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Balancing care with technology

Garcia-Dia, Mary Joy DNP, RN, FAAN

Program Director, Nursing Informatics Information Technology Department, NewYork-Presbyterian, New York, N.Y.

Q How do we get back to the caring environment amidst the proliferation of technology within and outside of our healthcare delivery system?

The Future of Nursing report suggested that nurses will be called upon to fill expanding roles and master technologic tools and information systems. Nursing is an information-based profession, and technology helps bring information to the point of care to support nurses' decision-making processes. 1 A goal of informatics is to use technology to increase efficiency, make healthcare safer and more effective, and improve quality and outcomes. 2 As part of nursing practice, clinical decision support tools have been designed to improve regulatory compliance, such as incorporation of automated measurement tools, point-of-care alerts, links to evidence-based standards of care and clinical guidelines available within the electronic health record, and graphs depicting trending critical parameters.

Amidst these technologies, nurses can use the “head, hand, and heart” approach, which incorporates practical know-how with empathic understanding and technical knowledge to provide humane and sensitive care. 3 Practicing respect, actively listening, committing to taking the time to sit with patients, and establishing trust and transparency will balance the ubiquitous presence of technology while promoting safe, quality care. The theory of technologic competency as caring in nursing illuminates the coexistence of technology and caring with three key nursing processes: technology knowing (the competent use of technology in treating and caring for the patient as a coparticipant), mutual designing (the nurse and patient codesign a care plan), and participative engagement (shared activities in implementing the care plan and evaluating the patient's response and outcomes). 4

Nurse leaders have a dual role in promoting a vision that ensures the clinical perspective is front and center. As experts on the care environment, we can advocate on behalf of our nurses in guiding technology decisions that affect their workflow and patients. This entails being at the table and driving conversations with key stakeholders to understand how technologic tools impact communication, patient interactions, and workflow efficiencies. By engaging staff members through council meetings and listening to how nurses are adopting and using the right tools to reduce patient frustration and improve the care experience, we can provide important feedback for our technology partners. And by defining compassionate and caring behaviors in our education and training, we can mentor staff members in delivering high-tech, evidence-based care and foster a reflective practice that prepares nurses to competently cope with the inevitable change that technology brings.

This year we celebrate the Year of the Nurse and Midwife along with the 200th anniversary of Florence Nightingale's birth, whose use of patient data through observations and analysis to guide care paved the way for nursing informatics. Remember the reason why you became a nurse and share your caring stories on social media using #yearofthenurse.

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All about Technology in the Healthcare Sector

In this article

According to the UK government , artificial intelligence, robotics and digital medicine could change the functions and roles of clinical staff by 2040. This is expected to deliver improvements in care and productivity and reduce costs.

Telemedicine (e.g., provision of care with IT) and telecommunications, wearable sensors and smartphone apps are all likely to be used. Things have progressed more swiftly with regard to remote consultations owing to the COVID-19 pandemic.

Just like technology has revolutionised many aspects of our day-to-day lives, the healthcare sector will be no exception. From improving patient care and outcomes to enhancing efficiency and accessibility, technology will continue to play a crucial role in transforming the healthcare industry. In this article, we’ll delve into the world of technology in the healthcare sector, exploring its types, significance, applications, benefits and potential risks.

What is technology in the healthcare sector?

When we talk about technology in the healthcare sector, we refer to the utilisation of digital tools, devices, software and systems to enhance the delivery, management and accessibility of healthcare services. This encompasses a wide range of technologies, including websites, mobile applications, wearables, electronic health records (EHRs), telemedicine, artificial intelligence (AI), robotics, and more.

Types of technology in healthcare

The number of different types of technology is increasing every year. Here are some of the things that have emerged or that are emerging in the healthcare sector:

• Websites and Mobile Applications Healthcare websites and mobile apps provide platforms for patients to access information, schedule appointments, refill prescriptions and communicate with healthcare providers. They also offer educational resources and remote monitoring capabilities.
• Wearable Devices Wearable technology like fitness trackers and smartwatches, has gained popularity in general and among healthcare staff. These devices can monitor vital signs, track physical activity, detect irregularities and transmit data to healthcare professionals for analysis.
• Electronic Health Records (EHRs) Electronic Health Records replace traditional paper-based patient records with electronic versions. This enables healthcare providers to store, access and share patient information more securely. These electronic records also streamline workflows, reduce errors and loss, facilitate data exchange, and enhance collaboration among healthcare professionals. They also take up much less room!
• Telemedicine Telemedicine involves the use of videoconferencing and telecommunications technologies to provide remote medical consultations, diagnoses and treatment. This provides excellent opportunities for consultations that don’t require physical examinations or tests. For example, consultations regarding mental health.Telemedicine also enables patients in remote or underserved areas to access healthcare services, and it’s also useful for those who would find it physically difficult to attend an appointment, like those with agoraphobia. Finally, videoconferencing and telemedicine reduce travel time and costs, and enhance the overall efficiency of healthcare delivery.
• Artificial Intelligence (AI) and Machine Learning (ML) AI and ML technologies are increasingly being applied in healthcare for tasks such as medical imaging analysis, personalised treatment recommendations, predictive analytics and automation of administrative processes. These technologies have the potential to improve diagnosis accuracy, optimise treatment plans and enhance patient outcomes.

Why is technology used in healthcare?

There are many reasons technology is used in healthcare.

Here are some of them:

1. Improved Efficiency: Digital tools automate routine tasks, streamline workflows and enable healthcare professionals to work more efficiently, reducing administrative burden and increasing productivity.

2. Enhanced Communication and Collaboration: Technology facilitates seamless communication and collaboration among healthcare providers. This enables the sharing of patient information, consultations and coordination of care.

3. Access to Information: Digital platforms provide patients and healthcare professionals with easy access to medical knowledge, research, educational resources and clinical guidelines. They support evidence-based decision-making.

4. Remote Monitoring and Care: Technology enables remote patient monitoring, allowing healthcare providers to track vital signs, symptoms and adherence to treatment plans from a distance. This approach is particularly beneficial for managing chronic conditions and postoperative care.

The development of telemedicine during COVID-19

Though the COVID-19 pandemic was a difficult period for the health service, it also significantly accelerated the development and adoption of telemedicine as a means of delivering healthcare services remotely.

The contagious nature of COVID-19 meant there was increased demand and necessity for telemedicine, particularly when it came to non-emergency consultations, follow-ups and mental health support.

The pandemic also drove technological advancements and innovation. Videoconferencing software like secure messaging platforms and remote monitoring devices were enhanced to accommodate the increased demand and provide a seamless virtual care experience.

There has also been more acceptance among patients too. In the past, patients were less likely to see the value in telemedicine. However, the contagious nature of COVID-19 meant that more and more people embraced it and saw the added benefits of reduced travel time and costs. Patient feedback to these experiences has also fuelled the development and refinement of telemedicine services.

Is technology important in the healthcare sector?

Technology is of significant importance in the healthcare sector. Firstly, technology can be used to enhance patient care and outcomes, which is the main goal of healthcare professionals. With accurate and up-to-date patient information, healthcare professionals can make informed decisions and provide timely interventions.

Like in many aspects of life, technology improves efficiency and productivity in the healthcare sector too. It can be used to automate routine administrative tasks, streamline workflows and reduce paperwork. This means that healthcare professionals can focus more on patient care. With digital systems, there is enhanced efficiency and less room for error.

Technology is also important in that it helps many patients overcome geographical barriers, which is important in rural areas, for example. Digital platforms and mobile applications can also provide valuable resources and information to empower patients about their health and to access services in a way that’s convenient for them.

The healthcare sector also benefits greatly from effective collaboration and communication as a result of new technologies. Professionals can also use technology to aid in their decision-making. They can gain valuable insights, which can help in disease prevention and the development of personalised treatment plans.

How can technology be used in healthcare?

There are many different ways in which the healthcare industry can use technology, some of which we’ve already mentioned.

Here are some common applications:

• Electronic Health Records (EHRs): EHRs are the digital systems that store patient health information like medical history, diagnoses, medications and test results. They enable healthcare providers to access and share patient data securely, leading to improved care coordination and continuity.
• Telemedicine: This refers to the remote delivery of healthcare services through videoconferencing, phone calls or online messaging platforms. Patients can consult with healthcare professionals, receive diagnoses and access treatment advice from the comfort of their homes.
• Wearable Devices and Remote Monitoring: Wearable devices like fitness trackers, smartwatches and medical sensors can monitor vital signs, physical activity and sleep patterns. These technologies enable healthcare providers to track patients’ health conditions, detect abnormalities and intervene when necessary.
• Health Apps and Patient Portals: Mobile applications and patient portals mean patients can access health information, schedule appointments, receive medication reminders and track their health.
• Artificial Intelligence (AI) and Machine Learning (ML): AI and ML technologies can be used to analyse large volumes of data to identify patterns, predict outcomes and assist in diagnoses. They can also automate administrative tasks, enhance clinical decision-making and personalise treatment plans based on individual patient characteristics.
• Robotics and Automation: These are increasingly used in healthcare for a range of purposes. For example, robots can assist surgeons during complex surgeries, provide support in physical therapy and automate repetitive tasks to improve efficiency.
• Health Information Exchange (HIE): HIE allows secure sharing of patient health information among different healthcare organisations and systems. It enables healthcare providers to access comprehensive patient records, resulting in more informed decisions and reduced duplication of tests or procedures.
• Data Analytics and Population Health Management: Data analytics tools can process large healthcare datasets to identify trends, patterns and population health insights. This information can guide public health interventions, resource allocation, and proactive disease prevention strategies.

Examples of wearable devices in practice

Most people these days are familiar with the concept of smartwatches and fitness trackers. Items such as Fitbit, Garmin and Apple watches are popular devices that monitor and track physical activity, heart rate, sleep patterns and calories burned. Some smartwatches can even detect blood pressure and oxygen saturation levels. In a general sense, they’re great at encouraging individuals to adopt healthier lifestyles and learn more about their health and bodies.

Another common item of technology that is already used well in society is the Continuous Glucose Monitor or CGM. CGMs are wearable devices used by people with diabetes to monitor their blood glucose levels continuously. The devices provide real-time data and alerts and help people to manage their insulin levels and make informed decisions about diet and medication.

Wearable ECG monitors are also available. These allow individuals to record their electrocardiograms wherever and whenever. The devices detect abnormal heart rhythms and provide important data that can help to diagnose and monitor heart conditions.

Benefits of technology in the healthcare sector

There are, of course, many benefits of the use of technology in healthcare. First and foremost, technology benefits patients as it enables healthcare providers to make diagnoses more accurately. This is also because it can be used to develop personalised treatment plans and check out patient progress more easily.

What’s more, technology makes for enhanced productivity and efficiency. Thanks to digital tools, administrative tasks, information exchange and workflows can be automated and streamlined. This results in more time to focus on patient care.

There’s also the benefit of increasing access to healthcare for patients in remote areas or for those who have mobility problems.

There’s also a lot of data and information made available thanks to healthcare technology. For example, patients themselves can be more empowered with access to their own health information as well as self-management tools and educational resources.

This access to data means that health information can be analysed on a wider scale. This analysis means professionals can gain valuable insights into disease patterns, health trends within the population, resource allocation and treatment outcomes.

Finally, technology also means better communication and collaboration among healthcare providers, which improves the overall quality and continuation of care.

Risks of technology in the healthcare sector

Like any part of our lives that use technology, there are risks for the healthcare sector too. Firstly, one of the biggest concerns for all is privacy and security. When it comes to health data, breaches, leaks or unauthorised access can compromise sensitive patient information . The biggest risks of this include medical fraud or identity theft.

Another risk is system failure or glitches. When we rely on technology, we expose the healthcare sector to potential disruption caused by technical glitches, system failures or even cyberattacks. There’s also the potential that AI algorithms might not be as accurate as humans, which could lead to incorrect diagnoses or treatment recommendations.

Now that the NHS operates in a trust system, different areas will use different systems and technologies. This can lead to an incompatibility, which can hinder the sharing of information as well as the coordination of care.

Finally, one huge concern is that there is the potential of becoming over reliant or even dependent on technology to such an extent that human interaction and clinical judgement are reduced. It’s crucial to strike a balance between technology and the expertise of existing healthcare professionals to ensure optimal patient care.

Final thoughts on technology in healthcare settings

Technology has brought numerous benefits to the healthcare sector and is responsible for improved patient outcomes, increased access to care, better communication and enhanced efficiency. However, it’s also important to address the risks associated with technology like privacy concerns and technical glitches. By carefully considering these risks and implementing appropriate safeguards, healthcare organisations can harness the full potential of new technologies while ensuring patient safety and privacy.

Looking ahead, the development of telemedicine during the COVID-19 pandemic has showcased the tremendous potential of technology in healthcare. It has accelerated the adoption of remote care, changed regulations and spurred technological advancements, paving the way for a future where telemedicine is an integral part of healthcare delivery.

As technology continues to advance, it is crucial for healthcare organisations, policymakers and stakeholders to embrace innovation, ensure patient safety and privacy and foster an environment that maximises the benefits of technology in healthcare. By leveraging technology effectively, the healthcare sector can enhance patient outcomes, improve access to care and transform the delivery of healthcare services for individuals and communities worldwide for the better.

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About the author

Louise Woffindin

Louise is a writer and translator from Sheffield. Before turning to writing, she worked as a secondary school language teacher. Outside of work, she is a keen runner and also enjoys reading and walking her dog Chaos.

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• Published: 18 September 2023

Barriers and facilitators to utilizing digital health technologies by healthcare professionals

• Israel Júnior Borges do Nascimento 1 , 2 ,
• Hebatullah Abdulazeem   ORCID: orcid.org/0000-0002-4529-7114 3 ,
• Lenny Thinagaran Vasanthan   ORCID: orcid.org/0000-0003-1776-6304 4 ,
• Edson Zangiacomi Martinez   ORCID: orcid.org/0000-0002-0949-3222 5 ,
• Miriane Lucindo Zucoloto 5 ,
• Lasse Østengaard   ORCID: orcid.org/0000-0001-7033-3722 6 ,
• Natasha Azzopardi-Muscat 1 ,
• Tomas Zapata   ORCID: orcid.org/0000-0002-7807-3520 1 &
• David Novillo-Ortiz   ORCID: orcid.org/0000-0001-9756-0984 1  

npj Digital Medicine volume  6 , Article number:  161 ( 2023 ) Cite this article

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Digital technologies change the healthcare environment, with several studies suggesting barriers and facilitators to using digital interventions by healthcare professionals (HPs). We consolidated the evidence from existing systematic reviews mentioning barriers and facilitators for the use of digital health technologies by HP. Electronic searches were performed in five databases (Cochrane Database of Systematic Reviews, Embase ® , Epistemonikos, MEDLINE ® , and Scopus) from inception to March 2023. We included reviews that reported barriers or facilitators factors to use technology solutions among HP. We performed data abstraction, methodological assessment, and certainty of the evidence appraisal by at least two authors. Overall, we included 108 reviews involving physicians, pharmacists, and nurses were included. High-quality evidence suggested that infrastructure and technical barriers (Relative Frequency Occurrence [RFO] 6.4% [95% CI 2.9–14.1]), psychological and personal issues (RFO 5.3% [95% CI 2.2–12.7]), and concerns of increasing working hours or workload (RFO 3.9% [95% CI 1.5–10.1]) were common concerns reported by HPs. Likewise, high-quality evidence supports that training/educational programs, multisector incentives, and the perception of technology effectiveness facilitate the adoption of digital technologies by HPs (RFO 3.8% [95% CI 1.8–7.9]). Our findings showed that infrastructure and technical issues, psychological barriers, and workload-related concerns are relevant barriers to comprehensively and holistically adopting digital health technologies by HPs. Conversely, deploying training, evaluating HP’s perception of usefulness and willingness to use, and multi-stakeholders incentives are vital enablers to enhance the HP adoption of digital interventions.

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Introduction.

Recent developments in health technology have positively affected multiple and essential sectors of the economy, especially the healthcare sector, by providing solutions that guarantee the exchange of medical knowledge and information and establish long-lasting health outcomes 1 , 2 . Digital health technologies, such as wearables devices, computerized decision support systems, and telemedicine improve the technical performance and satisfaction of healthcare employees, demonstrate potential to decrease direct and indirect costs of medical services, and enhance the quality of delivered care 3 . Worldwide, using digital solutions in practice seems inevitable, with modality-specific prevalence (e.g., 50.8% for telemedicine, 89.9% for electronic health records, and 91.9% for social media platforms) 4 , 5 , 6 . However, the prevalence of use might be even higher, as no previous study has collated and assessed the overall prevalence of using digital health technologies by healthcare providers. Likewise, several studies have suggested that ethnicity, race, geographic location, age, and medical specialty directly interfere in the adoption of technology use, evidencing the importance of understanding variables accounting for the digital divide and disparity of access 7 , 8 , 9 .

Several barriers to healthcare’s overall quality, transparency, and efficiency naturally arise during or following the creation, implementation, and maintenance of digital health technologies. Therefore, during the design of any health-related project, it is essential to identify and quanti-qualitatively analyze its risks and facilitators, enhancing the likelihood of obtaining favorable outcomes and optimizing the chances of success. The efficient implementation of digital technologies, characterized by proper implementation of a systematic management approach, including strategic planning, resource allocation, and control and evaluation processes, is fundamental to refining healthcare services, equipment, and technologies 10 , 11 , 12 . In reaction to these aforementioned elements, multiple efforts have strengthened healthcare systems through employing DHTs for healthcare professionals and stakeholders from low-, middle-, and high-income countries. For instance, the World Health Organization (WHO) endorsed in the 73rd World Health Assembly the institution of the Global Strategy on Digital Health 2020–2025, in which four guiding principles rely on the acknowledgment that the institutionalization of digital health in a national system requires a decision and commitment by countries, recognition that successful digital technologies require an integrated strategy, promotion of the appropriate use of digital interventions for health, and recognition of the urgent need to address the major impediments faced by least-developed countries implementing digital health technologies 13 . Furthermore, the Regional Digital Health Action Plan for the WHO European Region 2023–2030 has a critical regional focus area on strengthening digital literacy skills and capacity-building in the general population, with particular attention to the health workforce, for the use of digital health services and disease prevention and management 14 . Due to these global actions, numerous studies have focused on assessing barriers to and facilitators for many technologies 15 , 16 , 17 .

To date, hundreds of clinical trials based on specific technologies applied to the healthcare professionals’ environments have assessed the implementation of digital interventions in the healthcare system, while several systematic reviews have combined these publications, evidencing their effectiveness, safety, and feasibility. However, a summary of enablers and restraints to healthcare professionals’ coordinated and integrated use of digital health technologies has not been published yet, making the current evidence dispersed, misused, and overlooked. Therefore, in this overview of systematic reviews and semantic-based occurrence meta-analysis, we report all published evidence from existing systematic reviews covering and mentioning barriers and facilitators to the solid use of digital health technologies by healthcare providers.

Study selection and characteristics

Our database and PROSPERO search are shown in Fig. 1 . Our January 21, 2022 search retrieved 9,912 records, of which 139 underwent full-text review (Fig. 1 , section A). Based on the inclusion and exclusion criteria, 47 studies and seven ongoing studies were included. On March 1, 2023, 2,717 new publications were identified through an additional database search (Fig. 1 , section B). Of those, 142 studies were shortlisted for full-text assessment, and 60 reviews were added to our umbrella review. Two additional ongoing studies or protocols were identified. In total, this overview of systematic reviews included 108 primary systematic reviews and nine ongoing studies (Fig. 1 , section C). 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 One study was identified from alternative resources. 64 Justification for the exclusion of 165 studies is presented in Supplementary Information 1 (pp 2–7 ) . Included study characteristics are characterized in Table 1 and Table 2 . One study is pending classification as it required translation. No additional data needed to be requested from the corresponding authors.

Reason 1—wrong intervention or platform was unclear. Reason 2—the study did not provide any relevant outcome influencing healthcare providers. Reason 3—targeted population was not healthcare providers. Reason 4—study design used did not match our inclusion criteria.

Few studies ( n  = 20; 18.5%) initially targeted evaluating the creation, implementation, long-lasting use, and self-reported barriers and facilitators to using digital health technologies by healthcare professionals 25 , 27 , 29 , 43 , 45 , 51 , 66 , 68 , 70 , 72 , 73 , 74 , 82 , 86 , 93 , 96 , 98 , 101 , 107 , 120 . Thus, the remaining reviews were cautiously evaluated in order to identify a report of any barrier or facilitator to using digital health technologies by healthcare workers. Included reviews were heterogeneous in terms of the digital health technologies being assessed (e.g., alert systems, clinical reminders applications, computerized clinical decision support systems, electronic documentation systems, mobile health applications, social media platforms, and telemedicine tools) and enrolling different healthcare professionals (e.g., general practitioners and specialists, nurses, pharmacists, community healthcare workers) at several levels of care (primary, secondary, and tertiary health facilities).

Most reviews ( n  =  63 ; 58.3%) were executed in North America, Europe ( n  =  61 ; 56.4%), and Asia ( n  =  50 ; 46.2%). Thirty-three reviews suggested barriers and facilitators in the African territory (30.5%), while 28 reported data from Latin American and Caribbean regions (25.9%). Our study involved reviews from low- (e.g., Kenya, Rwanda, Uganda, and Ghana), middle- (e.g., Brazil, China, Russia, South Africa, and India), and high-income countries (e.g., Japan, the Czech Republic, United States of America, and Australia).

According to our bibliometric analysis, our data were classified into five clusters based on identifier clustering assessment, and recorded keywords by co-occurrence frequency are shown in Table 3 and Fig. 2 . The ten most common identifiers were “healthcare professionals,” “technology,” “review,” “barrier,” “care,” “systematic review,” “factor,” “patient,” and “implementation”.

Please note that in the network visualization, items are represented by their label and by default also by a rectangles. The size of the label and the circle of an item is determined by the weight of the item. The higher the weight of an item, the larger the label and the circle of the item. The color of an item is determined by the cluster to which the item belongs.

Taking into account the 37 (34.2%) records providing data regarding the number of healthcare professionals considered in primary studies, sample sizes ranged from 22 to 106,876 (totaling approximately 345,000 healthcare workers), with a mean of 3,197 (SD 12,364), and a median of 1,545 (IQR 258 to 9,016). Most studies did not precisely consider one medical specialty, disease, or condition. However, some reviews focused on diseases of the respiratory system (e.g., tuberculosis, asthma, and chronic pulmonary obstructive disease) 19 , 22 , 31 , 32 , 46 , 93 , 101 , 123 , pregnancy, childbirth, or puerperium (e.g., maternal health, postpartum hemorrhage, and reproductive health) 19 , 22 , 23 , 26 , 31 , 35 , 46 , 56 , 61 , 77 , 94 , certain infectious or parasitic diseases (e.g., malaria, human immunodeficiency virus infection, and influenza) 19 , 22 , 23 , 28 , 31 , 46 , 50 , 61 , endocrine, nutritional, or metabolic diseases (e.g., diabetes mellitus) 57 , 64 , 76 , 93 , 123 , mental and behavioral disorders (e.g., post-traumatic disorder syndrome, stress, depression, and burnout) 23 , 41 , 44 , 64 , 70 , 76 , 94 , 125 , neoplasms 50 , 67 , 85 , 123 , diseases of the circulatory system (e.g., hypertension) 19 , 25 , 48 , 50 , 57 , 67 , 93 , 123 , diseases of the blood or blood-forming organs (e.g., anemia) 22 , and diseases or disorders of orofacial complex (e.g., oral lesions) 28 , 42 . Identified reviews mostly included quantitative (randomized and non-randomized trials, surveys, economic analysis, structured questionnaires, and experimental studies), qualitative (e.g., non-structured interviews, literature reviews, focus groups, observation, and cultural reports), and mixed-method reviews (sequential exploratory and concurrent transformative studies). An additional description of included reviews is shown in Table 2 .

Barriers and facilitators identified in included reviews and potential recommendations

The final domains created based on the thematic analysis can be accessed in Figs. 3 , 4 , and the summary of findings of the top seven barriers and facilitators can be accessed in Table 4 . Our linguistic and semantic-based analysis stratified the data into 21 barriers and 19 recommendations. Predominant barriers were associated with infrastructure and technical (RFO of 6.4% [95% CI 2.9–14.1]), personal and psychological barriers (RFO of 5.3% [95% CI 2.2–12.7]), time and workload-related (RFO of 3.9% [95% CI 1.5–10.1]), training and educational (RFO of 3.4% [95% CI 1.3–8.9]), and legal- and ethical-related factors (RFO of 3.6% [95% CI 1.3–9.6]). Most predominant enablers related to the offer of training and educational activities (RFO of 3.8% [95% CI 1.6–9.0]), healthcare provider perception of digital health technologies usefulness and willingness to use (RFO of 3.8 % [95% CI 1.8–7.9]), the existence of government and multisector incentives (RFO of 3.0% [95% CI 1.4–6.6]), adherence promotion campaigns (RFO of 2.2% [95% CI 1.1–4.3]), involvement of healthcare providers in the process of digital health technologies development and implementation (RFO of 2.0% [95% CI 0.8–4.9]), and intuitive navigation in healthcare technology systems (RFO of 1.9% [95% CI 0.7–5.2]).

Frequencies (expressed as % and their confidence interval) are distributed among each categorized barriers as well as by healthcare technology modality.

Frequencies (expressed as % and their confidence interval) are distributed among each categorized facilitators as well as by healthcare technology modality.

As represented in Figs. 3 , 4 , several semantic clusters were described throughout included reviews. Herein, we outline and exemplify the five most common barriers and facilitators to the design, implementation, longitudinal maintenance, and evaluation of digital health technologies by healthcare professionals. The remaining barriers and facilitators are explained in detail in Supplementary Information 2 (pp 8). Infrastructure and technical barriers were the most frequently described barriers among included reviews, relating to issues with a limited or insufficient network, lack of existing technologies, lack of devices, compatibility with daily workflow, connectivity speed, healthcare capacity of technology integration, interconnectedness, absence of standardized/harmonized systems at different facilities, limited access to electricity, and requirement of a functional database system or large disk space. Notably, technical issues seem to be the worst in rural and countryside regions. Firstly, counteracting connectivity-related barriers involves ensuring availability (especially in rural areas) and affordability, guaranteeing high-speed fiber connectivity, and increasing the number of reliable local networks. In addition, we found reviews suggesting that to overcome infrastructure and technical barriers, the involvement of healthcare professionals in developing and implementing any health technology tools is fundamental, enhancing their capacity to manage such applications and increase their independence from co-workers and support centers. Remarkably, all reviews stated that user engagement and collaboration with system developers or associated stakeholders is crucial in all design and development stages, deployment, and continued utilization, as created applications are fit for purpose, based on understanding and addressing healthcare providers’ needs and expectations.

Personal and psychological barriers involved complex thematic components, including the healthcare professionals’ resistance to change, difficulty understanding the technology, perception of less human interaction, technophobia, ages, education levels, professional experience, low literacy, poor writing skills, linguistic features, adherence behavior, and fear of using particular health technology. Moreover, unwillingness, low expectations, skepticism from healthcare providers, and low motivation for compliance were also associated with personal barriers. For counterbalancing these barriers, healthcare professionals’ perception of usefulness and willingness was a highly cited facilitator, characterized by the degree to which the employees believe that using specific digital health technologies would enhance their performance and the proportion of participants intending to utilize that technology. Furthermore, personal and psychological barriers could be addressed by using and adopting training programs and educational activities appropriately tailored to healthcare professionals’ needs and coverage of deficient abilities. High-quality, real-time technical support and coaching also appeared as a component that increased healthcare providers’ efficiency, decreased implementation fear, and potentially could reduce internal conflicts during system adoption. Importantly, training programs may be developed with the ongoing involvement of the intended community to understand their needs and knowledge gaps. Moreover, evidence shows that user-friendly design, intuitive system navigation, and easy-to-use interfaces are critical to improving overall product performance and facilitating data collection and input, data processing, and further analysis.

Some reviews suggested that the limiting factors for the broad use of digital health technologies are associated with healthcare workers’ concerns about increased workload and altered workflow, which could hinder the sustainability of the digital health technologies. Additionally, these newly implemented technologies would require additional purchase time and increased set-up, implementation, training, access, adaptation, and establishment stages. In addition, healthcare professionals commonly stressed that digital health technologies would impact the quality of delivered care, as recently trained professionals would need a longer time to convert acquired data into the implemented system. However, although time might be required to acquire the right skills and operating competencies, with adequate training, continuous technical support, and peer-to-peer collaboration, threats associated with increased time to complete a specific task are significantly reduced. Useful written guidelines, instructions, and handouts appear to be important facilitators that could be easily implemented 73 . Likewise, incentives from government agencies and multisectoral organizations were shown to significantly improve digital health technologies’ effectiveness and chances of success in large-scale healthcare systems. Therefore, this conceptual perspective should be shown to healthcare providers, as increased effectiveness is directly related to the appropriate use of time and less wasteful processes.

Fourth, legal- and ethical-related barriers were shown to be a relevant factor for healthcare providers, as privacy and security concerns, national legislation, jurisdiction, and the existence of unclear legal liability regarding response protocols would directly affect healthcare professionals. Possible interventions for these barriers are associated with the development of safer data storage systems, the establishment of requirements on safety and security in cooperation with healthcare professionals and patients, or the creation of an international legal framework and legislative norm, which would clarify security regulation policies that could help ensure patients’ privacy and confidentiality, as well as define healthcare professionals’ liabilities.

Lastly, deficient or inexistent training and educational activities were evidenced to significantly impact the success and efficiency of digital health technologies in the healthcare environment . Some reviews highlighted that without training, healthcare providers tend to feel low self-efficacy when utilizing any digital health technologies, resulting in negative attitudes toward these technologies. In addition, as evidenced by healthcare workers, prior technology introduction, vendor training, in-depth seminars, workshops, or correlated training activities are unusual, and regular quality process assessment following implementation to ensure efficiency are also rare. Interestingly, reviews not only highlighted that training was fundamental to the success of using digital health technologies but also suggested that training per se would also be delivered through certain digital health technologies, such as mobile technologies and computers. Thus, the training offer positively affects healthcare professionals’ experience with digital health technologies, especially when monetary incentives are added to this variable, given the time invested in obtaining the proper abilities to operate any digital health technologies.

Using the AMSTAR 2 methodological quality assessment tool, most reviews had a very critically low overall methodological quality, as shown in Table 5 . Nine-nine reviews were classified as very low quality, six as low quality, and only three were rated to have a high methodological quality. Two top-ranked reporting inadequacies related to the lack of evaluating the presence and likely impact of publication bias (95.2%), and the disregard of the risk of bias when interpreting the results of the review (95.2%). Where judgment was lost, this generally associated with the lack of prior protocol (50.9%), absence of justification for excluding individual studies (88.8%), lack of risk of bias assessment from individual studies being included in the review (63.8%).

We mapped the aforementioned data and complementary results, as shown in Fig. 5 (also available for virtual access through the GitMind platform). 126 As evidenced in supplementary information 3 (pp 9), we found several terms with similar semantic structures. Thus, we coded each barrier or facilitator and identified recommendations, suggesting the possibility of a complex and broad linguistic connection and relationship amongst codes. These thematic relationships are not limited in our analysis and can be explored and exhausted.

Conceptual map of reported barriers and potential facilitators and recommendations to overcome these barriers.

To our knowledge, this is the first overview of systematic reviews to collate, cluster, and synthesize the quantitative, qualitative, and mixed methods body of literature associated with barriers and facilitators to and use of several digital health technologies by healthcare professionals at all levels of care. The decision for carrying out this valuable, but complex study, relies on the noticeable detachment of research data and investigation groups in the field of Medical Informatics, who usually inadvertently duplicate technical and financial resources given the existing gaps in the literature. Here we report 21 overarching barriers and 19 facilitators, mostly interconnected, containing a complex sequence of thematic describers and identifiers. Understanding and overcoming identified barriers to the fully integrated and coordinated use of DHTs by any class of healthcare providers and evaluating its facilitators could positively impact successful creation, implementation, adoption, training, and long-term services or product utilization.

The evidence suggests that healthcare providers and managers predominantly face infrastructure, technical-, training-, legal-, ethics-, time-, and workload-related barriers to using digital health technologies, regardless of the level of care or digital technology. In the second level of semantic occurrence, several restraining factors to the wide use of digital health technologies were combined and reported, including psychological and personal barriers, lack of supervisory support, ownership issues, and healthcare system-cultural-, social-, and financial-related limiting features. Nevertheless, we are aware that some of the classified items are interconnected, meaning that the prevalence of occurrence ranking should not be used as a priority guide for policymakers and health organizations when addressing these barriers. For instance, the highlighted barrier “81B” (regarding the simplicity of contents usually transferred in mobile applications or clinical alert systems) might be directly related (or potentially caused due to) to the technical limitations per se (considering devices screen’s reduced size (“2B”), the complexity of the systems themselves and the information they carry (“5B”), or even because the lack of standardization and customizability of such systems and technologies (“7B”). Therefore, the creation of artificial intelligence-based mind mapping representing these interconnections is of utmost relevance 126 .

Creating and applying digital health technologies to healthcare environments must be driven by a regime of comprehensive assumptions instead of empirical models and processes. Our results corroborate with published systematic reviews that have already evidenced patient-reported barriers and facilitators to utilizing digital health solutions for self-care 127 , 128 , 129 . For instance, self-management of low-back pain using mobile health applications was mainly challenging due to information technology, usability-accessibility, quality-quantity of content, tailoring-personalization, and motivation-support barriers 127 . In contrast, flexibly structured and intuitive navigation, trustworthy content and sources, content accounting for individual needs and priorities, and the opportunity to influence the application design appeared as relevant facilitators affecting the uptake and utilization of digital health interventions for self-management of lower back pain 127 . Likewise, Powell and colleagues suggested that a lack of awareness, self-motivation, training, privacy, and security concerns are the most common patient-derived barriers to using electronic portals 128 . Emphasized facilitators correlated with use engagement by a leader (i.e., physician), free access and control over health information, and an adequate communication profile. Therefore, as the relationships between our identified barriers and facilitators and existing patient-related evidence highlight, the development of digital healthcare solutions should consider multiple factors, which can facilitate or deteriorate broad goals of high-quality use of information technology in the healthcare environment.

During protocol modeling, our research group discussed the possibility of including reviews that summarize evidence on barriers and facilitators involving students in health fields. The decision was not to include these reviews because these students are not yet legally considered professionals or critically necessary workforce, and they are not considered essential in healthcare settings 130 , 131 . However, one aspect found in these excluded reviews was revealed in our overview with significant frequent and relevant findings: the use of digital health technologies for training and educational purposes. Although distance education dates from 1728 132 , 133 , e-learning or virtual learning started during the early 1980s at the University of Toronto 134 and has been developing ever since, particularly during the COVID-19 pandemic 135 , 136 . Currently, several high-income countries, such as New Zealand and the United States of America, have already integrated and implemented the Information and Communication Technology constructivist learning model in their national or statewide policies, ensuring that students have the chance to become digitally competent citizens 137 , 138 . These actions effectively decrease multiple barriers observed related to limited or no computer skills, restricted knowledge and technology literacy, and lack of reliability in technological tools. However, it has been suggested that numerous low- and middle-income countries still struggle with device acquisition, connectivity issues, tutors’ level of expertise and lack of motivation, absence of basic infrastructure, and the unwillingness of the government to implement such solutions 129 .

Foremost, we chose only six health solutions as systematic and feasible choices for comprehensive data processing. Nevertheless, we observed additional modalities of health solutions being implemented worldwide (e.g., laboratory and radiology automatic reporting systems, picture archiving and communication systems, cloud-based systems, and advanced and business analytics), and our synthesis may miss emerging or recent technologies 52 , 74 , 114 . For instance, studies have suggested that electronic laboratory reporting systems not only improve surveillance for notifiable conditions but can also be helpful in real-time laboratory testing in emergency departments and significantly improve organizational framework and efficiency 139 , 140 . Correspondingly, cloud-based computing systems have been increasingly applied in the healthcare system to ensure secure storage, handling, and processing of medical information 141 . Regardless of the digital health solution being implemented and utilized, healthcare workers and patients benefit from it. By improving real-time patient access to their results and providing better patient involvement with care, the incidence of unwanted tests or extra prescriptions decreases, and the overall quality of care is subsequently enhanced 142 , 143 .

We observed a limited number of reviews assessing the potential challenges and enablers for artificial intelligence models, machine learning algorithms, and platforms utilizing features such as augmented reality 40 , 54 , 63 , 70 , 78 , 85 , 94 , 99 . However, although the restricted number of studies assessing these subgroups in the field of digital technologies, core barriers and facilitators remained like other subgroups. Nevertheless, we highlight the need for further research with these technologies, as alternative barriers and facilitators would arise.

Due to the wide variety of digital health technologies currently being used in several medical specialties and levels of care, we had to restrict our report in different ways, limiting our certainty of evidence. Similarly, our series of analyses did not consider the existence of subgroup singularities by type of healthcare professional. As suggested in our map based on bibliometric data, only physicians, community health workers, and nurses appeared as recurrent keywords among all studies within the 42 systematic reviews eligible for inclusion. Therefore, studies analyzing impeding and enabling factors to the general use of digital health technologies in other healthcare providers (e.g., pharmacists, physiotherapists, physical educators, speech therapists, healthcare governmental agents, biologists, social services agents, healthcare managers, dentists, and psychologists) cause a “professional class bias” event that should be addressed in future studies. Likewise, factors like age, racial group, gender, country income index, or geographic location could affect a different subgroup (e.g., potential higher reporting of barriers of professionals practicing in low- or middle-income countries would focus more on technical and infrastructure features). Moreover, we neglected that digital health technologies utilized in the healthcare environment are usually concomitant and integrated. Thus, we may have considered the reported health solution independently instead of using a translational and adapted assignment methodology. Therefore, the provided RFO represented only the tendency of domain observance and reporting and not the identical picture of healthcare professionals’ reality. To conclude, we are aware that some highlighted barriers and facilitators could be assigned to a broader subtheme (e.g., lack of supervisory support in training and educational skills). However, during the overall execution, we observed that some terminologies and coding were commonly reported separately, so we decided to maintain them as individual elements to ensure the representativeness of the findings. Interestingly, the use of the AMSTAR 2 tool for evaluating the methodological quality of all included reviews should also be stated as a limitation, as the approach was primarily intended to systematic reviews of randomized controlled trials. Nevertheless, as most AMSTAR domains are on the elements that any review is structured (e.g., search strategy, protocol, extraction, combing studies, and publication bias), we believe that applying this methodology to our include reviews do not hinder the observed results. Likewise, although we Apart from these minor methodological limitations, the major strength of our study is the strict adhesion to international guidelines for reporting of systematic reviews (e.g., Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement and the Cochrane Handbook of Systematic Reviews and Meta-Analyses) and the execution of the entire study with international and blinded collaboration. We acknowledge that more than one methodology for evaluating the certainty of the evidence in qualitative research exists. We applied the GRADE CERQual method to check the overall quality of evidence for the seven most-reported barriers and facilitators. Generally, the evidence quality is high, with all considered domains without major concerns but with methodological limitations. We judged this domain as a moderate concern based on the phenomena of interest, adequate data collection and extraction, and quality in reporting observed data. In addition, expert groups have been discussing.

Although digital health technologies and their numerous types of technologies positively affect the healthcare environment, barriers impacting the successful creation, adoption, implementation, and sustainability of digital interventions are commonly reported by healthcare workers. Notwithstanding, the identification and deployment of different enabling factors allow the utilization of digital technologies in a holistic and integrated way. This overview of reviews emphasizes remarkable limiting features that should be considered by all stakeholders and provides advice to overcome these issues, with the expectation of increasing professional satisfaction and, perhaps, the quality of delivered care.

This overview of systematic and scoping review (herein referred to as “overview”) protocol was registered on PROSPERO (CRD42022304372, supplementary information 4 , pp 10–20) and it was part of a broader study conducted by the Data and Digital Health Unit of the Division of Country Health Policies and Systems of the World Health Organization, Regional Office for Europe 3 . This initiative provides strategic direction, technical assistance, and tailored support to countries and policymakers to strengthen their capacity to generate timely, credible, reliable, and actionable health-related data. The scientific community is currently defining an explicit, systematic, and transparent methodology to create evidence- and agreement-based reporting guidelines for overviews of reviews 144 . Therefore, we used the Preferred Reporting Items for Systematic Reviews and Meta-Analysis reporting recommendations 145 , the Cochrane Handbook guidelines 146 , and reports published by Fusar-Poli et al. 147 and Cornell et al. 148 guiding the practice on how to effectively conduct an umbrella review. As our study relies upon secondary data, ethics approval was waived. It is worthwhile mentioning that although in our protocol we initially stated that a standard meta-analysis would not be carried out, we decided to mathematically evaluate the obtained results. The technique utilized for the word- and sentence-based assessment (particularly associated with discourse analysis) is a well-known summarizing strategy used in the field of Human Sciences and was systematically presented and implemented in our research team after the protocol preparation. Therefore, in consonance with the requirements of continuous scientific evolvement and improvement, we decided to apply this newly introduced technique. However, this deviation does not alter the core of this project.

Data sources and searches

We searched five databases (Cochrane Database of Systematic Reviews, Embase ® , Epistemonikos, MEDLINE ® , and Scopus) and the PROSPERO protocol registration platform from inception to Jan 23, 2022, for systematic and scoping reviews evaluating barriers and facilitators to using digital health technologies by healthcare professionals worldwide. We also performed a manual search of reference lists of reviews shortlisted for full-text review and planned to contact the authors of included review to retrieve additional data.

An experienced information specialist and the expert team tailored search strategies to each database using Medical Subject Headings (MeSH) and free-text identifiers associated with the research topic 149 , 150 , 151 , 152 . The search included three main categories of key terms. Digital health technologies search identifiers included terms such as “telemedicine,” “telehealth,” “mobile health,” “mHealth,” “artificial intelligence,” “machine learning,” “social media,” “natural language processing,” and “computer decision support systems,” healthcare professional-related terms included “healthcare worker,” “healthcare provider,” and “healthcare support worker,” and systematic review filters used were “systematic review,” “meta-analysis,” and “scoping review.” Our terms are defined in recently published studies in the World Health Organization guidelines on digital health technologies for strengthening health systems, the World Assembly Resolution on Digital Health, and The Lancet Digital Health. In supplementary information 5 (pp 21-28), we present the detailed search strategy for the databases.

Study selection

Eligibility was evaluated by two independent investigators who primarily screened titles and abstracts and subsequently reviewed the full texts using Covidence ® (Veritas Health Innovation, Melbourne, Australia) 153 . Systematic and scoping reviews deemed eligible must have used at least two databases for their assessment, should have described the search methods, and evidenced the use of a transparent methodology for study selection and data extraction. Moreover, these reviews were only included if a qualitative analysis of barriers and facilitators to using digital health technologies by healthcare providers was clearly noted. We did not place limits on targeted healthcare professionals, medical specialty, level of care, language, and publication date. However, in order to avoid bias and results inflation, those studies strictly prioritizing the assessment of digital technologies for students and education in the field of health sciences were excluded.

Data extraction and quality assessment

Two independent researchers appraised the methodological quality of included systematic reviews using the AMSTAR-2 tool 154 . Following the initial evaluation, a third researcher cross-checked rated domains. The methodological quality of reviews was classified as “critically low,” “low,” “moderate,” and “high.” Our research team is aware that the AMSTAR 2 tool is not intended to generate an overall score of the review’s quality. Thus, we emphasize that we considered the appraisal methodology holistically, mostly related to the provision of an extensive evaluation of quality, particularly weaknesses associated with poor conduct of the review or word counting limitation endorsed by a determined journal.

Relevant data (first author identification, publication year, published journal, number of included databases, review objectives, primary study design, type of healthcare professional, type of digital technologies being analyzed, number of included primary studies, and barriers, facilitators, and recommendations for using digital health technologies) was extracted from included reviews by two independent researchers using Microsoft Excel (Microsoft Corporation, Redmond, USA) 155 . In the second stage, four independent volunteer collaborators reassessed extracted data to resolve inconsistencies.

Data synthesis and analysis

We used VOSviewer to assess research hotspots associated with digital health technologies based on the principle of co-occurrence analysis 156 . The minimum number of co-occurrences was set as 3, normalization method as an association, random starts as 1, random seed as 0, resolution as 1, and we merged small clusters. We attempted to clean the network map as much as possible, as some keywords were not meaningful. Thus, we extracted data from the top 100 author-provided keywords and mapped them into a single keyword co-existing network. Representative and frequent terms are expressed as larger nodes, and the thickness of the link between two or more nodes represents the strength of the relationships between them.

Our findings were evaluated and collated using an adapted version of a thematic synthesis developed by Thomas and Harden 157 . The 21 domains prioritized in the Enhancing Transparency in Reporting the Synthesis of Qualitative Research (ENTREQ) statement were followed 158 . First, qualitative data of included reviews on the main barriers and facilitators identified were coded line-by-line using QSR’s NVivo software (QSR International, Burlington, USA) 159 . In addition, primary highlighted concepts were re-evaluated by four volunteer collaborators who double-checked selected data and evaluated extraction errors or missing information. If needed, they also created new in-text selections. Furthermore, we organized free selections into similar themes to combine the preliminary results into descriptive themes. Lastly, we developed analytical themes that summarized barriers and facilitators closely related to the original remarks reported in included reviews. The explanatory delineation of thematic barriers and facilitators was a dynamic, deductive, and intuitive process, as different review authors had their peculiarities in academic and text writing. The alignment of thematic barriers and facilitators was discussed by all authors, resulting in the development of recommendations. In the result section, we have identified only the five most frequent barriers and facilitators. Recommendations were also emphasized for these five features. However, a complete list of barriers, facilitators, and recommendations can be accessed in supplementary information 2 (2.1 and 2.2). Where homogenous barriers were recognized (e.g., lack of leadership and local champions), guidance to overcome these barriers were prepared by the group of specialists (e.g., identification of processes weaknesses, implementation of improved strategies, and adjustment of progress based on stakeholder feedback). Similarly, the recommendations also considered the identified facilitators. Systematic reviews with similar research questions were expected to be included in our umbrella review. Consequently, the likelihood of two or more reviews including the same primary study in their analysis was meaningful 160 . Therefore, we carefully extracted and evaluated all references mentioned in the results section of each included review to exclude overlapping studies.

After establishing analytical themes, the frequency of occurrence for each categorized barrier and facilitator was aggregated into a standard meta-analysis of proportions. Certainty of the evidence was based on the GRADE-Cer-Qual approach 161 . Nominally identified results are indicated as the relative frequency of occurrence (RFO) and 95% confidence interval (CI). Analysis was executed using R software (version 4.1.1), using the metaprop function package. This study is deemed exempt as it does not assess data or intervene in humans.

Data availability

The authors hereby declare that all pertinent data has already been displayed within the article. Additional data can be accessed upon request to Dr. Israel Júnior Borges do Nascimento ([email protected]) or Dr. David Novillo-Ortiz ([email protected]).

Beer, P. & Mulder, R. H. The effects of technological developments on work and their implications for continuous vocational education and training: a systematic review. Front. Psychol. 11 , 918 (2020).

Article   PubMed   PubMed Central   Google Scholar  

Bronsoler, A., Doyle, J. & Van Reenen J. The Impact of New Technology on the Healthcare Workforce. 60. Available at https://workofthefuture.mit.edu/wp-content/uploads/2020/10/2020-Research-Brief-Bronsoler-Doyle-VanReenen.pdf .

Borges do Nascimento, I. J. et al. The global effect of digital health technologies on health workers’ competencies and health workplace: an umbrella review of systematic reviews and lexical-based and sentence-based meta-analysis. Lancet Digit Health 5 , e534–e544 (2023).

Article   PubMed   PubMed Central   CAS   Google Scholar  

Centers for Disease Control and Prevention. National Electronic Health Records Survey public use file national weighted estimates—NEHRS Survey Results and Products (2019). https://www.cdc.gov/nchs/data/nehrs/2019NEHRS-PUF-weighted-estimates-508.pdf .

Fischer, S. H., Ray, K. N., Mehrotra, A., Bloom, E. L. & Uscher-Pines, L. Prevalence and Characteristics of Telehealth Utilization in the United States. JAMA Netw. Open 3 , e2022302 (2020).

Khan, M. N. et al. A Study to See the Effect of Social Media Usage Among Healthcare Providers. Cureus 13 , e16350.

Christopher Gibbons, M. Use of health information technology among racial and ethnic underserved communities. Perspect. Health Inf. Manag 8 , 1f (2011).

PubMed   PubMed Central   Google Scholar  

Liu, C. et al. Academia-industry digital health collaborations: a cross-cultural analysis of barriers and facilitators. Digit Health 5 , 2055207619878627 (2019).

Samarasekera, U. The rise of racial minority health apps. Lancet Digit Health 4 , e218–e219 (2022).

Article   PubMed   CAS   Google Scholar  

Vinzant, D. H. & Vinzant, J. C. Strategy and organizational capacity: finding a fit. Public Product. Manag. Rev. 20 , 139–157 (1996).

Google Scholar  

Lavoie-Tremblay, M., Aubry, M., Richer, M.-C. & Cyr, G. A health care project management office’s strategies for continual change and continuous improvement. Health Care Manag (Frederick) 37 , 4–10 (2018).

Article   PubMed   Google Scholar  

Pickens, S. & Solak, J. Successful healthcare programs and projects: organization portfolio management essentials. J. Health. Inf. Manag 19 , 19–27 (2005).

World Health Organization. Global Strategy on Digital Health (WHO, 2020–2025).

Regional Committee for Europe, 72nd session. Seventy-second Regional Committee for Europe: Tel Aviv, 12–14 September 2022: Regional digital health action plan for the WHO European Region 2023–2030. World Health Organization. Regional Office for Europe (2022).

Schreiweis, B. et al. Barriers and facilitators to the implementation of ehealth services: systematic literature analysis. J. Med. Internet Res. 21 , e14197 (2019).

Weißenfeld, M. M., Goetz, K. & Steinhäuser, J. Facilitators and barriers for the implementation of telemedicine from a local government point of view - a cross-sectional survey in Germany. BMC Health Serv. Res. 21 , 919 (2021).

Jimbo, M. et al. Perceived barriers and facilitators of using a Web-based interactive decision aid for colorectal cancer screening in community practice settings: findings from focus groups with primary care clinicians and medical office staff. J. Med. Internet Res. 15 , e286 (2013).

Addotey-Delove, M., Scott, R. E. & Mars, M. Healthcare workers’ perspectives of mHealth adoption factors in the developing world: scoping review. Int. J. Environ. Res. Public health 20 , 1244 (2023).

Adepoju, I.-O. O., Albersen, B. J. A., De Brouwere, V., van Roosmalen, J. & Zweekhorst, M. mHealth for clinical decision-making in Sub-Saharan Africa: a scoping review. JMIR Mhealth Uhealth 5 , e38 (2017).

Alkhaldi, O., McMillan, B., Maddah, N. & Ainsworth, J. Interventions aimed at enhancing health care providers’ behavior toward the prescription of mobile health apps: systematic review. JMIR mHealth uHealth 11 , e43561 (2023).

Al-Rawashdeh, M., Keikhosrokiani, P., Belaton, B., Alawida, M. & Zwiri, A. IoT Adoption and application for smart healthcare: a systematic review. Sensors (Basel, Switzerland) 22 (2022).

Agarwal, S., Perry, H. B., Long, L.-A. & Labrique, A. B. Evidence on feasibility and effective use of mHealth strategies by frontline health workers in developing countries: systematic review. Trop. Med. Int. Health 20 , 1003–1014 (2015).

Amoakoh-Coleman, M. et al. Effectiveness of mHealth interventions targeting health care workers to improve pregnancy outcomes in low- and middle-income countries: a systematic review. J. Med. Internet Res. 18 , e226 (2016).

Arsad, F. S. et al. The impact of eHealth applications in healthcare intervention: a systematic review. J. Health Res. 37 (2023).

Aslani, N. et al. Advantages and challenges of telecardiology and providing solutions for its successful implementation: a scoping review. Int. Cardiovascular Res. J. 16 (2022).

Avoka, C. K., McArthur, E. & Banke-Thomas, A. Interventions to improve obstetric emergency referral decision making, communication, and feedback between health facilities in sub-Saharan Africa: a systematic review. Tropical Med. Int. Health https://doi.org/10.1111/tmi.13747 (2022).

Baluszek, J. B., Wiig, S., Myrnes-Hansen, K. V. & Bronnick, K. K. Specialized healthcare practitioners’ challenges in performing video consultations to patients in Nordic Countries—a systematic review and narrative synthesis. BMC Health Serv. Res. 22 , 1432 (2022).

Bervell, B. & Al-Samarraie, H. A comparative review of mobile health and electronic health utilization in sub-Saharan African countries. Soc. Sci. Med. 232 , 1–16 (2019).

Boonstra, A. & Broekhuis, M. Barriers to the acceptance of electronic medical records by physicians from systematic review to taxonomy and interventions. BMC Health Serv. Res. 10 , 231 (2010).

Brommeyer, M., Whittaker, M., Mackay, M., Ng, F. & Liang, Z. Building health service management workforce capacity in the era of health informatics and digital health—a scoping review. Int. J. Med. Inform. 169 , 104909 (2023).

Braun, R., Catalani, C., Wimbush, J. & Israelski, D. Community health workers and mobile technology: a systematic review of the literature. PLoS ONE 8 , e65772 (2013).

Brewster, L., Mountain, G., Wessels, B., Kelly, C. & Hawley, M. Factors affecting front line staff acceptance of telehealth technologies: a mixed-method systematic review. J. Adv. Nurs. 70 , 21–33 (2014).

Brown, J., Pope, N., Bosco, A. M., Mason, J. & Morgan, A. Issues affecting nurses’ capability to use digital technology at work: an integrative review. J. Clin. Nurs. 29 , 2801–2819 (2020).

Calleja, P., Wilkes, S., Spencer, M. & Woodbridge, S. Telehealth use in rural and remote health practitioner education: an integrative review. Rural Remote Health 22 (2022).

Cansdale, L. G. et al. Can mHealth improve recording of birth outcomes in LMICs—a scoping review. BJOG: Int. J. Obstet. Gynaecol. 129 , 98–99 (2022).

Cartolovni, A., Tomicic, A. & Lazic Mosler, E. Ethical, legal, and social considerations of AI-based medical decision-support tools: a scoping review. Int. J. Med. Inform. 161 , 104738 (2022).

Celes, R. S., Rossi, T. R. A., De Barros, S. G., Santos, C. M. L. & Cardoso, C. Telehealth as state response strategy: systematic review. Rev. Panam. Salud Publica Pan Am. J. Public Health 42 (2018).

Cen, Z. F. et al. Systematic literature review of adopting eHealth in pharmaceutical care during COVID-19 pandemic: recommendations for strengthening pharmacy services. BMJ Open 12 , e066246 (2022).

Chan, W. S. & Leung, A. Y. Use of social network sites for communication among health professionals: systematic review. J. Med. Internet Res. 20 , e117 (2018).

Chen, M. et al. Acceptance of clinical artificial intelligence among physicians and medical students: a systematic review with cross-sectional survey. Front. Med. 9 , 990604 (2022).

Article   Google Scholar  

Christensen, L. F., Moller, A. M., Hansen, J. P., Nielsen, C. T. & Gildberg, F. A. Patients’ and providers’ experiences with video consultations used in the treatment of older patients with unipolar depression: a systematic review. J. Psychiatr. Ment. Health Nurs. 27 , 258–271 (2020).

da Costa, C. B., Peralta, F. D. S. & Ferreira de Mello, A. L. S. How has teledentistry been applied in public dental health services? An integrative review. Telemed. J. E Health 26 , 945–954 (2020).

de Grood, C., Raissi, A., Kwon, Y. & Santana, M. J. Adoption of e-health technology by physicians: a scoping review. J. Multidiscip. Health 9 , 335–344 (2016).

Drissi, N. et al. A systematic literature review on e-mental health solutions to assist health care workers during COVID-19. Telemed. J. E Health 27 , 594–602 (2021).

Dutta, B. & Hwang, H.-G. The adoption of electronic medical record by physicians: a PRISMA-compliant systematic review. Medicine (Baltimore) 99 , e19290 (2020).

Early, J., Gonzalez, C., Gordon-Dseagu, V. & Robles-Calderon, L. Use of mobile health (mHealth) technologies and interventions among community health workers globally: a scoping review. Health Promot Pr. 20 , 805–817 (2019).

Ebneter, A. S., Sauter, T. C., Christen, A. & Eychmueller, S. Feasibility, acceptability and needs in telemedicine for palliative care. Swiss Med. Wkly. 152 , w30148 (2022).

Emmett, A., Kent, B., James, A. & March-McDonald, J. Experiences of health professionals towards using mobile electrocardiogram (ECG) technology: a qualitative systematic review. J. Clini. Nursing https://doi.org/10.1111/jocn.16434 (2022).

Ferdousi, R., Arab-Zozani, M., Tahamtan, I., Rezaei-Hachesu, P. & Dehghani, M. Attitudes of nurses towards clinical information systems: a systematic review and meta-analysis. Int Nurs. Rev. 68 , 59–66 (2021).

Fletcher, E. et al. Workload and workflow implications associated with the use of electronic clinical decision support tools used by health professionals in general practice: a scoping review. BMC Prim. Care 24 , 23 (2023).

Ftouni, R., AlJardali, B., Hamdanieh, M., Ftouni, L. & Salem, N. Challenges of telemedicine during the COVID-19 pandemic: a systematic review. BMC Med. Inform. Decis. Mak. 22 , 207 (2022).

Gagnon, M.-P. et al. Systematic review of factors influencing the adoption of information and communication technologies by healthcare professionals. J. Med Syst. 36 , 241–277 (2012).

Gagnon, M.-P., Ngangue, P., Payne-Gagnon, J. & Desmartis, M. m-Health adoption by healthcare professionals: a systematic review. J. Am. Med Inf. Assoc. 23 , 212–220 (2016).

Garvey, K. V. et al. Considering clinician competencies for the implementation of artificial intelligence–based tools in health care: findings from a scoping review. JMIR Med. Informatics 10 (2022).

Garavand, A., Aslani, N., Nadri, H., Abedini, S. & Dehghan, S. Acceptance of telemedicine technology among physicians: a systematic review. Informatics Med. Unlocked 30 (2022).

Ghimire, S., Martinez, S., Hartvigsen, G. & Gerdes, M. Virtual prenatal care: a systematic review of pregnant women’s and healthcare professionals’ experiences, needs, and preferences for quality care. Int. J. Med. Informatics 170 (2023).

Gonçalves, R. L. et al. Usability in telehealth systems for non-communicable diseases attention in primary care, from the COVID-19 pandemic onwards: a systematic review. J. Med. Internet Res. https://doi.org/10.2196/44209 (2023).

Grant, C., Jones, A. & Land, H. What are the perspectives of speech pathologists, occupational therapists and physiotherapists on using telehealth videoconferencing for service delivery to children with developmental delays? A systematic review of the literature. Aust. J. Rural Health 30 , 321–336 (2022).

Hagström, J. et al. Views, use, and experiences of web-based access to pediatric electronic health records for children, adolescents, and parents: scoping review. J. Med. Internet Res. 24 (2022).

Huang, R., Li, H., Suomi, R., Li, C. & Peltoniemi, T. Intelligent physical robots in health care: systematic literature review. J. Med. Internet Res. 25 (2023).

Ionescu, A., De Jong, P. G. M., Drop, S. L. S. & Van Kampen, S. C. A scoping review of the use of e-learning and e-consultation for healthcare workers in low- and middle-income countries and their potential complementarity. J. Am. Med. Inform. Assoc. 29 , 713–722 (2022).

Isidori, V. et al. Digital technologies and the role of health care professionals: scoping review exploring nurses’ skills in the digital era and in the light of the COVID-19 pandemic. JMIR Nurs. 5 , e37631 (2022).

Ismatullaev, U. V. U. & Kim, S. H. Review of the factors affecting acceptance of AI-infused systems. Human Factors . https://doi.org/10.1177/00187208211064707 (2022).

Jacob, C., Sanchez-Vazquez, A. & Ivory, C. Social, organizational, and technological factors impacting clinicians’ adoption of mobile health tools: systematic literature review. JMIR Mhealth Uhealth 8 , e15935 (2020).

Jimenez, G. et al. Digital health competencies for primary healthcare professionals: a scoping review. Int. J. Med. Informatics 143 (2020).

Jimma, B. L. & Enyew, D. B. Barriers to the acceptance of electronic medical records from the perspective of physicians and nurses: a scoping review. Inform. Med. unlocked 31 , 100991 (2022).

Joo, J. Y. Nurse-led telehealth interventions during COVID-19: a scoping review. Comput. Inform. Nurs. 40 , 804–813 (2022).

Jonasdottir, S. K., Thordardottir, I. & Jonsdottir, T. Health professionals? Perspective towards challenges and opportunities of telehealth service provision: a scoping review. Int. J. Med. Informatics 167 (2022).

Jose, A., Tortorella, G. L., Vassolo, R., Kumar, M. & Mac Cawley, A. F. Professional competence and its effect on the implementation of healthcare 4.0 technologies: scoping review and future research directions. Int. J. Environ. Res. Public Health 20 , 478 (2023).

Kane, H. et al. Opportunities and challenges for professionals in psychiatry and mental health care using digital technologies during the COVID-19 pandemic: systematic review. JMIR Human Factors 9 (2022).

Zhang, K. K. & Thompson, A. W. Effectiveness of electronic learning for continuing interprofessional education on behavior change of healthcare professionals: a scoping review. J. Interprofessional Care 1–11. https://doi.org/10.1080/13561820.2022.2071850 (2022)

Keyworth, C., Hart, J., Armitage, C. J. & Tully, M. P. What maximizes the effectiveness and implementation of technology-based interventions to support healthcare professional practice? A systematic literature review. BMC Med. Inf. Decis. Mak. 18 , 93 (2018).

Article   CAS   Google Scholar  

Koivunen, M. & Saranto, K. Nursing professionals’ experiences of the facilitators and barriers to the use of telehealth applications: a systematic review of qualitative studies. Scand. J. Caring Sci. 32 , 24–44 (2018).

Kolla, A., Lim, S., Zanowiak, J. & Islam, N. The role of health informatics in facilitating communication strategies for community health workers in clinical settings: a scoping review. J. Public Health Manag Pr. 27 , E107–E118 (2021).

Konnyu, K. J. et al. Changes to prenatal care visit frequency and telehealth: a systematic review of qualitative evidence. Obstet. Gynecol. 141 , 299–323 (2023).

Kruse, C. S. et al. Physician burnout and the electronic health record leading up to and during the first year of Covid-19: systematic review. J. Med. Internet Res. 24 (2022).

Laar, A. S., Harris, M. L., Shifti, D. M. & Loxton, D. Perspectives of health care professionals’ on delivering mHealth sexual and reproductive health services in rural settings in low-and-middle-income countries: a qualitative systematic review. BMC Health Services Research 22 (2022).

Lam, K. et al. Machine learning for technical skill assessment in surgery: a systematic review. NPJ digital Med. 5 , 24 (2022).

Lampickienė, I. & Davoody, N. Healthcare professionals’ experience of performing digital care visits—a scoping review. Life (Basel, Switzerland) 12 (2022).

Li, J., Talaei-Khoei, A., Seale, H., Ray, P. & Macintyre, C. R. Health care provider adoption of ehealth: systematic literature review. Interact. J. Med. Res. 2 , e7 (2013).

Li, L. & Cotton, A. A systematic review of nurses’ perspectives toward the telemedicine intensive care unit: a basis for supporting its future implementation in China? Telemed. J. E Health 25 , 343–350 (2019).

Lluch, M. Healthcare professionals’ organisational barriers to health information technologies-a literature review. Int. J. Med. Inf. 80 , 849–862 (2011).

Longhini, J., Rossettini, G. & Palese, A. Correction: digital health competencies among health care professionals: systematic review. J. Med. Internet Res. 24 , e43721 (2022).

Martin, G. et al. The impact of mobile technology on teamwork and communication in hospitals: a systematic review. J. Am. Med. Inf. Assoc. 26 , 339–355 (2019).

Marvaso, G. et al. Virtual and augmented reality as a novel opportunity to unleash the power of radiotherapy in the digital era: a scoping review. Appl. Sci. (Switzerland) 12 (2022).

Meunier, P. Y., Raynaud, C., Guimaraes, E., Gueyffier, F. & Letrilliart, L. Barriers and facilitators to the use of clinical decision support systems in primary care: a mixed-methods systematic review. Ann. Fam. Med. 21 , 57–69 (2023).

Moore, E. C., Tolley, C. L., Bates, D. W. & Slight, S. P. A systematic review of the impact of health information technology on nurses’ time. J. Am. Med Inf. Assoc. 27 , 798–807 (2020).

Muhiyaddin, R., Abd-Alrazaq, A. A., Househ, M., Alam, T. & Shah, Z. The Impact of Clinical Decision Support Systems (CDSS) on physicians: a scoping review. Stud. Health Technol. Inf. 272 , 470–473 (2020).

Mulita, F., Verras, G. I., Anagnostopoulos, C. N. & Kotis, K. A Smarter health through the internet of surgical things. Sensors (Basel, Switzerland) 22 (2022).

Namasivayam, P. et al. The use of telehealth in the provision of after-hours palliative care services in rural and remote Australia: A scoping review. PLoS ONE 17 , e0274861 (2022).

Nezamdoust, S., Abdekhoda, M., Ranjbaran, F. & Azami-Aghdash, S. Adopting mobile health applications by nurses: a scoping review. J. Res. Nurs. 27 , 480–491 (2022).

Nguyen, O. T. et al. A systematic review of contributing factors of and solutions to electronic health record-related impacts on physician well-being. J. Am. Med Inf. Assoc. 28 , 974–984 (2021).

Niazkhani, Z., Toni, E., Cheshmekaboodi, M., Georgiou, A. & Pirnejad, H. Barriers to patient, provider, and caregiver adoption and use of electronic personal health records in chronic care: a systematic review. BMC Med Inf. Decis. Mak. 20 , 153 (2020).

O’Connor, S. et al. Artificial intelligence in nursing and midwifery: a systematic review. J. Clinical Nursing . https://doi.org/10.1111/jocn.16478 (2022).

Odendaal, W. et al. Health workers’ perceptions and experiences of using mHealth technologies to deliver primary healthcare services: a qualitative evidence synthesis. Cochrane Database Syst. Rev . https://doi.org/10.1002/14651858.CD011942.pub2 (2020).

Osman, M. A. et al. Barriers and facilitators for implementation of electronic consultations (eConsult) to enhance access to specialist care: a scoping review. BMJ Glob. Health 4 , e001629 (2019).

Papadopoulos, I., Koulouglioti, C. & Ali, S. Views of nurses and other health and social care workers on the use of assistive humanoid and animal-like robots in health and social care: a scoping review. Contemp. Nurse 54 , 425–442 (2018).

Police, R. L., Foster, T. & Wong, K. S. Adoption and use of health information technology in physician practice organisations: systematic review. Inf. Prim. Care 18 , 245–258 (2010).

Prakash, S., Balaji, J. N., Joshi, A. & Surapaneni, K. M. Ethical conundrums in the application of Artificial Intelligence (AI) in healthcare—a scoping review of reviews. J. Personalized Med. 12 (2022).

Rahal, R. M., Mercer, J., Kuziemsky, C. & Yaya, S. Factors affecting the mature use of electronic medical records by primary care physicians: a systematic review. BMC Med Inf. Decis. Mak. 21 , 67 (2021).

Ramachandran, H. J. et al. Barriers and facilitators to the adoption of digital health interventions for COPD management: a scoping review. Heart Lung: J. Crit. Care 59 , 117–127 (2023).

Ratshidi, L., Grobbelaar, S. & Botha, A. Conceptualising the factors influencing community health workers’ preparedness for ICT implementation: a systematised scoping review. Sustainability (Switzerland) 14 (2022).

Vukušić Rukavina, T. et al. Dangers and benefits of social media on e-professionalism of health care professionals: scoping review. J. Med. Internet Res. 23 , e25770 (2021).

Saigí-Rubió, F. et al. The current status of telemedicine technology use across the World Health Organization European Region: an overview of systematic reviews. J. Med. Internet Res. 24 , e40877 (2022).

Sipanoun, P., Oulton, K., Gibson, F. & Wray, J. The experiences and perceptions of users of an electronic patient record system in a pediatric hospital setting: a systematc review. Int. J. Med. Informatics 160 , (2022).

Sullivan-Tibbs, M. A. et al. Social work’s response during the COVID-19 pandemic: a systematic literature review—balancing telemedicine with social work self-care during a pandemic. Soc. Work Public Health 37 , 499–509 (2022).

Tabaeeian, R. A., Hajrahimi, B. & Khoshfetrat, A. A systematic review of telemedicine systems use barriers: primary health care providers’ perspective. J. Sci. Technol. Policy Manag . https://doi.org/10.1108/JSTPM-07-2021-0106 (2022).

Tegegne, M. D. et al. Electronic medical record system use and determinants in ethiopia: systematic review and meta-analysis. Interact. J. Med. Res. 12 , e40721 (2023).

Tickner, C., Heinsch, M., Brosnan, C. & Kay-Lambkin, F. “Another tool in our toolbox”: a scoping review of the use of eHealth technologies in health social work practice. Soc. Work Health Care 62 , 1–18 (2023).

Torres-Castano, A. et al. Ethical, legal, organisational and social issues of teleneurology: a scoping review. Int. J. Environ. Res. Public Health 20 (2023).

Poissant, L., Pereira, J., Tamblyn, R. & Kawasumi, Y. The impact of electronic health records on time efficiency of physicians and nurses: a systematic review. J. Am. Med Inf. Assoc. 12 , 505–516 (2005).

Vejdani, M. et al. Electronic prescription system requirements: a scoping review. BMC Med. Inform. Decis. Mak. 22 , 231 (2022).

Verma, P. & Kerrison, R. Patients’ and physicians’ experiences with remote consultations in primary care during the COVID-19 pandemic: a multi-method rapid review of the literature. BJGP Open 6 , BJGPO.2021.0192 (2022).

von Wedel, P. & Hagist, C. Economic value of data and analytics for health care providers: Hermeneutic systematic literature review. J. Med. Internet Res . 22 , (2020).

Walle, A. D. et al. Readiness to use electronic medical record systems and its associated factors among health care professionals in Ethiopia: A systematic review and meta-analysis. Inform. Med. Unlocked 36 , 101140 (2023).

Walsh, L., Hong, S. C., Chalakkal, R. J. & Ogbuehi, K. C. A systematic review of current teleophthalmology services in new zealand compared to the four comparable countries of the United Kingdom, Australia, united states of America (USA) and Canada. Clin. Ophthalmol. 15 , 4015–4027 (2021).

Wisner, K., Lyndon, A. & Chesla, C. A. The electronic health record’s impact on nurses’ cognitive work: An integrative review. Int J. Nurs. Stud. 94 , 74–84 (2019).

Xyrichis, A. et al. Healthcare stakeholders’ perceptions and experiences of factors affecting the implementation of critical care telemedicine (CCT): qualitative evidence synthesis. Cochrane Database Syst. Rev. 2 , CD012876 (2021).

PubMed   Google Scholar  

Young, L. B., Chan, P. S. & Cram, P. Staff acceptance of tele-ICU coverage: a systematic review. Chest 139 , 279–288 (2011).

Zakerabasali, S., Ayyoubzadeh, S. M., Baniasadi, T., Yazdani, A. & Abhari, S. Mobile health technology and healthcare providers: systemic barriers to adoption. Health. Inf. Res. 27 , 267–278 (2021).

Zhang, J., Lu, V. & Khanduja, V. The impact of extended reality on surgery: a scoping review. Int. Orthop. 47 , 611–621 (2023).

Zhang, Z. et al. Smart glasses for supporting distributed care work: systematic review. JMIR Med. Inform. 11 , e44161 (2023).

Davis, M. M., Freeman, M., Kaye, J., Vuckovic, N. & Buckley, D. I. A systematic review of clinician and staff views on the acceptability of incorporating remote monitoring technology into primary care. Telemed. J. E Health 20 , 428–438 (2014).

Nizeyimana, E., Joseph, C., Plastow, N., Dawood, G. & Louw, Q. A. A scoping review of feasibility, cost, access to rehabilitation services and implementation of telerehabilitation: Implications for low- and middle-income countries. Digital Health 8 (2022).

Thomas Craig, K. J., Willis, V. C., Gruen, D., Rhee, K. & Jackson, G. P. The burden of the digital environment: a systematic review on organization-directed workplace interventions to mitigate physician burnout. J. Am. Med Inf. Assoc. 28 , 985–997 (2021).

Borges do Nascimento, I. J. Barriers, facilitators, and recommendations visual mind mapping. https://gitmind.com/app/docs/mv5g6b6x .

Svendsen, M. T., Tiedemann, S. N. & Andersen, K. E. Pros and cons of eHealth: a systematic review of the literature and observations in Denmark. SAGE Open Med . 9 (2021).

Powell, K. R. Patient-perceived facilitators of and barriers to electronic portal use: a systematic review. Comput. Inf. Nurs. 35 , 565–573 (2017).

O’Connor, S. et al. Understanding factors affecting patient and public engagement and recruitment to digital health interventions: a systematic review of qualitative studies. BMC Med. Inf. Decis. Mak. 16 , 120 (2016).

Miller, D. G., Pierson, L. & Doernberg, S. The role of medical students during the COVID-19 pandemic. Ann. Intern. Med. 173 , 145–146 (2020).

Menon, A., Klein, E. J., Kollars, K. & Kleinhenz, A. L. W. Medical students are not essential workers: examining institutional responsibility during the COVID-19 pandemic. Acad. Med. https://doi.org/10.1097/ACM.0000000000003478 (2020)

Bower, B. L. & Hardy, K. P. From correspondence to cyberspace: changes and challenges in distance education. N. Directions Community Coll. 2004 , 5–12 (2004).

Bethany D. W., Gattenberg R. W., & Freed K. ‘Societal Issues in Distance Learning’, September 2001, University of South Florida and ‘Technological Rug down the Hallways of Distance Education’. Wikipedia (2001).

Soumik Sarka. A. Brief History of Online Education. Adamas University https://adamasuniversity.ac.in/a-brief-history-of-online-education/ (2020).

Deepika, V., Soundariya, K., Karthikeyan, K. & Kalaiselvan, G. ‘Learning from home’: role of e-learning methodologies and tools during novel coronavirus pandemic outbreak. Postgrad. Med. J. 97 , 590–597 (2021).

Grafton-Clarke, C. et al. Pivot to online learning for adapting or continuing workplace-based clinical learning in medical education following the COVID-19 pandemic: A BEME systematic review: BEME Guide No. 70. Med. Teach. 44 , 227–243 (2022).

Jhurree, V. Technology integration in education in developing countries: Guidelines to policy makers. Int. Educ. J. 6 , 467–483.

Hipkins, C. New digital technologies for schools and kura. The Beehive https://www.beehive.govt.nz/release/new-digital-technologies-schools-and-kura .

Chang, J. et al. Real-time display of laboratory testing status improves satisfaction levels in an emergency department: A pilot study. J. Clin. Lab Anal. 34 , e23290 (2020).

Overhage, J. M., Suico, J. & McDonald, C. J. Electronic laboratory reporting: barriers, solutions and findings. J. Public Health Manag. Pr. 7 , 60–66 (2001).

CAS   Google Scholar  

EUROSTAT - Statistics Explained. Cloud computing—statistics on the use by enterprises. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Cloud_computing_-_statistics_on_the_use_by_enterprises (2021).

Cismondi, F. et al. Reducing unnecessary lab testing in the ICU with artificial intelligence. Int J. Med. Inf. 82 , 345–358 (2013).

Noblin, A., Cortelyou-Ward, K. & Servan, R. M. Cloud computing and patient engagement: leveraging available technology. J. Med. Pr. Manag. 30 , 89–93 (2014).

Pollock, M. et al. Preferred Reporting Items for Overviews of Reviews (PRIOR): a protocol for development of a reporting guideline for overviews of reviews of healthcare interventions. Syst. Rev. 8 , 335 (2019).

Page, M. J. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372 , n71 (2021).

Higgins J. P. T. et al. (eds) Cochrane Handbook for Systematic Reviews of Interventions version 6.3 . (Cochrane, 2022). Available from www.training.cochrane.org/handbook .

Fusar-Poli, P. & Radua, J. Ten simple rules for conducting umbrella reviews. Evid. Based Ment. Health 21 , 95–100 (2018).

Cornell, J. E. & Laine, C. The science and art of deduction: complex systematic overviews. Ann. Intern. Med. 148 , 786–788 (2008).

World Health Organization. Digital Health—draft Resolution Proposed by Algeria, Australia, Brazil, Estonia, Ethiopia, Germany, India, Indonesia, Israel, Italy, Luxembourg, Mauritius, Morocco, Panama, Philippines and South Africa (WHO, 2018).

World Health Organization. WHO Guideline: Recommendations on Digital Interventions for Health System Strengthening (WHO, 2019).

Jiang, Y. et al. Predicting peritoneal recurrence and disease-free survival from CT images in gastric cancer with multitask deep learning: a retrospective study. Lancet Digit Health 4 , e340–e350 (2022).

Venkateswaran, M. et al. A digital health registry with clinical decision support for improving quality of antenatal care in Palestine (eRegQual): a pragmatic, cluster-randomised, controlled, superiority trial. Lancet Digit Health 4 , e126–e136 (2022).

Covidence systematic review software. Melbourne, Australia: Veritas Health Innovation. www.covidence.org . Covidence.

Shea, B. J. et al. AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ 358 , j4008 (2017).

Microsoft Corporation. (2018). Microsoft Excel. Retrieved from https://office.microsoft.com/excel .

van Eck, N. J. & Waltman, L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84 , 523–538 (2010).

Thomas, J. & Harden, A. Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Med. Res. Methodol. 8 , 45 (2008).

Tong, A., Flemming, K., McInnes, E., Oliver, S. & Craig, J. Enhancing transparency in reporting the synthesis of qualitative research: ENTREQ. BMC Med Res Methodol. 12 , 181 (2012).

Jackson, K. & Bazeley, P. Qualitative Data Analysis with NVivo . (3rd Edition) (Sage Publications, 2019).

Borges do Nascimento, I. J. et al. Coronavirus disease (COVID-19) pandemic: an overview of systematic reviews. BMC Infect. Dis. 21 , 525 (2021).

Lewin, S. et al. Applying GRADE-CERQual to qualitative evidence synthesis findings: introduction to the series. Implement Sci. 13 , 2 (2018).

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Acknowledgements

This project is a result of an international task force to provide high-quality evidence in Medicine. Authors and contributors are mostly active Cochrane members. We are highly grateful for the research collaborators involved in the data extraction phase of this project, including Dr. Kusum Singal (Scientist Medical Doctor, ICMR—Evidence Based Center for Child Health, Post Graduate Institute of Medical Education and Research, Chandigarh, India), Anirudha Agnihoty (Biomedical Sciences, Arthur A Dugoni School of Dentistry, University of the Pacific, San Francisco, United States), Muhammad Ayyan (King Edward Medical University, Pakistan), and Atiya Karim (London School of Hygiene and Tropical Medicine, University of London, London, United Kingdom). We thank Anneliese Arno (University College London, England) for granting complimentary access to Covidence. Lasse Østengaard also represents the University Library of Southern Denmark, University of Southern Denmark, Odense, Denmark. Israel Júnior Borges do Nascimento is affiliated with the School of Medicine and University Hospital at the Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil. We thank Prf. Dr. Érika Amâncio Caetano (Department of Applied Linguistics at the Federal University of Minas Gerais, Brazil) for supporting and providing insights on the thematic analysis assessment. There was no funding for this project. D.N.-O., N.A.-M., and T.Z. are staff members of the WHO. The authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy, or views of the WHO.

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Borges do Nascimento, I.J., Abdulazeem, H., Vasanthan, L.T. et al. Barriers and facilitators to utilizing digital health technologies by healthcare professionals. npj Digit. Med. 6 , 161 (2023). https://doi.org/10.1038/s41746-023-00899-4

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The Role of Patient-facing Technologies to Empower Patients and Improve Safety

Perspective

Patient-centered care and patient engagement have become central components of the modern clinical encounter. The National Academy of Medicine defines patient-centered care as "care that is respectful of and responsive to individual patient preferences, needs, and values, and ensuring that patient values guide all clinical decisions."( 1 ) Technology, with its array of capabilities to measure data, manage information, and automate processes, plays an integral role in health care's ability to adequately respond to patient preferences, needs, and values in the guidance of clinical decisions. Central to fulfilling this role is ensuring that care is delivered in a safe and effective manner. With literally hundreds of thousands of preventable deaths and millions of avoidable adverse events happening each year, the stakes are simply too high not to employ every tool at our disposal to improve patient safety.

Health information technology (IT) represents one of the most important tools providers can employ to improve safety. Substantial gains in safety have been demonstrated with the enhanced management of health information using electronic health records that support standardized documentation and easier access to patient records, along with approaches that improve and standardize communication. Even though significant challenges are associated with this digital health age ( 2 ), the electronic management of health care information and the automation of clinical processes are making important strides to address the patient safety risk inherent with variation in care delivery.

However, these technology-enabled standardized approaches only get us so far to delivering patient-centered care. Information technology must also enable patient engagement across the continuum of care, so that the same level of attention toward preventing adverse events is paid to understanding and addressing patients' preferences, needs, and values. For example, the growing demand for improved and more efficient communication between health care providers and patients has created an impetus to harness patient-facing technologies and consumer e-health tools to promote patient engagement and empowerment.( 3,4 )

All in all, some of these patient-facing tools give patients the opportunity to be more responsible for their care by providing them with the ability to access health information, choose providers, and manage their health care.( 3 ) For example, patient-facing health records and patient portals allow patients to view, verify, and act on their health data from preferable access points outside of the traditional provider environment. Other tools allow patients to communicate directly with their care team, coordinate care across caregivers, and interact with other patients with similar health conditions, creating a broader and more connected health care network.( 3 ) Patients also now have access to mobile health apps that offer tailored information, education, networking, and monitoring that align with patient needs and values. These are only a few of the many illustrations of technology's ability to make the delivery of health care more patient centered.

Technologies that engage patients to report their care experience, outcomes, and follow-up activities empower them to improve the way health care providers deliver care and measure their performance. Telehealth and secure messaging open communication channels between providers, between patients and providers, or between similar patients. Together, these tools can increase transparency, manage expectations, and instill trust when patients are at their most vulnerable. Novel platforms like social media also create new opportunities for patients and families to participate actively in their care, self-manage their medical problems, learn from those with similar conditions, improve communication with their health care providers, and even report safety issues.( 5-7 ) Moreover, they give providers new channels to engage with patients in ways that patients prefer to communicate, furthering the move toward patient-centered care.( 5-7 )

Enhancing patient engagement via health IT has been shown to promote patient behavior that leads to positive health outcomes, improved satisfaction and care delivery efficiency, reduced costs, and better quality of care and patient safety.( 8,9 ) For example, in a study that we conducted recently in the medical intensive care unit (ICU), implementation of a structured patient-centered care and engagement training program and information technology platform (including an ICU safety checklist and patient-facing information tools) was associated with a reduction in adverse events and improved patient and care partner satisfaction.( 10 )

While technology holds immense promise to help improve patient safety and outcomes, it can only serve as an enabler for better care, rather than supplant the processes and people that deliver and receive it. As in any major change management initiative, three major components determine the endeavor's success: technology, processes, and people. Our recent study of key figures from leading US health systems, policymakers, and vendors found that success of health IT in general and new predictive analytics tools in particular depends less on the tools themselves than on getting buy-in at all levels from the start.( 11 ) Therefore, technology's next frontier in improving patient safety and quality of care is engaging the people that deliver and receive it.

Challenges to patient-facing technologies are many, ranging from low patient and provider awareness to structural challenges to technology implementation. The available adoption data suggest that ongoing patient usage rates of health IT modalities remain low.( 12,13 ) Some of the challenges to patient adoption of health IT could be related to lack of patient awareness, limited health literacy, lower socioeconomic status, older age, inadequate computer skills, and unmet technical support needs.( 3,13 ) Some of these challenges are related to organizational strategies (or lack thereof) for promoting patient and provider uptake of patient-facing technologies, such as personal health records.( 14 ) Organizations should make such uptake a strategic investment priority, targeting specific populations and monitoring their uptake.( 14 ) Another challenge is related to patient-facing mobile health applications (mHealth apps). While the number of available smartphones and mHealth apps has grown substantially, research suggest that few apps address the needs of the patients who could benefit the most, and many of the apps are not safe or secure.( 15 ) Finally, in some cases patient-facing health IT may raise ethical and patient privacy issues; for example, when patients lack decisional capacity and their proxy decision makers use the health IT tools on their behalf.( 16 ) Nevertheless, health IT tools that enable patient engagement are likely to grow in importance, as their potential is further understood and harnessed by policymakers, providers, and patients.

Patient-facing technology has the potential to improve quality and safety by enabling patients to take a more active role in their care. By maintaining focus on the people as much as the processes, technology will boost the progression toward patient-centered care—where patient preferences, needs, and values are emphasized as much as efforts to prevent adverse events. By combining these two endeavors, technology can play an unparalleled role in making health care safer, more efficient, and more coordinated.

Ronen Rozenblum, PhD, MPH Assistant Professor, Harvard Medical School Director, Unit for Innovative Healthcare Practice & Technology Brigham and Women's Hospital

David Bates, MD, MS Professor of Medicine, Harvard Medical School Chief, Division of General Internal Medicine and Primary Care Brigham and Women's Hospital Medical Director, Clinical and Quality Analysis, Information Systems Partners HealthCare System, Inc.

1. Corrigan JM, Donaldson MS, Kohn LT, eds. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academy Press; 2001.

2. Wachter R. The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine's Computer Age. New York, NY: McGraw-Hill; 2015. ISBN: 9780071849463.

3. Rozenblum R, Miller P, Pearson D, Marielli A. Patient-centered healthcare, patient engagement and health information technology: the perfect storm. In: Grando M, Rozenblum R, Bates D, eds. Information Technology for Patient Empowerment in Healthcare. Berlin: Walter de Gruyter Inc; 2015: 3-22. ISBN: 9781614515920.

4. Grando M, Rozenblum R, Bates DW, eds. Information Technology for Patient Empowerment in Healthcare. Berlin, Germany: Walter de Gruyter Inc.; 2015. ISBN: 9781614515920.

5. Rozenblum R, Bates DW. Patient-centered healthcare, social media and the internet: the perfect storm? BMJ Qual Saf. 2013;22:183-186. [go to PubMed]

6. Rozenblum R, Greaves F, Bates DW. The role of social media around patient experience and engagement. BMJ Qual Saf. 2017;26:845-884. [go to PubMed]

7. Hawkins JB, Brownstein JS, Tuli G, et al. Measuring patient-perceived quality of care in US hospitals using Twitter. BMJ Qual Saf. 2016;25:404-413. [go to PubMed]

8. Wagner PJ, Dias J, Howard S, et al. Personal health records and hypertension control: a randomized trial. J Am Med Inform Assoc. 2012;19:626-634. [go to PubMed]

9. Davis Giardina T, Menon S, Parrish DE, Sittig DF, Singh H. Patient access to medical records and healthcare outcomes: a systematic review. J Am Med Inform Assoc. 2014;21:737-741. [go to PubMed]

10. Dykes PC, Rozenblum R, Dalal A, et al. Prospective evaluation of a multifaceted intervention to improve outcomes in intensive care: the promoting respect and ongoing safety through patient engagement communication and technology study. Crit Care Med. 2017;45:e806-e813. [go to PubMed]

11. Kakad M, Rozenblum R, Bates DW. Getting buy-in for predictive analytics in health care. Harv Bus Rev. May-June 2017;95:2-5. [Available at]

12. Bates DW, Wells S. Personal health records and health care utilization. JAMA. 2012;308:2034-2036. [go to PubMed]

13. Ahern DK, Woods SS, Lightowler MC, Finley SW, Houston TK. Promise of and potential for patient-facing technologies to enable meaningful use. Am J Prev Med. 2011;40(suppl 2):S162-S172. [go to PubMed]

14. Wells S, Rozenblum R, Park A, Dunn M, Bates DW. Organizational strategies for promoting patient and provider uptake of personal health records. J Am Med Inform Assoc. 2015;22:213-222. [go to PubMed]

15. Singh K, Drouin K, Newmark LP, et al. Many mobile health apps target high-need, high-cost populations, but gaps remain. Health Aff (Millwood). 2016;35:2310-2318. [go to PubMed]

16. Brown SM, Aboumatar HJ, Francis L, et al; Privacy, Access, and Engagement Task Force of the Libretto Consortium of the Gordon and Betty Moore Foundation. Balancing digital information-sharing and patient privacy when engaging families in the intensive care unit. J Am Med Inform Assoc. 2016;23:995-1000. [go to PubMed]

In Conversation With… Wanda Pratt, PhD

Editor's note: Wanda Pratt is a Professor in the Information School with an adjunct appointment in the Division of Biomedical and Health Informatics in the Medical School at the University of Washington. Her research focuses on understanding patients' needs and designing new technologies to address those needs. We spoke with her about patient-facing technologies, including the opportunities and challenges for patient safety.

Dr. Robert M. Wachter : What are some general principles of both technology and human cognition that one needs to understand your work and the kinds of tools that actually are helpful?

Wanda Pratt : A lot of my work brings in material that we have learned from the field of human–computer interaction. Understanding what the computer does well, what people do well, how we can study people and their needs and come up with solutions that blend the best of both worlds, rather than creating technology that makes the problem worse. Our goal is to develop technologies that help people in ways that they cannot do alone. For example, people cannot remember things very well. Many studies have shown people can hold around seven items in working memory. Therefore, it is helpful to have technology be an external memory source that they can return to when they need the information and don't have to worry about holding it all in their head at the same time, which can create stress and other problems.

RW : What were your hopes and dreams about what patients might be able to do using technologies that weren't true at the time you entered the field in the mid-1990s? If you allowed yourself to fantasize out a decade or two, what did you think you might accomplish?

WP : My hope was that patients could be less dependent on clinicians as the only source of information and have some more independence and autonomy. In some ways that's a good goal, and in other ways that human interaction is really important. Then there are other aspects of information resources that could be automated and supplied in other ways.

RW : As you've seen that play out now over 20 or so years, what parts have worked particularly well, and what parts have been more challenging than you expected?

WP : Everything is slower than I expected, which is not too surprising. Many issues are less technological and more policy and social issues. We've developed a lot of interesting technology tools. But the policies, the social interactions, and the commercial world has some challenges, too, in not picking up a lot of advances from the research world and making technology better. The current electronic health record systems have a lot of problems from a user interface standpoint. We know a lot about the whole interaction and integration with work from a research perspective, but don't seem to be pulling into the commercial world where it could actually have a bigger impact with the systems that are out there now.

RW : Any idea what the gaps are about?

WP : Thinking about it from a patient's perspective, the primary role of my work, a lot of systems that were designed to have taken what was already designed for a clinician's interface, for nurses and physicians, and just switched it over to make it visible to a patient. Everything we know about human–computer interaction is that different types of people need different kinds of systems and support. I don't think that designers of these technologies have taken the perspective of what do patients need and how can we understand their needs and perspectives and develop tools to support them, rather than just turning on an information flow to patients that was designed for clinicians.

RW : Do you think the world is so different that those problems need to be solved by different companies—that the company that built an electronic health record for doctors and nurses cannot have, or is unlikely to build, the skillset and sensibility to appreciate what a patient might need?

WP : I think they could. They would need people on their team with different expertise and different perspectives. They wouldn't want the same design team that built the clinician-facing record, because they will come with biases and their own expertise and perspectives. It is time to bring on a team that has the patient perspective and look at it from that angle. I don't see any reason why the company itself couldn't do that.

RW : It is obvious the language has to be different, and someone who knows the language of medicine and thinks that way may not understand the health literacy level of an average patient. But are there other more subtle differences in how you design for a patient than for a nurse or a doctor?

WP : There are many differences. I would say that the language barrier is the smaller one. I've seen in my own research that people will adapt to the language; if it is important enough to them, they will learn it and figure it out. The issue is that we largely don't understand what the goals and perspectives of patients are. We found that even having a history of who has been in the room, which clinicians don't usually need or want because they know that information, is important. But the patient wants that information, and that has a strong role and some safety and well-being implications as well. Another perspective was thinking about the hospital environment for caregivers, particularly parents of young children feeling trapped in the room waiting for when a doctor could come take a look and have a valuable information exchange. Then they run to the bathroom or to get something to eat, and when they come back they've missed that appointment. So in some of our work, caregivers have designed different kinds of apps to address those problems. That all points back to the fact that we don't know what problems patients are experiencing and what information they need to meet their goals. It is not just their life as a patient. They have a whole life, so thinking about how that connects to the rest of their life is also really important.

RW : When you talked about the needs of patients in the beginning, were you thinking about peer-to-peer communities, or was it more about information access, retrieval, and communications with a more traditional health care system?

WP : When I first got started, I definitely wasn't thinking about peer-to-peer information exchange. Now I see that as extremely valuable. Particularly in rare diseases, patients sometimes know more about that particular health condition than the average clinician they interact with because the clinician might not see it very often. Yet the patient has been dealing with it for a long time, read about it on their own, and learned a lot . That valuable information resource is sometimes discounted and not recognized or supported by the health care community or by technology. Obviously, there are online communities, but they are not really tailored for the health angle or trying to exchange that expertise. The one exception would be Patients Like Me , which actually is doing a good job of trying to respect the peer-to-peer information exchange among patients.

RW : What is the design thinking that seems to make a difference in building a working community of patients?

WP : There are a variety of things. Some of our work has looked at whether it is helpful if a clinician is involved in the community. The answer is usually it is not helpful because the dynamic of the community changes and it often shuts down lines of communication. Yet there can be some worry about misinformation. So having resources and technology to help flag potential misinformation for moderators or other peers would be quite helpful. That said, from studies that I have done, peers are pretty good at policing that themselves and pointing out challenges in a way that keeps the conversation going.

RW : How structured does that need to be? Or is that a natural outgrowth of a community that somebody will call people out when they're getting off into cures or recommendations that have no scientific validity? Is that a natural evolution, or do you have to structure the technology or organize the moderation that facilitate that?

WP : I've seen it happen naturally pretty well. But moderating and maintaining a healthy community is a time-consuming and challenging job, so there is a lot of room for technology to help flag these situations, to ease the job of either the experienced participants who are trying to make sure that the community stays healthy and safe or to the official moderators.

RW : What are the risks to patient safety of patients becoming more engaged through digital tools, particularly engaged in their own care?

WP : The one I hear expressed is a concern that if patients get this information, they won't understand it well, which could create more misunderstandings between patients and care teams. Also, there are worries that patients will react poorly to certain information if it indicates that they have a poor prognosis. Or it could cause them to have an antagonistic relationship with their clinical care team if their interpretation of the information is different than their care team's interpretation. The big concerns are interactions and delays in care, possibly if patients are hesitant to follow the recommendations of the clinical care team.

RW : What does research say about those risks?

WP : Our survey work, interviews, and observations indicate that the vast majority of patients are very respectful of clinicians and their time and don't want to be "difficult patients." That is a barrier right now—that patients often will not speak up when they should because they don't want to be that difficult patient. It would be good to find ways that technologies could help make it easier for patients to flag issues without generating worry of getting in the way. But bringing in their own knowledge of what they're experiencing and what's unusual for them and being able to take that personal expertise and pull that into the safety role. I see that as really important.

RW : You've talked about patients seeing things in their record and identifying problems. You haven't talked much about patients truly doing self-care. We're moving to a world where they're Googling diagnoses, taking a picture of their rashes, and using an app to see if that's a skin cancer. How do you see that playing out in terms of safety threats or opportunities?

WP : I see that as an opportunity more than a threat. There are times when people are hesitant to seek care. If these kinds of technologies and tools can help them see when it is urgent for them to seek care outside of the home that would be very helpful. Even along the lines of peers supporting each other . Someone posting on social media, "This is happening to my child." And others coming in and saying, "I've heard of this. You should go to the ER and have this looked at by a professional." So in some ways, I see this as more likely to mitigate safety problems and concerns than cause problems.

RW : In the last several years, the rate of physician burnout has increased. Many attribute it to their electronic health records. Some of it is the clunkiness of it. But some of it seems like we've opened up the spigot for patients to be able to connect with their physicians in new ways. My primary care doctors at UCSF will talk about going home and having 3 hours of digital work to do that they didn't used to do before. How do you see that and do you see any solutions to those problems?

WP : That's a big problem. One solution could be some triaging of who deals with what kinds of problems. There are obviously challenges with important issues not getting triaged appropriately. The hope is if patients become more self-reliant, then maybe that is not as important, and maybe the interactions that do happen then are the more important ones as well as the more interesting ones from a clinician's perspective. I've talked with people who have been part of the OpenNotes Project and heard from physicians who were skeptical about it and worried about both the additional workload that it would create and the potential bad interactions that could happen between a physician and a patient. But by and large, it seems like most people have had very positive experiences and felt like they could actually have better interactions that weren't them repeating something over and over again, or having to justify previous things because the patient had time to look at the information and process it. Obviously, there would be exceptions to that. But the hope would be that it would make the work more rewarding and reduce the work that is fueling the burnout.

RW : You've done some work on the quantified self idea . I remember hearing about somebody who was monitoring every single bodily function for months at a time. What's your reaction to all this?

WP : I work in an information school; I think information is a very positive thing. Obviously, it needs to be processed and used in the right way. I see those technologies and information as potentially being very helpful for noticing potential problems earlier, for helping you reflect on your own behavior and how that influences your health and your well-being. But it could be taken to an extreme for people who have obsessive-compulsive challenges. Having that kind of information could fuel the anxiety of someone who is already very paranoid about their health when there are minor changes that are just fluctuations over time and not really indicative of health problems per se.

RW : When you think about the ability to monitor and manage things that maybe patients didn't have 10 years ago, can you think of a case where you think this is playing out in a very positive way?

WP : It has been particularly useful for different gastrointestinal issues, irritable bowel diseases, inflammatory bowel diseases, and people being able to keep track of how their own behaviors—whether it be stress, what they're eating, how much exercise they're getting, how much sleep they're getting—and be able to see correlations in their own disease states that they weren't able to see before and have been really powerful motivators in making changes to their health and lifestyle to improve their disease states. We have seen concrete evidence that is helping. I’d also include diabetes, where a person can take certain actions and it has an influence on their disease. Before these technologies, it was harder to notice those changes. There are some interesting trends for looking at the microbiome as well, and that has strong connections with these gastrointestinal issues, too.

RW : You said progress has gone more slowly than you expected. Do you think the same slow pace is likely to continue for the next decade or do you see an inflection point, and if you do, why?

WP : I do see an inflection point—the combination of the Internet, social media, and availability of information as well as new sensing technologies and the ability of patients to get their own health data. That will be a big change in both the technologies for health and probably in the way health is delivered. Patients will start making demands for things to change faster than they have in the past. They will be a forcing function in ways that health policies haven't been quite as effective. I think patients acting as their own safeguards is an underrepresented space in patient safety right now. But I don't think it is a panacea. It needs to be supported better. But it has the potential to be very helpful.

This project was funded under contract number 75Q80119C00004 from the Agency for Healthcare Research and Quality (AHRQ), U.S. Department of Health and Human Services. The authors are solely responsible for this report’s contents, findings, and conclusions, which do not necessarily represent the views of AHRQ. Readers should not interpret any statement in this report as an official position of AHRQ or of the U.S. Department of Health and Human Services. None of the authors has any affiliation or financial involvement that conflicts with the material presented in this report. View AHRQ Disclaimers

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Seven Nursing Technologies Transforming Patient Care

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From pizza ordering to financial security, new technology is changing the way we live our lives every day. And now more than ever before, new technology is finding ways to impact the healthcare industry.

A new survey of over 600 professional nurses found that 82% agree that new technology and equipment innovation will positively impact patient care.

How Do Nursing Technologies Help Patients?

As more of the population ages, as life expectancy increases, and as the nursing shortage continues, these new medical technologies are crucial for continued patient care and the healthcare system at large. New medical technologies can make life easier for medical professionals and patients alike. Certain technologies can make patient care easier and more efficient for the doctors and nurses who manage a large patient load. They can also assist patients in getting the care they need with more convenient and accessible options.

The nursing profession finds many of these new medical technologies help them with routine processes, as well as decrease human mistakes and errors that can come from too few nurses who are working long hours with too many patients.

While nurses agree that new healthcare technology and innovative medical devices can help them, they also agree that technology shouldn’t replace day-to-day human interactions. Working directly with patients is a huge element of healthcare, and nurses provide a crucial element of interaction that allows patients to feel at ease.

Working with families, explaining procedures, and helping to take a patient’s mind off their sickness are all part of nursing jobs. Many healthcare professionals worry that increased healthcare technology could try to remove that human element. Unlike in other sectors, healthcare’s human interaction is crucial for patient success. It’s critical to find the right balance between technology and the human nuances that make nursing and healthcare successful.

Professionals also largely agree there shouldn’t be an overreliance on healthcare software and technology, and that human eyes on both symptoms and needs should be as important as what healthcare technology is saying. While technological advancements aren’t a cure-all as healthcare solutions, new technology is changing the way nurses work in positive ways.

There are seven stand-out technologies transforming medical care. As nurses are educated about new medical technologies and practices, patients and providers benefit.

So, what nursing technologies are being used?

1. Automated IV Pumps

Automated IV pumps control the dosages and drips given to patients. Software and medical tech allows nurses to change the drip amounts and medication doses so patients aren’t waiting for changes. There are IV pumps for nutrition that give needed meals at the right times. Additionally, there are self-pumps that allow patients to increase a controlled amount of pain medication for themselves.

Automated IV pumps help speed up nursing processes and can be crucial if there is a need for immediate adjustment. Changing medication through an automated process also removes elements of human error that could present issues for clinical patients and hospitals.

Automated IV kits give nurses opportunities to focus on other areas of work, instead of having to measure and give medication or food. Hospitals all have different kinds of automated IV pumps, so training and education is often based on the specific hospital or clinic where nurses work.

Additionally, many nursing schools give training and information on new software and technology, including how automated IV pumps work and why they’re valuable.

2. Portable Monitors

Portable monitor equipment allows nursing professionals to check on patients, even if they’re on the move or busy helping someone else. Portable devices monitor vital signs like ECG, respiratory rates, and oxygen saturations while transmitting the information back to a central monitor. This means that nurses will get an alarm notification if there’s an emergency.

Most hospitals have nurses check levels hourly. Portable monitoring technology allows nurses to track and note stats at the right time, even if there are a lot of other things going on. This helps them constantly monitor patients from anywhere in the hospital. The alerts and alarms sent to nurses through the portable monitor can save lives.

Nurses learn at their specific hospital how to utilize portable monitors, and nursing schools help teach students the value and general use of many common pieces of equipment.

3. Smart Beds

Smart bed technology can help nurses track movement, weight, and even vitals. Smart beds also play a major role in keeping patients safe and comfortable during a long hospital stay. With the number of falls and patient injuries inside hospitals, smart beds are very important for patient safety.

Smart bed technology gives nurses a constant in-room monitor that provides them with regular updates and communications on a patient’s activities. It can also help them identify patterns, which can lead to a new diagnosis or a different understanding of a condition.

Nurses also spend less time coming in and adjusting supplies and medical equipment for comfort or safety because they can help control that with their smart bed. It allows providers to get back to other important work that only humans can do.

4. Wearable Devices

Wearable devices and mobile apps are transforming the healthcare industry. Devices that help track heart rates, exercise, sleep, respiration, and more are helping people take their health into their own hands.

With increased accessibility to iPhones, nurses also benefit from apps and devices that help them care for patients. For example, the Steth IO smartphone stethoscope is essentially a stethoscope app that allows nurses and doctors to simply use their smartphone to get breathing sounds and see heart rates. Using a phone can be less intimidating—especially for younger patients—and gives providers a full range of information and easy tracking of medical needs.

Wearable devices from health tracking to specific patient monitoring are often called the future of healthcare. With access to huge amounts of data, wearable devices can help the entire healthcare process, from diagnosis to recovery.

They also help remove elements of human error for nurses because the communication of data comes directly from the device itself. It allows for faster record keeping and helps patients and nurses maintain consistent monitoring of health.

Wearable devices from health tracking, to specific patient monitoring, are often called the future of healthcare. With access to huge amounts of data, wearable devices can help the entire healthcare process; from diagnosis to recovery.

Wearable devices help remove elements of human error for nurses, because the communication of data comes directly from the device itself. It allows for faster record keeping, and helps patients and nurses maintain consistent monitoring of health.

5. Electronic Health Records

Electronic Health Records (EHR) are replacing older paper filing methods. Electronic Health Records allow nursing experts to document care provided to patients and retrieve information that can help prioritize care. Additionally, information entered into computer systems can then be accessed by the care team, including doctors and even patients themselves when necessary.

While security continues to be a concern for Electronic Health Records, HIPAA laws ensure security and privacy of electronic records are maintained by healthcare organizations, and new technology like blockchain and cryptography are easing privacy concerns.

EHRs can tell registered nurses (RNs) whether there are further steps they need to take for a patient, monitor small condition changes, and give them information immediately as alerts or reminders.

Real-time health updates impact the speed and accuracy of medical care. RNs learn how to use software systems on the job, but their education and training will help them quickly understand what different indications on medical records mean and what their course of action should be to ensure improved patient outcomes.

6. Centralized Command Centers

One of the newest ideas for hospitals, centralized command centers promise improved patient experiences and better ways for RNs and doctors to manage supplies, clinical technology, and capacity. This is done through software applications such as dashboards that provide real-time updates.

With shorter or non-existent delays between transitions of care, nurses and doctors can actively be aware of room availability, OR schedules, and what individual patients still need before being discharged. This allows everyone to do their job more efficiently and help patients more effectively.

Specifically designed for capacity management, command centers are performing well around the country. Many hospitals report operating at higher capacity with overall improved patient experiences.

7. Telehealth and Apps

Telehealth is a valuable, newer element in healthcare. Hospitals and clinics allow patients to virtually video chat with a doctor or nurse to describe their symptoms or show doctors things like rashes or bumps. This helps patients with a quick diagnosis without leaving the comfort of their own home. They can find out if they need to come in for further testing or diagnosis, get a prescription for medicine, or get medical advice.

Telehealth saves both patients and doctors money and time. Similarly, it prevents sick patients from coming to public places and exposing other patients. This technology is changing the way clinics operate and how patients are cared for.

Similarly, medical apps and wearables help patients and doctors work to improve health. Doctors and nurses can monitor vital signs of patients without them being in the office. They can be utilized for overall health and wellness, or for specific medical concerns such as seizures or diabetes. Apps can also help patients understand when they should call a doctor and when a simple over-the-counter medication could help. This again conserves resources in clinics and helps patients save time and frustration.

Apps can also help address mental health issues. Mindfulness apps help individuals understand their mental health and energy and remind them to take time for these important aspects of wellness.

Increasing app and telehealth technology gives doctors, nurses, and patients themselves more control over their health.

Nursing Education

New clinical healthcare technology is exciting and transformative, with innovations launching every day that impact the industry. This can present a challenge to nurses who are tasked with learning about this new technology and implementing it into their work lives—not to mention, how this can impact their career advancement.

With the advancements of new technology in the healthcare industry, it’s crucial for nurses to keep abreast of these innovations and elevate their capabilities to match what both patients and the industry require for exceptional care. Education is key to navigating these new waters.

Whether you’re already an RN and looking to advance your career , or you’re transitioning into a nursing position and need the educational support to make your career leap, working with an online university such as WGU can help you get the training you need with flexible coursework that can fit in with your busy schedule. Not only can furthering your education help you become more comfortable in managing the technological needs of the advancing healthcare industry, but you can also learn to prepare yourself for future advancements and innovations that may impact your role as a nurse.

As the healthcare industry continues to change with the support of new technology, nurses can change right along with it—and elevate patient care in the process.

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Use of Technology in Healthcare Essay

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The modern health care industry is significantly transforming in terms of decision making, workflow, and information management. These changes are motivated by a federal policy focusing on building an electronic infrastructure that supports patient safety, service quality, and other healthcare initiatives. The National Strategy for Quality Improvement in Healthcare (further referred to as National Quality Strategy or NQS) has three primary purposes: to provide better and more affordable care and pursue healthy communities and populations (McBride & Tietze, 2018). Health information technology (HIT) is promoted as a critical element in this context. It implies the application of informatics as a tool for improving the health of populations served and the care delivered to them.

Several legislations have been implemented to achieve NQS goals. Most notable of them are the Affordable Care Act (ACA), the HITECH Act, and an incentive program for electronic health records (EHR) (McBride & Tietze, 2018). ACA was first implemented in 2009 to combat the uncontrollably escalating prices in the healthcare field (Zhao et al., 2020). The main idea is to manage the cost and simultaneously improve the quality of care services. An example of such control tools is the creation and support of accountable care organizations (ACO). These organizations supervise savings accounts for contracts at risk, which oblige the provider companies to fixate the rate of provided services.

Meanwhile, ACA establishes the general framework for qualitative performance evaluation. Its measures are systematized under five domains: patient and caregiver experience, safety, preventive health, at-risk population health, and care coordination (McBride & Tietze, 2018). To fulfill the requirements to be considered an ACO, organizations must implement sophisticated technology. In particular, ACOs require EHRs and health information exchange (HIE) data to be translated into actionable information representing comprehensive data management and extensive reporting capability. Consequently, HIT infrastructure is vital for advancement within the ACA healthcare delivery system.

The HITECH act holds responsibility for the promotion, adoption, and meaningful use (MU) of the HIT. It was implemented in 2009 with a focus on HIT support, college programs offering HIT training, and various grants supporting the research (Lin et al., 2019). In the context of HIT MU, HITECH developed three phases to accomplish the goals defined by the NQS. Each phase of MU emphasizes the technology it is designed to improve, which results in robust infrastructure and reliable outcomes (McBride & Tietze, 2018). Phase one focuses on implementing certified EHRs’ basic requirements, such as the ability to assess and report quality metrics and information exchange using electronic prescriptions. MU’s phase two targets consumer engagement (also referred to as “patient-centeredness”) and increases the assessing and reporting capacity of the data exchange concerning certified products. Phase three further expands the data exchange capacity requirement (more structured data, higher quality reporting) using HIEs at a more significant scope – within and across regions and states.

In the context of MU phases, the organizations that adhere to MU’s established standards are financially supported with payments from the EHR incentive program. In other words, the organization receives incentives when using a certified EHR and adhering to specific certified products’ criteria (McBride & Tietze, 2018). Starting in 2011, the incentive program extended over the years, with its timetable determined by the provider’s choice to either adhere to Medicaid or Medicare incentive programs. In the meantime, hospitals have access to both Medicaid and Medicare incentives; in many cases, these incentives can equate to millions of dollars.

Lin, Y. K., Lin, M., & Chen, H. (2019). Do electronic health records affect quality of care? Evidence from the HITECH Act . Information Systems Research , 30 (1), 306-318. Web.

McBride, S., & Tietze, M. (2018). Nursing informatics for the advanced practice nurse: Patient safety, quality, outcomes, and interprofessionalism (2 nd ed.). Springer Publishing Company.

Zhao, J., Mao, Z., Fedewa, S. A., Nogueira, L., Yabroff, K. R., Jemal, A., & Han, X. (2020). The Affordable Care Act and access to care across the cancer control continuum: a review at 10 years . CA: a cancer journal for clinicians , 70 (3), 165-181. Web.

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IvyPanda. (2023, November 29). Use of Technology in Healthcare. https://ivypanda.com/essays/use-of-technology-in-healthcare/

"Use of Technology in Healthcare." IvyPanda , 29 Nov. 2023, ivypanda.com/essays/use-of-technology-in-healthcare/.

IvyPanda . (2023) 'Use of Technology in Healthcare'. 29 November.

IvyPanda . 2023. "Use of Technology in Healthcare." November 29, 2023. https://ivypanda.com/essays/use-of-technology-in-healthcare/.

1. IvyPanda . "Use of Technology in Healthcare." November 29, 2023. https://ivypanda.com/essays/use-of-technology-in-healthcare/.

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IvyPanda . "Use of Technology in Healthcare." November 29, 2023. https://ivypanda.com/essays/use-of-technology-in-healthcare/.

Quality and Safety Education for Nurses

Strategy submission, healthcare technology innovation paper assignment: using informatics to promote quality and safety.

Cibele Webb

PhDc, MSN, RN, CNE

Assistant Professor

Julie Mack, DNP, RN

Institution:

Saint Mary's College

[email protected]

Competency Categories:

Informatics, Quality Improvement, Safety

Learner Level(s):

Pre-Licensure BSN

Learner Setting(s):

Strategy Type:

Paper Assignments

Learning Objectives:

Strategy Overview:

Submitted Materials:

286.QSEN-Teaching-Strategy-286-RubricFinal.docx - https://drive.google.com/open?id=1rto9baudbAIbtwWxaKQ6bJh7mdkWtR3v&usp=drive_copy

Additional Materials:

Assignment Description and Grading Rubric References: Forman, T. M., Armor, D. A., & Miller, A. S. (2020). A review of clinical informatics competencies in nursing to inform best practices in education and nurse faculty development. Nursing Education Perspectives, 41(1), E3-E7. https://doi.org/10.1097/01.NEP.0000000000000588 Foster, M., & Sethares, K. (Fall, 2017). Current strategies to implement informatics into the nursing curriculum: An integrative review. Online Journal of Nursing Informatics (OJNI), 21(3).

Evaluation Description:

2020: Emerging Technology in Global Nursing Care

Today, the global healthcare ecosystem begs for new processes, models, systems, and products that achieve the Quadruple Aim–to better manage populations, lower costs, and improve the patient and clinician experience (Bodenheimer & Sinsky, 2014). The nursing profession is enabling the achievement of these objectives by harnessing emerging technologies. Meeting these imperatives will not occur without nurses, as nurses are the largest group of practicing clinicians worldwide (Haddad & Toney-Butler, 2020) making them the most significant users of health technologies (Zadvinskis, et al.,, 2018). for patient care delivery.

With the distinguished honor of the World Health Organization deeming 2020 the Year of the Nurse and Midwife across the globe, it is time to take the perspective of nurses' care delivery now and how that will change in the future. Largely steeped in transforming healthcare and the global nursing profession is the tactical application of emerging technologies (Huston, 2013), those that eclipse electronic health record documentation and ordering systems, telehealth, smart devices, and mobile health. Emerging technologies beyond these now standard advances are taking over global health. This celebratory year is a perfect time to present evidence of the precise applications of emerging technologies in tactical care delivery and operations in all types of healthcare settings.

Emerging Technologies Impacting Global Health

In 2020, novel, intelligent, immersive, and connected technological advances have made their way into nursing care delivery settings globally. Due, in part, to globalization (Bradbury-Jones & Clark, 2017). stark illustrations of nurses who develop, champion, adopt, and apply emerging technologies to make better decisions at the point of care and support operations using these technologies have become a reality. These applications are rapidly occurring now, will continue, and are fortunately happening at a time when the need to improve global health outcomes is imperative. To meet this need, nurses are improving safe, quality care through the use of new technological innovations.

A change to optimize global health relies on nursing to improve the wellness of individuals, families, and communities, and to build a culture of health with the use of emerging technologies. The digital recognition of global social determinants of health (SDoH) (World Health Organization, 2018), improving the provision of effective, efficient, equitable, and accessible care across the care continuum using emerging technologies is happening now (Carroll, 2020). Nurses are committing to developing and putting novel technologies into practice to meet essential global health goals. Along with their unique and valuable knowledge and abilities to tackle these challenging health care objectives, nurses serve as crucial change agents in the creation and application of technological functionality that bridge the delivery of health care and social needs in both urban and rural communities. This trend will only increase in the next decade and beyond.

Emerging Technologies: The What and Why

Making waves in modern healthcare to achieve the Quadruple Aim across the globe requires groundbreaking technologies full of possibilities for nursing practice. These inventions may delay investors, end-users, and patients due to their unknown qualities and functionality. To demystify these transformative technologies, they are, in short, characterized as reasonable and consistent among the composition of people, institutions, and their patterns of relations that use them (Rotolo et al., 2015). They persist over time and have the potential to make a considerable impact on the socio-economic domains. Revolutionary technologies used in healthcare, are those that have radical novelty, fast growth, noticeable effect, and are uncertain and ambiguous.

What makes these emerging technologies different is their ability to fully penetrate society, which breaks down siloes and moves them into healthcare settings. Due to globalization, the mounting economic and social interdependence between countries that shift the patterns of health and disease (Bradbury-Jones & Clark, 2017) impacts global health. Those being health issues uncontained geographically that single countries cannot address alone. With this phenomenon, emerging technologies are now used across the globe to help nurses improve care delivery and operations, whether in highly evolved or underserved regions. The best use of intelligent, immersive, and connected technologies in nursing starts with innovations that are adaptable worldwide to improve global health outcomes.

Emerging Technology Applications in Global Nursing Care Settings

Patients and healthcare workers in developed countries benefit from the accessibility, and robust use of these technologies, particularly in rural settings and poverty-stricken nations, which leads to inequity as these emerging technologies are expensive and highly resource-driven (Wahl et al., 2018). However, the tide is changing. Emerging technologies, including Artificial Intelligence, Virtual Reality, and the Internet of Things, are steadily becoming available globally to nurses to optimize care delivery and routine operations critical to patient care worldwide.

Artificial intelligence (AI)

Artificial intelligence (AI) is the aptitude exhibited by smart machines through perceiving, thinking, planning, learning, and the ability to manipulate objects (National Institution for Transforming India (NITI) Aayog, 2018). This technology enables computer systems to perform tasks that usually require human intelligence (Pan, 2016). Clinical intelligence is the product of its use in healthcare (Health Information and Management Systems Society, 2018) as it enables more precise and expedited decision making, particularly for nurses (Simpson, 2012). AI used in nursing practice improves efficiencies and decreases low-value administrative tasks allowing nurses more valuable time to spend with patients in care settings (Carroll, 2019).

Application of AI in a nursing care setting. At the Massachusetts Institute of Medicine (MIT), nurses are using AI-driven robotics on a labor and delivery unit to help with resource allocation decision making such as bed management and nurse staffing. MIT’s robots use machine learning computer vision techniques to read the current status of the hospital unit using speech recognition. The robot receives feedback from the resource nurse, making auditory suggestions enabling nurses to consider the robot’s recommendations and put them into practice based on critical thinking (Gombolay, et al, 2018).

Virtual Reality (VR)

Newly used in healthcare and nursing practice settings, Virtual Reality (VR) is a technology that immerses and transports a patient into a ‘virtual world.’ (Ficarra, 2020). VR is a multi-sensory experience enabling a user to perceive being present in a simulated environment (Chirico et al., 2018). VR is an entirely imaginary digital experience, providing a realistic recreation of a three–dimensional environment experienced and controlled by the movement of the body (Ficarra, 2020). VR excludes the external (real-world) environment, and it resembles real-life interactions (Chirico et al., 2018; Chan et al., 2018). The computer-simulated environment is accessed through a head-mounted display (HMD) (Li et al., 2011) allowing patients to have a fully immersed, non–clinical, more comforting experience.

Application of VR in a nursing care setting. Nurses in the United Kingdom are using VR in burn units to decrease the traumatizing instances in patient wound dressing changes with excellent results (Furness, 2019). This revolutionary technology brings a better experience for both the patient and the nurse, through the use of distraction, to create a more comforting atmosphere in a highly stressful clinical environment.

Internet of Things (IoT)

The universal architecture of the Internet of Things (IoT) consists of sensors and mechanisms called things located at the data perception level, such as people, objects, and smart devices (Alqahtani, 2018). Technology developers place things on IoT–gateways and data acquisition systems, followed by a data center (or remote server) and then into the cloud (Edoh & Degila, 2019). IoT–enabled systems are sophisticated, embedded technologies for sensing, connecting, and processing that bring advanced applications and services in real-time and across geographies, particularly in healthcare and nursing care settings (Mieronkoski, et al, 2017).

Application of IoT in a nursing care setting. An IoT–based system is being used in India to transmit critical warning signs to nurses for expectant mothers at high-risk for fetal and maternal distress symptoms in rural areas of the country. A wearable device alerts nurses via the electronic medical record at the closest hospitals, allowing nurses to provide medical assistance to mothers by bringing them to hospitals to receive timely and critical maternal care (Garage Staff, 2018).

These applications provide clear evidence that nurses are on the cutting-edge of delivering care using revolutionary technologies and will continue to be in the coming years. Collectively, these everyday use cases show that despite the care setting, nurses do promote health and healing using emerging technologies. And the adoption in practice environments is a catalyst for changing roles that will evolve how nurses use emerging technologies to impact patient care in the future. In the year 2020, nurses are transforming global health practice by harnessing emerging technologies and are furthering innovation into the next phase of tackling critical healthcare challenges worldwide.

Education and Research

At the beginning of this century, finding actual nursing use cases and applications for novel technologies worldwide in the literature are still tricky. Much discussion still abounds about if and when using emerging technologies in nursing care will happen. In truth, healthcare organizations and public health systems are using emerging technologies, and it is imperative to bring this to light. More research, education, and documented evidence about nurses employing emerging technologies are urgently needed to advance nursing care delivery and the profession. Nurses charge is to study and create a narrative to share their experiences and wisdom to highlight and teach what they do with emerging technologies to make a case for further adoption and applications to achieve the Quadruple Aim. The continual use of these emerging technologies in nursing globally to advance and improve care delivery–in both highly-developed and underserved countries–depends on the disseminating knowledge about using innovations that impact nursing care delivery and improve patient experience and outcomes. There is no better time than during 2020, the Year of the Nurse and Midwife, to do so.

The views and opinions expressed in this blog or by commenters are those of the author and do not necessarily reflect the official policy or position of HIMSS or its affiliates.

Online Journal of Nursing Informatics

Powered by the HIMSS Foundation and the HIMSS Nursing Informatics Community, the Online Journal of Nursing Informatics is a free, international, peer reviewed publication that is published three times a year and supports all functional areas of nursing informatics.

Read the Latest Edition

Alqahtani, F.E. (2018). The application of the internet of things in healthcare. International Journal of Computer Applications, 180 (8). https://doi.org/10.5120/ijca2018916454 Bodenheimer, T., & Sinsky, C. (2014). From Triple to Quadruple Aim: Care of the patient requires care of the provider. Annals of Family Medicine, 12 (6), 573–576. https://doi.org/10.1370/afm.17 Bradbury-Jones, C., & Clark, M. (2017). Globalisation and global health: issues for nursing. Nursing Standard, 31 (39), 54–63. https://doi.org/10.7748/ns.2017.e10797 Carroll, W. M. (February 2019). Artificial intelligence, critical thinking and the nursing process. Online Journal of Nursing Informatics (OJNI), 23 (1). Retrieved from https://www.himss.org/library/artificial-intelligence-critical-thinking… Carroll, W. M. (Ed.). (2020). Emerging technologies and healthcare innovation . Emerging technologies for nurses–Implications for practice (pp. 13-16) . NY, NY: Springer Publishing Company.    Chan, E., Foster, S., Sambell, R., & Leong P. (2018). Clinical efficacy of virtual reality for acute procedural pain management: A systematic review and meta–analysis. PLoS One , 13 (7), e0200987. https://doi.org/10.1371/journal.pone.0200987 . Chirico, A., Ferrise F., Cordella, L., & Gaggioli, A. (2018). Designing awe in virtual reality: An experimental study.  Frontiers in Psychology , 8 , 2351.  https://doi.org/10.3389/fpsyg.2017.02351 Edoh, T. & Degila, J. (2019). IoT–Enabled health monitoring and assistive systems for in place aging dementia patient and elderly . I. Yasser (Ed.). Internet of Things (IoT) for automated and smart applications (p. 9 ) . London, UK: IntechOpen Limited . Ficarra, B. (2020). Virtual reality, augmented reality and mixed reality . Emerging technologies for nurses–Implications for practice (97-98) . W. M. Carroll (Ed.). NY, NY: Springer Publishing Company.    Furness, P. J., Phelan, I., Babiker, N. T., Fehily, O., Lindley S. A., & Thompson, A. R. (2019). Pain during wound dressings in burn care using virtual reality: A study of perceived impact and usability with patients and nurses. Journal of Burn Care & Research , 40 (6), 878–885. https://doi.org/10.1093/jbcr/irz106 Garage Staff. (2018, November). Using technology to fight maternal mortality worldwide. https://garage.ext.hp.com/us/en/impact/hp–wonder–project–maternal–mortality.html Gombolay, M., Yang, X. J., Hayes B., Seo N., Liu Z., Wadhwania, S., Yu, T., Shah, N., Golen, T., Shah, J. (2018). Robotic assistance in the coordination of patient care. The International Journal of Robotics Research, 37 (10), 1300–1316. DOI: https://doi.org/10.1177/0278364918778344 Haddad L. M., & Toney-Butler T. J. (2020). Nursing shortage. [Slide set]. Treasure Island, FL: StatPearls Publishing .   https://www.ncbi.nlm.nih.gov/books/NBK493175/ Health Information and Management Systems Society. (2018, December). HIMSS Insight Series: Artificial–clinical intelligence. https://indd.adobe.com/view/5c454ce9–309e–41bf–aed6–35f8f978e77c?aliId=eyJpIjoiZHZZVjRIMW9XcEZyTzBtZSIsInQiOiI3VjZNZXNKbzdUMzgybnQ2UUZaa05BPT0ifQ%253D%253D Huston, C. (2013). The impact of emerging technology on nursing care: Warp speed ahead. Online Journal of Issues in Nursing, 18( 2). doi.org/10.3912/OJIN.Vol18No02Man01 Li, A., Montaño, Z., Chen, V. J., & Gold, J. I. (2011). Virtual reality and pain management: Current trends and future directions.  Pain Management , 1 (2), 147–157. https://doi.org/10.2217/pmt.10.15 Mieronkoski, R., Azimi, I., Rahmani, A. M., Aantaa, R., Terävä, V., Liljeberg, P., & Salanterä, S. (2017). The internet of things for basic nursing care–a scoping review. International Journal of Nursing Studies , 69 ,78–90. https://doi.org/10.1016/j.ijnurstu.2017.01.009 National Institution for Transforming India (NITI) Aayog. (June 2018). Discussion paper. National Strategy for Artificial Intelligence. #AIforall. http://niti.gov.in/writereaddata/files/document_publication/NationalStrategy–for–AI–Discussion–Paper.pdf Pan, Y. (2016). Heading toward Artificial Intelligence 2.0.  Engineering , 2 (4), 409–413. https://doi.org/10.1016/J.ENG.2016.04.018 Rotolo, D., Hicks, D., & Martin, B. R. (2015). What is an emerging technology?  Research Policy , 44 (10), 1827–1843. https://doi.org/10.1016/j.respol.2015.06.006 Simpson, R. L. (2012). Technology enables value–based nursing care. Nursing Administration Quarterly , 36 (1), 85–87. https://doi.org/10.1097/NAQ.0b013e318237b7b4 Wahl, B., Cossy–Gantner, A., Germann, S., &Schwalbe, N. R. (2018). Artificial intelligence (AI) and global health: How can AI contribute to health in resource poor settings? BMJ Global Health , 3 (4), e000798. https://doi.org/10.1136/bmjgh-2018-000798 World Health Organization. (2018). About social determinants of health. http://www.who.int/social_determinants/sdh_definition/en/ Zadvinskis, I. M., Garvey Smith J., & Yen P. Y. (2018). Nurses’ experience with health information technology: Longitudinal qualitative study. JMIR Medical Informatics , 6 (2), e38. https://doi.org/10.2196/medinform.8734

Using technology to make evidence-based staffing assignments

As military leaders know, no battle plan survives its first contact in battle; too many variables exist. The same can be said of nurse staff¬ing. In health care, the “first contact” occurs when staff members call in sick and patient numbers or acuity increase or decrease more than planned.

Scheduling and staffing aren’t simply a matter of achieving a certain ratio; the cost of mistakes can be measured both in dollars and human lives. Applied technology is improving our ability to assemble real-time, actionable information to support staffing decisions and resource allocation. New evidence-based software computes large amounts of data and applies algorithms that help match the right nurse to the right patient at the right time—and at the right cost. This article describes how nursing leaders are using and benefiting from technology that integrates patient demand, nursing workforce as a resource, and evidence-based practice for staffing.

Resource-demand management

Catholic Health Initiatives (CHI) has combined technology, business processes, and a collaborative care management strategy to optimize care delivery and manage length-of-stay benchmarks. A national nonprofit health system based in Colorado, it operates nearly 90 hospitals in 18 states. To update each patient’s progress continuously, CHI uses a solution with real-time interfaces with the hospital’s admissions, discharges, and transfers (ADT); the electronic health record; and concurrent coding systems. As nurses document patient care, acuity is calculated based on the whole patient, including activities of daily living; physical, psychosocial, educational, and perceived needs; and family support. The system automatically calculates the staffing levels and skill mix needed to help the patient progress and adjusts the levels based on ADT activity.

The healthcare team rounds together in the patient room and uses the information obtained to manage the patient’s care toward a single departure date and time. Carol Wahl, chief nursing officer (CNO) at CHI’s Good Samaritan Health Systems in Kearney, Nebraska, states, “Patient satisfaction has skyrocketed… caregivers report that combining care management with case management is delivering better, more coordinated patient care.”

Workforce-management solutions

Midland Memorial Hospital (MMH), a not-for-profit hospital serving northwest Texas, uses workforce-management technology to optimize the quality of care and control costs. Centering on a web-based portal for real-time schedule management, the software is fully integrated with human resources, education, and time and attendance data. MMH has automated its scheduling, established self-scheduling practices, and created a fatigue-management guideline. Selected nursing competencies, such as advanced cardiac life support, are visible on the staffing page, alerting nurses to keep their licensure and certifications active. Nurses can self-schedule into an open slot only if they meet the requirements of that role.

MMH also uses a patient-assignment tool that recognizes the importance of continuity of care. The technology helps nurses and leaders achieve balanced assignments while creating an electronic record of primary and relief assignments. The nurse leader can use drag-and-drop functionality to assign nurses additional duties, such as crash-cart checks, narcotics counts, and refrigerator checks. Transparency of assignments can change nurses’ perception of the fairness and equity of those assignments (a key component of nurse satisfaction).

ShiftAlert is an important tool that frees up time for staffing offices and charge nurses, who typically spend hours each day calling nurses to fill gaps in the upcoming shift. This system communicates urgent, short-term staffing needs to qualified staff via text messages, email, and interactive voice response. Using the unit’s supporting business processes, ShiftAlert first offers the open shift to nurses qualified to work on that unit who wouldn’t be earning overtime or premium pay. The software eases the administrative burden of charge nurses, helping them focus more on patients and staff development.

The technology MMH uses to optimize the workforce and progress of patient care has yielded significant returns. According to CNO Bob Dent, “The improvements in costs were captured in the reduction in and elimination of high-cost labor, such as overtime and agency usage. At MMH, the return on investment for the technology happened within the first year.”

Technology that integrates operations

Florida Hospital System, an integrated system serving central Florida, is installing command centers to serve as operational headquarters where staff can see at a glance whether patient flow, staffing, and care coordination are operating at equilibrium. “Dashboard” views display bed management, surgery, emergency department, transportation, environmental services, and equipment status simultaneously in real time. Such integration of operations that previously existed in silos helps staff make actionable decisions to maximize operational efficiency and clinical excellence.

Nursing leaders make decisions on staffing resources every day, but determining if those decisions are good ones can pose a challenge. Dan Roberts, associate director for nursing at Stony Brook Medicine, a teaching healthcare system in Long Island, New York, uses technology to inform the following questions: “Did we maximize people, processes, and tools? Did we have the right patient on the right unit with the right plan of care with the right staff doing the right things?” He comments, “These new operational ‘rights’ of nursing point to access, quality, and cost. If you have these rights as a part of your nursing model, how do you know you have them correct…in real time? These questions are particularly important as governments and payers encourage reductions in length of stay and tie reimbursement to measures of quality and satisfaction.”

Systems that provide real-time staffing information

CHI, MMH, Florida Hospital System, and Stony Brook Medicine use technologies that provide real-time, actionable information on safe staffing. These technologies give nurses transparency about patient acuity, intensity, stability, and progress so they can more easily make assignments that take into account continuity of care, educational and professional characteristics, skill mix, and work environment. Nurses can use reports to predict patients’ needs prospectively and can use shared-governance models to create schedules using systems programmed to account for unit characteristics, union contracts, and labor law. With the aid of this technology, they can fill staffing gaps and understand the financial impact of moment-to-moment decisions. They can link demand for care and hours worked to nursing-sensitive quality measures.

Imagine a future where nursing is reimbursed for the value nurses bring—where nurses have easy access to staffing, patient progress, and financial information; where they maximize technology to clearly establish the relationships between an investment in nursing care and better patient outcomes; where they work with the finance officer to make the right investment. Imagine a future where technology helps us match the right nurse to the right patient at the right time. That future is now.

Selected references

American Nurses Association. ANA’s Principles for Nurse Staffing . 2nd ed. Silver Spring, Maryland: Author; 2012.

Caspers BA, Pickard B. Value-based resource management: a model for best value nursing care. Nurs Adm Q . 2013;37(2):95-104.

Creating value for patients for business success. Leadership . 2011;25-8. www.hfma.org/Content.aspx?id=3685 Accessed September 24, 2013.

Dent R, Bradshaw P. Building the business case for acuity based staffing. Nurs Leader . 2012;10(2):26-8. www.nurseleader.com/article/S1541-4612(11)00341-7/abstract . Accessed September 24, 2013.

Garcia A. A patient acuity checklist for the digital age. Nurs Manag . 2013;44(8):22-4.

The authors work at Cerner Clairvia in Kansas City, Missouri. Amy Garcia is the chief nursing officer and Kate Nell is the director.

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What is Healthcare Technology? How is it Changing the Future of Healthcare?

Published: June 26, 2024

Healthcare technology is revolutionizing the way we understand, diagnose, and treat illnesses. From advanced diagnostic tools to AI-driven patient care, the integration of technology in healthcare promises a future where medical services are more efficient, accessible, and effective. 

If you have an interest in healthcare or technology, now is an excellent time to enter the field. Emerging healthcare technologies are creating many new career opportunities for professionals. Whether your passion lies in patient care, health informatics, or biomedical engineering, there is a job for you in the healthcare technology sector.

What are the Most Used Technologies in Healthcare?

The healthcare sector is increasingly leveraging advanced technologies to enhance patient care and streamline operations. Here are some of the most utilized technologies:

Electronic Health Records (EHRs)

The most widely used technology in healthcare today is electronic health records (EHRs). EHRs provide a digital version of patients’ paper charts, enabling real-time, patient-centered records that make information available instantly and securely to authorized users.

Artificial Intelligence (AI)

AI is revolutionizing healthcare by enhancing diagnostic accuracy, personalizing treatment plans, and improving administrative efficiency. It supports digital communications by offering schedule reminders and tailored health tips, and it can also help identify patients at high risk for certain conditions, enabling early intervention. 

Other AI innovations include robot-assisted surgery, which enhances precision and allows for data-driven decision-making during operations. Virtual nursing assistants use AI to provide personalized care, help patients manage their health, and reduce the burden on healthcare professionals. Predictive analytics leverages historical data to forecast future health trends and improve patient outcomes by anticipating and preventing medical issues. Additionally, AI applications in diagnostics and treatment recommendations are streamlining clinical workflows and improving the accuracy of diagnoses.

mHealth (Mobile Health)

mHealth is being used to provide patients with convenient access to medical information, reminders for medication adherence, and tools for managing chronic conditions like diabetes. Mobile health apps enhance patient-centered care with features such as appointment scheduling, personalized health tips, and real-time communication with healthcare providers.

Internet of Medical Things (IoMT)

The Internet of Medical Things (IoMT) is connecting medical devices to healthcare information systems. It is primarily used for remote patient monitoring, where devices collect and transmit patient data such as heart rate, blood pressure, and glucose levels, allowing for continuous monitoring outside of clinical settings. IoMT also supports telehealth services, enabling virtual consultations and treatment, which enhances patient accessibility and convenience.

Telemedicine

Telemedicine is increasing access to medical services, particularly for patients in remote or underserved areas, and reducing the need for in-person visits. This saves time and costs for both patients and providers. Telemedicine also enhances the quality of emergency services, improves patient outcomes, and allows for better management of chronic conditions through continuous monitoring and follow-up care.

Wearable Devices

Wearable devices are revolutionizing healthcare by enabling real-time monitoring of vital signs, which helps in early disease detection and personalized treatment plans. These devices empower patients to take control of their health by providing continuous data and insights, enhancing patient engagement, and improving overall health outcomes.

3D Printing

3D printing enables the production of customized medical implants, prosthetics, and anatomical models, which improves surgical outcomes and patient-specific treatments. It also facilitates rapid prototyping and manufacturing of medical devices, reducing costs and accelerating the development process.

Virtual Reality (VR) 

Virtual reality (VR) is creating immersive training environments for medical professionals, where they can practice their skills without risking patient safety. Additionally, VR is being used for pain management, mental health therapy, and even assisting women in labor, thereby improving patient care and treatment outcomes.

How is Technology Changing the Future of Healthcare?

Advances in healthcare technology are poised to significantly improve patient health outcomes. Technologies like IoMT, AI, and telemedicine are enhancing the accuracy of diagnoses, personalizing treatments, and improving patient monitoring, which collectively leads to better health outcomes and potentially healthier populations. These advancements also contribute to a higher standard of care by enabling more precise, timely, and effective medical interventions.

The cost of healthcare may also be affected by these technological advancements. While initial investments in technology can be high, the long-term savings from reducing errors, preventing hospital readmissions, and improving overall efficiency could lower healthcare costs. Moreover, the integration of technologies such as telemedicine can make healthcare more accessible, reducing the financial burden on patients who might otherwise face high costs for in-person visits and long-term treatments.

Beyond cost and care quality, there are additional gains to be made from healthcare technology. Improved patient safety, enhanced data management, and the potential for remote healthcare services can transform the patient experience. These technologies can help in creating a more equitable healthcare system by reaching underserved populations and providing continuous care regardless of geographical barriers.

Pursuing a Career in Healthcare Technology

If you are interested in contributing to these transformative changes in healthcare, a degree in health science offers an excellent foundation. Health science programs provide comprehensive training in disease prevention, diagnostics, and treatment, equipping students with the skills necessary to improve health. This degree program is perfect for students wishing to deliver patient care. 

Meanwhile, if you are interested in creating and maintaining healthcare technology, you might consider a dual degree or double major in computer science and health science. Computer science programs emphasize the development of technological solutions, including software and algorithms, that drive innovations in healthcare. 

At UoPeople , our programs are designed to be accessible and affordable , ensuring that a wide range of students can gain the expertise needed to lead in the future of healthcare technology. One benefit UoPeople students enjoy is the ability to complete their degrees quickly thanks to our flexible structure and year-round enrollment options. These programs are designed to allow students to progress at their own pace, potentially completing their degrees faster than traditional timelines.

Additionally, UoPeople is tuition-free . This means we don’t charge students for taking courses, to enroll, or for books or other course materials. This unique online model of higher education allows us to bring the classroom to you, no matter where you are in the world. 

As technology continues to advance, the future of healthcare looks promising, with innovations poised to transform patient care, diagnostics, and treatment methods. Emerging technologies are not only enhancing the efficiency and effectiveness of healthcare delivery but also opening up a plethora of career opportunities in the field. 

For students aspiring to make a difference in healthcare, now is the perfect time to pursue a degree in health science. The skills and knowledge you’ll gain will prepare you to meet the demands of a rapidly evolving industry and contribute to groundbreaking innovations. By choosing this path, you will join a community of forward-thinkers dedicated to improving health outcomes and making a lasting impact on the well-being of individuals and communities worldwide.

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Patient-Reported Outcome Measures Within a National Multispecialty Surgical Quality Improvement Program

• 1 Surgical Health Outcomes and Research for Equity (SHORE) Center, Department of Surgery, University of Rochester Medical Center, Rochester, New York
• 2 Division of Colon and Rectal Surgery, Department of Surgery, University of Rochester Medical Center, Rochester, New York
• 3 Patient-Reported Outcomes, Value, and Experience Center (PROVE) Center, Department of Surgery, Brigham and Women’s Hospital, Boston, Massachusetts
• 4 Division of Plastic and Reconstructive Surgery, Department of Surgery, Brigham and Women’s Hospital, Boston, Massachusetts
• 5 Harvard Medical School, Boston, Massachusetts
• 6 Division of Surgical Oncology, Department of Surgery, Brigham and Women’s Hospital, Boston, Massachusetts
• 7 Division of General and Gastrointestinal Surgery, Department of Surgery, The Ohio State University, Columbus
• 8 Division of Surgical Oncology, Department of Surgery, Duke University School of Medicine, Durham, North Carolina
• 9 Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, Illinois
• 10 The David Geffen School of Medicine at UCLA, Los Angeles, California
• Invited Commentary Patient-Reported Outcome Measures in NSQIP Catherine B. Jensen, MD; Lesly A. Dossett, MD, MPH; Susan C. Pitt, MD, MPHS JAMA Surgery

Question   Is it feasible to leverage health information technology to collect patient-reported outcome measures (PROMs) within a national surgical quality improvement program at scale?

Findings   This pragmatic cohort study of 65 hospitals (including 130 365 patients) participating in the American College of Surgeons National Surgical Quality Improvement Program achieved the 30% or greater target collection rate after the implementation of 15 strategies over a 3-year period. Fifty-eight hospitals (89.2%) achieved collection rates of 30% or greater, and 9 (13.8%) achieved collection rates 50% or greater; platform functionality and patient engagement were the keys to success.

Meaning   The large-scale electronic collection of PROMs into a national multispecialty surgical registry was feasible.

Importance   Patient-reported outcome measures (PROMs) are increasingly recognized for their ability to promote patient-centered care, but concerted health information technology (HIT)–enabled PROM implementations have yet to be achieved for national surgical quality improvement.

Objective   To evaluate the feasibility of collecting PROMs within a national surgical quality improvement program.

Design, Setting, and Participants   This was a pragmatic implementation cohort study conducted from February 2020 to March 2023. Hospitals in the US participating in the American College of Surgeons National Surgical Quality Improvement Program and their patients were included in this analysis.

Exposures   Strategies to increase PROM collection rates were identified using the Institute for Healthcare Improvement (IHI) Framework for Spread and the Consolidated Framework for Implementation Research and operationalized with the IHI Model for Improvement’s Plan-Do-Study-Act (PDSA) cycles.

Main Outcomes and Measures   The primary goal was to accrue more than 30 hospitals and achieve collection rates of 30% or greater in the first 3 years. Logistic regression was used to identify hospital-level factors associated with achieving collection rates of 30% or greater and to identify patient-level factors associated with response to PROMs.

Results   At project close, 65 hospitals administered PROMs to 130 365 patients (median [IQR] age, 60.1 [46.2-70.0] years; 77 369 female [59.4%]). Fifteen PDSA cycles were conducted to facilitate implementation, primarily targeting the Consolidated Framework for Implementation Research domains of Inner Setting (ie, HIT platform) and Individuals (ie, patients). The target collection rate was exceeded in quarter 3 (2022). Fifty-eight hospitals (89.2%) achieved collection rates of 30% or greater, and 9 (13.8%) achieved collection rates of 50% or greater. The median (IQR) maximum hospital-level collection rate was 40.7% (34.6%-46.7%). The greatest increases in collection rates occurred when both email and short-message service text messaging were used, communications to patients were personalized with their surgeon’s and hospital’s information, and the number of reminders increased from 2 to 5. No identifiable hospital characteristic was associated with achieving the target collection rate. Patient age and insurance status contributed to nonresponse.

Conclusions and Relevance   Results of this cohort study suggest that the large-scale electronic collection of PROMs into a national multispecialty surgical registry was feasible. Findings suggest that HIT platform functionality and earning patient trust were the keys to success; although, iterative opportunities to increase collection rates and address nonresponse remain. Future work to drive continuous surgical quality improvement with PROMs are ongoing.

• Invited Commentary Patient-Reported Outcome Measures in NSQIP JAMA Surgery

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Temple LKF , Pusic AL , Liu JB, et al. Patient-Reported Outcome Measures Within a National Multispecialty Surgical Quality Improvement Program. JAMA Surg. Published online June 26, 2024. doi:10.1001/jamasurg.2024.1757

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Ten key considerations for the successful implementation and adoption of large-scale health information technology

Kathrin m cresswell.

1 The School of Health in Social Science, The University of Edinburgh, Edinburgh, UK

David W Bates

2 Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA

3 The Department of Health Policy and Management, Harvard School of Public Health, Boston, Massachusetts, USA

Aziz Sheikh

4 eHealth Research Group, Centre for Population Health Sciences, The University of Edinburgh, Edinburgh, UK

The implementation of health information technology interventions is at the forefront of most policy agendas internationally. However, such undertakings are often far from straightforward as they require complex strategic planning accompanying the systemic organizational changes associated with such programs. Building on our experiences of designing and evaluating the implementation of large-scale health information technology interventions in the USA and the UK, we highlight key lessons learned in the hope of informing the on-going international efforts of policymakers, health directorates, healthcare management, and senior clinicians.

Introduction

Large-scale, potentially transformative, implementations of health information technology are now being planned and undertaken in multiple countries. 1 2 The hope is that the very substantial financial, human, and organizational investments being made in electronic health records, electronic prescribing, whole-system telehealthcare, and related technologies will streamline individual and organizational work processes and thereby improve the quality, safety, and efficiency of care. The reality is, however, that these technologies may prove frustrating for frontline clinicians and organizations as the systems may not fit their usual workflows, and the anticipated individual and organizational benefits take time to materialize. 3 4 In this article, we reflect on our mapping of the literature (see box 1 ) and complement this with our experiences of studying a range of national evaluations of various large-scale health information technology systems in the UK and USA to provide key pointers that can help streamline implementation efforts. 4 52–54 In so doing, we hope to inform policy and practice development to support the more successful integration of technology into complex healthcare environments. This is particularly timely given the US Health Information Technology for Economic and Clinical Health (HITECH) Act, which includes a $19 billion stimulus package to promote the adoption of electronic health records and associated functionality. 55

Factors associated with effective implementation identified in the literature 5–51

Technical: usability, system performance, integration and interoperability, stability and reliability, adaptability and flexibility, cost, accessibility and adaptability of hardware

Social: attitudes and concerns, resistance and workarounds, expectations, benefits/values and motivations, engagement and user input in design, training and support, champions, integration with existing work practices

Organizational: getting the organization ready for change, planning, leadership and management, realistic expectations, user ownership, teamwork and communication, learning and evaluation

Wider socio-political: other healthcare organizations, industry, policy, professional groups, independent bodies, the wider economic environment, international developments

This paper complements a previous publication by Bates and colleagues on ‘Ten commandments for effective clinical decision support’, 11 which focused on lessons learned in relation to clinical decision support systems. We have developed a technology lifecycle approach to highlight key considerations at four stages: establishing the need for change, selecting a system, implementation planning, and maintenance and evaluation ( figure 1 ).

Summary of the lifecycle stages of health information technology and the ten key considerations.

Ten key considerations for the successful implementation of health information technology

1. clarify what problem(s) the technology is designed to help tackle.

Many health information technology procurements are based on assumed benefits, which are often poorly specified. This can result in difficulties agreeing on a shared vision across the healthcare organization. While terms like ‘improved quality of care’ and ‘improved efficiency’ are often used, detailed outcomes resulting from specific functionality are hard to measure and to anticipate as most implementations require fundamental changes to operational processes and many organizations do not even attempt this. 3 4 52–54 Thus, organizations often encounter difficulties conceptualizing the required short-, medium-, and long-term transformations.

A thorough mapping of existing local processes before implementation can mitigate this risk and help to identify existing problems as well as areas for improvement. In an ideal scenario, this groundwork would result in agreement on the problem(s) to be addressed by a specific functionality (eg, duplication of information) and, based on this, the development of a long-term strategic vision (eg, a common patient record that is populated by all health professionals). However, new technology may not always be the answer. It is therefore also important to assess if, and to what extent, existing/new health information technology can support these strategic goals and whether other approaches may also need to be considered.

2. Build consensus

Professional, managerial, and administrative consensus needs to be built around the strategic vision, in addition to creating the means to support the realization of this vision. 56 This may involve considering whether to aim for radical changes across the organization (eg, through implementing electronic health record functionality), or whether to focus on streamlining specific processes (eg, electronic prescribing) initially and then expanding functionality over time. Many authors in the field of organizational change have highlighted that high-level strategic leadership of senior management including both administrative and clinical leaders is vital, and this is accurate, but it is also essential to involve and get the buy-in of different professional stakeholder groups (eg, doctors, nurses, administrative staff, managers) in order to facilitate co-ownership and ensure commitment. 57 58 From our experience, this balance is best achieved through the creation of a high-level strategic group that not only includes senior managers, but also clinical and administrative leads who represent different end-user groups.

An important factor to keep in mind is that attempts to align perspectives through, for example, consensus building activities, need to be skillfully handled with cognizance of the means to overcome rather than perpetuate existing professional hierarchies. One approach that we have successfully used is to identify domains in which there is already broad agreement versus those which need specific attention from different professional stakeholders. For the latter, efforts promoting the participation and empowerment of different groups by actively searching for inclusive solutions, have the highest potential to achieve coordinated implementation efforts. 59 Nurses, for instance, will have different needs to doctors, but all groups tend to agree that the provision of high standards of care should be the focus of activities. Patient-centered discussions could therefore be a point of convergence between different professional viewpoints.

3. Consider your options

Once the need for a technological system has been established, it is important to commit adequate time and resources to thoroughly consider different options in terms of which system(s) to choose. We have found that this aspect of planning and the associated writing of business cases and procurement considerations are sometimes under-estimated and often rushed. 11 52 It is, for example, important to be aware of the full range of system providers, and network with potential suppliers in order to understand the ethos and values of the companies with which the organization is considering embarking on a long-term relationship. Visiting other healthcare settings that have implemented similar technology can prove very helpful. 52

Once available commercial systems have been appraised, it is appropriate to reflect on whether to build a customized system tailored to local needs, whether to customize an existing system, or whether to use an ‘off-the-shelf’ standardized solution. 58 The literature and our experiences indicate that there are inherent risks and benefits to each of these approaches. For example, although ‘home-grown’ customized systems tend to be better accepted by local users than standardized solutions, they are also not a cheap option and often do not easily integrate with other technological systems in the organization. 4 52–54 In addition, considerable time is needed to customize systems, and such efforts are often led by individuals or small groups of enthusiasts and so may not have longevity. In the USA, for example, commercial systems have markedly improved in recent years and now dominate the marketplace. Internationally, most organizations will, we anticipate, also choose commercial systems in the future due to cost and interoperability considerations. Commercial systems are cheaper to purchase (as they are not customized to individual organizations and can therefore be produced in bulk) and they are also likely to be interoperable due to common data standards and architectures (such as, for example, the Health Level Seven International interoperability standards).

4. Choose systems that meet clinical needs and are affordable

Once a decision on the basic type of system has been made, it is important to base the final choice not only on organizational, but also on clinical needs. 60 A system should be both fit for organizational purpose and fit for clinical practice. There are countless examples of systems that have been procured but never used (eg, if they are perceived to undermine professional values) or are deployed in unintended ways, which will then typically result in a failure to realize the hoped for improvements (see box 2 , example 1). 4 61

Examples of ‘failures’ in implementation

Example 1 : Rejection by users 61

May and colleagues evaluated the implementation of a videophone, which was intended to be used by primary care physicians to refer patients to a community mental health facility. The team conducted qualitative interviews and observations with clinicians, managerial staff, and patients in order to explore the acceptability of the technology. They found that some professional groups, including community psychiatric nurses and occupational therapists, resisted using the system as they felt that it impacted adversely on the therapeutic relationship with their patients.

Example 2 : Bandwidth undermining system performance (authors’ own experience)

At one large hospital, operational management was told by the information technology leadership that the hospital's network was at maximal bandwidth at budget time. The management decided that the hospital would wait a year to upgrade the network, and instead purchased an expensive new imaging technology. However, several months later the institution's systems began to ‘brown out,’ and it was taking up to 30 minutes for a single screen change. Although the leadership immediately reversed field and authorized a network upgrade, this took several months to implement and care delivery was substantially impaired in the interim.

Example 3 : The importance of user feedback (authors’ own experience)

Getting an ‘early fix’ on how long a new system is taking to use is especially critical. At one hospital which was a pioneer in order entry, a system was implemented and users were told they had to use it. However, the leadership had no clear idea how long it was actually taking front-line clinical users to do their work—something that took an hour before implementation was now taking several hours, which resulted in an unworkable situation for front-line users. This eventually resulted in a computer monitor being thrown through a hospital window, and a work action by the clinical users. That got the leadership's attention and major changes were made.

Example 4 : Tracking system performance (authors’ own experience)

Maintenance includes tracking how the system is performing, and how the decision support within it is performing. Such tracking is much easier if it is built in from the beginning. At one large hospital, the allergy over-ride rates were initially very low. However, a series of apparently innocuous changes in the decision support system were made by the responsible committee with the result that several years later, large numbers of alerts were being delivered, with nearly all being over-ridden. After these data were reviewed, the system could be tuned, and the unimportant alerts were turned off.

A system therefore needs to fulfill a range of requirements on a variety of levels. It needs to be usable for end-users (not cumbersome for clinicians and beneficial for patients), cost-effective for organizations, and interoperable to allow secondary uses of data. These purposes are often difficult to align as requirements of different domains may result in trade-offs for others. 60 For example, it has repeatedly been found that many health information technologies slow down the work practices of users, despite improving overall organizational efficiency. 62 Speed is of the essence and any initiative that slows down key clinical tasks is likely to be strongly resisted by frontline staff. This issue can to some extent be addressed by purchasing systems that allow a large degree of customization, but these are often expensive to acquire and run, necessitating a careful balancing act between affordability and desired functionality. Our experiences suggest that the associated system costs are often under-estimated, particularly those relating to infrastructure, support, and maintenance.

5. Plan appropriately

It takes both targeted and reflective efforts to plan for transformative organizational ventures of any kind. Although flexibility in strategy is required, there are some general pointers that tend to characterize effective preparation across organizations and technologies. These include the aforementioned necessity to engage extensively with potential suppliers and other organizations who have already implemented, but also the decision to prioritize the implementation of functionality that can bring benefits to the greatest number of end-users as early as possible. 4 52–54 Other factors relate to the avoidance of ‘scope-creep’ (ie, the tendency to increase the scope of a project when it is already underway) and maintaining open channels of communication between management and users.

Implementation strategies need to be tailored to organizational circumstances and systems, whether they involve ‘phased’ or ‘big-bang’ implementation approaches. The former relates to introducing incremental functionality slowly, while the latter relates to introducing functionality across the organization all at once. We suggest avoiding the running of parallel systems (both paper and electronic) wherever possible, as this tends to increase workloads for end-users and may inadvertently introduce new threats to patient safety. 4 52–54

6. Don't forget the infrastructure

Developing the right infrastructure is an essential part of planning activity. If this is not afforded sufficient attention, then software systems may perform sub-optimally (eg, if wireless networks are unavailable or bandwidth is too narrow), or may be inaccessible to users altogether (eg, if there is a lack of available hardware). Again, this increases the possibility that systems are not used at all or used in ways other than intended, potentially compromising benefits and increasing risks associated with technological systems. 4 We have repeatedly found that inappropriate infrastructure can negatively shape user attitudes towards software systems themselves, as it can impact on usability and performance. Inappropriate infrastructure, such as a slow wireless connection, may for example, reduce the speed of a system, which is an important (if not the most important) factor in determining adoption ( box 2 , example 2). 11

7. Train staff

Trained users tend to be more satisfied with new technologies than those who have not been adequately trained. 63 This may be due to a lack of understanding of system capabilities, which can in turn lead to workarounds whereby the new systems are used in unintended ways—or worse still—avoided completely.

The most effective training is that which is tailored to the individual roles of users, without being too restrictive as this can undermine understanding of how the whole system functions. Training needs to allow users to practice ‘hands-on’ and as closely simulate the actual working environment as possible. 58 64 It is also ideally conducted shortly before the implementation as otherwise staff may forget important functions. There may be a need for compulsory (eg, in relation to approaches to maintaining patient confidentiality) as well as voluntary components, and some individuals may need more training than others. For instance, older users may never have used a computer and may therefore require more basic training than younger individuals, who tend to be more accustomed to computers. For infrequent users and in relation to systems that are subject to regular upgrades, continuous training may be necessary. From our experience, training should typically total about 40% of an implementation budget, but is the area most often left short.

8. Continuously evaluate progress

Although it is now widely recognized that evaluation is important when considering new technologies, the reality is that it is still, more often than not, an afterthought as immediate implementation activities take priority. 65 Real-time, longitudinal data collection strategies providing formative feedback are desirable as emerging results can be incorporated in on-going implementation activity, but this is costly and time-consuming. However, it is essential to capture user feedback about problems that are identified and respond to it in a timely manner ( box 2 , example 3). In our experience, investments in evaluation activities are always worth it. These should begin with assessing existing and anticipated organizational and individual workflows, monitoring desired and undesired consequences, and tracking new innovative ways of working. 4 52–54 It is also crucially important that this work is carried out over an appropriate length of time, as it may well take years for benefits and consequences to emerge. 60 Following developments over the long term can further help identify when systems have become obsolete and when there is a need for new solutions.

9. Maintain the system

Maintenance is in many ways related to all of the above points as these issues need to be re-visited periodically throughout the technology lifecycle (see figure 1 ). Nevertheless, maintenance deserves particular attention as it is often under-estimated in relation to associated activities and cost. 66 This is not only the case in relation to on-going costs (eg, pertaining to support, infrastructure, and system upgrades), but also costs relating to potential system changes as the strategic aims of organizations and therefore the capabilities of existing technological systems are likely to change over time ( box 2 , example 4).

10. Stay the course

The benefits of major transformative ventures are notoriously difficult to measure and may take a long time to materialize. 3 4 52–54 However, this is not to say that they are non-existent, rather they need to be tracked by appropriate evaluation work assessing how the new system is used and re-invented locally. This also requires an appreciation of the timelines surrounding the realization of expected benefits, allowing enough time for technologies to embed and data to be exploited for secondary uses. 60 Our work has shown that in many cases the expectations of organizations and individual users far exceed what is achievable in the short term. The managing of expectations is, therefore, important as otherwise there is a danger that stakeholders disengage with the initiative and negative attitudes may emerge. 4 52–54

Conclusions

Careful planning and on-going, critical evaluation of progress are central to the successful implementation of major health information technology. Taking a lifecycle perspective on the implementation of technological systems will, we hope, help organizations to avoid some of the all too commonly encountered pitfalls and improve the likelihood of successful implementation and adoption (see figure 1 ). It is, however, important to keep in mind that, although the stages and considerations discussed here were depicted in a linear manner, they may to some extent overlap. This is consistent with the complex nature of large-scale health information technology implementations, where a range of different inter-related factors are at play.

Acknowledgments

We are very grateful to all participants who kindly gave their time and to the extended project and program teams of work we have drawn upon. We are also grateful to two anonymous expert peer reviewers who commented on a previous version of this manuscript.

Contributors: AS conceived this work. AS is currently leading a National Institute for Health Research-funded national evaluation of electronic prescribing and medicines administration systems. KMC is employed as a researcher on this grant and led on the write-up and drafting of the initial version of the paper, with DWB and AS commenting on various drafts.

Funding: This work has drawn on data funded by the NHS Connecting for Health Evaluation Programme (NHS CFHEP 001, NHS CFHEP 005, NHS CFHEP 009, NHS CFHEP 010) and the National Institute for Health Research (NIHR)-funded Programme Grants for Applied Research scheme (RP-PG-1209-10099). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health.

Competing interests: None.

Provenance and peer review: Not commissioned; externally peer reviewed.

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AI and rural health care: A paradigm shift in America’s heartland

By Bill Gassen June 26, 2024

T he use of artificial intelligence is happening in “unlikely places.” So says a recent working paper published by the National Bureau of Economic Research. While much of the nation is debating the future of AI, health care providers in rural America are pioneering new uses of it in their practices. As the leader of the U.S.’s largest rural health care system, I predict the impact of AI on rural health care will be consequential.

After a decade of decline before the pandemic, a recent study by the U.S. Department of Agriculture indicates that the population in rural areas is rising a bit. Rural counties across the country — defined as those with cities of up to 50,000 people — grew one-quarter of a percent from 2020 to 2022.

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That tiny population surge, however, isn’t likely to mend the greatest demographic challenge for rural health care: recruiting and retaining enough clinicians to work in the medical specialties that are in short supply across rural America.

Three out of five federally designated medical provider-shortage areas are in rural regions. Rural communities have only 30 specialists per 100,000 people , compared to 263 specialists per 100,000 in urban regions. Meanwhile, 25% fewer rural physicians will be practicing by 2030 due to an aging workforce with looming retirements.

A paradigm shift

Nearly all of America’s doctors are experiencing burnout as the pace of their practice has relentlessly sped up in recent years. But many are encouraged by the potential for AI to help them improve efficiencies in ways that allow them to refocus on their patients, rather than trying to keep up with electronic health records.

Related: Rural hospitals face uncertainty with health care proposals

A paradigm shift is happening in rural America as rural health providers come to embrace the idea that what we do won’t change, but how we do it must.

Clinicians working in Nebraska corn country at Bryan Health are now using AI-enabled software that takes notes on their phones with the press of a button so they can look their patients in the eyes instead of clicking away at a keyboard. The technology securely records a provider’s conversation with a patient during a visit using “ambient listening,” which is then transcribed in the electronic medical record.

Bryan Health CEO Russ Gronewold told me that one physician in Grand Island called this “career extension technology.” Another in Lincoln said he’ll look back at this moment as “one of the most pivotal moments” in his medical profession.

In the next few weeks, Bryan Health will also launch a new generative AI tool in its electronic medical record that’s designed to reduce the significant amount of time physicians spend responding to large volumes of patient messages. The new tool will “pre-populate responses,” but physicians will have the ability to tailor and edit each message before sending.

While recent studies have shown that the use of large language models may not actually save clinicians time, saving time might not be the only measurement that matters. As Dr. Michael Pfeffer, the chief information officer and associate dean at Stanford Health Care and Stanford School of Medicine recently shared, his team found that generated draft responses for patient messages “reduced cognitive burden” by giving physicians a place to start.

Related: Artificial intelligence: crossing the border between health care and tech

In the dairy country of Wisconsin and the upper peninsula of Michigan, Marshfield Clinic Health System is taking a slightly different approach when it comes to combating physician burnout related to patient messages. Marshfield will soon deploy AI technology within its electronic medical record to “reduce noise” for physicians by sorting and routing messages to the appropriate member of the care team.

According to Marshfield’s chief information and digital officer, Jeri Koester, nearly 60% of the system’s patient-initiated messages are related to prescription refills, scheduling, or completing forms — tasks that can be managed by a nurse or medical assistant. This new tool will “remove clutter” from physicians’ inboxes and allow them to instead focus on clinical and urgent messages that require their expertise and immediate attention.

When the U.S. Preventive Services Task Force recommended colorectal cancer screenings begin at age 45 , clinical teams at Sanford Health gathered to determine how to manage screening for 100,000 newly eligible individuals in the rural Dakotas, where there was a limited supply of gastroenterologists. They developed their own AI model that includes additional risk factors that may put people at heightened risk for colon cancer. The model is designed to help physicians understand the risk of the patient in front of them without having to scroll through medical records, saving doctors time they can spend with the patient instead.

AI’s next chapter

AI can and will do much more than streamline administrative tasks. These technologies will soon serve as another tool in clinicians’ black bags — wherever their practices are located — metropolis, suburb, or farming town. AI-enabled clinical decision-support tools will help to identify serious health threats, improve diagnoses and calibrate the precision of medical treatments.

A new effort, led by the White House, is focused on developing a voluntary framework of health care AI commitments . The initiative does not shy away from the trust barriers that must be addressed, both for consumers and health care practitioners, including ensuring data that an AI model is trained on is representative of the population it will serve as a guard against bias .

Related: Watch: Treating Rural America: The telehealth solution

So far, 38 payers and health systems have come together in this collaboration to determine how to harness AI models safely, securely and transparently. Bringing diverse voices to the table is a critical component of this work.

The challenges are not the same for urban and rural health care systems.

Rural America has some of the highest rates of late-stage breast cancer diagnoses in the country. A recent report from the Centers for Disease Control and Prevention found that rural Americans are more likely to die early from preventable causes like cancer. The potential to reverse this trend through new AI technologies that forecast the risk of disease will be a game-changer for rural clinicians and the patients they care for.

Preterm birth is another rural issue. In rural northern Minnesota, one of the poorest and most geographically isolated regions in the state, OB-GYNs at Sanford Bemidji Medical Center launched a pilot using FDA-cleared AI-enabled non-stress test belts to monitor fetal heart rate and the presence of contractions among patients who may be at higher risk of pre-term delivery, allowing them to intervene earlier to ensure the best possible maternal health outcomes.

My children are enamored with a movie called “The Croods,” which tells the story of a family of prehistoric cave-dwellers about as far from high-tech living as one could imagine. In the original 2013 film, the stubborn, cautious father (“Fear keeps us alive!”) refuses to let anyone leave the cave except for brief forays at daybreak to gather food, admonishing his children to “never not be afraid.” But they defy that edict and eventually make it to the other side of the mountain to see a peaceful paradise there.

It makes sense to be cautious about AI in health care, no matter where one lives or practices medicine. But some health care providers, including those in the most remote and rural locations in our nation, have already crept over the mountain and have seen a new world of promise.

Bill Gassen is president and CEO of Sanford Health , the largest rural health system in the United States, with headquarters in Sioux Falls, South Dakota.

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What Are the Benefits of Natural Language Processing Technology?  

Using natural language processing technology, researchers can sort through unstructured data to improve patient care, research efforts, and disease diagnosis.  .

• Erin McNemar, MPA

To deliver quality care and positive patient outcomes, researchers and clinicians need comprehensive patient data and medical literature. 

 However, since 80 percent of essential data lies in unstructured clinical notes, scientific papers, and conference articles, gaining immediate access to clinical information is difficult. 

Researchers can utilize artificial intelligence methods to sort through unstructured data by using natural language processing. The data can then provide valuable insights into patient care, research efforts, and disease diagnosis.  

What is natural language processing? 

Natural language processing  is the overarching term that describes using computer algorithms to identify key components in everyday language, exact meaning from the unstructured or written input, and turn it into usable data. NLP requires the use of  artificial intelligence , computational linguistics, and other machine learning methods. 

 Some of the specific tasks for NLP systems can include: 

• Summarizing lengthy blocks of narrative text, including clinical notes or academic journal articles, by identifying key concepts or phrases present in the material 
• Mapping data elements present in unstructured text to structured fields in an electronic health record to improve clinical data integrity 
• Converting data in the other direction from machine-readable formats into natural language for reporting and educational purposes 
• Answering unique free-text queries that require the synthesis of multiple data sources 
• Engaging in optical character recognition to turn images, like PDF documents or scans of care summaries and imaging reports, into text files that can then be parsed and analyzed 
• Conducting speech recognition to allow users to dictate clinical notes or other information that can then be turned into text 

Many NLP systems “learn” over time, reabsorbing results from previous usages as feedback regarding which results were accurate and which did not meet the research needs. 

How can NLP improve patient care and research? 

To better manage  unstructured data  and research efforts, Georgetown University Medical Center adopted artificial intelligence-based text-mining tools in  electronic health records . The tool allows physicians to quickly search through large amounts of medical literature to support real-time clinical decision-making.  

“I work with physicians a lot and talk to them about data searching. Their main complaint is that they can’t pull out the precise information they want quickly when they do a search. The search is very imprecise. It takes them too long and too much work to get to the information that they want,”” Clinical Informaticist at Georgetown university, Johnathan Hartmann, told  HealthITAnalytics .  

The Linguamatics  text-mining tool  uses natural language processing to sort through text for specific key phrases. The pulled information can then identify the best course of treatment for patients.  

Using that same  AI technology,  research teams can search literature and medical records to discover genes associated with certain diseases to improve their understanding of molecular processes and to advance drug targeting.  

While traditional text-mining tools perform similar tasks, Linguamatics significantly lightens the load on physicians.  

“The tools allow clinicians to search the text of a larger number of articles very quickly and efficiently, pulling out the information that they’re interested in precisely. Traditional search engines like PubMed, might search and pull up 50 articles, some of which may contain the information they’re looking for,” Hartmann continued.  

“However, clinicians would have to read through either the abstract or even the full text themselves to identify maybe one or two articles that have the information they’re looking for. Using Linguamatics, they could identify those two articles immediately and not have to sift through 50 articles.” 

Additionally, Linguamatics uses  natural language processing  capabilities to search the entire text of an article to identify concepts and relationships in literature to deliver high-quality care. Traditional text-mining search methods without natural language processing capabilities are unable to perform the same tasks. 

Using comprehensive data allows physicians to provide high standards of patient care. According to Hartmann, there is plenty of information in unstructured data and literature, which can play an important role in  clinical decision-making .  

How can NLP assist with disease diagnosis? 

In addition to identifying the correct treatment options for patients,  NLP can also assist clinicians in disease diagnosis .  

 A recent study by Kaiser Permanente demonstrated the value of natural language processing (NLP) technology with clinicians identifying more than 50,000 patients with aortic stenosis. 

The study was conducted by Matthew Solomon, MD, PhD, a cardiologist at The Permanente Medical Group and a physician researcher at the Kaiser Permanente Division of Research in Oakland, California. 

According to Solomon, while healthcare is currently in an era of  big data  and  data analytics , it remains challenging to identify patients with complex conditions such as valvular heart disease, creating challenges with it comes to studying the disease, tracking practice patterns, and  managing population health . 

“Currently, health systems track patients using diagnosis or procedure codes, which are mostly created for billing purposes. These can be very non-specific and are not useful for clinical care or research,” Solomon told  HealthITAnalytics .  

“Without accurate and systematic case identification, population management and research on valvular heart conditions and many other complex conditions aren’t possible. We set out to tackle this problem by developing natural language processing algorithms that make it possible to teach a computer how to do this for us.” 

The research team trained the NLP to sort through over a million electronic medical records ( EMR ) and echocardiogram reports to detect certain abbreviations, words, and phrases associated with aortic stenosis. 

Within minutes, the software recognized almost 54,000 patients with the conditions, a process that would have likely taken years for physicians to perform manually. 

“It was a magical moment when we were able to apply our developed and validated algorithms on our entire population and to then identify our large cohort of patients with aortic stenosis,” Solomon said. 

“We could immediately imagine a not-too-distant future where these methods could be used to take population management, which Kaiser Permanente Northern California has excelled at for the past two decades, to the next level.” 

As artificial intelligence continues to grow in healthcare, Solomon said providers should be confident that investing in new technologies will significantly improve  patient outcomes . 

“These AI techniques will be able to assist doctors and other providers to care for their patients in ways that were not previously possible,” Solomon said. 

With NLP technology, researchers can enhance patient care, research efforts, and disease diagnosis methods. 

• What Is the Role of Natural Language Processing in Healthcare?
• 4 Key Use Cases for Patient Data Access, Patient Portals
• Ethical Artificial Intelligence Standards To Improve Patient Outcomes

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