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Artificial Intelligence: How is It Changing Medical Sciences and Its Future?

Kanadpriya basu, ritwik sinha, treena basu.

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Address for correspondence: Dr. Kanadpriya Basu, Covisus Inc, Monrovia, CA - 91016, USA. E-mail: [email protected]

Received 2020 May; Accepted 2020 May.

This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.

Artificially intelligent computer systems are used extensively in medical sciences. Common applications include diagnosing patients, end-to-end drug discovery and development, improving communication between physician and patient, transcribing medical documents, such as prescriptions, and remotely treating patients. While computer systems often execute tasks more efficiently than humans, more recently, state-of-the-art computer algorithms have achieved accuracies which are at par with human experts in the field of medical sciences. Some speculate that it is only a matter of time before humans are completely replaced in certain roles within the medical sciences. The motivation of this article is to discuss the ways in which artificial intelligence is changing the landscape of medical science and to separate hype from reality.

K EY W ORDS : Artificial intelligence , deep convolutional neural network , medical use

Introduction

Artificial intelligence (AI) in varying forms and degrees has been used to develop and advance a wide spectrum of fields, such as banking and financial markets, education, supply chains, manufacturing, retail and e-commerce, and healthcare. Within the technology industry, AI has been an important enabler for many new business innovations. These include web search (e.g., Google), content recommendations (e.g., Netflix), product recommendations (e.g., Amazon), targeted advertising (e.g., Facebook), and autonomous vehicles (e.g., Tesla).

Humans reap the benefits of artificially intelligent systems every day. Starting from the spam free emails that we receive in our inboxes, to smart watches that use inputs from accelerometer sensors to distinguish between mundane activities and aerobic activity, to buying products on online shopping sites, like Amazon that recommend products based on our previous purchase records. These examples represent the use of AI in a variety of fields, such as technology and retail. AI has transformed our everyday lives, with an effect on the way we perceive and process information.

This article aims to present various aspects of AI as it pertains to the medical sciences. The article will focus on past and present day applications in the medical sciences and showcase companies that currently use artificially intelligent systems in the healthcare industry. Furthermore, this article will conclude by highlighting the critical importance of interdisciplinary collaboration resulting in the creation of ethical, unbiased artificially intelligent systems.

What is AI?

AI is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. Some applications of AI include automated interfaces for visual perception, speech recognition, decision-making, and translation between languages. AI is an interdisciplinary science.[ 1 ]

It is widely accepted that the term AI was first coined in 1956 when American computer scientist John McCarthy et al . organized the Dartmouth Conference.[ 2 ] Prior to that, work in the field of AI included the Turing test proposed by Alan Turing[ 3 ] as a measure of machine intelligence and a chess-playing program written by Dietrich Prinz.[ 4 ]

Artificially intelligent systems in healthcare have the following typical pattern. Such a system starts with a large amount of data, on these data machine-learning algorithms are employed to gain information, this information is then used to generate a useful output to solve a well-defined problem in the medical system. Figure 1 captures the typical workflow of an AI solution. Applications of AI in the field of medical sciences include matching patient symptoms to appropriate physician,[ 5 ] patient diagnosis,[ 6 ] patient prognosis,[ 7 ] drug discovery,[ 8 , 9 ] bot assistant that can translate languages,[ 10 ] transcribe notes, and organize images and files.[ 11 ]

Illustration outlining the development of an artificially intelligent model

History of AI in Medical Field

Great advances have been made in using artificially intelligent systems in case of patient diagnosis. For example, in the field of visually oriented specialties, such as dermatology,[ 12 , 13 ] clinical imaging data has been used by Esteva et al .[ 6 ] and Hekler et al .[ 14 ] to develop classification models to aid physicians in the diagnosis of skin cancer, skin lesions, and psoriasis. In particular, Esteva et al .,[ 6 ] trained a deep convolutional neural network (DCNN) model using 129,450 images to classify images into one of two categories (also known as binary classification problem in machine learning) as either keratinocyte carcinoma or seborrheic keratosis; and malignant melanoma or benign nevus. They further established that the DCNN achieved performance at par to that of 21 board-certified dermatologists. Their research demonstrated that AI systems were capable of classifying skin cancers with a level of competence comparable to dermatologists and required only a fraction of the time to train the model in comparison to physicians who spend years in medical school and also relied on experience they developed through patient diagnosis over decades.

Much work has also been done in the realm of AI and patient prognosis. For instance, researchers at Google[ 7 ] developed and trained a DCNN using 128,175 retinal fundus images to classify images as diabetic retinopathy and macular edema for adults with diabetes. There are several advantages of the existence of such an artificially intelligent model, such as:

Automated grading of diabetic retinopathy leading to increased efficiency in diagnosing many patients in shorter time;

Serving as a second opinion opthalmologists;

Detection of diabetic retinopathy in early stages due to capability of the model to study images at the granular level-something impossible for a human opthalmologist to do;

Vast coverage of screening programs reducing barriers to access.

Huge strides have been made in application of AI systems to drug discovery[ 15 ] and providing personalized treatment options.[ 16 ] Companies, such as Verge Genomics, focus on the application of machine-learning algorithms to analyze human genomic data and identify drugs to combat neurological diseases, such as Parkinson's, Alzheimer's, and amyotrophic lateral sclerosis (ALS) in a cost-effective way.

Artificially intelligent systems are also being applied in the healthcare sector to enhance patient experience, patient care, and provide support to physicians through the use of AI assistants. Companies, such as BotMD have built systems that can help 24 h with clinical related issues regarding:

Instantly finding which physicians are on call and scheduling the next available appointment; the AI system can also search multiple scheduling systems across different hospitals

Answering prescription related questions, like drug availability and cost-effective alternative drugs

Assisting doctors search hospital protocol, list of available clinical tools, and available drugs all through the use of a mobile application, thus improving workflow in the hospital.

Companies Using AI in Medical Sciences

Table 1 below lists just a few of hundreds of companies in the field of technology, healthcare, and pharmacies that conduct research on artificially intelligent systems and their applications in the healthcare industry. Additionally, applications of artificially intelligent systems in healthcare can be broadly classified into three categories[ 17 ] (for the companies in Table 1 , the type of AI system is also noted):

Some major companies around the world using artificial intelligence in medical sciences

Patient-oriented AI

Clinician-oriented AI and

Administrative and Operational-oriented AI.

Present Day Use of AI

The most recent application of AI in global healthcare is the prediction of emerging hotspots using contact tracing, and flight traveler data to fight off the novel coronavirus (COVID-19) pandemic.

Contact tracing is a disease control measure used by government authorities to limit spread of a disease. Contact tracing works by contacting and informing individuals that have been exposed to a person who has contracted the disease and instructing them to quarantine to prevent further spread of the disease. As reported by Apple Newsroom,[ 18 ] tech giants like Google and Apple have joined forces to create a contact tracing platform that will use artificial intelligent systems through the use of application programming interfaces commonly referred to as API's on smartphones. The platform will enable users who choose to enroll to report their lab results. Location services will then allow the platform to contact people who may have been in the vicinity of the infected person.

Canadian company BlueDot creates outbreak risk software that mitigates exposure to infectious diseases.[ 19 ] BlueDot published the first scientific paper[ 20 ] on COVID-19 that accurately predicted the global spread of the virus. The company uses techniques such as natural language processing (NLP), machine learning (ML), along with automated infectious disease surveillance by analyzing approximately 100,000 articles from over 65 countries every day, travel itinerary information and flight paths, an area's climate, temperature and even local livestock to help predict future outbreaks.

Myth Versus Reality in AI

There is a lot of hope that AI will be able to advance the healthcare sector in a variety of ways, not just for patient diagnosis, patient prognosis, drug discovery, but also to serve as an assistant for physician and provide a better and more personalized experience for patients. This hope has been fueled by some successful applications of AI in healthcare. Side-by-side however, there are unrealistic expectations of what AI can do and what the landscape of the healthcare industry will look like in the future.

Dr. Anthony Chang was one of 2019's invited speakers for the Society for Artificial Intelligence in Medicine (AIME) conference held in Poznan, Poland, where he presented a lecture entitled: Common Misconceptions and Future Directions for AI in Medicine: A Physician-Data Scientist Perspective. Below we list two of the more common myths regarding the application of artificially intelligent systems in healthcare.

Clinicians will be replaced by AI:

While nobody can entirely predict the future, the fact is that physicians who understand the role of AI in healthcare will likely have an advantage in their career. For instance, the American College of Radiology (ACR) posted a job advertisement for a Radiologist:

https://jobs.acr.org/job/radiologist-for-teleradiology- ai-practice/50217408/

listing two requirements for the job:

• Must be American Board of Radiology Certified
• Must be enthusiastic, well-trained radiologist excited about a future where radiologists are supported by world-class AI and machine learning.

Programming knowledge is necessary to successfully use AI:

The use of AI in any field of study consists of many components and programming is just one of them. For the continued growth, development and success of AI applications in healthcare, physicians and data scientists need to continue collaboration to build meaningful AI systems. Physicians need to understand what AI is capable of achieving and need to evaluate how their role can be improved with AI. Physicians need to communicate this information to data scientists who can then build an AI system. The collaboration does not end here. Together physicians and data scientists must figure out what kind of data they have available to use for model training and, further, once the model is built its performance must be analyzed and interpreted, both of which require collaboration between physicians and data scientists. A further trend is the significant commoditization of AI software. For instance, today it is possible to use a visual tool (requiring no coding) to build a visual classifier. An example of such a tool is Teachable Machine by Google.

Limitations and Challenges in the Application of Artificially Intelligent Systems in Medical Science

The application of artificially intelligent systems in any field including healthcare comes with its share of limitations and challenges. The time has come to change our mindset from being reactive to being proactive with regard to downfalls of new technology. Here we discuss those challenges focusing more on those that pertain particularly to healthcare.

Availability of data

The first step towards building an artificially intelligent system (after problem selection and development of solutions strategy) is data collection. The creation of well performing models relies on the availability of large quantities of high quality data. The issue of data collection is shrouded in controversy due to patient privacy and due to recent incidents of data breaches by major corporations. Advances in technology have resulted in increased computational and analytic power as well as the ability to store vast amounts of data. Technology such as facial recognition and gene analysis provides a path for an individual to be identified from a pool of people. Patients and the public in general have a right to privacy and the right to choose what data, if any, they would like to share. Data breaches now make it possible for patient data to fall into the hands of the insurance companies resulting in a denial of medical insurance because a patient is deemed more expensive by the insurance provider due to their genetic composition. Patient privacy leads to restricted availability of data, which leads to limited model training and therefore the full potential of a model is not explored.

Creating biased models

Biased data

Artificially intelligent systems are then trained with a portion of the data that was collected (also known as training data set) with the remaining data reserved for testing (also known as testing data set). Thus, if the data collected is biased, that is, it targets a particular race, a particular gender, a specific age group then the resulting model will be biased. Thus the data collected must be a true representation of the population for which its use is intended.

Data preprocessing

Even after unbiased data has been collected, it is still possible to create a biased model. The collected data must be preprocessed before it can be used to train an algorithm. The raw data that has been collected often contains errors due to manual entry of data or a variety of other reasons. These entries are sometimes modified through mathematical justification or are simply removed. Care should be taken that data preprocessing does not result in a biased pool of data.

Model selection

With the existence of several algorithms and models to choose from, one must select the algorithm that is best suited for the task at hand. Thus, the process of model selection is extremely important. Bias models are ones that are overly simple and fail to capture the trends present in the dataset.

Presenting biased models

It is important for a user of an artificially intelligent system to have a basic understanding of how such models are built. This way a user can better interpret the output of the model and decide how to make use of the output. For instance, there are many metrics that one could use to evaluate the performance of a model, such as accuracy, precision, recall, F 1 score , and AUC score .[ 21 ] However, not every metric is appropriate for every problem. When the user of an artificially intelligent system is presented with performance metrics of a model, they need to make sure that the metrics appropriate to the problem are being presented and not just the metrics with the highest scores.

Fragmented data

Another limitation of the application of AI is that models that one organization spends time and effort to design and deploy for a specific task (regression, classification, clustering, NLP, etc) cannot be seamlessly transitioned for immediate use to another organization without recalibration. Due to privacy concerns, data sharing is often inaccessible or limited between healthcare organizations resulting in fragmented data limiting the reliability of a model.

Artificial Intelligent systems have a reputation of being blackboxes due to the complexity of the mathematical algorithms involved. There is a need to make models more accessible and interpretable. While there is some recent work in this direction, there is still some progress to be made.[ 23 ]

Conclusion: The Future of AI in Medical Sciences

Despite the above limitations, AI looks well positioned to revolutionize the healthcare industry. AI systems can help free up the time for busy doctors by transcribing notes, entering and organizing patient data into portals (such as EPIC) and diagnosing patients, potentially serving as a means for providing a second opinion for physicians. Artificially intelligent systems can also help patients with follow-up care and availability of prescription drug alternatives. AI also has the capability of remotely diagnosing patients, thus extending medical services to remote areas, beyond the major urban centers of the world. The future of AI in healthcare is bright and promising, and yet much remains to be done.

The application of artificially intelligent systems in healthcare for use by the general public is relatively unexplored. Only recently the FDA (U.S Food and Drug Administration) approved AliveCor's Kardiaband (in 2017) and Apple's smartwatch series 4 (in 2018) to detect atrial fibrillation. The use of a smartwatch is a first step toward empowering people to collect personal health data, and enable rapid interventions from the patient's medical support teams.

There are many negative effects of modern technology on mental health. However, researchers at the University of Southern California (USC) in collaboration with Defense Advanced Research Projects Agency and the U.S. Army found that people suffering from post-traumatic stress and other forms of mental anguish are more open to discussing their concerns with virtual humans than actual humans for fear of judgment. This research[ 23 ] has promising results for the role of virtual assistants resulting in the collection of honest answers from patients that could help doctors diagnose and treat their patients more appropriately and with better information.

Most global pharmaceutical companies have invested their time and money on using AI for drug development of major diseases, such as cancer or cardiovascular disease. However, development of models for diagnosing neglected tropical diseases (malaria and tuberculosis) and rare diseases remains largely unexplored. The FDA now incentivizes companies to develop new treatments for these diseases through priority vouchers.[ 24 ]

Given the impact that AI and machine learning is having on our wider world, it is important for AI to be a part of the curriculum for a range of domain experts. This is particularly true for the medical profession, where the cost of a wrong decision can be fatal. As identified here, there is a lot of nuance in how an AI system is built. Understanding this process and the choices it entails are important for appropriate usage of this automated system. The data used to learn from and the optimization strategy used has a deep impact on the applicability of the AI system to solve a particular problem. An understanding and appreciation of these design decisions is important for medical profession.

AI has the potential to help fix many of healthcare's biggest problems but we are still far from making this a reality. One big problem and barrier from making this a reality is data. We can invent all the promising technologies and machine learning algorithms but without sufficient and well represented data, we cannot realize the full potential of AI in healthcare. The healthcare industry needs to digitize medical records, it needs to come together to agree on the standardization of the data infrastructure, it needs to create an iron-clad system to protect the confidentiality and handle consent of data from patients. Without these radical changes and collaboration in the healthcare industry, it would be challenging to achieve the true promise of AI to help human health.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

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• Research article
• Open access
• Published: 26 February 2018

The use of advanced medical technologies at home: a systematic review of the literature

• Ingrid ten Haken 1 ,
• Somaya Ben Allouch 1 &
• Wim H. van Harten 2 , 3  

BMC Public Health volume  18 , Article number:  284 ( 2018 ) Cite this article

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The number of medical technologies used in home settings has increased substantially over the last 10–15 years. In order to manage their use and to guarantee quality and safety, data on usage trends and practical experiences are important. This paper presents a literature review on types, trends and experiences with the use of advanced medical technologies at home.

The study focused on advanced medical technologies that are part of the technical nursing process and ‘hands on’ processes by nurses, excluding information technology such as domotica. The systematic review of literature was performed by searching the databases MEDLINE, Scopus and Cinahl. We included papers from 2000 to 2015 and selected articles containing empirical material.

The review identified 87 relevant articles, 62% was published in the period 2011–2015. Of the included studies, 45% considered devices for respiratory support, 39% devices for dialysis and 29% devices for oxygen therapy. Most research has been conducted on the topic ‘user experiences’ (36%), mainly regarding patients or informal caregivers. Results show that nurses have a key role in supporting patients and family caregivers in the process of homecare with advanced medical technologies and in providing information for, and as a member of multi-disciplinary teams. However, relatively low numbers of articles were found studying nurses perspective.

Conclusions

Research on medical technologies used at home has increased considerably until 2015. Much is already known on topics, such as user experiences; safety, risks, incidents and complications; and design and technological development. We also identified a lack of research exploring the views of nurses with regard to medical technologies for homecare, such as user experiences of nurses with different technologies, training, instruction and education of nurses and human factors by nurses in risk management and patient safety.

Peer Review reports

As a result of demographic changes and the rapidly increasing number of older patients, there is a need for cost savings and health reforms, which include an increased move from inpatient to outpatient care in most industrialized countries over the last 10–15 years [ 1 , 2 ]. As a consequence, the transfer of advanced medical devices into home settings was considerable and it is expected that there will be a further increase in the near future [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ].

When ‘an increase’ in the number of medical technologies used at home is mentioned, it is not clear which and how many technologies are involved. Today, there are an estimated 500,000 different kinds and types of medical devices available on the world market [ 8 , 9 ]. The European Commission (EC) publishes data regarding legislation and regulations for medical devices, but the actual figures for medical technologies in outpatient practice are not available [ 10 ]. The U.S. National Center for Health Statistics (NCHS) stated that technologies have shifted from hospitals into the home, but it too does not illustrate its findings with statistics [ 11 ]. We searched for data with regard to the actual number of medical technologies used in home settings and it proved difficult to find any systematic data sets available throughout the international landscape.

An important condition for the application of medical technology in the home setting is that quality of care and patient safety must be guaranteed [ 6 ]. From a historical perspective medical technologies were designed for hospital settings [ 12 , 13 ]. This means that specific factors regarding the implementation and use at home now need to be taken into account [ 7 , 14 , 15 ]. In general, risks with medical technologies can be classified regarding (a) environmental factors; (b) human factors and (c) technological factors [ 16 ]. Human factors, however, are very important in patient safety in both hospital and in home settings [ 1 , 6 , 12 ]. For example, a major risk factor is the number of users and handovers in the chain of care. In home settings, a sometimes impressive number of different users of medical technology, often with various levels of training, instruction or education, are involved. Although patient empowerment moves control to the patient and/or relatives, an important user group is that of professional nurses. Understanding user experiences and information about adverse events and near incidents are important aspects for developing knowledge regarding implementation and use in home care setting. Sharing this knowledge can support patients and caregivers, and especially nurses in their professional work and will also contribute to patient safety and quality of care.

Therefore, there is a need to address the question first, which types of technologies are used at home; second, how frequently are they used and third, what trends can be distinguished. Additional research questions are whether there are any scientific data regarding particular user experiences; training, instruction and education; safety and risks, and finally, what can be concluded about the role of nurses in using medical technologies in the home environment. The objective of this paper therefore is to present a systematic literature search on the international state of art concerning various aspects of the use of advanced medical technologies at home.

Definitions

First, we want to clarify some definitions. In general, ‘health technology’ refers to the application of organized knowledge and skills in the form of devices, medicines, vaccines, procedures and systems developed to solve a health problem and improve quality of life [ 17 ]. The World Health Organization [ 8 ] uses the definition of ‘medical device’ as ‘An article, instrument, apparatus or machine that is used in the prevention, diagnosis or treatment of illness or disease, or for detecting, measuring, restoring, correcting or modifying the structure or function of the body for some health purpose …….’. A specification for a home use medical device is: ‘A medical device intended for users in any environment outside of a professional healthcare facility. This includes devices intended for use in both professional healthcare facilities and homes’ [ 18 ].

The landscape of medical devices is diverse with technologies varying from relatively simple to very complex devices. Wagner et al. [ 19 ] stated that ‘high-tech dependency’ (for children) matches with ‘technology-dependency’ if it concerns ‘a medical device to compensate for the loss of a vital bodily function and substantial and ongoing nursing care to avert death or further disability’. ‘The needs of these patients may vary from the continuous assistance of a device and highly trained caretaker to less frequent treatment and intermittent nursing care’ [ 20 ]. Although patients dependent of advanced medical technologies at home are often medically stable, they sometimes have high technical needs and may be expected to need long-term recovery. They also require skilled nursing [ 21 ] and a considerable degree of advanced decision making, planning, training and oversight [ 22 ]. An overall definition of ‘advanced medical technology’ is: ‘Medical devices and software systems that are complex, provide critical patient data, or that directly implement pharmacologic or life-support processes whereby inadvertent misuse or use error could present a known probability of patient harm’ [ 23 ]. Examples of advanced medical technologies used at home include ventilators for respiratory support, systems for haemo- or peritoneal dialysis and infusion pumps to provide nutrition or medication.

In the Netherlands, the National Institute for Public Health and the Environment (RIVM) [ 24 ] uses the following definition:

Advanced medical technology or high-tech technology in the home setting is defined as technology that is part of the technical skills in nursing and meets the following conditions:

technology that is advanced or high-tech, for example equipment with a plug, an on/off switch, an alarm button and a pause button;

technology that had been applied formerly only in hospital care, but that is now also often applied in home settings;

technology that can be categorized as ‘supporting physiological functions’, ‘administration’ or ‘monitoring’.

Within the Dutch classification of advanced medical technologies 19 different devices are identified (see Table  1 ), which will be used in this review as a basis to categorize the technologies. It is a classification format in which specific advanced technologies are defined. Terms as ‘advanced medical technology’ (from now on abbreviated as AMT) will be used consistently as synonyms for ‘complex medical technology’ and ‘high-tech medical technology’. The term ‘technology’ will be used in the meaning of ‘device’ or ‘equipment’. The target is on technologies that are instrumental and ‘hands on’ use by nurses in the care for patients. This means that information technology (IT) based technologies as domotica (automation for a home) are not part of the study.

Eligibility and search strategy

The systematic review of the literature was conducted early 2016. Key concepts for the review were ‘medical technologies’ or ‘medical devices’, and ‘home settings’. The concept of ‘home settings’ is related to the terms ‘home nursing’ and ‘home care service’, of which the stem is ‘home’. Combining the key concepts provided the search string: (‘medical technology’ OR ‘medical device’). As domotica is not part of the study, the search string was extended with: AND NOT (eHealth OR telecare OR telemedicine). The exact search string is (“medical technology” OR “medical devices”) AND home AND NOT (ehealth OR telecare OR telemedicine). Online databases MEDLINE, Scopus and Cinahl were searched electronically using the search string to obtain data.

Inclusion and exclusion criteria

Criteria for selection were defined prior to the search process. General criteria for inclusion were:

Year of publication: 2000–2015.

An abstract or an article (with or without abstract) has to be available, containing reference to AMT information.

The article is published in English, German, French or Dutch/Flemish language.

If medical technology is cited, it has to conform to the definition of ‘advanced medical technology’ [ 24 ].

The abstract or the article has to contain empirical material. For the purpose of this review, ‘empirical material’ has been defined as: AMT which is designed for the home setting, or where the design or choices took into account the setting of the home, or where the medical technology has been tested for the home or if the medical technology is already on the market and being used in the home setting.

For further selection, inclusion criteria related to the key concepts for title and abstract were applied, such as ‘advanced medical technology’, ‘high-tech medical technology’, ‘home-centred health-enabling technology’ and ‘care at home’. The classification of the RIVM (see Table 1 ) has been taken as a basis to categorize technologies in this review. Domotica and telemonitoring technologies scored under ‘monitoring’, such as fetal cardiotocography, and respiratory and circulatory monitoring, were left out. If the abstract or article was about electronic health records, ‘smart home’, ambient intelligence, pervasive computing, software of devices, smartphone or surgical robots, the article was also removed from selection. Technologies as ‘VAD (ventricular assist device)’, ‘dental devices’ and ‘AED (automatic external defibrillator)’ were not seen as part of the technical nursing process and these records were left out as well. Studies conducted in the hospital, hospice or nursing home settings were also excluded. An overview of all inclusion and exclusion criteria can be found in Table  2 .

Screening process

The search in the online databases using the search string, identified a total of 1287 references. After checking for duplicates, 1070 articles remained. Those articles were reviewed by a reviewer for titles and abstracts on basis of the inclusion and exclusion criteria. A double check was performed by two reviewers, who independently screened random samples of 20% of the articles. There was an initial agreement of 88%. In case of disagreement about the inclusion of an article, the decision was based on a joint discussion by all three reviewers to an agreement of 100% and the resulting screening policy was applied to the rest of the abstracts. Based on the selected titles and/or abstracts, articles were retrieved or requested in full text and assessed for eligibility. Some articles were excluded from further study, for reasons of ‘full text not available’ or the article contained no empirical material. Finally, 87 studies remained which were included in the analysis (see Table  3 ). A graphical representation of the screening process has been included in Fig.  1 .

PRISMA flowchart

Appraisal of selected studies

To conduct the systematic literature search on the international state of art concerning various aspects of the use of advanced medical technologies at home, several sources are consulted. To guarantee a scientific standard, only articles were retrieved from academic databases. MEDLINE refers to journals for biomedical literature from around the world; Cinahl contains an index of nursing and research journals covering nursing, biomedicine, health sciences librarianship, alternative medicine, allied health and more. These databases related to discipline have been supplemented with Scopus, which is considered to be the largest abstract and citation database of peer-reviewed literature. Grey literature, such as national and international reports on regulations and safety of medical technologies, is also used to illustrate the background of the problem statement and describe definitions. The Classification of advanced medical technologies in the Netherlands according to the National Institute for Public Health and the Environment (RIVM) has been used as a framework to categorise the medical technologies in the selected articles. No methodological conditions of selected studies were applied in advance and the quality criterion we applied was that of the article had to contain empirical material, as we wanted to obtain an comprehensive overview of published studies of any design and to get insight in a variety of contents.

Categorization of included articles

The characteristics of the included articles are outlined in Table  3 . All included articles were categorized by year of publication and the type of research, like the designs, methods and used instruments in the studies. Research features were synthesized where possible into overarching categories. For example, ‘systematic review’ and ‘narrative review’ were scored as ‘review’ and instruments as ‘semi-structured interview’ and ‘in-depth individual interview’ were both assigned to the category ‘interview’.

For each study, the medical technology or technologies on which the study was based was scored. The categorization was in accordance with the classification of AMTs (see Table 1 ). For example, the devices ‘continuous positive airway pressure (CPAP)’ and ‘negative pressure ventilation (NPV) have both been categorized as ‘respiratory support’; and the devices ‘jejeunostomy tube’ and ‘gastronomy tube’ as ‘enteral nutrition’. With regard to the category ‘dialysis’, further subdivision was made by using ‘haemo dialysis’ and ‘peritoneal dialysis’. If in an article a medical technology was mentioned as an example, but was no subject of study, then the technology was not scored.

‘Medical diagnosis (or diagnoses)’ as mentioned in the studies, was included in the analysis only if it was related to the medical technology as the subject of study, not if it has been mentioned as an example. In some cases, an underlying cause of diagnosis was indicated. For example, ‘chronic respiratory failure due to congenital myopathy’, in itself a neurological disorder, has been scored as ‘neurological disorder’. Diseases or disorders have been classified as much as possible under the overarching name. For example ‘pneumonia’ and ‘cystic fibrosis’ are categorized under ‘respiratory failure’, and ‘gastroparesis’ and ‘Crohns disease’ under ‘gastrointestinal disorder’. The category ‘other’ contains diagnoses which occur only once, such as ‘chromosomal anomaly’, or which are not yet determined, like ‘chronic diseases’ or ‘congenital abnormalities’.

In relation to the research questions, articles were classified regarding one of the following categories and, where appropriate, into subcategories:

User experiences

Training, instruction and education, safety, risks, incidents and complications.

From an analysis of the articles, additional categories of content emerged:

Design and technological development

Application with regard to certain diseases or disorders, indication for and extent of use

Policy and management

Types of medical technologies used, frequency of use and trends.

In four of the 87 articles (5%) there were no specific medical technologies mentioned as a subject of study (see Table  4 ). Almost half of the studies (45%) considered medical technologies for respiratory support and 39% devices for dialysis, either haemo- ( n  = 18), peritoneal- ( n  = 15) or dialysis not specified ( n  = 1). Of the studies, 29% reported on devices for oxygen therapy. In addition, there has been relatively more research conducted on equipment for ‘infusion therapy’ ( n  = 19; 22%), parenteral nutrition and enteral nutrition with a score of 20% each ( n  = 17). Relatively little research has been carried out on suction devices (8%), external electrostimulation (5%), nebulizer (5%), insulin pump therapy (3%), sleep apnea treatment (2%), patient lifting hoists (2%), vacuum assisted wound closure (1%) and continuous passive motion (1%). None of de studies considered medical technologies with regard to decubitus treatment, skeletal traction or UV (ultraviolet) therapy.

Table 4 shows that on the years 2000 and 2001 no relevant articles on the subject were found. Over the period 2000–2005, 17 articles were published, the same number over 2006–2010, and there has been a substantial increase in the number of publications to 54 over the years 2011–2015. In general, it can be concluded that more frequent investigated technologies show a fairly even distribution of publications over the years 2000–2015. Technologies, on which little research had been done, except for nebulizers, have been mainly investigated since 2010. An increase of published articles over the years 2000–2015 is apparent particularly for haemo dialysis and to a lesser extent, for devices for enteral- and parenteral nutrition. As mentioned before, several studies reported on the increase of the number of medical technologies used in home settings, but concrete data are not available. However, the number of studies and the visible trends may be indicative of the frequency of use.

In 63% of the cases ( n  = 55), a medical diagnosis (or diagnoses) was mentioned in the article. Where a diagnosis has been mentioned, in almost half of the studies ( n  = 26; 47%) it concerned diagnoses in the field of respiratory failure (see Fig.  2 ). This is not surprising, since ‘respiratory support’ is the medical technology most commonly found in the articles, similarly ‘oxygen therapy’ has also been considered relatively often. Diagnoses with regard to neurological disorders occurred in 42% of the studies ( n  = 23). Just over a quarter of the studies (27%) considered diagnoses ‘other’, such as ‘sepsis’, ‘chromosomal anomaly’ or other not specified medical disorders, nearly a quarter (24%) considered ‘cancer’ and 22% kidney disorders ( n =  12).

Number of medical diagnoses mentioned in articles on AMTs ( n  = 87, multiple answers possible)

An analysis of the used research designs identified that 64% ( n  = 56) of the studies used an observational (non-experimental) design and only a small part of the studies ( n  = 5; 6%) used an experimental design, such as a Randomized Control Trial (RCT). Of the included studies 19 were reviews and 8 were essays. A quantitative design ( n  = 37) was used more frequently than a qualitative design ( n  = 25); and only one study applied ‘mixed methods’ (quantitative and qualitative). Just over one-third of the studies (35%) used a descriptive design, and a similar number used a cross-sectional study (36%). Case series were used in 12% of the articles and a cohort-study in 9%. A phenomenological approach was applied in 16% of the records. Research instruments most frequently used were interviews (33%) and survey/questionnaires (21%). In 10% of the cases other instruments were used, including different types of assessments or tests.

With regard to the categories of content, most research has been carried out on ‘user experiences’ (see Fig.  3 ): just over one-third of the articles ( n  = 31; 36%) focused on this topic. Of these articles almost all studies focused on experiences of patients or informal caregivers ( n  = 29) and only a small number ( n  = 2) considered the user experiences of nurses or other professionals (see Table  5 ). More than half of the studies ( n  = 19) used a qualitative research design; of these 13 used a phenomenological approach. The goal of these studies was to elicit the essence of human phenomena as experienced by the users. Seven studies used a quantitative design and one an integrated mixed method. Three of the studies applied a grounded theory approach and two an experimental design (randomized controlled trial). The research instruments in this content category to collect data were interviews, either semi-structured or in-depth, and a survey. About two-thirds of the articles regarding ‘user experiences’ were published in the period 2011–2015, with an accent on the psychosocial impact of patients or informal caregivers.

Number of articles on AMTs with main content categories ( n  = 87)

Relatively little research was found on ‘training, instruction, education’ ( n  = 7), for the use of AMTs in home settings. It was remarkable that all the studies identified as focusing on this topic, concentrated on one category of AMT. Respiratory support was the subject of study in four instances and in the other three, the focus was on technologies for enteral nutrition, haemo dialysis and external electro-stimulation. Four of the seven articles utilized quantitative methods, among which three of them used an observational non-experimental design and one was an experimental randomized double-blind clinical trial. Another study within the initial seven articles used a qualitative observational non-experimental design, one was a review and another was in essay format.

In total, 22% of the articles discussed topics on safety, risks, incidents and complications ( n  = 19). In the majority of cases ( n  = 13) general aspects about the subject, for instance safe use, factors affecting safety, a safe transfer of the equipment and monitoring of assessing safety were considered. One article described technological factors with regard to safety, three articles reported on environmental factors and two explored human factors. Safety aspects were explored over a wide range of medical technologies. Five articles were reviews and one an essay. Quantitative methods were used in ten of the cases, particularly for monitoring, evaluating and assessing safety, technological and environmental factors. Only three studies used a qualitative design. Retrospective chart reviews or case series were used to collect data in some cases of unforeseen events. Table 5 shows about a doubling of published articles in the period 2011–2015 regarding this content category, compared to the previous period 2000–2010.

Approximately 20% of the selected articles considered the content category ‘design and technological development of the medical device’ ( n  = 17). The studies each focused on only one type of AMT and treated a relative wide range of eight different categories, such as ‘respiratory support’, ‘oxygen therapy’, ‘haemo dialysis’, ‘infusion therapy’, ‘insulin pump therapy’ and ‘enteral nutrition’, but also ‘external electrostimulation’ and ‘patient lifting hoists’. Interestingly, in this group of articles, relatively often ( n  = 6) no medical diagnosis was mentioned. Around half of the studies ( n  = 8) referring to this topic were in review or essay format. All other studies used a quantitative research design and throughout the search no application of qualitative designs were found. Two studies used an experimental study design (randomized crossover trial) to obtain data and two described a prospective cohort study. The majority of papers ( n  = 11) were published in the period 2011–2015 and six in the preceding period up to and including 2010.

Seven articles concerned the application of AMTs, all of them devices with regard to at least respiratory support and/or nutritional support. Five studies used a non-experimental quantitative design including the analysis of clinical data, such as record reviews or cohort studies, and two articles were reviews. Most articles on this subject ( n  = 5) were published in the period 2012–2015.

Six articles described policy or management systems in different countries regarding the use of AMTs at home. The majority of the articles ( n = 4 ) were in essay or review format. The other papers concerned a qualitative cross-sectional case study analysis and an observational quantitative study in which data are collected prospectively using a database. The categories of content will now be discussed in greater detail.

Content description and trends to secondary research questions

In this category, 22 articles described the psychosocial impact on patients or informal caregivers from the use of medical technologies at home. Living at home with the assistance of medical technology needs a range of adjustments. Fex et al. [ 25 , 26 ] state that self-care is more than mastering the technology, in terms of the health-illness transition, it requires ‘…. an active learning process of accepting, managing, adjusting and improving technology’. When it comes to children, they have to learn to incorporate disability, illness and technology actively within their process of growing up [ 27 ]. It seems that the use of medical technologies in the home can have both a positive and a negative psychosocial impact on patients and their families, which in turn causes ambivalence in experiences [ 27 , 28 ]. On the one hand, patients in general gain more independence, an enhanced overall health and a better quality of life [ 29 , 30 , 31 , 32 , 33 , 34 ]. On the other hand, for some patients the experience is one of dependency on others for executing daily activities, and these circumstances, to some extent, a social restricted live and perceived stigmatization [ 29 , 30 ]. The situation in which patients need to use medical technology at home also affects family functioning and requires next of kin responsibilities [ 35 , 36 , 37 ]. As a result, next of kin caregivers are frequently faced with poor sleep quality and quantity, and/−or other significant psychosocial effects [ 38 , 39 , 40 , 41 ]. Nevertheless, family members had a positive attitude to the concept of bringing the technology into the home [ 42 ]. Knowledge of how to use the technology and permanent access to support from healthcare professionals and significant others, enabled next of kin caregivers to take responsibility for providing necessary care and to facilitate patients learning to provide self-care [ 25 , 36 , 42 , 43 , 44 ]. Bezruczko et al. [ 45 , 46 ] developed a measure of mothers’ confidence to care for children assisted with medical technologies in their homes. To provide high quality sustainable care, nurses have to recognize and understand the psychosocial dimensions for both patients and family members which arise as a result of changing role and providing care for the patients. The need to provide emotional support and support with appropriate coping strategies is a key professional role [ 25 , 26 , 47 ]. Insight into the psychosocial effects on those involved can be used to assist designers of medical devices to find strategies to better facilitate the integration of these technologies into the home [ 28 ].

Seven articles reported on the usability, barriers and accessibility experienced by patients or informal caregivers. Findings in these studies showed that several technologies were rarely perceived as user-friendly and that home medical devices inadequately met the needs of individuals with physical or sensory deficits [ 48 , 49 ]. An accessible design which meets the diversity of individual user needs, characteristics and features would be better able to help patients manage their own treatment and so could contribute to the quality of care and safety of patients and lay users [ 50 , 51 ]. Munck et al. [ 52 ] stated that restricted patients were reminded daily of the medical technology and were more dependent on assistance from healthcare professionals than masterful patients.

In contrast to the group of patients or informal caregivers, only two papers in this content category focused on the user experiences of nurses or other professional caregivers. The review demonstrates that to maintain patient safety, more education on application of medical devices for users is needed together with improved awareness and understanding of how to use the medical technology correctly in a patient-safe way [ 53 , 54 ]. More collaboration between all involved ‘actors’ in the process of care is also requisite. Continuity among carers, trust between patient and carers and supportive communication between informal and professional caregivers are important factors for the successful implementation of medical technologies in the home environment while maintaining patient safety [ 44 , 51 , 53 , 54 , 55 ].

Three articles regarding this topic focused on nurses or other professionals and four on the patients or informal caregivers. The results showed that successful use of advanced medical technologies at home requires adequate staff education and training programmes. Although many topics in educational programmes are suitable for different types of professionals in care provision, the focus for the level and application of information can vary for Registered Nurses and unregistered care staff. In addition, for overall learning experiences to be of maximum benefit there is a need for a clear focus on the specific client groups [ 56 ]. According to Sunwoo et al. [ 57 ], in the case of home non-invasive ventilation the degree of clinical support needed is extremely variable given the mixed indications for this respiratory support. A relatively simple procedure, such as the replacement of a feeding tube, can be performed by nurses, the patient and informal caregivers, provided they are trained well [ 58 ]. However, several studies revealed the complexity of the education needed by patients and informal caregivers for the use of advanced medical technologies at home [ 59 , 60 ]. Nevertheless, the studies revealed that a structured education programme, specific training, or the support of a dedicated discharge coordinator has several advantages [ 59 , 61 , 62 ]. It was evident that good preparation by patients or informal caregivers may result in a shorter length of stay in hospital, a better performance with regard to the use of the equipment or less requests by patients and/or families for assistance.

Most articles regarding this topic ( n  = 13) reported on safety in general, like aspects of safe use, factors affecting safety, complications and prevention of incidents in the home. Some identified the risk factors and the complications that may arise [ 63 , 64 , 65 ], where Stieglitz et al. [ 66 ] also emphasize that human error is the main reason for critical incidents and that regular instruction for medical staff and patients is necessary. To prevent untoward and adverse events, evidence based guidelines, recommendations on the preferred methods for managing the equipment, troubleshooting techniques for potential complications and monitoring activities are necessary [ 67 , 68 ]. Faratro et al. [ 68 ] added that key performance and quality indicators are important mechanisms to ensure patient safety when using a medical device in the home. Methods to address or evaluate patient safety issues are for example, a home visit audit tool, a nationwide adverse event reporting system, programs such as the Medical Product Safety Network HomeNet, or, in the case of peripherally inserted central catheters (PICCs) a central catheter stabilization system [ 69 , 70 , 71 , 72 ]. However, a study conducted by Pourrat and Neuville [ 73 ] in France found that there are very few internal medical devices vigilance reports found within organizations that deliver devices for home parenteral nutrition and that safety management could be improved. The safe transfer of medical devices from a hospital setting to the home and vice versa, comes with several challenges regarding technological, environmental and human factors [ 14 ]. While many hospitals have developed policies to control the pathways of home-used devices in the hospitals, in case patients take them into the hospital when they are admitted for treatment [ 74 ]. Improvement of the safety of devices intended for use in home settings, implies also improvement of safety when their transfer to the hospital settings is urgently needed.

One article considered the technological factors, three the environmental and two the human factors. An example of research on the technological factors of safety related aspects of medical technologies used in home settings by Hilbers et al. [ 75 ] found that manufacturers pay insufficient attention to safety-related items in technical documentation for the use in the home setting. For instance, the environmental factor of electricity blackout leads to electrically powered medical devices failing. Studies show that this type of event causes a dramatic increase in appeal for access to emergency or hospital facilities, and that disaster preparation needs to include the specific needs of patients reliant on electrically driven devices [ 76 , 77 , 78 ]. Regarding human factors impacting on safety aspects, one article assessed the suitability of a particular theoretical framework for understanding safety-critical interactions of patients using medical devices in the home [ 79 ], while Tennankore et al. [ 80 ] described adverse events in home haemodialysis by the use of patients. It was remarkable that none of the articles focused on human factors with regard to the use of medical technologies at home by nurses or other professional caregivers.

Of those articles that focused on this topic, ten reported on the comparison between different types of medical technologies, or their advantages and disadvantages. The comparison of different devices for oxygen therapy was made by two articles [ 81 , 82 ] and one reported on the comparison of two types of enteral nutrition tubes [ 83 ]. Some studies regarding respiratory support considered the process of making a choice between different types of devices [ 84 , 85 , 86 ] while one paper considered the conditions for home-based haemo dialysis [ 87 ]. A minority, explored the individual characteristics and the clinical applications of several devices for respiratory support [ 88 , 89 ] and one considered devices for insulin pump therapy [ 90 ]. Seven papers discussed the technological development or effectiveness of medical technologies. The testing of devices for external electro-stimulation was described in two papers [ 91 , 92 ], with the testing of a new design patient lift was subject of one study [ 93 ]. Hanada and Kudou [ 94 ] explored the current status of electromagnetic interference with medical devices in the home setting, an issue of importance as more devices are considered for home use. The technological development of respiratory support for home use was part of one study [ 95 ], as were the possibilities of solar-assisted home haemo dialysis [ 96 ]. While the study by Pourtier [ 97 ] describes the advantages of analgesia pumps that can be read remotely by nurses, but also emphasizes the central position of a professional nurse in the transfer of information within a multi-disciplinary team.

Application with regard to certain diseases or disorders, indications for and extent of use

All articles described several aspects that need to be considered for use, such as clinical characteristics of the patients, indications for the use in the home setting, the technical availability of devices, the extent of their use at home or eventual complications and morbidity. It was important to note that all but one article ( n  = 6) were about children or related to adults with what are usually regarded as paediatric diseases. Results show that the use of AMTs at home among children after hospital discharge is common (in 20%–60% of cases), or is standard for patients with some disorders [ 98 , 99 , 100 , 101 ]. The timely application of advanced home medical technology benefits patients and can help to reduce respiratory morbidity [ 102 ]. Nevertheless, the rate of death of patients with Möbius syndrome using the devices at home was high (30%) [ 98 ], as was that of patients with intestinal failure dependent on home parental nutrition therapy in Brazil (75% for 5 years) [ 103 ]. The average cumulative survival of children needing home ventilation was found to be between 75 and 90%, depending on the medical diagnosis [ 104 ].

Three of the papers were concerned with costs and/or reimbursement. The application of medical technologies in the home environment can be cost-effective when compared to institutionalized care [ 22 , 105 , 106 ]. Nevertheless, successful employment of medical technologies in the home necessitates medical guidelines for the indicators for use, careful identification of patients as well as careful planning and attention to details [ 105 , 106 , 107 ]. Two studies concerned the dilemma’s for implementation of the technologies in home healthcare and emphasized the importance of cooperation in the chain of key stakeholders to maximize efficiency of high-tech healthcare at home, one with regard to the purchasing policy of medical technologies [ 108 ] and one with regard to the interventions of local community service centres and hospitals supporting optimal use of these technologies in the home setting [ 5 ].

The use of medical technologies in the home setting has drawn increased attention in health care over the last 15 years, as the feasibility of this type of medical support has rapidly grown. This article systematically reviewed the international literature with regard to the state of the art on this subject, in order to provide a comprehensive overview.

Trend analysis over the period 2000–2015 shows that most research has been conducted about respiratory support, dialysis and oxygen therapy; relatively little about vacuum assisted wound closure and continuous passive motion, and no about decubitus treatment, skeletal traction and UV therapy. A substantial increase in publications was found in the period 2011–2015. Although the number of studies on technologies is indicative of the extent to which they are used in home settings, however, no firm conclusions can be drawn about this.

This review also identified that most research is conducted with regard to ‘user experiences’ of medical technologies in the home, ‘safety, risks, incidents and complications’, and ‘design and technological development of medical technologies’. There have been relatively few studies which have explored the topic of training, instruction and education. Content analysis showed that the use of AMTs in the home setting can have both a positive and a negative psychosocial impact on the patients and their families, and that it has become part of self-management and patient empowerment. Successful use of advanced equipment requires adequate education and training programmes for both patients, informal caregivers and nurses or other professionals. When trying to maximize or assure safety, technological, environmental and human factors have to be taken into account, and it is evident that human factors are the main reason for critical incidents. Studies on the design and technological development of medical technologies emphasize that research is necessary to improve its possibilities and effectiveness. The research found on the application of the technologies focused predominantly on children and the results indicate that the rate of the use of home medical devices among children after hospital discharge is common. Also that when compared to institutionalized care, the application of medical technologies in the home environment can be cost-effective. Much is known, but information on several key issues is limited or lacking.

An important finding was that in almost all the reviewed articles, the study subjects were patients or informal caregivers with very few studies focused on the role and activities of nurses or other professionals as users. This was unexpected as nurses are the main group of users of AMTs at home and they have to transfer knowledge and skills on how to use the devices to patients and other caregivers. Nurses also have a key role in setting up and maintaining collaboration between all actors involved in the process of care with regard to the use of home medical technologies and in giving support to patients and family members in this respect. There is need to initiate further in depth research on AMTs use at home focusing on the role of specifically nurses.

Another interesting result was that, despite the fact that most adverse events with AMTs at home are caused by human factors, hardly any studies conducted on this subject were found. None of the articles focused on related human factors regarding the use by nurses or other professional caregivers, although this is the main user group. Research on this area could contribute to improved patient safety and quality of care. The results also revealed the tension between the advantages and disadvantages of medical technologies as experienced by patients at home. Important aspects needed to promote the benefits include improving the user-friendliness of the devices and attuning their designs for the use in home settings. This emphasizes the importance of professionals (and patient groups) working together with the designers with regard to sharing knowledge and user experiences of the use of AMTs at home in order to improve quality of care and patient safety. This collaboration emerged as of key importance in the successful use of AMTs in the home as well.

Although all included articles were retrieved from academic databases and served our purpose, there was considerable heterogeneity of quality of the studies. Most of the studies have explicitly described their research design, albeit to a greater or lesser extent. On the other hand, there were a few studies that did not even mention their methodological approach, though it could be derived from the description. Most included reviews are of moderate quality. Although findings are almost always described clearly, the search strategy and selection criteria used are often lacking. The quantitative studies are generally well described in different methodological aspects, such as selection of respondents, research design, data collection methods and analyses. Studies of qualitative nature show more variation in the depth with which the design is described. However, almost all qualitative studies have described the research instruments very well, such as semi-structured interviews or questionnaires. Despite the varying quality of the studies, we believe that the whole of different methodological approaches and the relatively large number of included studies ( n  = 87) has yielded a fairly reliable overview on the international state of art concerning various aspects of the use of advanced medical technologies at home. For future research, we recommend to emphasize the development of a more detailed methodological design, zooming in on specific technologies, using large databases or conducting large surveys, and focusing on specific groups of respondents. Both in quantitative and in qualitative studies, a good definition of the research question(s), selection of respondents, development of instruments and analysis of findings, contributes to validity, consistency and neutrality.

Some limitations do have to be taken into account with this review. Although we used the RIVM-definition of ‘advanced medical technology’, not all devices are considered as ‘complex devices’ by nurses in practice. For example, the use of an anti-decubitus mattress in the context of ‘decubitus treatment’ and ‘patient lifting hoists’ are considered by nurses as being of less or lower complexity. However, overall the RIVM-classification was found to be a good starting point, and provided a practical and useful framework from which to work to gain an insight and overview of available medical technologies. Of some of the chosen technologies defined using the RIVM-classification of AMTs, questions do have to be asked as to whether they really are part of the technical skills in nursing process. For example, ‘external electrostimulation’ and ‘continuous passive motion’ are mainly applied by physiotherapists, although with appropriate training nurses can apply them. Then too, devices regarded as only ‘monitoring’ were excluded from the review.

This systematic review study was designed to fill a gap in the current research by investigating what is known about different aspects of medical technologies used in the home. From the results it is obvious that a wide and growing range of medical technologies are used at home. Different types of technologies have been subject of study, increasingly –also in scope- over the period 2011–2015.

Professional nurses have a central role in the process of homecare which has to be recognized when considering use of AMTs at home. Nurses have to support patients and family caregivers and in consequence have a key role in providing information for, and as a member of multi-disciplinary teams. Closer collaboration by all actors involved in the process of care and feedback of user experiences to the designers is essential for the provision of high quality of care and patient safety.

This review also identified a lack of research exploring the perspectives of nurses in the processes involved in introducing and maintaining technology in homecare. Most of the research has been conducted regarding the experiences of patient experience and how informal caregivers perceive their role in using medical technologies at home. The few studies that were found, demonstrate the need for more research focused on the experiences of nurses working with advanced technologies in the home. The same applies to research on training, instruction and education to use medical technologies, as in these areas too, there was limited available research so here again there is need for further research. Despite the fact that most adverse events with medical technologies in home settings are caused by human factors, our findings also identified a lack of research in this area for nurses.

This study demonstrates that, although there is increasing attention on and recognition of the need for the use of medical technologies in the environment of the home, the research has not kept pace with the advances in care. Subjects such as user experiences of nurses with different technologies, training, instruction and education of nurses and human factors by nurses in risk management and patient safety urgently need to be investigated by further research.

Abbreviations

Automatic external defibrillator

Advanced medical technology

Continuous positive airway pressure

European Commission

Information technology

National Center for Health Statistics

Negative pressure ventilation

Peripherally inserted central catheters

Randomized Control Trial

National Institute for Public Health and the Environment

Ultraviolet

Ventricular assist device

World Health Organization

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The authors thank Ronnie van de Riet, head of the Medical Technical Care Team of the hospital ZiekenhuisGroep Twente, for his time and commitment to this project.

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All authors meet the criteria for authorship and all those entitled to authorship are listed as authors. ITH made the conception and design of the study; acquisition, analysis and interpretation of data; and drafting the article. SBA and WVH have made substantial contributions to the conception and design of the study; the analysis and interpretation of data; and revising the article critically for important intellectual content. All authors have approved the final article, this submission and its publication.

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Ingrid ten Haken is researcher in the research group Technology, Health & Care at Saxion University of Applied Sciences, Enschede, The Netherlands. Somaya Ben Allouch is head of the research group. Wim van Harten is professor at the University of Twente, Faculty Behavioural, Management and Social Sciences, department Health Technology & Services Research and CEO of Rijnstate general hospital, Arnhem, The Netherlands.

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ten Haken, I., Ben Allouch, S. & van Harten, W.H. The use of advanced medical technologies at home: a systematic review of the literature. BMC Public Health 18 , 284 (2018). https://doi.org/10.1186/s12889-018-5123-4

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