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• Published: 09 July 2024

From silos to synergy: integrating academic health informatics with operational IT for healthcare transformation

• Devin M. Mann   ORCID: orcid.org/0000-0002-2099-0852 1 , 2 ,
• Elizabeth R. Stevens   ORCID: orcid.org/0000-0001-6063-1523 1 , 2 ,
• Paul Testa   ORCID: orcid.org/0000-0002-1512-9638 2 &
• Nader Mherabi 2  

npj Digital Medicine volume  7 , Article number:  185 ( 2024 ) Cite this article

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We have entered a new age of health informatics—applied health informatics—where digital health innovation cannot be pursued without considering operational needs. In this new digital health era, creating an integrated applied health informatics system will be essential for health systems to achieve informatics healthcare goals. Integration of information technology (IT) and health informatics does not naturally occur without a deliberate and intentional shift towards unification. Recognizing this, NYU Langone Health’s (NYULH) Medical Center IT (MCIT) has taken proactive measures to vertically integrate academic informatics and operational IT through the establishment of the MCIT Department of Health Informatics (DHI). The creation of the NYULH DHI showcases the drivers, challenges, and ultimate successes of our enterprise effort to align academic health informatics with IT; providing a model for the creation of the applied health informatics programs required for academic health systems to thrive in the increasingly digitized healthcare landscape.

We have entered a new age of health informatics. The ubiquitous and rapidly evolving use of technology in healthcare demands that institutions adapt to integrate operational and innovative academic informatics resources. Historical organizational models of health informatics that silo academic health informatics departments from operational information technology (IT) hinder a health system’s ability to adapt, scale, and respond to new technological developments required for achieving healthcare goals. As the science of using data, information, and knowledge to improve human health and healthcare services, health informatics plays an ever more critical role in how healthcare is delivered. To fulfill these roles, the field of health informatics has dynamically adapted to the growing digitization of healthcare. Nevertheless, for health informatics to address our healthcare systems’ current and future requirements, it must undergo further evolution. This evolution requires direct intervention and concerted, collaborative efforts from health systems and academic health informaticists. By showcasing the successes and challenges of our institution’s ongoing endeavors to align academic health informatics with IT, we aim to underscore the necessity for integrating health informatics programs with IT and present this model for others to consider when developing their own strategies to address the needs of this new age of health informatics. This new integration model is crucial to effectively respond to the technological demands of healthcare, both now and in the future.

An evolution of health informatics

Health informatics, having grown up in a classic academic tradition—i.e., built by researchers at academic institutions with external research funding (such as from the National Institutes of Health) with the explicit purpose of creating knowledge—has often been steered towards the aspirational goal of indirectly influencing healthcare delivery and human health by providing new tools and evidence for healthcare decision makers and providers 1 . With the digitization of healthcare itself, accelerated through federal policies such as the HITECH Act 2 , informatics became critical to health system operations, generating the growth of the previously undefined “operational informatics.” Thus, a hybrid system was created where classic informatics and operational informatics have worked in parallel—with opportunistic collaboration—generally remaining siloed from each other.

Informaticists within academic healthcare institutions have frequently been separated from IT teams—and do not even exist in non-academic settings. IT teams manage the hardware and software required to run hospital clinical systems and support other health system functions such as education and research (e.g. statistical software, basic science hardware/software, library systems, etc.). Alternatively, informaticists have been tasked with focusing on more conceptual innovation and research to develop new digital health tools and systems 3 . However, with the widespread adoption of enterprise electronic health records (EHRs) and steps being taken towards digitizing medicine, this compartmentalization has become unsustainable. As a result, IT and informatics teams have been impelled to intersect in more meaningful and pragmatic ways 4 ; however, significant barriers to these collaborations remain without unified efforts to facilitate these intersections.

Against this backdrop, we have seen the emergence of a new age of informatics—applied health informatics—where digital health innovation cannot be pursued without considering operational needs 5 . Within many institutions, actions have been taken to address the need for consideration of clinical care operations within IT, with the creation of positions such as the Chief Medical Information Officer (CMIO), however, additional strategies are needed to address the challenges faced in within more academic-oriented informatics. Seemingly paradoxically, with the rise of digitization in operational settings, informaticists have seen more significant restrictions to accessing data and implementing novel tools into clinical care for their research. EHRs and the digitization of medicine dramatically elevated the consequences and financial implications of IT, where health systems that leveraged digital tools well were at an advantage compared to their peers; leading to increased guarding of IT resources 6 . Furthermore, the interconnectivity of health IT systems has led to fewer interventions being isolated. Even straightforward and effective tools, such as clinical decision support alerts, have rapidly become overwhelming, resulting in issues like alert fatigue that carry significant consequences at a health system level 7 .

This new reality has enhanced scrutiny by operational and IT leadership with concomitant growth of operational IT programs as a necessity in facilitating academic informatics research. It is now often the case that minimal informatics research can be performed without health system leadership oversight and approval. IT teams have become the new stewards to the treasure trove of informatics research enabled by the EHR and other dimensions of digital medicine applied to innovative digital health research studies, including patient portals, smartphones, and apps. In many academic medical centers, this gatekeeper dynamic has created an undefined and inconsistent partnership between IT and informatics teams that is likely to prove untenable moving into the future. In this new digital health era, where applied health informatics will predominate, creating an integrative system—where a true partnership is established to join operational and academic informatics people, resources, and missions—will be essential for health systems to achieve informatics healthcare goals effectively. Indeed, many calls have been made to create processes that unite informatics innovation with operational IT 8 , 9 , 10 , and steps have been taken to accomplish this goal, including creating positions such as Chief Research Informatics Officers (CRIOs) 11 , however further actions are needed to develop systems that represent a structural integration between informatics research and operational IT.

Developing an integrated system

In this new era, there is a fundamental need for ongoing experimentation and innovation through researchers’, IT’s, and operational leadership’s collaborative efforts. Achieving this necessitates an optimized organizational infrastructure, which can be realized by fully integrating IT and health informatics. While it might be expected that this integration would naturally occur in response to technological advancements, without a deliberate and intentional shift towards unification, operational IT and health informatics convergence is unlikely to happen successfully. Such unification is essential for effectively addressing the constantly evolving landscape of health technology 8 . Recognizing this, NYU Langone Health’s (NYULH) Medical Center IT (MCIT) executed a strategic plan and intentional, collaborative system-level effort to create an integrated system that allows for advancement in real-world applications, as well as innovation at scale. This endeavor united MCIT and academic health informatics, giving operational and academic innovation teams access to the wealth of health informatics expertise and greater freedom to experiment. This deliberate restructuring has already begun to show benefits as new disruptive technologies—like generative artificial intelligence (GenAI)—have emerged, and the efforts made by NYULH may serve as a roadmap for other health systems seeking a strategy to advance their own capabilities to adapt to rapidly emerging technological changes.

As with many academic health systems, historically, NYULH MCIT and health informatics were fundamentally unrelated endeavors. Despite successfully addressing the current technological needs of a digitizing health system through progressive strategies, such as transitioning from an application-based structure to an experience-based one (i.e., project portfolios centered on the user rather than the technical system), these restructuring efforts did not fully integrate MCIT and informatics. In this context, there was a recognition that maintaining the status quo and relying on incremental changes to legacy structures that governed the relationship between IT and health informatics would constrain NYULH’s ability to maintain excellence across its tripartite mission.

The early stages of this transformation began more than a decade ago with incremental reorganization from exclusively IT application-based teams (clinical, hardware, security, etc.) to include joint structures based on shared goals around a theme e.g., a digital experience-based portfolio (patient digital experience, clinician digital experience, researcher digital experience, etc.). This shift reflected the transition to integrated digital experiences and the reorganization of MCIT’s work to better align with its fundamental roles in a large academic health system. These early steps set the stage for the recent full vertical integration of operational IT and health informatics with the NYULH MCIT Department of Health Informatics (DHI) launch in 2023. Establishing DHI within MCIT was unconventional and strategically disruptive. Unlike traditional biomedical or health informatics departments, MCIT DHI is a corporate operations department within IT, not an academic department. It does not confer academic promotion nor “own” faculty or grants—instead, it serves as an enterprise-level hub of informatics activity that spans the full breadth of the health system’s missions—clinical care, research, and education (Fig. 1 ). This hub integrates the domains of a CMIO and CRIO bringing together clinical and technical operational personnel, researchers, educators, and clinicians seeking to leverage technology to drive scaled innovation across the enterprise. Through this informatics structural innovation, the legacy silos between academic and operational entities within an academic health system were mitigated, amplifying the impact of informatics on clinical operations and research.

The Department of Health Informatics creates a new model to promote collaboration for integrated informatics by bringing together stakeholders in care delivery, academics, and operational resources.

The DHI comprises eight divisions—Clinical Informatics, Health IT Safety, Digital Health Innovation, Digital Health Equity, Applied AI, Research Informatics, Nursing Informatics, and Educational Informatics. However, these divisions are not formal entities like the divisions of an academic department; rather, they serve as concentration areas within a matrixed organizational structure 12 . Within this matrixed structure, all core DHI faculty play multiple roles, serving IT operational roles that complement their informatics and academic roles. For example, the Director of the Division of Health Equity in DHI spearheads institution-wide efforts to leverage digital health tools in support of enterprise equity goals across all missions (clinical, research, education, community); a role that jointly reports to (and is partially funded by) the CMIO in MCIT and the Director of the NYULH Institute for Excellence in Health Equity (an enterprise-wide initiative). She is also a tenure-track physician investigator studying clinical decision support tools in the NYULH Department of Population Health (her academic home). Acting as foci of common work, the Divisions do not have their own prioritization structures or committees, instead using established decision-making bodies to facilitate knowledge sharing, remove redundancy and coordinate efforts horizontally across potential silos.

This matrixed role underscores the deliberate nature of a fully integrated health informatics initiative within an academic health system. Consistent with this approach, IT resources and staff are not assigned to specific divisions. Instead, the DHI and Divisions request and get assigned corporate IT resources (EHR analysts, reporting resources, software architecture resources) as needed. They are prioritized in the same enterprise prioritization processes that all IT requests get reviewed. Similarly, non-DHI academic faculty and staff are recruited to lend expertise to DHI initiatives. They are provided an official channel to raise their health informatics research for support from DHI. This allows projects to be easily worked on by multiple teams and divisions organized around the user experience they seek to improve at scale.

Adaptability in a new technological age

By putting health informatics within an enterprise IT department and creating vertical integration, the unique and intentional structure of the NYULH DHI reflects the needs of academic healthcare systems in the current age. New technological innovations frequently have implications in broad domains. Restructuring how informatics and IT interact within an organization can avoid the resource waste and stifling of progress that can arise by approaching the use of these technologies in a siloed, piecemeal fashion. An integrated system also allows for better measurement of resource use and evaluation of implementation impact. Furthermore, with an increasing number of regulations placed on digital health solutions, implementing academic health informatics as part of operational IT can reduce the challenges associated with system compliance monitoring. Conversely, the creation of DHI has generated challenges and exposes our organizations to potential “threats.” Integrating informatics research, operations and education creates potential role and responsibility confusion, an organizational disruption that requires attention and iterative adjustments and organizational patience as people and teams adapt to the new structures. Additionally, even though DHI is designed to not compete with established organizational structures, leadership needs to be attuned to the risk that over time DHI could develop its own “walls” that create new siloes rather than breaching old ones. Integrating operational and academic IT also highlights the differences in standards, cultures, and other norms between these traditions. For example, while auditable standards may be required for operational IT systems, academic innovations often have less stringent standards. Operational innovation sandboxes 13 and other approaches to create reduced risk innovation zones within an academic health system represent potential avenues for bridging these differences.

Generative AI (GenAI) exemplifies the fundamental advantages of this organizational approach. This disruptive technology has broad applications within an academic medical center, including in research, education, and clinical care delivery. It is also complex and rapidly evolving, requiring significant expertise, oversight, and resources to implement effectively and safely. Furthermore, due to its very nature, GenAI becomes a more powerful tool when it is integrated with data and additional applications. In a traditional structure, innovations using GenAI would be siloed between the various health system functions, with separate efforts occurring for academic investigations, clinical operations improvement projects, and explorations of its value in medical education.

The DHI leveraged its role as an organizational hub to rapidly establish the first patient health information (PHI) safe OpenAI GPT-4 environment for organizational experimentation, host the first healthcare “prompt-a-thon”, rapidly implement strategically aligned pilots in all of its missions and deploy a unified oversight of GenAI applications 14 . Moreover, the integrated structure facilitates remarkable transparency and transferability of ideas and expertise across domains. This allows IT resources to be more efficiently deployed in support of experimenting with how this important new technology can help drive forward all the academic health system’s missions. Thus, while the creation of a new structure within MCIT has taken significant vision and labor to implement, in the long-term, the effective reorganization and incorporation of health informatics and IT is ultimately reducing the costs associated with implementing these novel and broadly reaching technologies.

New technological innovations, such as GenAI, present both exciting opportunities and challenges for health systems to revolutionize their capabilities at scale in all aspects of their missions. The rapid evolution of these technologies necessitates an organizational structure that integrates traditional IT and health informatics into a symbiotic system, fostering innovative ideas, deploying novel clinical tools, and translating research into practice. NYULH is a model for achieving this mutual collaboration and may inspire other organizations. Most importantly, the example of NYULH DHI highlights the significance of strategic planning and deliberate efforts on the part of health system leadership to bring together IT and informatics resources to tackle the challenges that will arise in the new AI-driven era of healthcare innovation.

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Mann, D.M., Stevens, E.R., Testa, P. et al. From silos to synergy: integrating academic health informatics with operational IT for healthcare transformation. npj Digit. Med. 7 , 185 (2024). https://doi.org/10.1038/s41746-024-01179-5

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SPECIALTY GRAND CHALLENGE article

Health informatics—ambitions and purpose.

• School of Computing and Information Systems, The University of Melbourne, Melbourne, VIC, Australia

The current transformation of the digital health landscape is not only technological, it's also social, cognitive, and political, with the end goal participatory health—a partnership with digital devices collecting data and generating insights with new models of care evolving through partnerships of clinicians, patients, and carers.

Most people are likely aware of the enormous challenges facing the world to deliver equitable, affordable healthcare to a world population that is growing, aging, undergoing increasing urbanisation, and suffering more chronic preventable diseases. Consumer expectations of their health practitioners are growing, and the cost of devices and medications are constantly rising. Increasing antibiotic resistance may challenge the very existence of hospital based care. Added to this, the looming impacts of climate change on communicable disease patterns, social disruption, and migration patterns together with the cost of climate mitigation will reduce the financial pool available for health care ( 1 ).

How then can we use new technologies to keep up, or better yet, get in front of the curve, to reduce cost, improve safety, increase equity of access, reduce the burden on limited hospital facilities, and increase the overall health and well-being of the community at large?

Regardless of the social or funding model, reducing avoidable complications through evidence based, personalised, connected care is essential. Increasing patient focus and patient control are necessary elements of the individual responsibility for health and wellness. Managing social determinants of health in our population also has the potential for increasing the return on investment of our health expenditure ( 2 ).

For those of us working in health, the current convergence of wireless communications, miniaturisation of physiological sensors, Electronic Medical/Health Records (EMRs), mobile computing interfaces (including smartphones), advanced analytics, personalised medicine, and connected data repositories provides a source of optimism that dramatic transformation and improved cost effectiveness can be achieved in the foreseeable future. Examples include prediction return to work after rehabilitation or understanding factors that may lead to complications in health procedures ( 3 , 4 ).

There remain significant hurdles to overcome in our quest. System disruption from consumer wearable devices improves consumer-directed care, but this increasing independence may paradoxically isolate the patient from their care providers. Connection between data systems is dramatically increasing, but data aggregation and analysis is still limited by different vendor EMR data structures, competing terminologies and ontologies, jurisdictional data sharing, and privacy legislative differences. Telehealth has improved access to many forms of clinical support, however rural and remote communities still suffer from physical isolation and access to interventional services.

As acute hospitals reduce the need for inpatient care, do we have the trained workforce to care for patients at home? Will telemedicine, remote monitoring, GP and community sectors be able to assume the load? How will the economic and regulatory enablers be established to promote appropriate outcomes? What then will be the role of digital technologies in identifying and supporting patients most at risk?

As the EMR becomes more embedded, some jurisdictions have discovered that unparalleled ability to capture data including repetitive clinical documentation for compliance purposes may reduce the capacity of the system overall through staff burnout and clinical resistance.

A necessary step in improvement is understanding that our most dramatic increase in capability is derived from our closure of the data cycle. The advent of EMR, connected Acute and family medicine—General Practice data systems, real-time pathology and radiology results, device-generated physiology and mobility data enables rapid cycle individual outcomes assessment and population level reporting, as well as algorithmic notification of early disease markers, medication safety, conformance with evidence-based care, and pragmatic assessment of changes in health system delivery. These enhancements can collectively be seen as Learning Health Systems and will be essential in providing safe, effective, and evidence based care, regardless of the economic and political framework.

At a research level, we now have numerous examples of machine learning and artificial intelligence impacting on the routine clinical diagnostic process. However, these algorithms must become trusted partners in a sustainable health system. The algorithms must be validated prior to implementation, embedded in the workflow and enhance the operational capacity of the human sponsors. Re-tasking and supporting human resources in the most effective manner may enable more humanistic interactions between clinicians and patients, allow development of newer techniques or simply just enable us to keep up with the ever increasing workload.

Regardless of which scenarios play out in the future, the social dimensions of these changes will be far reaching, and are just as worthy of study as the technical achievements themselves.

Health Informatics as a Public Good

Health Informatics is the practice of acquiring, studying and managing health data, and applying medical concepts in conjunction with information technology systems to help clinicians provide better healthcare. We believe useful insights can be gained by viewing Health Informatics in general, and electronic healthcare records in particular, as a “public good.” A public good is any service or resource that cannot be withheld from an individual due to inalienable characteristics relating to citizens' rights ( 5 ). Examples of public good resources include city parks, street lighting or freeways, which are funded by the state but available to all.

Economists have extensively studied public goods and developed so-called public goods games to simulate and understand people's behaviour ( 6 ). It has been observed through such studies that players adjust their contribution according to the behaviour of other players. For instance, an initial willingness for contribution might change due to learning about other players' contribution behaviour. Broadly, players can be classified into four types based on their aggregate contributions ( 6 ):

• Unconditional Contributors: Players who contribute regardless of the behaviour of other players.

• Conditional Co-operator: Players who show more willingness to contribute when other players contribute more.

• Free Riders: Players who do not contribute to the project regardless of other players' contribution status.

• Triangle Contributors: Players whose contribution rises to a point then starts to decline in relation to other players' contributions.

Looking at Health Informatics through this lens opens up new perspectives on how to address problems such as data handling challenges. For instance, using Artificial Intelligence methodologies in clinical decision support raises issues such as explainability (why), interpretability (how), and trust (who). By understanding we are dealing with a public good, we learn that we need to cooperate rather than compete. Therefore, neither clinicians nor patients can simply be seen as consumers of health information systems, they must be co-creators.

Workforce Development Required for the Future

We need a much more extensive program for workforce development and education than currently exists to reach a digital transformation of health.

Targeted students should include:

• Clinicians providing patient care.

• Data scientists, analysts and IT professionals working in hospitals, insurance companies, government and the health tech industry.

• Students enrolled in professional programs in the health field, such as medicine, nursing, physiotherapy, nutrition, etc.

• Students enrolled in degree-granting programs such as informatics, computer science, mathematics, psychology, and economics who are interested in applying their knowledge and skills in the health domain.

• Clinician scientists who combine research with practice to effect change in health care delivery.

• CEOs and other executives in health care delivery systems.

• Politicians making regulatory decisions.

To reach such a broad group of students we require layers of resources that not only vary in content, but also in the amount of depth that is provided and in teaching methods. Resources including:

• Online lectures, interviews, and case studies that can inspire and motivate.

• Hands-on skill building activities that provide experience wrangling, analysing, and interpreting health data sets and demonstrating how to transform data into actionable insights.

• Interaction with electronic medical records, business intelligence tools, and AI applications that provide experience with systems used now and in the future.

• Sociotechnical activities such as interactive simulations, observations, and individual or focus group interviews that provide insight into the social and cognitive aspects of digital health technology embedded in health settings.

• Intensive mentoring programs that are necessary for training in digital health and informatics research.

Those who have taught in this space are painfully familiar with the benefits and challenges of a multidisciplinary classroom. Successful training of clinical trainees requires skill development in data management, programming, and information modelling. Whereas, students without a clinical background need knowledge of the healthcare environment, medical terminology and physiology, and healthcare economics. These topics are best integrated into existing training activities so they are not considered add-ons to already full programs.

Considering these points, it becomes obvious that we need a two-way “conversion” or indeed “confluence,” from health to IT and vice versa. Thus, ideal programmes are those based on Computer Science or Information Systems, but with a specialisation in health or medicine, for example a Master of Information Systems with a Health Specialisation. In such degrees it is crucial that students have opportunities for industry-based learning in the IT and health sectors and that the curriculum is aligned with national and international guidelines of the foundational discipline of Health Informatics. Ideally, elective streams are available in areas such as Information Systems Project and Change Management, IT Service Provision, Business Analytics, IT Innovation and Interaction Design, and Spatial Information.

Extensive infrastructure is required to support hands-on learning, involvement in clinical settings, and participation in research or quality improvement topics. If Health Informatics can be viewed as a public good, so can workforce development and education in this space. However, with current incentive structures, it isn't clear whether one or a few entities could host and manage materials made and consumed by diverse players. We need more innovation in informatics education delivery.

There are some early efforts to make educational resources modular for use and re-use in micro-credentialing, professional development, and coordinated programs of study. However, there is still much to be done to align incentives of trainees, universities, professional societies, and industry in a student-centred way so that everyone has access to the learning they need.

How Will This Journal Contribute

This section is looking for interdisciplinary high-quality submissions that integrate information technology with health science. Importantly, we are not only encouraging traditional academic contributions (such as data-driven or discovery-led articles), but we are also eager to receive real-world case studies and review papers written by practitioners. We believe this is essential to illuminate the barriers and catalysts to successful adoption, innovation, development, implementation, and evaluation in such healthcare technologies and applications. Furthermore, we welcome submissions about education and workforce development, social and ethical implications of digital health, and economic analysis.

This approach to article curation is supported by the distinct reviewing and epitomal systems employed by Frontiers. Rather than the typical adversarial “authors vs. referees” approach, we operate an open review and editorial process where we collaboratively, as a team, improve, and perfect articles with the common aim of making the journal the best it can be.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: health informatics, health informatics applications, health informatics and information systems, grand challenge, digital health

Citation: Aickelin U, Chapman WW and Hart GK (2019) Health Informatics—Ambitions and Purpose. Front. Digit. Health 1:2. doi: 10.3389/fdgth.2019.00002

Received: 17 November 2019; Accepted: 09 December 2019; Published: 23 December 2019.

Edited and reviewed by: Björn Wolfgang Schuller , Imperial College London, United Kingdom

Copyright © 2019 Aickelin, Chapman and Hart. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Uwe Aickelin, uwe.aickelin@unimelb.edu.au

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Informatics: Research and Practice

What is informatics.

Biomedical and health informatics applies principles of computer and information science to the advancement of life sciences research, health professions education, public health, and patient care. This multidisciplinary and integrative field focuses on health information technologies (HIT), and involves the computer, cognitive, and social sciences.

Informatics is the science of how to use data, information and knowledge to improve human health and the delivery of health care services. Health IT is part of informatics and an essential aspect of AMIA, but technology and technological considerations are only one component of the association’s work. Health IT enables advancements in health care by providing the tools with which to set knowledge in motion. Biomedical and health informatics has developed its own areas of emphasis and approaches that sets it apart from other professions and disciplines Biomedical informatics (BMI) is the interdisciplinary, scientific field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving and decision making, motivated by efforts to improve human health.

• BMI develops, studies and applies theories, methods and processes for the generation, storage, retrieval, use, and sharing of biomedical data, information, and knowledge.
• BMI builds on computing, communication and information sciences and technologies and their application in biomedicine.
• BMI investigates and supports reasoning, modeling, simulation, experimentation and translation across the spectrum from molecules to populations, dealing with a variety of biological systems, bridging basic and clinical research and practice, and the healthcare enterprise.
• BMI, recognizing that people are the ultimate users of biomedical information, draws upon the social and behavioral sciences to inform the design and evaluation of technical solutions and the evolution of complex economic, ethical, social, educational, and organizational systems.

The growing role of HIT has created the need to broaden and deepen the pool of workers who are able to help organizations deal effectively with their investment in information technology and, thus, enhance the prospects for major improvements in the safety, quality, effectiveness and efficiency of care. Biomedical and health informaticians understand the workflow of organizations as well as the potential and limitations of information technology. Informaticians conduct research and apply findings to improve processes and propose solutions to technical, clinical, and organizational challenges hampering successful technology implementations.

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Areas of Practice

AMIA supports the following practice areas:

Translational Bioinformatics

Translational Bioinformatics is the development of storage, analytic, and interpretive methods to optimize the transformation of increasingly voluminous biomedical data, and genomic data, into proactive, predictive, preventive, and participatory health. Translational bioinformatics includes research on the development of novel techniques for the integration of biological and clinical data and the evolution of clinical informatics methodology to encompass biological observations. The end product of translational bioinformatics is newly found knowledge from these integrative efforts that can be disseminated to a variety of stakeholders, including biomedical scientists, clinicians, and patients.

Clinical Research Informatics

Clinical Research Informatics involves the use of informatics in the discovery and management of new knowledge relating to health and disease. It includes management of information related to clinical trials and also involves informatics related to secondary research use of clinical data. Clinical research informatics and translational bioinformatics are the primary domains related to informatics activities to support translational research.

Clinical Informatics

Clinical Informatics is the application of informatics and information technology to deliver healthcare services. It is also referred to as applied clinical informatics and operational informatics.

Consumer Health Informatics

Consumer Health Informatics is the field devoted to informatics from multiple consumer or patient views. These include patient-focused informatics, health literacy and consumer education. The focus is on information structures and processes that empower consumers to manage their own health--for example health information literacy, consumer-friendly language, personal health records, and Internet-based strategies and resources. The shift in this view of informatics analyzes consumers' needs for information; studies and implements methods for making information accessible to consumers; and models and integrates consumers' preferences into health information systems. Consumer informatics stands at the crossroads of other disciplines, such as nursing informatics, public health, health promotion, health education, library science, and communication science.

Public Health Informatics

Public Health Informatics is the application of informatics in areas of public health, including surveillance, prevention, preparedness, and health promotion. Public health informatics and the related population informatics, work on information and technology issues from the perspective of groups of individuals. Public health is extremely broad and can even touch on the environment, work and living places and more. Generally, AMIA focuses on those aspects of public health that enable the development and use of interoperable information systems for public health functions such as biosurveillance, outbreak management, electronic laboratory reporting and prevention.

Changing the Way We Approach Health and Healthcare

Through education, training, accreditation and certification, AMIA supports the current and next generation of informatics professionals by:

• Providing members opportunities to grow professionally, no matter what their career level or discipline.
• Fostering collaboration and networking to support members’ work to improve people’s lives.
• Expanding members’ leadership opportunities within the association and in the field.

Daily News in Your Inbox

Get streamlined, need-to-know informatics news and AMIA updates straight to your inbox with Informatics SmartBrief. This new daily e-Newsletter was designed with informatics professionals in mind.

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MS - Health Informatics Concentration

What is Health Informatics?

Health informatics is the intersection of health care, information technology, and data science. It involves the collection, management, and use of information to improve patient care, health care outcomes, and the overall efficiency and effectiveness of the health care system and health interventions.

Terika McCall, PhD, MPH, MBA, Assistant Professor in the Biostatistics Department (Health Informatics Division) at the Yale School of Public Health, discusses what brought her into health informatics, as well as the real-world impact of her work and the field.

About the Program

The science of health informatics drives innovation-defining future approaches to information and knowledge management in biomedical research, clinical care, and public health. Health informatics (HI) comprises applied research and the practice of informatics across clinical and public health domains. Informatics researchers develop, introduce, and evaluate new biomedically motivated methods in areas as diverse as data mining, natural language or text processing, cognitive science, human-computer interaction, decision support, databases and algorithms for analyzing large amounts of data generated in public health, clinical research and genomics/proteomics.

The MS degree will provide well-rounded training in Health Informatics, with a balance of core courses from such areas as information sciences, clinical informatics, clinical research informatics, consumer health and population health informatics, data science and more broadly health policy, social and behavioral science, biostatistics and epidemiology. The length of study for the MS in HI is two academic years. First-year courses survey the field; the typical second-year courses are more technical and put greater emphasis on mastering the skills in health informatics. The degree also requires a capstone project in the second year.

Applicants should typically have an undergraduate degree with a focus in health, computer science or mathematics/statistics. Students with a master’s degree or other related degrees may be allowed to enroll in additional elective courses in lieu of required courses, if they can demonstrate prior proficiency in required courses.

The length of study for the MS in Health Informatics is two years. Part-time enrollment is not an option.

This program does not require General GRE scores.

For more information and to apply to the MS program, visit the Yale Graduate School of Arts and Sciences website. Please choose "Public Health" as the program. Then select Health Informatics as the concentration. Do not try to use SOPHAS.

I love the mentality and supportive atmosphere at YSPH. The confidence and the passion from faculty members and students were inspiring and made me want to join this big Y family.

Degree Requirements - MS in Health Informatics

2024-25 matriculation.

All courses count as 1 credit unless otherwise noted.

MS Required Courses (10 course units)

• BIS 633 Population and Public Health Informatics
• BIS 634 Computational Methods for Informatics
• BIS 560/ CBB 740 Introduction to Health Informatics *BIS 560 is a prerequisite for BIS 685& 686
• BIS 550/CBB 750 Topics in Biomedical Informatics and Data Science
• EPH 508 (fall) Foundations of Epidemiology and Public Health or EPH 509 (spring) Fundamentals of Epidemiology
• EPH 608 Frontiers of Public Health*
• BIS 638 Clinical Database Management Systems and Ontologies
• BIS 562 Clinical Decision Support or BIS 640 User-Centered Design of Digital Health
• BIS 685 and BIS 686 Capstone in Health Informatics- 2 units *BIS 560 is a prerequisite for BIS 685& 686

Informatics, Statistics and Data Science Electives: Minimum of 4 of the following REQUIRED

• BIS 540 Fundamentals of Clinical Trials
• BIS 555 Machine Learning with Biomedical Data
• BIS 567 Bayesian Statistics
• BIS 568 Applied Machine Learning in Healthcare
• BIS 620 Data Science Software Systems
• BIS 621 Regression Models
• BIS 623 Advanced Regression Models
• BIS 628 Longitudinal and Multilevel Data Analysis
• BIS 630 Applied Survival Analysis
• BIS 662 Computational Statistics
• BIS 645/GENE 645/CB&B 647 Statistical Methods in Human Genetics
• BIS 691 Theory of Generalized Linear Models
• BIS 692/CB&B 645 Statistical Methods in Computational Biology
• CB&B 555 Unsupervised Learning for Big Data
• CB&B 567 Topics in Deep Learning: Methods and Biomedical Applications
• CB&B 663/CPSC 552/AMTH552 Deep Learning Theory and Applications
• CDE 534 Applied Analytic Methods in Epidemiology
• CDE/EHS 566 Causal Inference Methods in Public Health Research
• CPSC 540 Database Design and Implementation
• CPSC 546 Data and Information Visualization
• CPSC 564 Algorithms and their Societal Implications
• CPSC 577 Natural Language Processing
• CPSC 581 Introduction to Machine Learning
• CPSC 582 Current Topics in Applied Machine Learning
• CPSC 583 Deep Learning on Graph-Structured Data
• CPSC 670 Topics in Natural Language Processing
• EMD 533 Implementation Science
• EMD 553 Transmission Dynamic Models for Understanding Infectious Disease
• ENAS 529 Medical Device Design and Innovation
• ENAS 544 Medical Imaging
• EPH 510 Health Policy and Health Care Systems
• HPM 559 Big Data, Privacy, and Public Health Ethics
• HPM 560 Health Economics and U.S. Health Policy
• HPM 570 Cost-Effectiveness Analysis and Decision-Making
• HPM 573 Advanced Topics in Modeling Health Care Decisions
• IMED 625 Principles of Clinical Research
• MGT 525 Competitive Strategy
• MGT 534 Personal Leadership: Leading the Self Before Others
• MGT 612/ GLBL 6590/ ENV 632 Social Entrepreneurship Lab MGT 656 Managing Software Development
• S&DS 517 Applied Machine Learning and Causal Inference
• S&DS 530 Data Exploration and Analysis
• S&DS 562 Computational Tools for Data Science
• S&DS 563 Multivariate Statistical Methods for the Social Sciences
• S&DS 565 Introductory Machine Learning
• S&DS 583 Time Series with R/Python
• S&DS 584 Applied Graphical Models
• S&DS 610 Statistical Inference
• S&DS 663 Computational Methods for Data Science
• S&DS 664 Information Theory

*Students entering the program with an MPH or relevant graduate degree may be exempt from this requirement.

Please note, CDE 538 Soda Politics is not approved as an elective.

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What Is Health Informatics? A Complete Guide

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Health informatics is a dynamic and evolving field crucial in today’s healthcare system. In this comprehensive guide, we will explore the essence of informatics, its definitions, current trends, and the vast array of opportunities it presents for professionals. Whether you’re contemplating a career change or looking to expand your knowledge, this blog will be your go-to resource for all things health informatics.

Key Takeaways

• Health informatics combines healthcare, information technology, and data management to improve patient care, streamline processes, and support public health and research.
• From data analysts to quality managers, an MHI degree offers diverse roles with competitive salaries ranging from $90,000 to $100,000—driven by high demand for specialized skills.
• The informatics field is expanding rapidly due to technological advancements and the healthcare industry’s reliance on data-driven decision-making, ensuring a strong job outlook and numerous opportunities for professionals.

What Exactly Is Health Informatics?

Health informatics—also known as healthcare informatics—refers to the intersection of healthcare, information technology, and data management. It is motivated by the need to create innovative solutions that enhance the quality and safety of patient care while managing costs, promoting the advancement of public and population health, and supporting biomedical research and population data. This multidisciplinary field is expected to expand significantly over the next few years because of the high demand for these roles in healthcare.

What are Health Informatics Professionals, and What Do They Do?

Health informatics professionals cover a broad spectrum of roles within the healthcare industry, from specialized positions like Data Analysts and Applications Specialists to managerial roles such as Data Quality Managers. These diverse opportunities cater to individuals with an enthusiasm for data-related work who aspire to contribute their skills in a healthcare setting.

Whether you are inclined towards analyzing health data, developing applications, or ensuring data quality integrity, healthcare informatics offers a rich array of positions suitable for data enthusiasts seeking meaningful roles in healthcare.

Professionals in the field typically command competitive salaries, a testament to the high demand for their specialized skills. Managers and Specialists occupying key roles can expect to earn substantial incomes, with salary ranges falling between $90,000 and $100,000. The specific compensation depends on factors such as education level and professional experience, highlighting the rewarding financial prospects available to those with expertise in informatics.

Job Outlook

With the relentless advancement of technology, the expected surge in demand for health informatics professionals is on the horizon. Additionally, the ever-evolving healthcare landscape increasingly relies on data-driven decision-making processes, underscoring the pivotal role skilled individuals play in healthcare informatics. These professionals are crucial architects of the future healthcare system, where their ability not only meets current demands but also pioneers the data-centric strategies shaping the trajectory of the entire industry.

Healthcare Informatics Trends & Things to Know

Staying informed about the latest trends in informatics is crucial for professionals in the field. Today’s informatics professionals are shaping the healthcare industry’s future by adopting cutting-edge technological innovations in the medical industry. Since the federal mandate for healthcare industries to transition to electronic health records, more patient data has been generated than ever before.

The science of Health Informatics aims to manage current technology and create new technology to harness this unprecedented access to information to improve human health.

Discover How UC Online Can Help You

UC Online ’s Master of Health Informatics (MHI) program allows you to earn your degree from an award-winning accredited university from anywhere in the world. UC Online offers a unique, multidisciplinary approach that arms students with real-world skills and the opportunity to build and grow their professional networks.

Do you want to learn more about UC Online’s MHI program today? Reach out to one of our knowledgeable Enrollment Services Advisors to learn everything you need to know before getting started.

Frequently Asked Questions (FAQs)

What is health informatics in simple terms.

In simple terms, health informatics uses technology and data management to improve healthcare. It involves organizing, analyzing, and managing health information effectively, with the goal of enhancing patient care, streamlining healthcare processes, and improving overall health outcomes. Informatics combines healthcare and information technology elements to make healthcare more efficient, accessible, and patient-centered.

Is Health Informatics the Same as Health IT?

While related, health informatics and health information technology (health IT) are different.

Health informatics is a broader field that encompasses the use of information technology in healthcare. It involves the application of technology and data management to enhance the delivery and efficiency of healthcare services.

However, health information technology (health IT) refers to implementing and maintaining information technology systems in the healthcare industry. It focuses on using technology to manage and exchange health information, such as electronic health records (EHRs), health information exchange systems, and other technologies that facilitate the electronic storage and transmission of healthcare data.

In short, health informatics is a broader concept that includes the strategic use of technology and information management in healthcare. At the same time, IT explicitly addresses implementing and supporting technology systems within the healthcare domain.

Is It Worth Getting a Master’s Degree in Health Informatics?

Pursuing a master’s in health informatics depends on your career goals and the demand for informatics roles in your area. A master’s degree can enhance qualifications, provide in-depth knowledge, and open up advanced career opportunities with potentially higher salaries. It would be best to consider factors such as networking opportunities, job satisfaction, and adaptability to technological advancements when evaluating the worth of a degree.

Is Health Informatics in High Demand?

Yes, health informatics is in high demand. The continuous integration of technology in healthcare has led to an increasing need for professionals who can effectively manage, analyze, and use health data. As the healthcare industry embraces digital transformation and relies on data-driven decision-making, the demand for skilled individuals in healthcare informatics has risen significantly. This trend will continue as the field evolves, making healthcare informatics a sought-after and dynamic career path.

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The IHI Rochester Report 2022 on Healthcare Informatics Research: Resuming After the CoViD-19

• Research Article
• Open access
• Published: 01 May 2023
• Volume 7 , pages 169–202, ( 2023 )

Cite this article

You have full access to this open access article

• Carlo Combi 1 ,
• Julio C. Facelli 2 ,
• Peter Haddawy 3 ,
• John H. Holmes 4 ,
• Sabine Koch 5 ,
• Hongfang Liu 6 ,
• Jochen Meyer 7 ,
• Mor Peleg 8 ,
• Giuseppe Pozzi 9 ,
• Gregor Stiglic 10 ,
• Pierangelo Veltri 11 , 12 &
• Christopher C. Yang 13  

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In 2020, the CoViD-19 pandemic spread worldwide in an unexpected way and suddenly modified many life issues, including social habits, social relationships, teaching modalities, and more. Such changes were also observable in many different healthcare and medical contexts. Moreover, the CoViD-19 pandemic acted as a stress test for many research endeavors, and revealed some limitations, especially in contexts where research results had an immediate impact on the social and healthcare habits of millions of people. As a result, the research community is called to perform a deep analysis of the steps already taken, and to re-think steps for the near and far future to capitalize on the lessons learned due to the pandemic. In this direction, on June 09th–11th, 2022, a group of twelve healthcare informatics researchers met in Rochester, MN, USA. This meeting was initiated by the Institute for Healthcare Informatics— IHI , and hosted by the Mayo Clinic. The goal of the meeting was to discuss and propose a research agenda for biomedical and health informatics for the next decade, in light of the changes and the lessons learned from the CoViD-19 pandemic. This article reports the main topics discussed and the conclusions reached. The intended readers of this paper, besides the biomedical and health informatics research community, are all those stakeholders in academia, industry, and government, who could benefit from the new research findings in biomedical and health informatics research. Indeed, research directions and social and policy implications are the main focus of the research agenda we propose, according to three levels: the care of individuals, the healthcare system view, and the population view.

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• Artificial Intelligence
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Avoid common mistakes on your manuscript.

1 Introduction

On June 09th–11th, 2022, a group of twelve health informatics researchers from academia and major research centers met in Rochester, MN, to assess a research agenda for biomedical and health informatics ( BMHI ) for the next decade. This meeting was modeled after similar meetings in related research areas [ 1 , 2 ], and it was initiated by the Institute for Healthcare Informatics— IHI [ 3 ]. IHI is a non-profit professional organization and has the goal of connecting an interdisciplinary global community concerned with the application of novel approaches in computer, information, and data sciences, through suitable information and communication technologies, “to address problems in healthcare, public health, medicine, everyday wellness as well as the related social and ethical issues.” IHI is sponsoring the IEEE International Conference of Healthcare informatics ( ICHI ) where the authors met as a pre-conference event of ICHI 2022 in Rochester, MN.

The goal of the meeting was to discuss the current biomedical and health informatics research agenda in light of the changes and the lessons learned from the CoViD-19 pandemic and to report recommendations for a future biomedical and health informatics research agenda for enabling data-driven citizen-centered health and well-being. This document reports the discussed topics and directions for the future of biomedical and health informatics as result of the meeting.

Biomedical and health informatics [ 4 ] is the modern term for the field previously known as biomedical informatics [ 5 ]: “the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem-solving and decision making, motivated by efforts to improve human health. Application areas range from bioinformatics to clinical and public health informatics and span the spectrum from the molecular to population levels of health and biomedicine.” Other terms, such as “computational medicine/health sciences,” “health data science,” “digital health sciences,” and “AI in medicine,” have been adopted to deal with similar/partially overlapping topics of the area [ 6 , 7 ].

Research in biomedical and health informatics has been evolving for several decades and has achieved a level of maturity comparable to any modern biomedical science sub-discipline. Many high-quality journals [ 8 ] and conferences have become widespread, reaching worldwide diffusion.

In 2020, the CoViD-19 pandemic took the world by surprise, with threats and consequences that have been experienced only very rarely at such a global scale. This pandemic modified approaches, priorities, and social behaviors in multiple healthcare and medical contexts. The CoViD-19 pandemic tested, validated, and challenged some of the achievements of the research in our field. The pandemic also revealed some of the weaknesses and some of the uncovered areas of research that our field should consider in the incoming years. As a result, the research community is called to perform a deep analysis of the steps already taken, and to encourage more exploratory, more innovative, and long-haul work, focusing on the most promising and necessary directions to follow.

The literature already includes reports from a technical/computer science point of view about applications and approaches responding to CoViD-19 -related challenges [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ] as well as about the role of machine learning in controlling the pandemic from a clinical perspective [ 17 , 18 ] and recommendations for evidence-based health informatics to combat the pandemic [ 19 ]. The current paper takes a BMHI perspective.

The remainder of this paper is organized as follows. Section  2 discusses the driving forces of the new research agenda; Section  3 proposes a research agenda for the next decade and Section  4 summarizes major the research directions. Finally, Section  5 finalizes some conclusions.

2 Post- CoViD-19 Challenges and Opportunities

The major driving forces and underlying pillars that are driving the research agenda include phenomena that started before CoViD-19 , but became more pervasive and potentiated since the pandemic. These phenomena, described below, are enablers for BMHI applications and research.

The data revolution allows us to infer from larger volumes and much faster and diverse data streams, information (summarization of data into population-level facts), knowledge—which applies the information to yield instructions and know-how, such as clinical recommendations and decision options, and finally wisdom—which incorporates ethical and aesthetic values and judgment to support making effective, efficient, and explainable decisions toward the clinical and societal goals [ 20 ].

Important movements have been shaping medical care in recent years. Among them, we mention:

evidence-based medicine [ 21 ], which argues that clinical decisions need to be based on clinical evidence;

personalized medicine [ 22 ], which tailors healthcare to the individual patient based on their predicted response or risk of disease, often using predictive algorithms;

patient participation [ 23 ] and empowerment [ 24 ], which understands that the health of patients will improve when they take responsibility in becoming active and knowledgeable partners in shared clinical decision-making; and finally

the biopsychosocial model of health [ 25 ], which stresses the importance of dealing not only with physical disease but also with mental health and well-being.

According to these perspectives, some research directions in biomedical and health informatics are completely new, but most of them are well-known issues, which need to be addressed in an (at least partially) new way based on the CoViD-19 experience. Sections  2.1 describes existing phenomena that are driving forces; Sections  2.2 ,  2.3 ,  2.4 ,  2.5 ,  2.6 are focused on phenomena related to CoViD-19 .

2.1 The Data Revolution

In his book “Sapiens: A Brief History of Humankind,” Harari describes the revolutions of mankind. After the agricultural and scientific revolutions, we are now in the new age of the data revolution [ 26 ]. In this new age, human beings and society depend on data and on information technology ( IT ) in their daily lives. In the following, we report issues regarding data evolution w.r.t. life science:

big data and ease of storage: Humans and organizations create data and leave their digital signatures everywhere, intentionally or as a by-product of daily work and life. In addition, data storage is very cheap and almost costless. We are collecting petabytes of new data, without the need to care about storage costs, though this may have deleterious energy and environmental effects. Data have variable formats, like alphanumeric (free text or coded/partially coded information, including semantic web), signal, image, video, or personal multimedia data [ 27 ]; new issues and concerns about data privacy arise [ 28 ].

maturing and effective artificial intelligence ( AI )–based techniques for data analysis: Deep learning and transfer learning produced algorithms and tools that have revolutionized personal lives (e.g., Google translate, reverse image search) as well as clinical diagnosis. In the domain of radiology, some claim that trained professionals will be shortly replaced or highly aided by AI algorithms. Although this could be classified as an extremely revolutionary view of AI integration in healthcare, we believe that breakthrough discoveries in recent years have introduced many new challenges that will need to be addressed in the near future. In the case of radiologists, we might see solutions that will represent a mandatory toolkit for every radiologist to improve their performance, which could also mean that working without the support of AI would be considered non-compliant in some environments for the benefit of the patient [ 29 ]. This could be followed by new rules for many other healthcare specialists where AI has the potential to make a big difference in how they diagnose, predict, or do other related tasks, taking into account also the recently highlighted social fairness [ 30 ].

mobile health ( mHealth ): Data from wearable devices are helping people of all ages, genders, and geographical regions to connect, become educated, receive services, and live independently [ 31 ]. Smartphones are becoming the personal hub for health information management, and the usage of many health apps is free. However, there are also negative sides, including the lack of warranty and a poor regulatory framework for quality control of the wearables and the app itself. Lack of rigorous clinical trials that demonstrate the effectiveness of such apps, uncertainty about meaningful uses, and difficulty of integration of mHealth -collected data with Hospital Information Systems ( HIS ) and the Electronic Health Record ( EHR ) continue to be unresolved challenges [ 15 , 16 , 32 ].

Internet of Things/Internet of Healthcare Things ( IoT / IoHT ): Internet of Things/Internet of Healthcare Things, and the Ubiquitous Internet are collecting data automatically and continuously, in households, personal and professional environments. In addition, environmental sensor networks are providing continuous real-time data relevant to human health at low cost [ 33 ]. Integration of these data, with a great deal of time and spatial resolutions and qualities, into health decision-making, will remain a key research challenge for the foreseeable future.

2.2 Prospective and Retrospective Data Collection for Learning

Historically, data requirements in health science research were defined by clinical researchers and informaticians prospectively, to support patient management, or to answer a research question. Furthermore, to generate clinical evidence, informatics and clinical research methods were defined. Obtaining evidence from data that is available but was collected for a different primary use is an avenue that has been explored over the last 50 years, and recognizes the great potential of big data availability as well as the need to study and address bias in data collection and data analysis [ 34 ]. During this quest, researchers need to explore ways in which the resulting knowledge, in the form of predictive machine-learning models, could make a difference in clinical care and could “translate to practical improvement in clinical processes or outcomes” [ 35 ].

Generating, collecting, and acquiring data has become easy, routine, and pervasive: wherever we turn, we discover devices that acquire and store data helping us to perform everyday tasks. The pillars of the data revolution, described in Section  2.1 , made this possible at costs that become smaller each day, and their volume and resolution every day get larger and higher. Yet, the focus of the data, information, and knowledge was usually commercial and not necessarily related to well-being. Furthermore, most of that data is collected for different use and without a specific health-related purpose. From the pandemic, we have learned lessons on how we could collect, analyze, and use existing data and IT for improving health and well-being.

So, what has changed further during the pandemic and how can we leverage the change into a new informatics agenda for enabling data-driven citizen-centered health and wellbeing? This will be discussed in the following subsections.

2.3 Data and IT as Essential Elements for Society and Individuals

As the pandemic emerged, it rapidly became evident that the only way to effectively contain it was to share data among individuals, hospitals, industry, and government at a national [ 36 ] and international level. Administration and individuals alike, in all sectors, including health, education, and labor, understood that we must use IT to cooperate and to control and mitigate CoViD-19 . Also, because IT was effective in helping people to deal with the personal consequences of the pandemic, many people wanted to use IT . The most salient example was platforms that enabled people to remotely perform daily life activities (whether adults or school children) in the new reality that restricted movement and gatherings. Finally, organizations and governments, as well as many individuals, wanted to obtain knowledge and explanations from data , in order to be informed about the current state of the world, of their own personal state, about the near future, as well as being alerted about fake news spreading at an even higher speed than the virus did.

As the pandemic evolved, government and international healthcare agencies sought to provide the public with detailed and up-to-date information, including incidence and mortality statistics, prevention measures, vaccines, and treatments. An unprecedentedly large and broad segment of society was presented with information about a healthcare topic at an unprecedented volume and rate. This provided an opportunity to gauge public interest in such information, as well as the public’s capacity to absorb and process the information.

The public faced a number of challenges to making effective use of the information provided. The understanding of the virus and how it spreads evolved over time, which resulted in the public being presented with uncertain information and a frequently shifting picture. In addition, because the information was provided by a variety of sources, including healthcare agencies, websites, news outlets, and politicians, the public was often confronted with conflicting information and recommendations. This confusing picture was exacerbated by widely disseminated misinformation. One study found that as much as 75% of the sampled population in the USA reported being confronted with conflicting information about CoViD-19 [ 37 ]. In the words of WHO Director-General Tedros Adhanom Ghebreyesus, the world was fighting an “infodemic.” Some people were able to cope with this volume and shifting mix of information and reach informed decisions. Others suffered from information overload, resulting in poor prevention behavior decisions [ 38 ]. Correlations were also found between the amount of time people spent reading about CoViD-19 on social media and the incidence of anxiety and depression [ 39 ].

On the positive side, many citizens became more educated about the interpretation of information (and even data, to some extent) and about health, and realized the real value of data. Before the pandemic, most people realized the commercial value of data, like having their name, surname, and credit card number stored as cookies inside the browser of their smartphone to easily support purchases. People saw, on television and in the news, charts presenting data about the pandemic and visualizations of different key indicators over time; many became interested in the meaning that stems from the way in which the data was collected. For example, much of the public may now be aware of how viruses spread and they may understand the exponential spread of the virus. They may also understand that measuring the number of people with positive CoViD-19 tests depends on the sensitivity of the test that was used (home antigen test vs. PCR viral mRNA amplification test) and on the percentage of infected persons actually performing and recording the tests at medical centers. They understand that vaccines can fight against infection but also understand that viruses undergo mutations and changes that make the vaccines less effective and also that the immune systems of people vary and that older persons have weaker immune systems and are more at risk from the virus also due to comorbidities. They can see tradeoffs in measuring the number of infections vs. the number of severely ill CoViD-19 patients. However, the scientific community has not done a great job explaining that in these types of complex systems, there are always uncertainties and that science is constantly evolving, making new discoveries and reformulating prediction models.

2.4 A Citizen-Centered Health and Wellbeing Care System

As a result of public interest in CoViD-19 , many citizens have the appreciation and the need for attaining basic informatics and medical literacy to allow them to make correct inferences from data, information, and knowledge, and to act wisely. With increased awareness and literacy, many understood the value of data as well as personal initiative and hence are potentially willing to share their data, look at their data and interpret it, and act wisely upon it (i.e., react to events and rapidly map information into knowledge and processes, resulting in informed decision making). They want to make autonomous decisions about their behavior, exercising self-judgment that is informed by science but weighs in the trade-offs of their behavior. These tradeoffs account for physical and for mental health, which is compromised when they are in isolation. The everyday decisions that citizens make regard for example, when to visit elderly family members in uncertain situations, when to allow their children to attend a school trip, how many days a week to work from home vs. at their institution, and whether to attend an international conference—and what they could do in order to increase their own safety and that of others.

Data about health and the health of a single person are just a small brick in the wall that we need to build in order to protect ourselves against the pandemic and to build physical and mental resilience for future events. With the importance of health data being understood by all, the basic step in the direction of resilience building is—with no doubt—that of sharing data. The concept of citizen-acquired data is thus a key factor, and it embraces all the data collected by individuals as part of self-tracking and of a digital lifestyle, and which may be collected toward some primary goal, or incidentally. CoViD-19 has made it visible (but it’s not new) that users/citizens, their data, and their interaction with data are crucial for a new, citizen-centered healthcare system. The pandemic has also made it more apparent that there is a significant knowledge divide in modern societies that is exacerbated by political, religious, and social contexts that may disregard scientific facts and capitalize on science uncertainty to construct unfounded theories that rapidly propagate as misinformation.

2.5 Telehealth and Virtual Care Systems

The advancement of technological innovation and digitalization in healthcare has brought tremendous opportunity in transforming healthcare by greatly expanding access to healthcare services at relatively low cost [ 40 ]. To lower costs, specialist care has been centralized with fewer but more specialized clinics whereas healthcare has been decentralized leading to a shift from in-hospital to primary care and advanced home care [ 41 ]. The resulting fragmentation of care calls for digital solutions, but adoption has been less than comprehensive.

The CoViD-19 pandemic has profoundly accelerated the use of digital health technologies where the integration of virtual and in-person care has been deployed widely across healthcare, demonstrating that it is possible to create a healthcare system that is more accessible, scalable, and sustainable. For instance, w.r.t. accessibility, it is indubitable that the future of healthcare will be more accessible which goes beyond virtual patient visits. Remote patient monitoring ( RPM ) systems have become very popular since CoViD-19 [ 42 , 43 , 44 ]. Advanced care at home with remote monitoring, and on-demand immediate medical care management, along with rapid response teams to deliver supplies and services that report to accessibility and remote services. Scalability regards remote diagnostic and monitoring options, and data integration processes (e.g., from electronic health records to remote diagnostics and AI ). Finally, the healthcare systems should be able to allow sustainable services and more efficient partnerships among public and private providers. This may be able to transform healthcare into a platform for delivering patient-centric healthcare, where patients can seek care from a healthcare system anytime and anywhere and get support for self-management and prevention.

2.6 Resilience and Agility

Health information systems play an important role in terms of the possibility of recovering from an emergency, not only strictly related to health systems but also related to social and working life. Data and context evolve rapidly and resilience models should be defined as able to react in terms of services for improving the quality of life for patients, with single healthcare systems, but also by governments.

The CoViD-19 pandemic also highlighted the importance of considering new kinds of information systems. Indeed, even by using cloud computing and social media, healthcare and medical information systems had to be merged and integrated within wider, often worldwide, information systems, aiming at supporting the (possibly) coordinated actions/decisions about safety policies, health, and medical nation-wide policies, worldwide travel restrictions, prevention of pandemic diffusion, teaching and education modalities, new working habits, and so on. We may define such a kind of information system supporting the activities of this “worldwide organization,” as a social information system.

Social information systems should be able to collect data that has to be quickly transformed or mapped into reliable and useful information. CoViD-19 tests, for instance, gave an example of collecting and transforming data into reliable information and knowledge to guide administrations in defining containment rules. This also implies:

Motivational aspects (that are crucial for activating procedures, such as containment, vaccinations, and roles);

Cultural constraints to implement and obtain the same global effects; and

Reproducibility (replicate strategies/implementation/policy and measure outcomes in different contexts and regions). This is also related to scalability, discussed above. In the quest for resilience, we should recognize that in some countries (USA for example) there has been great concern about privacy issues [ 45 ].

Hence, there is a need to balance privacy preserving vs. public advantages.

The urgent need of addressing social and mental health, which incidence increased during CoViD-19 due to isolation, should be supported by a continuum of care involving different systems: education (e.g., school), social work and welfare, and the healthcare system. This calls for a vision in which educators and social workers—professionals and researchers—collaborate with healthcare professionals and researchers to address wellbeing in a holistic and translational way.

3 The Agenda

The section discusses the major topics of the new research agenda, motivated by the driving forces presented in Section  2 . To ease the reading, we group the topics into nine major areas.

3.1 Prospective and Retrospective Data Collection

In times of crises, data collected incidentally (e.g., location data from which exposure and contact between a patient and other individuals may be inferred, or data from wearables that citizens donate to measure a population’s health [ 46 ]), though valuable, should be complemented by the collection of other prospective data, in which specific clinical questions and requirements are defined ahead of time. There is a need for semantic, technological, and legal approaches that allow the rapid and accurate collection of data that are needed to investigate and manage crises. Another issue that arose during the CoViD-19 pandemic concerns the timely collection and reporting of surveillance data, which is needed to effectively track disease spread and to provide input to population-based epidemiologic predictive models. This issue can be addressed through better data flow from clinics and through sensor networks or edge computing for environmental monitoring. Thus, in this regard, methodologies are required to be able to specify in a strong and reproducible way the process for data collection. This process should continually check and validate data collection, so that data are trustworthy within the expected uncertainty boundaries that characterize any measurement [ 47 ], and the results based on such data are reproducible within well-specified tolerances [ 48 , 49 ]. As far as we know, the monitoring related to CoViD-19 is probably the first time that data were provided daily at a worldwide scale, to both understand how a disease was evolving and to try to predict its evolution. Haendel et al. [ 50 ] demonstrated that in case of emergency, collaborations can form quickly and data can be integrated at an unprecedented speed and magnitude, as in the case of the USA National CoViD-19 Cohort Collaborative ( N3C ) [ 50 ]. An international effort at rapid data collection and integration from the electronic health records of over 350 hospitals in eight countries was created in early 2020, during the early stages of the pandemic [ 51 ].

Related to data collection, we pose the following specific research challenges (see Table  1 ).

At the individual level, can we harness the power of mass data (“patients like me”) to answer questions that an individual is not even aware of, but are important for him/her?

At the healthcare system level, can we develop novel methods for data collection that quickly and precisely collect from citizens the needed prospective data necessary to manage the crisis?

How can we accelerate the acquisition of data relevant to public health decisions?

3.2 Data Sharing and Integration

Gathering enough data to represent all classes of patients in a dataset that serves as the training set for a machine learning ( ML ) model is crucial, and could potentially be done via data sharing for clinical applications. We speculate that when we face future crises, individuals would be interested in sharing daily-life data for research without having specific clinical questions defined ahead of time (prospectively). Such data could be used retrospectively to collect evidence regarding effective interventions from the large quantity of population data shared by individuals. An example is the German Corona data donation app with currently around 190.000 monthly active donors [ 46 ]. Considering that evidence has been so far collected mainly by prospective studies, novel data integration methods would need to be developed to allow unbiased and statistically sound integration of retrospectively secondary use data with prospectively collected data; even without integration, simply interpreting retrospective data and generating proper evidence from it is not easy. An example of such a study can be found in [ 52 ], where a retrospective approach was developed to evaluate potential adverse outcomes associated with delay of procedures for cardiovascular and cancer-related diagnoses, in the context of CoViD-19 .

The pandemic highlighted the urgency for data integration, particularly in epidemiologic predictive models. Indeed, data must often be integrated from a variety of sources that influence the spread of the virus, including:

the virus properties;

the properties of the environment in which the virus is spreading; and

the human behavior by different social groups within the environment which dictates exposure [ 53 ].

Data exist in medical institutions, or are collected at a national level by the government—from medical groups and from individuals (e.g., via self-reporting apps). Since many groups may be working with the same national datasets, data integration should not be done anew for each ML model that is developed. Integration of data from different national healthcare environments can be complicated by the adoption of different standards in each national context. Initiatives such as the European GAIA-X infrastructure project [ 54 ] may provide the technical foundation for such integrations. In combination with the European Health Data Space regulation [ 55 ], new opportunities are currently being made possible and explored, e.g., by the German “Health–X” project [ 56 ]. CoViD-19 is only one of the prominent examples where data integration from different regional and national healthcare and clinical systems is required to provide support to achieve effective solutions [ 57 ]. Other examples come from other infectious diseases [ 58 ], environmentally sustainable development, biodiversity, and climate change monitoring and evaluation [ 59 , 60 , 61 , 62 ]. Another example of data sharing and integration is between government ministries responsible for human health, animal health, and the environment in order to better detect and control the spread of zoonoses [ 63 ]. However, not all countries have the infrastructure or legal framework needed for such data sharing and integration on a national and international level.

Data integration methods should allow the integrating of different types of data, like EHR data and data shared by citizens, coming from sensors, as well as self-reported in structured forms, free text, wave, or image formats. Many challenges exist in transferring the integrated data to an EHR system, including syntactic and semantic issues as well as legal and ownership regulations. This requires a framework and standards to support and encourage organizations to share their data. Considering public health, the CoViD-19 scenario confirms our arguments: during the pandemic, cross-border sharing of epidemiologic data became essential to track the transnational spread and inform control measures [ 64 ]. While less dramatic, this is also the case with other infectious diseases [ 65 ]. Additional efforts should be made at the global and local levels to encourage data collection and reduce the fraction of missing data. The impact of missing data on the effectiveness of the solutions built on these data is significant [ 66 ]. In order to guarantee that electronic health record data is optimally used for patient and public benefit, government and professional organizations must prioritize efforts, like the following: (i) providing lifelong learning opportunities for healthcare experts to address these limitations; (ii) informing the general public, whose support is critical, about the social advantages of properly sharing data.

Data federation is an approach for data integration that offers a means of querying and analyzing information from multiple systems as if it all resides within a single, harmonized data store, without consolidating the data into a single store. Health record banks [ 67 ] could provide services for integrating and accessing clinical and genomic data into patient-centric longitudinal and cross-institutional health records. Consultants can recommend the level of “insurance” that is good for the patient. They could obtain the recommendations from different ML models and say which program they recommend for you (e.g., what diet you should follow, based on your microbiome). This will allow patient data to be captured comprehensively and consistently with legal/policy and techniques for privacy-preserving techniques. Such multimodal data fusion techniques are being developed [ 68 ] but there are still many questions to be answered. In which contexts should data be integrated? When is data federation appropriate? When is early or late data fusion indicated for features feeding the ML model?

Related to data sharing and integration, we pose the following research directions (see Table  1 ).

At the individual’s level, how can the citizens curate their data to add meaning and context?

At the healthcare system level, can we develop methodologies to interpret retrospective data available in an institutional EHR , which is different from traditional (prospective/purposeful) medical data?

At the population/research level, we offer several specific research directions: can we design and define new models able to gather and integrate information from different and heterogeneous prospective biomedical research studies with citizen-acquired data?

How can context and environmental data be added to the data? As an example, social and behavioral determinants of health ( SDoH ) [ 69 ] play a key role. Social determinants of health ( SDoH ) are the conditions in the environments where people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning, and quality-of-life outcomes and risks: major issues are on economic stability, education access and quality, healthcare access and quality, neighborhood and built environment, social and community context. Some more facets need to be considered to better focus the entire environmental data landscape.

We also pose the following policy implications questions (see Table  1 ).

At the individual’s view, can we develop data collection methods that quickly and precisely collect the needed prospective data from citizens that are necessary to manage the crisis?

At the healthcare system view, can we develop usable interfaces, similar to that of PubMed search, to allow accessing shared ML models and shared data sets; these may be used by genetic consultants or endocrinologists, radiologists, neurologists, and other professionals?

At the population/research view, what legal policies and regulations should be developed for exchanging, sharing, and receiving data for research (research done by research institutions in academia, public sector, industry, and government) at an integrated national and international level?

3.3 Data and Model Privacy

The need of sharing and analyzing large amounts of data coming from a wide population highlighted the further issue of health data privacy [ 28 ]. Data privacy, especially related to the healthcare domain, was already a discussed topic before the CoViD-19 pandemic and was also the main focus of recent laws (see, for example, the recent GDPR [ 70 ]). “In light of recent changes in technology, applications, social media, and other platforms, and the increasing generation, collection, use, sharing, and selling of personal health information,” the USA Senate introduced in 2022 the Health Data Use and Privacy Commission Act. This act is intended to modernize the Health Insurance Portability and Accountability Act of 1996 ( HIPAA ) and account for emerging healthcare technologies [ 71 ]. This represents a shift of paradigm from “data privacy” to “data use privacy,” which is the current approach in the USA for genomic data [ 72 ].

It is now even more evident that it is necessary to also consider the overhead for conducting research if the right to privacy is perceived in isolation and without considering in a holistic way all the social and healthcare consequences. Indeed, there is the need to strike a balance of data privacy vs. public interest, which is both from a long-term perspective (basic research) and a short-term perspective (applied research to forecast and manage pandemic events). From a user perspective, a—possibly only perceived—lack of proper privacy may considerably hinder the acceptance of digital health solutions, as shown by the discussion around the German Corona warning app that, although probably a prototypical solution from a privacy point of view, still received considerable mistrust and refusal.

A technology that may potentially help in reestablishing trust is blockchain [ 73 ]. Blockchain technology uses a decentralized and distributed environment without central authority; entries are simultaneously secure and trustworthy when using state-of-the-art cryptographic principles. While blockchain is widely used in cryptocurrencies, its use in health informatics has been very limited. The readers are referred to for reviews [ 74 , 75 ], and examples of blockchain applications in healthcare [ 76 , 77 ].

As an example, federated learning in healthcare can get profitable advantages from blockchain technology: federated machine learning ( ML ) may run algorithms over data that are neither shared in full nor locally stored, thus easing the privacy issues which arise when dealing with healthcare data [ 78 , 79 ].

Related to the research agenda, we pose the following research questions (see Table  1 ).

At the individual’s level, can we develop methods for detecting and mitigating legal issues of rights that may contradict? The right to own your data and the right to protect the public may interact. An individual may wish to provide his/her data to receive recommendations for him/herself but not to share the data with others.

At the population/research level, can we develop technological tools that would allow sharing data in a secure, privacy-conserving yet meaningful way (e.g., encryption, de-identification), balancing between the right for privacy and the quest to improve the ways in which we fight pandemics?

3.4 Temporal Prediction Frameworks

The importance of temporal prediction frameworks for guiding national and organizational policies and regulations for fighting the pandemic was essential; governments, school systems, and multiple community organizations were informed by predictive models to determine and implement policies relating to mitigation, such as limitations on gatherings, masking, distancing, and lockdowns, as well as to prevention via vaccination. An important and often underestimated aspect of the predictive models was the inherent timing of what was being modeled and the ability of the models to consider that timing. As an example, the prediction of CoViD-19 spread is meaningful only with respect to the time needed to observe the outcomes of the actions taken. Indeed, in a short period, it is well-known that lockdown measures will not have measurable effects immediately [ 80 ]. In addition, lags in data reporting can have a significant impact on the accuracy of predictive models. This has been studied, for example, in the case of real-time dengue prediction [ 81 ].

In our new research agenda, we need to ask ourselves, related to the population/research level (see Table  1 ): what new questions could we answer with longitudinal data, and can we invent new AI methods to answer them?

3.5 Quality of Data and of ML Models Developed from Datasets

The pandemic underlined the issues related to the quality of data . Data quality and completeness is a multi-faceted issue: it has to be considered both at model construction time and at inference time. Indeed, even though the model has been defined by using high-quality data, at inference time, a model may perform significantly worse than in testing during construction due to missing data or poor-quality data. This can be further complicated by the fact that user-generated data may have originally been collected for entirely different purposes and is now re-purposed for different use. Such a situation, long well-known, is becoming even more evident with pandemic data when decisions potentially impacting a large part of the population are based also on data having a partially known and controlled acquisition and transformation process. New concepts based on the idea of “fit for purpose” may be useful in this area as different types of studies may need different data quality characteristics, making the concept of absolute data quality less desirable [ 82 , 83 ].

Uncertain and incomplete data caused many false positive cases in CoViD-19 contact-tracing systems [ 84 ]. In Israel, a collaboration between the Ministry of Health and the domestic security agency, conducted widespread tracking and relied on a classified database that has existed for 18 years, did not rely on informed consent, but was much more effective [ 84 ]; it increased the sensitivity and specificity of contact tracing and was accepted by public opinion as necessary. However, such an approach is not expected to be adopted universally, considering political climates and strong anti-establishment sentiments in a substantial portion of the population.

Because ML models are created from data that may not be complete, the model may have issues with generalizability and transferability . Model accuracy may be different for different clinical settings or for different geographic regions beyond those from which the original data came. A well-known classical example is that of the Leeds abdominal pain diagnostic system, developed by de Dombal and colleagues in the early 1970s, was very reliable for the local population but did not reach the same accuracy elsewhere [ 85 , 86 ]. In the USA and other countries without a national health care system, this is especially problematic for underserved populations that receive care in settings in which informatics infrastructure and the use of electronic health records are suboptimal. This can make it difficult to aggregate data at the national level [ 87 , 88 , 89 ].

Considering the social changes induced by the pandemic, it has been widely recognized that the CoViD-19 pandemic caused a further distancing in many different contexts (between developed and developing countries, between groups of different ethnicity in a single country, between people with different incomes and different levels of education.) For data analytics, it means different demographic groups may not be equally represented in the predictive models. An important question is how to guarantee and measure fairness? Indeed, poor accuracy can arise because of groups underrepresented in the data and groups with limited positive cases. Moreover, different demographic groups may need different sets of attributes for allowing the proposed models to reach results of acceptable quality. “Fair AI ” has drawn significant attention in recent years [ 30 ]. Fairness needs to start from the data collection stage, collecting data from all subgroups of the population [ 30 ]. Algorithmic approaches for preventing or correcting bias can be done at the pre-processing, in-processing, and post-processing stages [ 90 ]. The pre-processing approach performs data transformation to reduce biases or discrimination of the training data by mitigating the sensitive variables. The transformation may include removing attributes that are highly correlated with the sensitive attribute (suppression), assigning weights to the tuples in the training dataset (reweighting), and stratified sampling [ 91 ]. The in-processing approach intends to develop an optimized model that maximizes fairness and performance. The post-processing approach applies a transformation to model output to improve prediction fairness. Several metrics have been identified to measure fairness. For example, statistical demographic parity defines fairness as an equal probability of the predicted class for all demographic groups and such metric requires the predicted class independent of the demographic groups [ 92 ].

Shall we be able to apply fairness methods effectively to create a fair healthcare system, and to characterize fairly the subgroups affected by a pandemic or other health crisis? The legislation would be established to define these requirements.

3.6 Explainable and Responsible AI

Historically, communication among care teams was based on language, and in the clinical setting was information-dense. When healthcare delivery becomes data-intensive, it requires explainable and interpretable AI to assist decision-making by healthcare professionals as well as by patients or citizens, which requires explanations at different levels of health literacy and information needs. For example, practitioners require explanations of recommendations from clinical AI –based decision support systems in order to obviate the “black box” problem, in which the reason for a recommendation is either opaque or missing. On the other hand, the lay user of a medical recommendation system may need explanations that are simpler, do not require clinical knowledge, and are consonant with the user’s health and general literacy level. In both cases, explainability leads to trust in what the system is telling the user.

Explainable AI ( XAI ) is currently an important multidisciplinary research topic in biomedical and health informatics, bringing in computer science, bioethics, and implementation science researchers more broadly. At the same time, foundational research efforts are still needed to go beyond the presentation of features and their positive or negative contribution, as facilitated by SHAP (SHapley Additive exPlanations) [ 93 ]. Holmes et al. highlight and focus on the main related concepts and technical aspects underlying such term XAI [ 70 ]. XAI in medicine requires considering the meaning and the relationships among terms such as interpretability, understandability, but also usability, and usefulness. XAI has to be related to data and to their quality, in order to allow stakeholders to understand why the AI -based systems are providing some (possibly partially unexpected) results. Most of the current work on XAI is concerned with generating explanations of inferences from deep learning models since these are the best-performing models for large classes of problems. Techniques focus on identifying the important features of the predictive model by saliency maps or generating post hoc explanations by approximating the complex and opaque models [ 94 ]. These techniques are helpful to artificial intelligence and machine learning scientists in enhancing their models but not necessarily offering a meaningful explanation to health professionals. At the same time, techniques such as Bayesian networks use knowledge representations that are inherently explainable. Unfortunately, for many problems, e.g., medical diagnostic image interpretation, Bayes nets are not the best performing technique. A promising recent approach, applied in radiology, combines deep learning models with Bayesian networks, using the deep learning model for feature recognition and the Bayes net for differential diagnosis generation [ 95 ]. Similarly, DeepProbLog integrates probabilistic logic programming with deep learning models by adding neural predicates for feature extraction [ 96 ].

On the other end, XAI has to be heavily connected to the final users (i.e., stakeholders), be they nurses, physicians, public health officers, or lay citizens. Indeed, both the way and the concepts used in explanations have to be finely tuned with respect to the role and the background of the expected user. During CoViD-19 , citizens increased their consumption of health data and the results of analyses. However, a variety of information was presented to citizens in a variety of formats. It then becomes a challenge for citizens to be able to draw conclusions concerning risk and implications for their own behavior. Explainability becomes an important issue here both in terms of understanding the information and in terms of trust in the information.

The process of explanation is socio-cognitive. The cognitive process determines an explanation with sufficient information for a given event and the social process is transferring knowledge between the AI systems and the users through interaction. Continuous interaction is important given the initial explanations of the prediction, it facilitates further interrogation with user-driven questions by the healthcare professionals. There are two types of explanations, information-based explanation and instance-based clarification [ 94 ]. The information-based explanation can be extracted from the documentation of the implemented predictive model to address questions related to input, output, process, and performance. However, the instance-based clarification will need to be generated by examining the instances through executing the predictive model to address questions such as why, why not, and what if. For example, users may want to know what the prediction model may recommend if certain parameters of an instance are changed or how much the parameters are changed to change the prediction results. Can education help non-technical people consider results and interpret them relative to their own personal contexts?

Furthermore, we pose some research questions related to explainable and responsible AI (see Table  1 ).

At the individual’s level, we ask, can we invent novel explanations and education methods and tools that can help persons to make sense of their data?

At the healthcare system level, we ask, can we develop methods that could convert the patient’s self-tracked data into a summary or visualization that is meaningful for the medical actors (doctors) and integrate it with the patients’ EHR ?

At the population level, we ask, can we develop methods for the analysis of the new citizen-generated data, considering biases?

3.7 Quality Assessment of AI Models

Data quality, model performances, and explainability—together with the need of introducing AI -based data analytics for clustering, prediction, and decision support in real clinical settings—push for having quality assessment procedures for AI software tools. Regulations should be put into place for evaluating and reporting on AI -based decision-support systems, such as Transparent reporting of a multivariable prediction model for individual prognosis ( TRIPOD ) [ 97 ] or the machine learning ( AI / ML )–based software as a medical device ( SaMD ) proposal [ 98 ]. The final goal would be having a reproducible pipeline of experiments witnessing the correct behavior of the software tools in controlled and verified conditions, with some progressive steps, similarly to the preclinical and clinical phases of drug trials, followed by an authorized certification, and by a post-marketing monitoring [ 99 ]. It is important to note that ML models work well on the majority of cases but may not work well for some populations that are underrepresented or may even have a systematic bias towards certain populations. For example, Obermeyer [ 100 ] finds a racial bias in an AI algorithm that is widely used in the USA, reducing the number of black patients in need of care by half, compared to white patients. In such cases, the ML models should alert that human inspection is needed (e.g., the radiologist should inspect the radiology report because the AI determined that the conclusion is not justified by the findings or because the patient is different than the population on which the ML model was trained).

The regulatory framework by the USA FDA for machine learning applications is still evolving. The FDA has followed the well-established principle that if the machine learning application is used by a provider and is mediated (i.e., does not trigger an automatic intervention), then the FDA does not have to regulate the application. But there have been considerable discussions arguing both in favor and against a much higher level of regulatory framework [ 98 , 101 , 102 , 103 , 104 ].

Education also plays an important role here. The ease of applying powerful ML approaches to medical data means that technical people are often creating ML models without fully understanding the issues and limitations (e.g., potential biases) in producing reliable models. As researchers, but also as teachers, we must teach people about responsible AI and potential pitfalls in applying AI techniques.

Educators, physicians, and psychologists, and health managers, can work together to define requirements for data collection from the education, social, and healthcare systems and from citizens, as well as define the purpose of ML models and evaluate these models—especially for mental health management, applications, support, and prevention via mobile health (mindfulness, positive psychology, isolation, and social impact).

Another very important aspect of ML applications in medicine is the development of strong frameworks to evaluate the accuracy of individual predictions and not the commonly reported merit functions like AUC (Area Under the ROC Curve) or predictive values for entire populations. To accomplish this, we advocate looking into other fields and adapting well-established methods like end-to-end uncertainty quantification [ 105 , 106 ]. A review on how to use conformal prediction for this purpose has been published recently by one of the authors [ 107 ].

Two research questions, at the healthcare system level, that our community should be answering related to quality assessment of AI models are (see Table  1 ):

Can we develop practical methods to allow organizations to screen the quality of the information they provide to the AI model to avoid misinformation (ethics)?

ML models work well on the majority of cases but may not work well for some populations that are underrepresented. In this case, the ML model should alert that human inspection is needed (e.g., the radiologist should inspect the radiology report because the AI determined that the conclusion is not justified by the findings).

Can we develop methods and tools to support this?

Finally, a policy implication at the population level is that national health services could take a ML model from an organization that developed it on a smaller dataset and will evaluate it on a national dataset, and then move it to an international level.

3.8 Learning Health System

The new challenges regard the evolution of health informatics structures and systems to be able to react to emergency conditions, at institutional, national, and international levels. Indeed, in the last 2 years, since the beginning of 2020, health administration and services changed their approaches to health informatics systems, moving from a mechanism of supporting health systems in data management and storing, to mechanisms able to anticipate and predict event evolution (e.g., prediction of pandemic evolution and diffusions, as well as simulation of vaccines and virus evolution).

Addressing urgent needs for the CoViD-19 pandemic builds the foundation for how to plan the future for care delivery with a nimbler, patient-centered digital healthcare system that can better stand up to future challenges. During the CoViD-19 pandemic, the need for agile and resilient clinical and research information systems became rapidly manifest. One approach to address this need was the Learning Health System ( LHS ) model [ 108 ], which provides a conceptual framework for identifying and leveraging information and organizational resources in the context of a pipeline that has a repetitive, cyclic structure, as shown in Fig.  1 .

Learning Health System ( LHS ) cycle model from [ 108 ]

The LHS and variations thereof have been particularly important as frameworks for mounting informatics-centric responses to the CoViD-19 pandemic. For example, an LHS model was adopted and adapted at the University of Alabama at Birmingham to enhance resilience and a proactive response to the pandemic [ 109 ]. Through the course of developing this model, the investigators identified seven contributing organizational components of their health system and academic center that were critical to achieving effective responses; informatics capabilities figured prominently in this model, involving informatics and information technology groups that partnered to leverage the substantial information resources at the institution. Another example is the system established at Assistance Publique-Hôpitaux de Paris ( AP - HP ), a public health hospital that instantiated the Health Data Space ( HDS ) as an extension to its clinical data warehouse in response to the pandemic [ 12 ]. The HDS is described as a “key facilitator for data-driven evidence generation and making the health system more efficient.” While the LHS provides a robust model for learning from clinical data, relatively few reports of a successful learning pipeline have been reported. Dash et al. have created such a pipeline that could be effective in responding in an agile and rapid way to events like the CoViD-19 pandemic [ 110 ]. Payne et al. make a strong case for the integration of informatics and healthcare IT in establishing a robust LHS for coordinating the surveillance of and response to the CoViD-19 pandemic and other public health emergencies [ 111 ].

In addition to LHS approaches, others have created networks that facilitate resilient and rapid responses to public health emergencies such as the CoViD-19 pandemic. For example, Duchen et al. report on a system that includes neighborhood-level data for tracking CoViD-19 vaccine uptake [ 112 ]. An extensive clinical information network was established in Kenya, focusing on 22 pediatric hospitals that have been used for CoViD-19 surveillance [ 113 ]. Although this network was not set up as an LHS , it provides the basic infrastructure for developing one. Vahidy’s work on developing a retrospective research task force is an excellent example of how the LHS model can be adapted and exploited to support rapid observational research using clinical and other data [ 114 ].

An LHS could provide decision support for increasing resilience and health literacy. What are some new opportunities for interventions or applications building on top of a person’s own health data, that are inspired by the public’s interest in resilience-supporting health and wellbeing?

As a first example, we envision a lifelong health support system accompanying and supporting every citizen throughout their whole life. Such a system would integrate seamlessly and unobtrusively with daily life. It would be based on data incidentally collected from wearables, IoT , health records, and the digital traces that we all leave as part of our digital lives. In times of stability, when everything is fine, the system would be mostly silent, being there merely upon request. During phases of changes in the person’s life well-being, such as when entering a new stage of life, the system may become active, offering decision support and behavior-change interventions. Changes in health, which may go slow and undetected, may be identified early by such a system, and warnings and recommendations may be issued, encouraging a more intense, purposeful interaction to help find reasons for changes and take appropriate measures.

A second example is to rely on AI -based systems that increase resilience and proactively engage people at risk of mental and social problems. When the social surrounding is not available or not functioning, a technical system is at least the second-best choice [ 115 , 116 , 117 ].

Finally, once reliable datasets and models used in a LHS have been created, they need to be curated for further reuse. Curation of the datasets and ML models should consider what approaches and algorithms will still be usable in the future, after the environment would change, for example, due to immunization and the evolution of the CoViD-19 virus. Note that data acquisition is becoming less expensive, but the cost of data curation which is still done very much by skilled humans is very costly. More research in autonoetic annotation and scalable and reproducible concept extractions is needed.

Table  1 presents some research questions that we pose to our community. At the individual’s level, we propose two specific ideas for new opportunities for interventions/applications building on top of a person’s own health data:

lifelong health support systems that reflect phases of changes in the person’s wellbeing (where purposeful tracking with decision support and behavior-change interventions is desired) and times of relative stability (with merely incidental tracking);

systems that increase resilience and proactively engage the people at risk. When the social surrounding is not available/not functioning, a technical system is at least the second-best choice.

A challenge at the healthcare system level is to develop acts, technology, and standards for continuity of care. These could allow clinicians to use tools to obtain evidence-based advices based on traditional clinical guidelines and on predictive models; such models are based on data from patients similar to the patient being taken care of. Changes in legislation are needed to allow accessing and defining inclusion and exclusion criteria for the “other” similar population, which can come from the same healthcare organization, but potentially also from national or international shared data.

Finally, at the population level, we ask:

what questions can be answered with citizen-acquired data, and what are the new opportunities (with longitudinal data) that can help governments and local governance agencies to establish policies based on AI models developed using citizen-acquired datasets?

3.9 Digital Disparity and Trust

As a discipline, biomedical and health informatics is at the interface of technology, people, and process where the implementation and dissemination of informatics solutions depend on our ability to formulate the solutions through a social, ethical, and trustworthy framework.

Health disparities, referring to preventable differences in the burden of disease, injury, violence, or opportunities to achieve optimal health, have been experienced by socially disadvantaged populations. Even though various policy efforts are aimed at reducing health disparities, evidence mounts that population-level gaps in healthcare quality are increasing [ 118 ]. This widening of disparities is likely to worsen over the coming years due, in part, to our increasing reliance on Internet-based technologies to disseminate health information and services. For example, even though telehealth has been considered a way to close the healthcare gap between the rural and marginalized urban populations, the CoViD-19 pandemic has surfaced the disparity in the access to technology, i.e., the digital divide as a social determinant of health [ 16 , 119 ].

Research has shown that the benefits of advanced medical technologies disparately benefit people belonging to different demographic groups. For example, in the USA people who identify as Black or African American have been shown to receive less benefit from an AI algorithm, which uses claims data as surrogates for resource needs and tends to identify people in need of extra health resources [ 100 ]. Meanwhile, they also suffer more hidden hypoxemia due to inaccurate non-invasive pulse oximetry measurements [ 120 ].

There are opportunities as well as challenges in leveraging digital innovations to address health disparities. It is critical to gain the trust of people in improving the data capture of social determinants of health information, generating evidence-based interventions, and implementing them in the practice of healthcare aiming for optimizing health outcomes for those socially disadvantaged populations. More research is needed to identify important social determinants of health, variables needed to be collected from patients, how to generate better data-gathering approaches that mitigate cultural or socioeconomic differences in reporting, and how to engage patients and communities to generate and deliver fair, inclusive, and trustworthy digital innovations. To move the field forward, in June 2021, World Health Organization ( WHO ) published a guidance on Ethics and Governance of Artificial Intelligence for Health, which acknowledges that digital solutions hold great promise to improve diagnosis, treatment, health research, and drug development and to support governments carrying out public health functions, including surveillance and outbreak response but must put ethics and human rights at the heart of their design, deployment, and use [ 63 ].

Trustworthy in AI is strongly connected to policy-making based on social, ethical, and trustworthy frameworks. In this regard, curation of the AI models is important. This curation process should consider the following question (see Table  1 ): what approaches, algorithms, and models will still be usable, equitable, and trusted in the future after the environment changes due to immunization and evolution of the CoViD-19 virus?

4 Research Directions and Discussion

We discussed above the main directions and issues learned from the CoViD-19 pandemic, vis-à-vis health informatics. We report in Table  1 a summary of such issues as the results of the Rochester meeting.

Research directions and social and policy implications are the main objectives of the newly proposed research agenda. These can be viewed on three levels:

the care of individuals;

the healthcare system view, which provides patient-centric medical care for sets by clinicians and by clinical institutions, customized for people with similar characteristics and needs; and

the population view, where research aspects and policy have to be considered in a large scale perspective.

Table  1 summarizes several major research questions and policy implications, arranged according to the three views. These were also noted at the end of each subsection of Section  3 . The same major research questions, policy implications, and views of Table  1 are graphically depicted by Fig.  2 . For instance, research topics related to the individual’s view include devising big-data-based methods to answer questions that are important to an individual, even if that individual is not even aware of them. Another example is creating methods for generating explanations, educational materials, and tools that can help a person to make sense of their data. Policy and social implications at the individual’s level regard, for instance, the right of vendors and health actors to access an individual citizen’s data (wearable, behavioral, clinical) to help in improving health services (both private and public) and in preventing or minimizing pandemic effects. The right of an individual to the usability refers to being able to understand the specific functionalities of healthcare services, and the consequences of performing or not performing an action through the digital health system. This can be also considered a policy issue in the individual’s view. At the healthcare level, research methods and tools should be invented for data collection (e.g., involving telemedicine), data aggregation, visualization, and analysis. Regulations at the healthcare level should address, for instance, standards for evaluating ML -based models (e.g., for performing risk calculations). These regulations should go beyond current efforts, such as TRIPOD [ 97 ] or SaMD [ 98 ].

Conceptual map of the research questions (RQ) and of the policy implications (PI) from Table  1 . Concepts (ellipses) correspond to views (Individual, Healthcare System, Population) and topics of the research agenda. RQ/PI represent connections between concepts

Methods for semantic data integration and analysis are some of the new research directions. Health policies have to reconsider ethical issues and technological standards for allowing sharing of data in a secure, privacy-conserving, yet meaningful way at the population level.

5 Conclusions

In this report, we discuss health informatics issues and related lessons learned from the CoViD-19 pandemic. At the same time, we provide directions for future development of research and the application of the research results in the post-pandemic era, divided into nine themes. The proposed themes of the research agenda are organized into three levels: individual care, healthcare system, and population view, with research topics and their potential policy implications, described for each level. An overarching observation of this group is that, while we recognize that there is substantial room for new informatics innovation, the pandemic demonstrated that there are many mature informatics techniques that are not routinely used in health informatics due to policy and/or public perception arguments, but that proved to be of great value when we operated in crisis mode during the pandemic.

To complete our report, we would like to underline some key points, especially in the context of policy implications and the practical value of the proposed research agenda. One of them is the right to access vendor-specific data and metadata from wearables and sensors of an individual, but also from all online activities of an individual, in a standardized way that allows the data to be analyzed and integrated into the personal EHR . Technical specifications must balance the completeness of established medical standards (e.g., HL7 , FHIR ) with the vendors’ needs for lightweight and relatively simple interfaces.

Usable interfaces should be developed for sharing ML models and datasets so that they can be accessed by genetic consultants, radiologists, neurologists, and other professionals. Legislation should be established to define these requirements, as well as the quality of data and ML models developed from datasets. A comprehensive evaluation of ML models, beyond standard ROC and predictive values, merit functions should be required for submissions in scientific literature as well as in the introduction of ML models in clinical practice, with an understanding of the role that end-to-end uncertainty of the individual predictions has to play.

Regulatory frameworks are needed to allow accessing and defining inclusion and exclusion criteria for patients who are similar in characteristics to the patient at the point of care. Acts, technology, and standards for continuity of care should allow clinicians to use tools to obtain evidence-based advice based on AI -supported systems.

On the population level, we identified the need for technological tools to allow sharing of data in a secure, privacy-preserving yet meaningful way (e.g., encryption, de-identification, and blockchain). A step in this direction is represented by the European Health Data Space ( EHDS ) regulations that offer the possibility or mandate of sharing ML models and data. Another possible solution could include the integration of simulated population data with the same properties as the data of the real organization (i.e., digital twins).

Although this report proposes many new directions and possible solutions to known issues, there are still many open questions that will need to be addressed in the near future. For example, what are the best tools to ensure that data is shared in a secure, privacy-conserving yet meaningful way? Or how can we accelerate the acquisition of data relevant to public health decisions?

Moving to the sensitive argument related to funding strategies and research directions, health informatics investments in terms of research funding models have been strongly influenced by pandemics. For instance, the USA NIH research funding model specific to the pandemic has been defined and used to represent a relevant issue that has to be considered.

A significant limitation of this report that needs to be acknowledged here is that we have not conducted a detailed analysis of the implications of this research agenda on healthcare financial models, a task that will be challenging due to the high variability of these models, even in well-developed countries with advanced healthcare systems.

We conclude with a final issue that will deserve further discussion in the next few years. Indeed, in these years the (public) role of scientists, and that of biomedical and health informatics scientists, has changed. In many situations some colleagues had to manage unexpected overexposure in the mass media, overcoming all the issues related to the communication of scientific content, having a possibly heavy impact also on policy decisions and social behaviors, in a plain and widely understandable language. Such a new role requires specific attention and skills, not completely considered previously, which deserve specific actions also in the way BMHI scientists make their research results accessible and usable worldwide, avoiding misuses and wrong expectations.

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Acknowledgements

All the authors deeply thank the Institute for Healthcare Informatics ( IHI ) for initiating the meeting and Mayo Clinic for hosting the meeting: without their valuable help, this meeting would have not been possible.

Open access funding provided by Politecnico di Milano within the CRUI-CARE Agreement. C.C. is partially funded by the Ministry of University and Research, MIUR, Project Italian Outstanding Departments, 2018-2022. J.C.F. is partially funded by the National Institutes of Health (USA) Clinical Translational Science Award UL1-TR002538. P.H. is partially funded by the Hanse-Wissenschaftskolleg Institute for Advanced Study and by the Mahidol University Office of International Relations under the MIRU phase-II award. J.H. is partially funded by the National Institutes of Health (USA) Clinical Translational Science Award, UL1-TR001878. G.P. is partially funded by the EU H2020 program: “PERISCOPE: Pan European Response to the Impacts of CoViD-19 and future Pandemics and Epidemics” (grant no. 101016233). G.S. is supported in part by the Slovenian Research Agency under the grants ARRS N2-0101 and ARRS P2-0057. P.V. is partially funded by the research project PON VQA (Validated Query Answer) co-funded by the Ministry of Economic Development (MISE) 2019–2022. C.C.Y. is supported in part by the National Science Foundation (USA) under the Grant IIS-1741306, IIS-2235548, and the Department of Defense (USA) Data Science Award.

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Combi, C., Facelli, J.C., Haddawy, P. et al. The IHI Rochester Report 2022 on Healthcare Informatics Research: Resuming After the CoViD-19 . J Healthc Inform Res 7 , 169–202 (2023). https://doi.org/10.1007/s41666-023-00126-5

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DOI : https://doi.org/10.1007/s41666-023-00126-5

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The Health Informatics field is diverse and expanding, with demand being fueled by the healthcare industry's focus on evidence-based medicine, quality improvement, data security, and patient accessibility. 

What is Health Informatics?

Approximately 30% of the world's data is generated by the healthcare industry, and it is expected to rise to 36% by 2025. The ability to effectively analyze and deploy this data is critical to the successful operation of healthcare organizations.

However, a recent report by Arcadia  found that less than 60% of data generated by healthcare organizations is being used to make intelligent business decisions. At the same time, 93% of healthcare leaders agree that quality data is critical to their performance. But investing in analytics remains a common challenge across healthcare organizations large and small.

This information explosion, the advancement of technology in the medical field, and the need to keep sensitive data confidential, has created a robust market for health or medical informatics specialists , who work at the intersection of information science, computer science, and health care.

What do Health Informatics professionals do?

• Apply the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem-solving, and decision making.
• Use data analytics and artificial intelligence to develop insights and drive innovations in the health care industry.
• Improve patient portals, create cloud-based health care systems, and help personalize treatment plans.
• Help improve patient outcomes and make health care systems more efficient by optimizing health care delivery and centralizing information such as medical records.
• Develop  new medical technology, design public health strategies, and digitize of health care records, systems, and processes.
• Integrate electronic health records with existing systems, aiming for minimal organizational disruption and even improved delivery of healthcare services.

What’s driving the need for health informatics professionals?

The health informatics field is diverse and expanding, with demand being fueled by the health care industry's focus on evidence-based medicine, quality improvement, and data security and accessibility for patients.

• Electronic Health Record (EHR): Perhaps the most well-known application of health informatics is the adoption of electronic health records. In the US, the Affordable Care Act of 2009 requires that healthcare institutions transition to a fully digital medical record system. This requirement impacts every aspect of a healthcare institution’s operations.
• Predictive Medicine: Health informatics is helping to shape a new era of predictive medicine using Big Data and AI, leveraging the huge quantities of data now available through sources such as wearable medical devices. Predictive tools have the potential to help clinicians better predict who will get sick when and how best to intervene to improve outcomes.
• Epidemic Tracking: Health informaticists are assisting in capturing and translating data into usable information to track infectious diseases and create systems to predict and prevent epidemics.

What fields are part of Health Informatics?

• Artificial intelligence
• Chemical informatics
• Consumer health informatics
• Data informatics
• Data privacy
• Decision support systems
• Dental informatics
• Global health informatics
• Information security
• International healthcare systems
• Nursing informatics
• Telemedicine
• Translational research informatics

What are some career areas in Health Informatics?

• Public health informatics focus on how to use information technology to educate the public. They study computer science and use their computer skills to keep track of current medical research. They also design and implement new methods in the field.
• Organizational informatics is the study of both communication within medical organizations and the collation of data used by such organizations.
• Social informatics involves research on the social implications of computerization and the way that information technology affects society's perception of these systems. Social informatics is based more on research and theory.
• Clinical informatics involves the study of the ways that information technology affects clinical research and medical education. When coupled with social informatics, it also complements patient education and perception of the process.

What do Health Informatics professionals earn?

Job titles and career paths are diverse in the health informatics field. 

See additional computing salary information .

Where do Health Informatics professionals work?

• Computer/information security firms
• Governmental agencies
• Health informatics firms
• Health insurance companies

Hospital and healthcare systems

• Long-term care facilities

Medical billing firms

• Medical clinics and doctor's offices
• Medical insurance firms
• Medical oversight firms

Medical software and technology firms

• Multi-center hospital systems
• Public health organizations

Research laboratories

What are some Health Informatics job titles?

• Health informatics specialist
• Clinical analyst
• Clinical informatics specialist
• Nursing informatics specialist
• Pharmacy or nutrition informaticist
• Clinical informatics manager
• Health informatics consultant
• Informatics nurse
• Healthcare IT project manager
• Informatics director

How do I become a Health Informatics professional?

Because health informatics is a technical and business-oriented occupation, most health informatics positions require a bachelor's degree or master's degree, which is typically preferred for higher-level, higher-paying roles. Some common health informatics-related degrees are:

• Bachelor of Science in Health Informatics
• Bachelor of Information Technology
• Bachelor of Science Business—Information Technology Management
• Master of Science in Health Informatics
• Master of Information Systems
• Master of Health Informatics
• Master of Nursing Informatics

What do Health Informatics majors study?

• Software, databases, and analytical tools that process biological information.
• How to design and implement innovative applications and promote new technologies in health care, such as medical decision support systems, telemedicine applications, and medical ethics and biostatistic guidelines
• How to use various resources, devices, and methods to learn to optimize the acquisition, storage, retrieval, interpretation, and use of health and biomedicine information.
• How to retrieve and share information efficiently, think critically while problem solving, and make decisions based on the best possible patient outcomes.

What skills do Health Informatics specialists need?

• Computer Programming. Some health informatics specialists design computer programs to automate the application of statistical analysis techniques to clinical data, drawing out insights with the aid of technologies like artificial intelligence.
• Data Analytics. The role of data analytics in health care is expansive, and health informatics pros use descriptive, predictive, and prescriptive analytics to discover patterns, forecast, and problem solve.
• Health Care IT: Health informatics specialists work closely with health information technology like electronic health records (EHR) and clinical health data systems. They are comfortable working with data generated by technologies such as telemedicine, wearable health devices, electronic prescription services, patient portals, and consumer health care apps.
• Management: Senior and executive positions in health informatics, such as director of health informatics or chief medical information officer, involve managing teams of informatics specialists or heading up strategic project management.

The Future of Health Informatics

The digitization of healthcare is well underway, and today's rapid progression in artificial intelligence, data security standards, and big data is affecting our daily lives more and more. The need to respond to these changing technologies assures the future of the health informatics field, as these professionals will lead the efforts to adapt in this new landscape.

• Electronic Health Records (EHRs) and Interoperability: EHR adoption and the focus on interoperability to facilitate data sharing among healthcare providers.
• Telehealth and Remote Monitoring: Accelerated adoption of telehealth and remote monitoring technologies for virtual healthcare delivery.
• Big Data and Analytics: Increasing reliance on big data and analytics for predictive analysis, personalized medicine, and population health management.
• Artificial Intelligence (AI) and Machine Learning: Growing use of AI and machine learning in healthcare for tasks like image analysis, predictive analytics, and drug discovery.
• Mobile Health (mHealth) and Apps: The role of mobile apps and wearable devices in health monitoring, medication management, and patient engagement.
• Ethical, Legal, and Security Considerations: Ongoing discussions and regulations related to ethical use of health data, patient privacy, data security, and legal compliance.

Health Informatics at Michigan Tech

There is a growing need for health informatics credentials among professionals in the fast-growing, data-driven healthcare sector. Students and working professionals can acquire these credentials through Michigan Tech’s MS in Health Informatics program . The Michigan Tech MS in Health Informatics can be completed at your own pace , fully online, on campus, or as an accelerated master's . Flexibility is built in.

The Master of Science in Health Informatics is designed to:

• Deepen understanding and knowledge of medical informatics and computer/information security.
• Provide a flexible curriculum that's workable for both traditional and nontraditional graduate students.
• Provide research opportunities within the field of medical informatics.

A Flexible, Stackable Master's Program

Ranked as a top program in Michigan, the interdisciplinary 30-credit-hour MS in Health Informatics at Michigan Tech is composed of four stackable graduate certificates that can be completed in any order. Students who successfully complete the required courses work will earn an MS in Health Informatics degree plus 3 graduate certificates (1 foundational certificate + 2 focus area certificates). Completion of the individual certificates may also count toward employer continuing education requirements.

Foundations of Health Informatics

You'll begin with the 12-credit Foundations of Health Informatics certificate, then complete two of the three focus-area certificates listed below. A custom plan of study for is formulated for each individual as they enter the program. The Foundations certificate courses are offered in the fall and/or spring semesters.

SAT 5114: Artificial Intelligence in Healthcare (3 credits) SAT 5141: Clinical Decision Support Modeling (3 credits) EET 4501: Applied Machine Learning (3 credits) 

Learn more about artificial intelligence in healthcare

SAT 5283: Information Governance and Risk Management (3 credits) SAT 5815: Digital Forensics (3 credits) SAT 5817: Security Penetration Test and Audit (3 credits)

Learn more about security and privacy in healthcare

3 of the 4 courses listed below are required.

SAT 5165: Introduction to Big Data Analytics (3 credits) SAT 5317: Medical Internet of Things (3 credits) SAT 5424: Population Health Informatics (3 credits) KIP 4740: Epidemiology (3 credits)

Learn more about public health informatics

Foundations of Health Informatics (12 credits)

Provides academic training in fundamental topic areas such as security and privacy, data analysis, programming, and system analysis. The Foundations certificate can be completed in two semesters, on-campus or fully online. Successful completion of the Foundations certificate and two of the three focus area certificates leads to a Master of Science in Health Informatics.

Artificial Intelligence in Healthcare (9 credits)

The AI in Health Informatics sector needs professionals of many kinds to support, implement, assess, teach, and research AI healthcare solutions. The field is young and opportunities are plentiful and well-compensated.

Public Health Informatics (9 credits)

Computing has become pervasive in the healthcare sector, and managing and deriving valuable information related to public health is critical. Learn fundamental knowledge and competencies in the application of public health informatics.

Security and Privacy in Healthcare (9 credits)

Healthcare privacy is complex, and there is a delicate balance between keeping patient data secure and safely sharing it. The importance of safeguarding the personal healthcare information, as well as protecting hardware and software systems against attacks, cannot be overstated.

Health Informatics Research at Michigan Tech

• Proactive and Responsive Holistic Wellness Solutions in K-12
• Enhancing Emotional Well-being through AI-Enabled Self-Regulation Interventions
• Improved Automated Quality Control of MSK Radiographs using Deep Learning
• Improved Fracture Risk Predictions through Opportunistic Screenings
• Improved Public Health Disease Surveillance Architecture and Modeling
• Enhancing Behavioral Health Capacity and Clinical Decision Modeling
• #3 Best Medical Informatics Colleges in Michigan
• #6 Best Online Master’s in Health Informatics
• #8 Best Master’s in Health Informatics Degree Programs
• Top 3 Best Online Health Informatics Master's Degrees in Michigan

Best Reviewers Awards 2023

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Ieee-embs international conference on biomedical and health informatics (bhi’23).

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J-BHI publishes original papers describing recent advances in the field of biomedical and health informatics where information and communication technologies intersect with health, healthcare, life sciences and biomedicine.

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IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI’24)

Editor-in-chief.

Professor Dimitrios I. Fotiadis

Dr. Fotiadis is Prof. of Biomedical Engineering and Director of the Unit of Medical Technology and Intelligent Information Systems (MEDLAB), University of Ioannina, Ioannina, Greece. Dr Fotiadis is the founder of MEDLAB, which now is one of the leading centers in Europe in Biomedical Engineering with activities ranging from the development of health monitoring systems to big data management and multiscale modelling. The Unit is an active center for many R&D projects and is considered as a center of excellence for human tissues modelling activities with international collaborations with the research community, industry and public organizations. Dr Fotiadis is affiliated researcher of the Biomedical Research Dept. of the Institute of Molecular Biology and Biotechnology, FORTH, and member of the board of Michailideion Cardiac Center.

Impact Factor

Eigenfactor, article influence score, the ieee journal of biomedical and health informatics volume 27, issue 1 has been published..

• September 6, 2024

August 2024 Highlights

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) 2024 10 – 13 November 2024 Houston, TX USA Non-Contact Blood Pressure Estimation from Radar Signals by a Stacked Deformable Convolution…

• July 26, 2024

July 2024 Highlights

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) 2024 10 – 13 November 2024 Houston, TX USA Adaptive Multi-dimensional Weighted Network with Category-aware Contrastive Learning for Fine-grained Hand Bone…

• June 18, 2024

June 2024 Highlights

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) 2024 10 – 13 November 2024 Houston, TX USA RegFSC-Net: Medical Image Registration via Fourier Transform with Spatial Reorganization and Channel…

• May 20, 2024

May 2024 Highlights

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) 2024 10 – 13 November 2024 Houston, TX USA An Automated Analysis Framework for Epidemiological Survey on COVID-19 Lin, Zichao; Lin,…

• April 24, 2024

April 2024 Highlights

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) 2024 10 – 13 November 2024 Houston, TX USA Discovering consensus regions for interpretable identification of RNA N6-methyladenosine modification sites via…

• March 25, 2024

March 2024 Highlights

Enhancing Motor Sequence Learning via Transcutaneous Auricular Vagus Nerve Stimulation (taVNS): An EEG Study C. Tang, L. Chen, Z. Wang, L. Zhang, B. Gu, X. Liu, D. Ming, Dong Motor…

Support Open Access Initiatives by Storing Datasets in IEEE DataPort ™

IEEE DataPort is a great way to gain exposure for your research, serving as an easy-to-use and secure platform for data storage, and a way to ensure compliance with many funding agency open access requirements.

Join researchers around the globe who rely on IEEE DataPort to store, share, and manage their research data, by uploading your dataset today! This universally accessible, web-based portal accepts open access datasets up to 2TB. Currently, datasets can be uploaded to IEEE DataPort at no cost and each dataset is assigned a Digital Object Identifier (DOI).

Uploading datasets as open access helps both individuals and their institutions meet funding agency requirements and helps ensure compliance with data requirements. Right now, individuals can use the promotion code OPENACCESS1 to upload one open access dataset at no cost .

In addition, all IEEE DataPort users have the opportunity to freely access all open access datasets and are able to analyze and use them with proper citation.

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Join the EMBS

In an era where technology is expanding at a rapid rate and the needs for medical application of these technologies has never been greater, the intersection between engineering, medicine and biology is a critical place to be. The IEEE Engineering in Medicine and Biology Society is well-positioned to serve as a central gathering point for both of these major disciplines.

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• College of Allied Health Sciences
• Allied Health Professions

Health Information Administration

Improving Health Care Through Technology

Be on the leading edge of today's health care environment. Implement and use IT communications to promote success in health care reform. Managing and protecting personal health information has never been more important.

  Health Sciences Campus

Health Sciences Building

706-721-4650

[email protected]

706-721-6067

Lori H. Prince, MSA, RHIS, CCS

What it Means

The Role of a Health Information Administrator

HIA professionals work in a growing diversification of settings in addition to the traditional hospital environment. They may be employed as:

• Clinical data specialist
• Patient information coordinator
• Data quality manager
• Information security manager
• Data resource administrator or researcher
• Decision support specialist

Bachelor of Science in Health Information Administration (BS-HIA)

• Four-year, 2+2 program
• Complete two years of core curriculum prerequisites in liberal arts and sciences courses
• Transfer to Augusta University College of Allied Health Sciences for two years of online or traditional classroom specialty education in health information administration
• Students begin the program in the fall semester of their junior year

Program Outcomes

Graduation Rate (2020)

Retention Rate (2020)

Satisfaction Rate Among Graduates (2020)

Graduates Employed within 1-Year of Graduation (2020)

National Exam

Our graduates are eligible to sit for the AHIMA national registration examination. Passing this examination entitles one to use the designation Registered Health Information Administrator (RHIA) after their name. The RHIA credential is recognized by employers throughout the continuum of health care. 

Credential Disclosure Statement

Our graduates are eligible to sit for the AHIMA national registration examination. Passing this examination entitles one to use the designation Registered Health Information Administrator (RHIA) after their name. The RHIA credential is recognized by employers throughout the continuum of health care. The Augusta University College of Allied Health Sciences and the Department of Allied Health Professions has determined the BS HIA degree program satisfies the requirements of all US states and territories for the Registered Health Information Administrator (RHIA) credential.

Accreditation

The Bachelor of Science Degree, Health Information Administration at Augusta University, is accredited by the Commission on Accreditation for Health Informatics and Information Management Education (CAHIIM).

Health Information Administration faculty and students are committed to the promotion and expansion of excellence in research and scholarly activities related to health information management and education. Our students are encouraged to explore opportunities for research through their coursework and to present their findings at annual professional meetings such as the Georgia Public Health Association and the Georgia Health Information Management Association.

Our research mission: to lead the development of knowledge collaboration among professionals in health informatics and health systems management, and to positively influence such interactions so as to improve human health and enhance quality of life in Georgia, the nation and the world.

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Science, health, and public trust.

September 4, 2024

Communicating About Data Security and Privacy in Health Research

By Katie Rush, Director of Public Relations and Science Communications, and Jilliene Drayton, Director of Participant Communications and Outreach, at the All of Us Research Program

When a person considers participating in research, there can be a natural struggle between their hope to contribute to science and their interest in protecting their privacy and data. Those of us working with NIH’s All of Us Research Program know this challenge well. Since 2018, All of Us has been inviting participants from across the United States to contribute their information to help researchers better understand how genetics, environmental factors, and life experiences impact our health.

We understand the sensitivity of what we’re asking participants to contribute. That’s why we’ve put data security and privacy at the forefront of our work. Beyond protecting people’s data, we know we need to ensure that participants have the information they need to understand and feel confident in how their data is protected.

When sharing information about research efforts like All of Us , it’s crucial for communicators to understand and convey the data protection and privacy safeguards that studies are using. There are four key approaches that we focus on at All of Us:

• Be Specific and Translate: The technical details of security standards can feel like a foreign language. That’s why it’s important for us to describe the key aspects of protections in plain language that’s accessible to participants and prospective participants. At All of Us , we emphasize several aspects of our data security and privacy measures. We share details about system security. These include strict rules on access, ongoing monitoring designed to detect unusual activity, and tests that can help us find vulnerabilities so they can be fixed. We highlight our privacy measures, such as the removal of direct identifiers (like names and addresses) prior to making data available for research, and encryption—or scrambling of the data to prevent it from being used by people who don’t have permission. We also ensure that participants know how we’re holding researchers accountable for responsible use of data through strict policies and required training.
• Talk About It on Repeat: Reiterating information about data protections and privacy is critical when talking about research to the public. This is especially true for longer studies or programs like All of Us that depend on people being engaged over time. Science and health communicators should look for opportunities to share—and re-share—helpful information that can support education, awareness, and confidence throughout participation in research programs. That means creating and promoting materials designed to increase understanding. For All of Us , these include videos that accompany the informed consent, specific sections of our websites that focus on our safeguards, dedicated fact sheets, and clear messages and FAQs that our frontline staff at health care and community organizations and at our call center can use to quickly and directly address questions from participants and the public.
• Provide Visibility into Data Use: Very often, participants are not made aware of how researchers are using their data. To help participants feel confident about sharing their information, communicators should create and share resources that explain who is using the data, how they’re using it, how the program is holding them accountable for using it responsibly, and what they’re learning. We do this in several ways. We post a list of organizations that have a Data Use and Registration Agreement in place. Researchers must be working with one of these organizations before they can sign up to become a registered user. We also maintain a directory of research projects underway. If something doesn’t look right, participants—or anyone else—can flag a research project for review by our Resource Access Board to ensure researchers are fulfilling their obligations for responsibly using the data. Finally, participants can see the impact of their data contributions through our listing of scientific publications that include All of Us data, and through selected research summaries, our Research Highlights , that we deliver to participants in a monthly newsletter.
• Be Real: It’s important to be upfront that even with the best protections, risks still exist. Ultimately, everyone is willing to accept a different level of risk. Sometimes the informed consent process ends with “No, thank you.” It’s the job of researchers and communicators to help potential participants understand what our safeguards are and what their limitations may be. With that information in hand, people can decide if taking part in the research program is the right choice for them.

Science and public health communicators are crucial to the success of research efforts. They can help people better understand the risks of taking part in research, the protections in place, and the greater societal benefits. And those efforts yield results. Every day we’re inspired by the more than 825,000 people who have joined the All of Us Research Program to share their health data and drive new discoveries, to pave the way to a healthier future for all communities. With effective communication about data privacy and protections, continued progress like this is possible.

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Research on Tap: Women’s Health Research and Cultivating a Community at BU

Wednesday, november 6 | 4-6 pm, kilachand center eichenbaum colloquium room (room 101) 610 commonwealth ave boston, ma 02215.

Hosts: Catherine Klapperich, PhD, Biomedical Engineering; Joyce Wong, PhD, Biomedical Engineering; Emelia Benjamin, ScM, MD, Medicine and Epidemiology; Elisha Wachman, MD, Pediatrics

For much of the 20th century, clinical research predominantly involved male participants, with the assumption that findings would apply equally to women. Concerns about hormonal fluctuations and reproductive health risks led to the exclusion of women, particularly those of childbearing age. This exclusion resulted in a significant lack of data on how various diseases and treatments affect women, contributing to inequities in healthcare outcomes. The emergence of women’s health as its own field has allowed health researchers to study and address biological differences, the unique needs of women, and social factors that significantly impact women’s health.

This Research on Tap will feature women’s health research from across BU campuses, and is part of a broader effort to establish a BU research community focused on women’s health. All investigators, faculty, and trainees interested in the health of women, intersex people, and gender minorities are invited to share ideas and form new collaborations. BU is uniquely positioned to address long-standing challenges in these areas with investigators working in basic science, social science, engineering, clinical, translational, and implementation science at BU and Boston Medical Center.

The event is aligned with the recent effort to create a new Evans Center pre-Affinity Research Collaboration related to women’s health, which will convene monthly meetings alternating between the Charles River and Medical Campuses.

About Research on Tap

The Research on Tap series, sponsored by the BU Office of Research, brings together groups of BU researchers around important topics. At each event, 10-12 researchers present a maximum of four slides and deliver a four-minute “elevator pitch” of their work. Research on Tap events are open to faculty, staff, postdocs, and graduate students. Each presentation is followed by refreshments and lively discussion with colleagues and potential collaborators.

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Certification of Health IT

Health information technology advisory committee (hitac), health equity, hti-1 final rule, hti-2 proposed rule, information blocking, interoperability, patient access to health records, clinical quality and safety, health it and health information exchange basics, health it in health care settings, health it resources, laws, regulation, and policy, onc funding opportunities, onc hitech programs, privacy, security, and hipaa, scientific initiatives, standards & technology, usability and provider burden.

Access and Use of Electronic Health Information by Individuals with Cancer: 2020-2022

ASTP Data Brief | No. 73 | August 2024

In 2024, an estimated 2 million new cases of cancer will be diagnosed in the US (1). As of January 2022, there were an estimated 18.1 million cancer survivors in the US, which is projected to increase to 22.5 million by 2032 (1). For cancer survivors and those managing a recent diagnosis, having access to online medical records via patient portals or smartphone-based health apps is critical to managing complex health needs such as viewing lab results or clinical notes, communicating with providers, and sharing information with caregivers and other providers involved in their care. In early 2024, as part of the Cancer Moonshot initiative, the White House reaffirmed their commitment to enhancing patient navigation in cancer care (2). Ensuring patients and caregivers have easy access to information they need to manage their health and care is critical to enabling patients to navigate a recent cancer diagnosis or cancer survivorship. This brief pools data from two years of the National Cancer Institute’s nationally representative Health Information National Trends Survey (HINTS) to examine the access and use of health information by individuals with cancer in 2020-2022.

• More than 6 in 10 individuals with a recent cancer diagnosis were offered and accessed their online medical records in 2020-2022, a significant increase from 2017-2018.
• Individuals with a recent cancer diagnosis frequently accessed their online medical records: 40% accessed their records 6 or more times in the past year – nearly twice the rate of those who have never had cancer (23%).
• Frequent users and individuals with a recent cancer diagnosis had higher rates of using multiple methods (both an app and website) to access their online medical records.
• Nearly all users with a recent cancer diagnosis used their online medical records or patient portal to view test results.

More than half of individuals with a recent cancer diagnosis reported having multiple patient portals or online medical records.

The share of individuals offered access to their online medical records increased significantly between 2017-2018 and 2020-2022, particularly among individuals with a recent cancer diagnosis..

★ Eighty percent of individuals with a recent cancer diagnosis were offered access to their online medical records in 2020-2022—a 38% increase from 2017-2018 (58%).

★ Between 2017-2018 and 2020-2022, a greater share of individuals who were offered online access to their records reported accessing them at least once in the past year. While rates increased across all categories, they were highest among individuals with a recent cancer diagnosis (65%) compared to cancer survivors (52%) and those who never had cancer (47%).

Figure 1: Change in the percent of individuals who were offered access to their online medical records by cancer status, 2017-2018 vs. 2020-2022. 

Source: HINTS 5, Cycle 1 (2017); HINTS 5, Cycle 2 (2018); HINTS 5, Cycle 4 (2020); HINTS 6 (2022) Notes: Denominator represents all individuals and percentages reflect weighted national estimates. Recent cancer diagnosis was defined as individuals who reported that their first cancer diagnosis occurred in the past 5 years. Cancer survivor was defined as individuals who reported that their first cancer diagnosis occurred more than five years ago. Never had cancer was defined as individuals who reported that they have never been diagnosed with cancer. *Indicates statistically significant difference from the “Never had cancer” group (p<0.05). Original source of 2017-2018 statistics: Access and Use of Electronic Health Information by Individuals with Cancer: 2017-2018 | HealthIT.gov .

Forty percent of individuals with a recent cancer diagnosis reported accessing their online medical records or patient portal 6 or more times in the past year.

★ Individuals with a recent cancer diagnosis accessed their information more frequently: 40% of individuals with a recent cancer diagnosis accessed their online medical records 6 or more times in the past year (“frequent users”) – nearly twice the rate of those who have never had cancer (23%).

★ Less than 1 in 5 cancer survivors and those with a cancer diagnosis (18%) reported accessing their online medical records 1 or 2 times in the past year (“infrequent users”) as compared to over a quarter (27%) of those who never had cancer.

Figure 2: Frequency of access within the past year among those offered a portal by cancer status, 2020-2022. 

Source: HINTS 5, Cycle 4 (2020); HINTS 6 (2022) Notes: Denominator represents individuals who were offered a portal by their health care provider or insurer. *Indicates statistically significant difference from the “Never had cancer” group (p<0.05).

Frequent users and individuals with a recent cancer diagnosis had higher rates of using multiple methods to access their information electronically.

★ In 2020-2022, individuals with a recent cancer diagnosis—who tend to be frequent users (Figure 2)—had higher rates of using multiple methods (both a smartphone-based health app and web-based portal) to access their online medical records (31%) (Panel A).

★ While many individuals used only web-based methods to access their records, frequent users had higher rates of using multiple methods of access, regardless of cancer status (Panel B).

Figure 3: Methods individuals used to access their online medical records by cancer status and frequency of access, 2020-2022. 

Source: HINTS 5, Cycle 4 (2020); HINTS 6 (2022)  Notes: Denominator represents individuals who accessed their online medical records at least once within the past year. Don’t know responses to the methods of access question were excluded. *Indicates statistically significant difference from the “Never had cancer” (Panel A) or “6 or more times” (Panel B) reference group (p<0.05).

Nearly all users with a recent cancer diagnosis used their access to view test results.

★ Viewing tests results and clinical notes were the most common uses of patient portals or online medical records among those who access them, regardless of cancer status.

★ Cancer survivors and individuals with a recent cancer diagnosis used their online medical record or patient portal to view test results (93%) or clinical notes (77%) at higher rates than individuals who have never had cancer (88% and 68%, respectively).

★ Rates of portal use for downloading health information or transmitting information to a 3rd party did not differ substantially by cancer status.

Figure 4: Individuals’ use of online medical records or patient portal to view, download, or transmit information by cancer status, 2020-2022. 

Source: HINTS 5, Cycle 4 (2020); HINTS 6 (2022) Notes: Denominator represents individuals who accessed their online medical records at least once within the past year. For 'view clinical notes' data come from two different questions. In 2020, the survey asks whether respondents' online medical records include clinical notes (Yes vs. No or don’t know). In 2022, the survey asks whether respondents used their online medical record to view clinical notes (Yes vs. No). Missing values were excluded from the denominator. *Indicates statistically significant difference from the “Never had cancer” group (p<0.05).

★ Individuals with a recent cancer diagnosis had significantly higher rates of having multiple online records or patient portals (59%, 2.2 portals on average) compared to cancer survivors (48%, 1.2 on average) and those who have never had cancer (43%, 1.7 on average)

★ Individuals with a recent cancer diagnosis reported using a 3rd party app to organize information from multiple portals or online medical records at higher rates (8%) than cancer survivors (4%) and those who have never had cancer (5%).

★ A greater share of individuals, regardless of cancer status, reported having a patient portal or online medical record through their primary care provider (63% nationally).

Table 1: Organization or provider types with which individuals have an online medical record or patient portal and use of 3rd party apps to organize information from multiple records or portals, 2022. 

Source: HINTS 6 (2022) Notes: Denominator represents all individuals. National estimates reported in parentheses in Column 1. Questions were available in HINTS 6 (2022) only. Missing values excluded from the denominator. *Indicates statistically significant difference from the “Never had cancer” group (p<0.05).

Cancer is one of the most prevalent chronic diseases in the US. Nearly 40% of individuals will be diagnosed with cancer at some point during their lifetimes, (1) which speaks to the importance of ensuring individuals navigating a recent diagnosis or cancer survivorship have easy access to tools to navigate their care. In 2020 and 2022, more than three-quarters of cancer survivors (73%) and individuals with a recent cancer diagnosis (80%) were offered access to their online medical records or patient portal by a health care provider or insurer – a significant increase from 2017-2018. This increase likely reflects broader growth in patients being offered online access to their electronic health information during the COVID-19 pandemic—which likely spurred demand for online access to medical records including test results. This increased demand was supported by the implementation of the Cures Act Final Rule, which aimed to make it easier and more convenient for patients to access their electronic health information using smartphone- or web-based health apps (3-5).

In 2020-2022, most patients who were offered access to their online medical records or patient portal reported accessing them at least once in the past year. Rates of access were higher among individuals with a recent cancer diagnosis (82%) compared to cancer survivors (71%) and those who never had cancer (72%). Individuals with a recent diagnosis were also more frequent users: 40% accessed their online medical records or patient portal 6 or more times in the past year compared to about a quarter of cancer survivors (26%) and those who have never had cancer (23%). Frequent access among individuals with a recent diagnosis may be attributable to more frequent health care visits following a diagnosis. Having online access to medical records allows patients to view test results and clinical notes, communicate with providers, and download or share information with other members of the care team.

Patient portals and smartphone-based health apps, which help enable individuals manage information from multiple online medical records or patient portals, can help bridge the gap in cancer care by providing an electronic means of navigating care and facilitating patient-provider communication. Studies have shown that patient portal use is associated with greater perceived patient-centered communication among individuals with cancer and other chronic conditions (6, 7). Individuals with a recent cancer diagnosis—who tend to be frequent users—had higher rates of using multiple methods (both an app and a web-based portal) to access their information electronically. Individuals with a recent cancer diagnosis also had higher rates of having multiple online records or patient portals (59%, 2.2 portals on average)—particularly portals or records with other health care provider (e.g., specialists)—compared to cancer survivors (48%, 1.2 on average) and those who have never had cancer (43%, 1.7 on average).

One option available to individuals to share information across portals is to download or transmit their electronic health information. However, rates of downloading or transmitting information have been consistently low—even among those with a recent cancer diagnosis—suggesting there may be differences in availability, lack of awareness, or lower demand for these functionalities. Aggregating data from multiple records using 3rd party health apps is a more recently available option to manage information contained in different portals. Individuals with a recent cancer diagnosis reported using 3rd party health apps to organize information from multiple portals or online medical records at slightly higher rates (8%) than cancer survivors (4%) and those who have never had cancer (5%). Despite relatively low use of these portal organizing apps, this difference in use suggests greater utility for individuals with cancer or another chronic condition to streamline access to information contained in multiple records.

Ensuring patients and caregivers have easy access to information they need to manage their health and care is critical to enabling patients to navigate a recent cancer diagnosis or cancer survivorship. Targeted efforts to improve patient access and simplify patient navigation can help further promote patient-centered communication, empower patients to make informed decisions about their health and care, and aid the delivery of person-centered care. As part of the Cancer Moonshot initiative, the White House recently announced CancerX —a multi-stakeholder public-private partnership aimed at developing innovative approaches to reduce the burden of cancer, including by focusing on ways to leverage existing technology and advancing the development and commercialization of new digital tools to enhance patient access and ease the burden of cancer navigation. One such initiative that aligns with the Cancer Moonshot priorities is the Centers for Medicare & Medicaid Services’  Enhancing Oncology Model (EOM), an innovative payment model that aims to improve health care providers’ ability to deliver patient-centered care, enhance coordination across all of a patients’ health care providers, and support personalized services that help patients navigate and manage their care.

In addition to ongoing efforts to promote enhance patient navigation, there are parallel efforts aimed at increasing the standardized capture of data elements that will assist patients, providers, researchers, and public health practitioners in gaining access to the information needed to further cancer prevention, diagnosis, research and care. The  USCDI+ Cancer Initiative —a collaboration between the National Cancer Institute and the Office of the Assistant Secretary for Technology Policy—aims to support the adoption and use of interoperable cancer health IT standards and advance the development and adoption of cancer-specific use cases (e.g., clinical trial matching; timeliness of cancer registry reporting) to more broadly support the cancer community. Furthermore, in alignment with  The White House’s call to action regarding improving cancer care through better electronic health records (EHRs), CancerX is working closely with partners, members, and healthcare technology companies to contribute input to the development of and ultimately  support broad industry adoption of USCDI+ Cancer to improve the usability and accessibility of cancer data to benefit patient care everywhere.

Reducing the burden of cancer is a national priority. Several federal efforts are underway to advance cancer-focused research, reduce the burden of navigating cancer care, and enhance patient access. Patient portals and apps can help patients navigate cancer by enabling easy, secure access to information needed to manage their health and care. Looking forward, it will be important to ensure that emerging tools and technologies are widely accessible to patients and navigators in various stages of navigating cancer survivorship or a recent diagnosis. 

DATA SOURCES AND METHODS

Data come from two waves of the National Cancer Institute’s (NCI) Health Information National Trends Survey (HINTS). Since 2003, NCI has conducted the HINTS to assess the impacts of health communication, specifically measuring: how people access and use health information, how people use information technology to manage their health and health information, and the degree to which people are engaged in health behaviors. The Office of the Assistant Secretary for Technology Policy (ASTP) works with NCI to develop survey content related to individuals’ access and use of information contained in their online medical records.

This brief pooled data from HINTS 5, Cycle 4 (2020) and HINTS 6 (2022) to achieve a more robust sample of individuals with a recent or prior cancer diagnosis. HINTS 5, Cycle 4 was a single-mode mail survey fielded February through June 2020. HINTS 6 (2022) was fielded as both a paper and web-based survey in March through November 2022. The sample design for each survey consisted of two-stages. In the first stage, a stratified sample of addresses were selected from a file of residential addresses. In the second-stage, one adult was selected within each sampled household. The sampling frame consisted of a database of addresses used by Marketing Systems Group (MSG) to provide a random sample of addresses. For HINTS 5, Cycle 4, complete data were collected from 3,865 respondents and the final response rate was 37%. For HINTS 6, complete data were collected from 6,252 respondents and the final response rate was 28%. All results were weighted to account for non-response and generate national estimates. More details regarding sample selection, data collection, and weighting can be found in the Methodology Reports on the  HINTS website .

• National Cancer Institute. Cancer Statistics [Internet]. Bethesda (MD): National Cancer Institute. Available from: https://www.cancer.gov/about-cancer/understanding/statistics.
• The White House. WHAT THEY ARE SAYING: As Part of the Cancer Moonshot, First Lady Jill Biden, Leading Health Insurers & Oncology Practices Nationwide Highlight New Actions to Expand Patient Navigation [Internet]. Washington (DC): The White House. Available from: https://www.whitehouse.gov/briefing-room/statements-releases/2024/03/27/what-they-are-saying-as-part-of-the-cancer-moonshot-first-lady-jill-biden-leading-health-insurers-oncology-practices-nationwide-highlight-new-actions-to-expand-patient-navigation/ .
• Strawley C. and Richwine C. Individuals’ Access and Use of Patient Portals and Smartphone Health Apps, 2022. Office of the Assistant Secretary for Technology Policy and Office of the National Coordinator for Health Information Technology. Data Brief: 69. 2023. Available from: Individuals’ Access and Use of Patient Portals and Smartphone Health Apps, 2022 | HealthIT.gov .
• Richwine, C. Progress and Persistent Disparities in Patient Access to Electronic Health Information. JAMA Health Forum. 2023;4(11). Available from: Progress and Persistent Disparities in Patient Access to Electronic Health Information | Health Policy | JAMA Health Forum | JAMA Network .
• Office of the Assistant Secretary for Technology Policy. The ONC Cures Act Final Rule: Delivering on the Patient’s Right to Their Medical Records and Promoting a Modern Health App Economy [Internet]. Washington (DC): Office of the Assistant Secretary for Technology Policy. Available from: TheONCCuresActFinalRule.pdf (healthit.gov)
• Zaidi M, Amante DJ, Anderson E, Ito Fukunaga M, Faro JM, Frisard C, Sadasivam RS, Lemon SC. Association Between Patient Portal Use and Perceived Patient-Centered Communication Among Adults With Cancer: Cross-sectional Survey Study. JMIR Cancer. 2022 Aug 9;8(3). Available from: Association Between Patient Portal Use and Perceived Patient-Centered Communication Among Adults With Cancer: Cross-sectional Survey Study - PMC (nih.gov)
• Stewart MT, Hogan TP, Nicklas J, Robinson SA, Purington CM, Miller CJ, Vimalananda VG, Connolly SL, Wolfe HL, Nazi KM, Netherton D, Shimada SL. The Promise of Patient Portals for Individuals Living With Chronic Illness: Qualitative Study Identifying Pathways of Patient Engagement. J Med Internet Res 2020;22(7). Available from: Journal of Medical Internet Research - The Promise of Patient Portals for Individuals Living With Chronic Illness: Qualitative Study Identifying Pathways of Patient Engagement (jmir.org) .

ACKNOWLEDGEMENTS

The authors are with the Office of Standards, Certification, and Analysis, within the Office of the Assistant Secretary for Technology Policy (ASTP). The data brief was drafted under the direction of Mera Choi, Director of the Technical Strategy and Analysis Division, Vaishali Patel, Deputy Director of the Technical Strategy and Analysis Division, and Wesley Barker, Chief of the Data Analysis Branch.

SUGGESTED CITATION

Richwine C. Access and Use of Electronic Health Information by Individuals with Cancer: 2020-2022. Office of the Assistant Secretary for Technology Policy. Data Brief: 73. September 2024.

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Evidence-Based Health Informatics as the Foundation for the COVID-19 Response: A Joint Call for Action

Luis fernandez-luque.

1 Adhera Health Inc., Palo Alto, California, United States

Andre W. Kushniruk

2 School of Health Information Science, University of Victoria, Victoria, Canada

Andrew Georgiou

3 Australian Institute of Health Innovation, Macquarie University, Macquarie, New South Wales, Australia

Arindam Basu

4 School of Health Sciences, University of Canterbury, Christchurch, New Zealand

Carolyn Petersen

5 Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, United States

Charlene Ronquillo

6 Daphne Cockwell School of Nursing, Ryerson University, Ryerson, Toronto, Canada

Chris Paton

7 Department of Information Science, University of Otago, Dunedin, New Zealand

8 Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom

Christian Nøhr

9 Centre for Health Informatics and Technology, Maersk McKinney Moller Institute, University of Southern Denmark, Denmark

Craig E. Kuziemsky

10 Office of Research Services, MacEwan University, Edmonton, AB, Canada

Dari Alhuwail

11 Department of Information Science, Kuwait University, Kuwait

12 Health Informatics Unit, Dasman Diabetes Institute, Kuwait

Diane Skiba

13 University of Colorado, Denver, Colorado, United States

Elaine Huesing

14 IMIA CEO

Elia Gabarron

15 Norwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway

Elizabeth M. Borycki

16 School of Health Information Science, University of Victoria, Victoria, Canada

Farah Magrabi

Kerstin denecke.

17 Institute for Medical Informatics, Bern University of Applied Sciences, Bern, Switzerland

Linda W. P. Peute

18 Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands

19 Columbia University Medical Center, Data Science Institute, Columbia University, Columbia, United States

Najeeb Al-Shorbaji

20 Amman, Jordan

Paulette Lacroix

21 University of Victoria, Victoria, Canada

Romaric Marcilly

22 Univ. Lille, Inserm, CHU Lille, ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales, F-59000 Lille, France

Ronald Cornet

Shashi b. gogia.

23 Society for Administration of Telemedicine and Healthcare Informatics, New Delhi, India

Shinji Kobayashi

24 National Institute of Public Health, Japan

Sriram Iyengar

25 The University of Arizona, Arizona, United States

Thomas M. Deserno

26 Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany

Tobias Mettler

27 Swiss Graduate School of Public Administration, University of Lausanne, Lausanne, Switzerland

Vivian Vimarlund

28 Department of Computer and Information Science (IDA), School of Engineering and Technology, Linköping University, Linköping, Sweden

29 Center for Biomedical Data Science, Yale University, New Haven, Connecticut, United States

Background  As a major public health crisis, the novel coronavirus disease 2019 (COVID-19) pandemic demonstrates the urgent need for safe, effective, and evidence-based implementations of digital health. The urgency stems from the frequent tendency to focus attention on seemingly high promising digital health interventions despite being poorly validated in times of crisis.

Aim  In this paper, we describe a joint call for action to use and leverage evidence-based health informatics as the foundation for the COVID-19 response and public health interventions. Tangible examples are provided for how the working groups and special interest groups of the International Medical Informatics Association (IMIA) are helping to build an evidence-based response to this crisis.

Methods  Leaders of working and special interest groups of the IMIA, a total of 26 groups, were contacted via e-mail to provide a summary of the scientific-based efforts taken to combat COVID-19 pandemic and participate in the discussion toward the creation of this manuscript. A total of 13 groups participated in this manuscript.

Results  Various efforts were exerted by members of IMIA including (1) developing evidence-based guidelines for the design and deployment of digital health solutions during COVID-19; (2) surveying clinical informaticians internationally about key digital solutions deployed to combat COVID-19 and the challenges faced when implementing and using them; and (3) offering necessary resources for clinicians about the use of digital tools in clinical practice, education, and research during COVID-19.

Discussion  Rigor and evidence need to be taken into consideration when designing, implementing, and using digital tools to combat COVID-19 to avoid delays and unforeseen negative consequences. It is paramount to employ a multidisciplinary approach for the development and implementation of digital health tools that have been rapidly deployed in response to the pandemic bearing in mind human factors, ethics, data privacy, and the diversity of context at the local, national, and international levels. The training and capacity building of front-line workers is crucial and must be linked to a clear strategy for evaluation of ongoing experiences.

Introduction

The current COVID-19 pandemic demonstrates that there is an urgent need to focus on evidence-based implementation of digital health. This gains paramount importance since there is frequently a tendency to focus attention on what could be highly promising despite being poorly validated in times of urgency. Rigor and evidence need to be taken into consideration to avoid delays and unforeseen negative consequences.

Further, this pandemic has created a unique opportunity to create quality data which can enable the achievement of the “Learning Healthcare System (LHS)” paradigm which implies that knowledge generated within the health care systems in the daily practice is used systematically to produce the continual improvement in care. This involves the intersection of digital platforms to create seamless efficient delivery of health care that also aligns itself continually to changing demands. 1 2

This, in turn will result in a long-term improvement of the quality of the health care system. This article is a joint call to action from the working groups and special interest groups of the International Medical Informatics Association (IMIA) for adopting an evidence-based approach toward deployment of digital health technologies during the current COVID-19 crisis. We believe that embracing an interdisciplinary scientific approach will not only consolidate the response, but will also reduce the risk of increasing health disparities and increase our digital preparedness for future challenges (e.g., pandemics and natural disasters).

Evidence-Based Health Informatics in Times of Crisis

The current COVID-19 pandemic is affecting health care systems across the globe in an unprecedented scale affecting not only the prevention and management of the COVID-19 emergency but also how health care is delivered as a whole. There is a rapid expansion of the use of health informatics innovations often overcoming legal and organizational barriers that have been in place for decades. The World Health Organization (WHO) defines in its web site eHealth as “the use of information and communication technologies for health care purposes.” 3 Further, the 71st World Health Assembly (WHA) in 2018 highlighted the importance of using digital technologies to reinforce public health resilience including “to build capacity for rapid response to disease incidents and public health emergencies,” as stayed in the minutes of the WHA. 4 These digital technologies include many different types of approaches and subtypes (e.g., telehealth, mobile health, online health and digital therapeutics), but all of them require, as any health technology, to be built on the best practices and evidence. The use of information technology (IT) in medicine and health care continues to evolve into different branches and focus areas. The discipline of health informatics is the interdisciplinary research field focused on how ITs can support the practice of health care and public health, this can be seen as a crucial part of medical informatics or more broadly biomedical informatics, 5 6 thus encompassing the scientific foundations of innovation such as telemedicine, eHealth, mHealth, and a long list of terms to describe those informatics-based innovations in the health sector.

In the context of a public health crisis, access to accurate evidence-based information about how safe and effective the health informatics technologies are in aiding the public health interventions is of paramount importance. 7 8 Clinical trials and data of different types (such as public health registries or Electronic Health Records) are rapidly emerging to validate therapeutic and preventive pharmacological treatments. However, eHealth solutions are deployed at a large scale often without rigorous and methodologically sound scientific assessment. Evidence has been published about the use of eHealth tools in previous public health emergencies such as the Ebola virus 9 10 11 12 and the Zika virus. 13 In many instances, there have been reports about the need to ensure methodologically sound evaluation which will support evidence-based decision-making and building a strategy to reinforce and strengthen the health care systems to increase resilience and preparedness for the next crisis. 14

Evidence is broadly defined as the available body of information that attests whether a proposition is valid. Sackett et al, in his definition of evidence-based medicine, perhaps the most well-developed area of evidence-based practice, have defined the field in terms of the “conscientious, explicit, and judicious use of the current best evidence in making decisions about the care of individual patients,” 15 and it has been further expanded into areas such as public health. 16 The contexts into which digital health systems are implemented are usually highly complex making randomized controlled trials (RCTs) challenging and often infeasible. This means that following a hierarchy of evidence that relies solely on RCTs and meta-analyses may not be appropriate nor practical for evidence-based health informatics where much of the body of available information on the effectiveness of informatics comes from observational studies, although there are fast growing numbers of RCTs in health informatics. Notwithstanding these differences, evidence should remain be the basis of clinical and public health practice. It enables decision-making to be based on the best available peer-reviewed quantitative and qualitative researches. This would mean systematic usage of data/information systems, application of planning frameworks such as learning health care systems models, community involvement in decision-making, and evaluation. Dissemination of knowledge to the key stakeholders is important, since acceptance of technology is often related to how it has been communicated and perceived, also in addition to the training and skills of intended users.

One of the main challenges stems from the sociotechnical nature of digital health technologies. These are complex interventions that require well-planned integration into complex organizational settings where human factors, as well as privacy and security, play a major role. Consequently, building evidence-based digital health strategies requires an interdisciplinary and intersectoral approach combining expertise across disciplines (e.g., health care, informatics, and management,) and sectors (e.g., health care systems, higher education, health technology industry, and policymakers) to avoid negative or unintended consequences. 17 We know that implementation challenges, including training and human factors, might hamper the translation of evidence into health practice, thus requiring an interdisciplinary approach. 8 Research on implementation challenges have been acquiring extra attention in recent years, including in eHealth, as a mean to facilitate the meaningful introduction of new solutions into the health domain. 18

As in many health innovations, including pharmacological treatments, formative research needs to happen before clinical trials. In health informatics, usability and design research can provide early evidence on how innovations will be accepted into clinical practice. An additional challenge during the current novel coronavirus disease 2019 (COVID-19) pandemic is to ensure that experiences are shared rapidly as part of a “crisis informatics” approach, 19 and ensuring high-quality data. 20

An interdisciplinary approach to study the use of ITs in the health care field is not new, and the field of health informatics has been an interdisciplinary endeavor for over 60 years. An example of such scholarly community is IMIA, which with 53 years of existence encompasses over 60 national medical informatics societies and regional associations, and over 20 groups dealing with special aspects via special interest groups, task forces, and working groups. Furthermore, the global approach of such type of scientific societies does allow for rapid sharing of knowledge and expertise not only across disciplines but also across geographies that might represent very different socioeconomic and cultural contexts. In addition, scientific societies do represent independent bodies where knowledge can be freely shared within the scientific principles. IMIA and its national and regional member societies and associations have already published some core recommendations in the context of COVID-19, aiming at guiding public health organizations. 19

In this paper, we urge for a joint call for action to use and leverage evidence-based health informatics as the foundation for the COVID-19 response and public health interventions. We provide an overview of how the health informatics scientific community is helping to support the COVID-19 crisis response through tangible examples of how the working groups and special interest groups of IMIA are helping to build an evidence-based response to this crisis. Further, we provide some recommendations on key aspects that should be addressed and/or avoided related to the use of digital health during the current crisis based on decades of experience in health informatics research.

Using a qualitative approach, the IMIA board approached leaders of working groups and special interest groups via e-mail for an overview of the scientific efforts being taken as the COVID-19 pandemic was spreading across the globe. Each working group and special interest group compiled its activities and submitted a short summary of their efforts.

The process for creating this manuscript was led by the IMIA Vice President for Working Groups and Special Interest Groups. The IMIA Chief Executive Officer (CEO) sent e-mail invitations to the leadership of the working groups using the IMIA mailing database (currently 26 groups). That e-mail included short questions (web form) to describe the role of the different working groups (WGs)/special interest groups (SIG) during the COVID-19 crisis. After that, the WGs and SIGs were invited to participate in two brainstorming sessions using video conference where the role of WGs and SIGs were discussed and early versions of the manuscript were developed. Once a more matured version of the manuscript was ready, a second round of invitations to all WGs/SIGs was sent for comments or additional contributions. Discussions on the latest version of the manuscript were done by circulating the word documents for comments.

The Role of the Health Informatics Scientific Community and the International Medical Informatics Association

The IMIA WGs and SIGs have made contributions in the context of COVID-19 that is summarized in Table 1 .

Abbreviations: COVID-19, novel coronavirus disease 2019; IMIA-NI, International Medical Informatics Association Nursing Informatics; IT, information technology; SIG, special interest group; WG, working group.

Results: A Call for Evidence-Based Informatics Response to COVID-19

With the COVID-19 outbreak, research concerned with forecasting and predictive analytics for syndromic surveillance 21 have received remarkable media attention. Increasing reliability and validity of forecasting or developing mechanisms for blending official datasets, like case statistics published by the World Health Organization or Centers for Disease Control and Prevention (CDC), with unofficial channels, such as data feeds from social media or telecommunications service providers, 22 seem to be “the” most important concern right now. Already, there are examples of meaningful data sharing initiatives such as the international consortium 4CE, 23 (p3) where electronic health record (EHR) data of COVID-19 patients from nearly a hundred hospitals is being shared. However, to a much lesser extent, researchers are focusing on organizational preparedness and postcrisis learning. 24 Even though there is strong evidence that a coordinated approach and small but directed changes in culture, processes, and IT-reliant solutions may prevent a breakdown of health care providers in times of crisis, 25 relatively little efforts have been made on this topic (as compared with crisis response). Our call to action is not only directed toward the crisis response but actually addressing a long-term perspective including preparedness and postcrisis learning.

Based on the combined discussion among the scientific working groups of the IMIA, we have created a list of actions that should take place during the current COVID-19 crisis ( Table 2 ), as a mean to reinforce the response and health care systems with the best evidence-based knowledge in health informatics. Underpinning these recommendations is the expertise of the IMIA community in the multidisciplinary perspectives, understanding of human factors, and thoughtful and critical, ethical considerations that should be of central importance in the development and implementation of digital health tools that have been rapidly deployed in response to the pandemic. With these foundations in mind, this involves both things to avoid and things to promote. We should consider that the right approach will enable the creation of the global Learning Health System built on real-world evidence and robust scientific foundations. We consider that training and capacity building is of crucial importance to ensure recovery and preparedness for the next crisis. This needs to be linked to a clear strategy for evaluation of ongoing experiences, and the fair and meaningful practices for data sharing and privacy. All these aspects need to be considered at the local, national, and international levels through methodological planification and guidelines which include addressing ethics and human factors.

Abbreviations: COVID-19, novel coronavirus disease 2019; IMIA, International Medical Informatics Association; IT, information technology; SIG, special interest group; WG, working group.

Conclusion: A Call for Interdisciplinary Collaboration in Digital Health during the COVID-19 Pandemic

Collaboration is our recommendation as the best way forward toward a more robust and equitable global public health system after the COVID-19 pandemic. The involvement and collaboration of multidisciplinary stakeholders across sectors (i.e., policymakers, governments, research institutes, consumers, and others) can foster and enable the desired outcomes and health system. Therefore, we do call on other scientific societies and any stakeholders involved in the crisis response, including consumers of health care services, to proactively seek collaboration with the IMIA working groups, as well as with national and regional associations that do have also related working groups. In this paper, we provide a substantial corpus of knowledge and evidence; however, we should consider it to be limited due to the exponential growth on research and implementation of digital health. To get actionable insights from the implementation of digital health during the COVID-19 is going to be a research tasks for many years to come.

Together, we can move digital health from hope and hype to reality and in the service of consumers and public health. To do that, we would like to encourage the wider scientific communities to raise awareness about evidence-based digital approaches for COVID-19 by disseminating them in social media, publishing complementary viewpoints, and consensus statements, so we can be better prepared for the next crisis both at the microlevel (e.g., patient interaction), mesolevel (health care organization and community), and macrolavel (e.g., policy). 26

Conflict of Interest L.F.L. is Chief Scientific Officer and shareholder at Adhera Health Inc (USA). All the other authors report no conflict of interest.

Note: For complete IMIA affiliations, please refer to Appendix A .

Luis Fernandez-Luque 1

Andre W. Kushniruk 2

Andrew Georgiou 3

Arindam Basu 4

Petersen Carolyn 5

Charlene Ronquillo 6

Chris Paton 7

Christian Nøhr 8

Craig E. Kuziemsky 4

Dari Alhuwail 7

Diane Skiba 9

Elaine Huesing 10

Elia Gabarron 5

Elizabeth M. Borycki 2,11

Farah Magrabi 3

Kerstin Denecke 5

Linda W. P. Peute 12

Max Topaz 6

Najeeb Al-Shorbaji 4,13,14

Paulette Lacroix 13

Romaric Marcilly 12

Ronald Cornet 15

Sriram Iyengar 4

Shashi B. Gogia 4

Shinji Kobayashi 7

Thomas M. Deserno 17

Tobias Mettler 11

Vivian Vimarlund 2

Xinxin Zhu 8

1 IMIA Vice-President for Working Groups and Special Interest Groups

2 IMIA Health Informatics for Patient Safety Working Group

3 IMIA Technology Assessment & Quality Development Working Group

4 IMIA Telehealth Working Group

5 IMIA Participatory Health and Social Media Working Group

6 IMIA Students and Emerging Professionals Working Group

7 IMIA OpenSource Working Group

8 IMIA Organizational and social issues Working Group

9 IMIA Nursing Informatics Special Interest Group

10 IMIA CEO

11 IMIA Smart Homes and Ambient Assisted Living Working Group

12 IMIA Human Factors Engineering for Healthcare Informatics Working Group

13 IMIA Ethics, Privacy and Security of Health Informatics Working Group

14 IMIA Vice-President Medinfo

15 IMIA Language and Meaning in BioMedicine Working Group

16 IMIA Open Source Working Group

17 IMIA Accident & Emergency Informatics Working Group

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Whole Person Health: What It Is and Why It's Important

.header_greentext{color:greenimportant;font-size:24pximportant;font-weight:500important;}.header_bluetext{color:blueimportant;font-size:18pximportant;font-weight:500important;}.header_redtext{color:redimportant;font-size:28pximportant;font-weight:500important;}.header_darkred{color:#803d2fimportant;font-size:28pximportant;font-weight:500important;}.header_purpletext{color:purpleimportant;font-size:31pximportant;font-weight:500important;}.header_yellowtext{color:yellowimportant;font-size:20pximportant;font-weight:500important;}.header_blacktext{color:blackimportant;font-size:22pximportant;font-weight:500important;}.header_whitetext{color:whiteimportant;font-size:22pximportant;font-weight:500important;}.header_darkred{color:#803d2fimportant;}.green_header{color:greenimportant;font-size:24pximportant;font-weight:500important;}.blue_header{color:blueimportant;font-size:18pximportant;font-weight:500important;}.red_header{color:redimportant;font-size:28pximportant;font-weight:500important;}.purple_header{color:purpleimportant;font-size:31pximportant;font-weight:500important;}.yellow_header{color:yellowimportant;font-size:20pximportant;font-weight:500important;}.black_header{color:blackimportant;font-size:22pximportant;font-weight:500important;}.white_header{color:whiteimportant;font-size:22pximportant;font-weight:500important;} what is whole person health.

Whole person health involves looking at the whole person—not just separate organs or body systems—and considering multiple factors that promote either health or disease. It means helping and empowering individuals, families, communities, and populations to improve their health in multiple interconnected biological, behavioral, social, and environmental areas. Instead of just treating a specific disease, whole person health focuses on restoring health, promoting resilience, and preventing diseases across a lifespan.

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Health and disease are not separate, disconnected states but instead occur on a path that can move in two different directions, either toward health or toward disease.

On this path, many factors, including one’s biological makeup; some unhealthy behaviors, such as poor diet, sedentary lifestyle, chronic stress, and poor sleep; as well as social aspects of life—the conditions in which people are born, grow, live, work, and age—can lead to chronic diseases of more than one organ system. On the other hand, self-care, lifestyle, and behavioral interventions may help with the return to health.

Chronic diseases, such as diabetes, cardiovascular disease, obesity, and degenerative joint disease, can also occur with chronic pain, depression, and opioid misuse—all conditions exacerbated by chronic stress. Some chronic diseases increase the immediate and long-term risks with COVID-19 infection. Understanding the condition in which a person has lived, addressing behaviors at an early stage, and managing stress can not only prevent multiple diseases but also help restore health and stop the progression to disease across a person’s lifespan.

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Some health care systems and programs are now focusing more on whole person health.

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The VA’s Whole Health System of Care and Whole Health approach aims to improve the health and well-being of veterans and to address lifestyle and environmental root causes of chronic disease. The approach shifts from a disease-centered focus to a more personalized approach that engages and empowers veterans early in and throughout their lives to prioritize healthy lifestyle changes in areas like nutrition, activity, sleep, relationships, and surroundings. Conventional testing and treatment are combined with complementary and integrative health approaches that may include acupuncture, biofeedback, massage therapy, yoga, and meditation.

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The Total Force Fitness program arose within the U.S. Department of Defense Military Health System in response to the need for a more holistic approach—a focus on the whole person instead of separate parts or only symptoms—to the demands of multiple deployments and the strains on the U.S. Armed Forces and their family members. The focus extends the idea of total fitness to include the health, well-being, and resilience of the whole person, family, community, and U.S. military.

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Established in 2020, the Whole Health Institute’s Whole Health model helps people identify what matters most to them and build a plan for their journey to whole health. The model provides tools to help people take good care of their body, mind, and spirit, and involves working with a health care team as well as tapping into the support of family, friends, and communities.

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The North Carolina Department of Health and Human Services has incorporated a whole person health approach into its health care system by focusing on integrating physical, behavioral, and social health. The state has taken steps to encourage collaborative behavioral health care and help resolve widespread inequities in social conditions, such as housing and nutritious food access.

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The Ornish Program for Reversing Heart Disease is an intensive cardiac rehabilitation program that has been shown to reverse the progression of coronary heart disease through lifestyle changes, without drugs or surgery. The program is covered by Medicare and some health insurance companies. The program’s lifestyle changes include exercise, smoking cessation, stress management, social support, and a whole-foods, plant-based diet low in total fat. The program is offered by a team of health care professionals who provide the support that individuals need to make and maintain lasting changes in lifestyle.

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} What does research show about whole person health?

A growing body of research suggests the benefits of healthy behaviors, environments, and policies to maintain health and prevent, treat, and reverse chronic diseases. This research includes several large, long-term epidemiological studies—such as the Framingham Heart Study, Nurses’ Health Study, and Adventist Health Studies—that have evaluated the connections between lifestyle, diet, genetics, health, and disease.

There is a lack, however, of randomized controlled trials and other types of research on multicomponent interventions and whole person health. Challenges come with conducting this type of research and with finding appropriate ways to assess the evidence. But opportunities are emerging to explore new paths toward reliable and rigorous research on whole person health.

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Yes, NCCIH plans to fund research on whole person health . (Details can be found in the NCCIH Strategic Plan FY 2021–2025: Mapping a Pathway to Research on Whole Person Health . )

By deepening the scientific understanding of the connections that exist across the different areas of human health, researchers can better understand how conditions interrelate, identify multicomponent interventions that address these problems, and determine the best ways to support individuals through the full continuum of their health experience, including the return to health.

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Nccih clearinghouse.

The NCCIH Clearinghouse provides information on NCCIH and complementary and integrative health approaches, including publications and searches of Federal databases of scientific and medical literature. The Clearinghouse does not provide medical advice, treatment recommendations, or referrals to practitioners.

Toll-free in the U.S.: 1-888-644-6226

Telecommunications relay service (TRS): 7-1-1

Website: https://www.nccih.nih.gov

Email: [email protected] (link sends email)

Know the Science

NCCIH and the National Institutes of Health (NIH) provide tools to help you understand the basics and terminology of scientific research so you can make well-informed decisions about your health. Know the Science features a variety of materials, including interactive modules, quizzes, and videos, as well as links to informative content from Federal resources designed to help consumers make sense of health information.

Explaining How Research Works (NIH)

Know the Science: How To Make Sense of a Scientific Journal Article

Understanding Clinical Studies (NIH)

A service of the National Library of Medicine, PubMed® contains publication information and (in most cases) brief summaries of articles from scientific and medical journals. For guidance from NCCIH on using PubMed, see How To Find Information About Complementary Health Approaches on PubMed .

Website: https://pubmed.ncbi.nlm.nih.gov/

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• Aggarwal M, Ornish D, Josephson R, et al. Closing gaps in lifestyle adherence for secondary prevention of coronary heart disease. American Journal of Cardiology. 2021;145:1-11.
• Centers for Medicare & Medicaid Services. Decision Memo for Intensive Cardiac Rehabilitation (ICR) Program—Dr. Ornish’s Program for Reversing Heart Disease (CAG-00419N). Accessed at https://www.cms.gov/ on April 26, 2021.
• Deuster PA, O’Connor FG. Human performance optimization: culture change and paradigm shift. Journal of Strength and Conditioning Research. 2015;29(suppl 11):S52-S56.
• Gaudet T, Kligler B. Whole health in the whole system of the Veterans Administration: how will we know we have reached this future state? Journal of Alternative and Complementary Medicine. 2019;25(S1):S7-S11.
• Malecki HL, Gollie JM, Scholten J. Physical activity, exercise, whole health, and integrative health coaching. Physical Medicine and Rehabilitation Clinics of North America. 2020;31(4):649-663.
• National Center for Complementary and Integrative Health. NCCIH Strategic Plan FY 2021–2025: Mapping a Pathway to Research on Whole Person Health. National Center for Complementary and Integrative Health website. Accessed at https://www.nccih.nih.gov/about/nccih-strategic-plan-2021-2025 on May 14, 2021.
• North Carolina Department of Health and Human Services website. Healthy Opportunities and Medicaid Transformation. Accessed at https://www.ncdhhs.gov/about/department-initiatives/healthy-opportunities/healthy-opportunities-pilots/healthy on April 26, 2021.
• Military Health System website. Total Force Fitness. Accessed at https://health.mil/Military-Health-Topics/Total-Force-Fitness on April 26, 2021.
• Tilson EC, Muse A, Colville K, et al. Investing in whole person health: working toward an integration of physical, behavioral, and social health. North Carolina Medical Journal. 2020;81(3):177-180.
• U.S. Department of Veterans Affairs website. Whole Health. Accessed at https://www.va.gov/wholehealth/ on April 26, 2021.
• U.S. Department of Veterans Affairs website. Whole Health Library. Accessed at  https://www.va.gov/wholehealthlibrary/ on April 26, 2021.
• Vodovotz Y, Barnard N, Hu FB, et al. Prioritized research for the prevention, treatment, and reversal of chronic disease: recommendations from the Lifestyle Medicine Research Summit. Frontiers in Medicine (Lausanne). 2020;7:585744.
• Whitehead AM, Kligler B. Innovations in care: complementary and integrative health in the Veterans Health Administration Whole Health System. Medical Care. 2020;58(9S)(suppl 2):S78-S79.

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• Alborzkouh P, Nabati M, Zainali M, et al. A review of the effectiveness of stress management skills training on academic vitality and psychological well-being of college students. Journal of Medicine and Life. 2015;8(4):39-44.
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Acknowledgments

NCCIH thanks Mary Beth Kester, M.S., and Helene M. Langevin, M.D., NCCIH, for their review of this publication.

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NCCIH has provided this material for your information. It is not intended to substitute for the medical expertise and advice of your health care provider(s). We encourage you to discuss any decisions about treatment or care with your health care provider. The mention of any product, service, or therapy is not an endorsement by NCCIH.

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