Digital health: meeting the ethical and policy challenges

Vayena, Effy; Haeusermann, Tobias; Haasteren, Afua van; Blasimme, Alessandro

doi:10.4414/smw.2018.14571

Cited by 104 publications

(82 citation statements)

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“…In addition, as digital health entails new forms of communication (eg, virtual consultations, email, chatbots), it is imperative for health care workers to be able to relate information accurately yet efficiently, timely, and delicately to patients, colleagues, and other collaborating stakeholders [ 68 ]. Furthermore, the rise in the adoption and utilization of digital technologies has spurred new issues relating to the use of IT [ 69 ], which corresponds to the next two common domains of competencies (ie, ethics, legal, or regulations, and privacy and security). These domains stress the importance of health care workers’ adherence to legal and regulatory requirements and to keep up to date with privacy and security policies pertaining to the appropriate use of digital technologies.…”

Section: Discussionmentioning

confidence: 99%

A Digitally Competent Health Workforce: Scoping Review of Educational Frameworks

Nazeha¹,

Pavagadhi²,

Kyaw³

et al. 2020

J Med Internet Res

121

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Background Digital health technologies can be key to improving health outcomes, provided health care workers are adequately trained to use these technologies. There have been efforts to identify digital competencies for different health care worker groups; however, an overview of these efforts has yet to be consolidated and analyzed. Objective The review aims to identify and study existing digital health competency frameworks for health care workers and provide recommendations for future digital health training initiatives and framework development. Methods A literature search was performed to collate digital health competency frameworks published from 2000. A total of 6 databases including gray literature sources such as OpenGrey, ResearchGate, Google Scholar, Google, and websites of relevant associations were searched in November 2019. Screening and data extraction were performed in parallel by the reviewers. The included evidence is narratively described in terms of characteristics, evolution, and structural composition of frameworks. A thematic analysis was also performed to identify common themes across the included frameworks. Results In total, 30 frameworks were included in this review, a majority of which aimed at nurses, originated from high-income countries, were published since 2016, and were developed via literature reviews, followed by expert consultations. The thematic analysis uncovered 28 digital health competency domains across the included frameworks. The most prevalent domains pertained to basic information technology literacy, health information management, digital communication, ethical, legal, or regulatory requirements, and data privacy and security. The Health Information Technology Competencies framework was found to be the most comprehensive framework, as it presented 21 out of the 28 identified domains, had the highest number of competencies, and targeted a wide variety of health care workers. Conclusions Digital health training initiatives should focus on competencies relevant to a particular health care worker group, role, level of seniority, and setting. The findings from this review can inform and guide digital health training initiatives. The most prevalent competency domains identified represent essential interprofessional competencies to be incorporated into health care workers’ training. Digital health frameworks should be regularly updated with novel digital health technologies, be applicable to low- and middle-income countries, and include overlooked health care worker groups such as allied health professionals.

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Section: Discussionmentioning

confidence: 99%

A Digitally Competent Health Workforce: Scoping Review of Educational Frameworks

Nazeha¹,

Pavagadhi²,

Kyaw³

et al. 2020

J Med Internet Res

121

View full text Add to dashboard Cite

show abstract

“…For now, suffice to say that a traditional thematic review methodology (following Abdul, Vermeulen, Wang, Lim, & Kankanhalli, 2018) was used to find literature from across disciplinary boundaries that highlighted ethical issues unique to the use of AI algorithms in healthcare. This means that the review did not focus on issues such as lack of evidence, privacy and security (Vayena, Tobias, Afua, & Allesandro, 2018), or definitions and secondary uses of healthcare data, as these are ethical issues for digital health at large and not unique to AI. More detailed discussion of these issues is highlighted in To ensure that the focus stayed on the unique ethical issues, the map, developed by (Mittelstadt et al, 2016), of the epistemic, normative, and overarching ethical concerns related to algorithms was used as a base.…”

Section: Methodology In Briefmentioning

confidence: 99%

The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review

Morley¹

2019

Preprint

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Healthcare systems across the globe are struggling with increasing costs and worsening outcomes. This presents those responsible for overseeing healthcare with a challenge. Increasingly, policymakers, politicians, clinical entrepreneurs and computer and data scientists argue that a key part of the solution will be ‘Artificial Intelligence’ (AI) – particularly Machine Learning (ML). This argument stems not from the belief that all healthcare needs will soon be taken care of by “robot doctors.” Instead, it is an argument that rests on the classic counterfactual definition of AI as an umbrella term for a range of techniques that can be used to make machines complete tasks in a way that would be considered intelligent were they to be completed by a human. Automation of this nature could offer great opportunities for the improvement of healthcare services and ultimately patients’ health by significantly improving human clinical capabilities in diagnosis, drug discovery, epidemiology, personalised medicine, and operational efficiency. However, if these AI solutions are to be embedded in clinical practice, then at least three issues need to be considered: the technical possibilities and limitations; the ethical, regulatory and legal framework; and the governance framework. In this article, we report on the results of a systematic analysis designed to provide a clear overview of the second of these elements: the ethical, regulatory and legal framework. We find that ethical issues arise at six levels of abstraction (individual, interpersonal, group, institutional, sectoral, and societal) and can be categorised as epistemic, normative, or overarching. We conclude by stressing how important it is that the ethical challenges raised by implementing AI in healthcare settings are tackled proactively rather than reactively and map the key considerations for policymakers to each of the ethical concerns highlighted.

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Section: Methodology In Briefmentioning

confidence: 99%

The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review

Morley

Machado²,

Burr

et al. 2019

SSRN Journal

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Healthcare systems across the globe are struggling with increasing costs and worsening outcomes. This presents those responsible for overseeing healthcare with a challenge. Increasingly, policymakers, politicians, clinical entrepreneurs and computer and data scientists argue that a key part of the solution will be 'Artificial Intelligence' (AI)-particularly Machine Learning (ML). This argument stems not from the belief that all healthcare needs will soon be taken care of by "robot doctors." Instead, it is an argument that rests on the classic counterfactual definition of AI as an umbrella term for a range of techniques that can be used to make machines complete tasks in a way that would be considered intelligent were they to be completed by a human. Automation of this nature could offer great opportunities for the improvement of healthcare services and ultimately patients' health by significantly improving human clinical capabilities in diagnosis, drug discovery, epidemiology, personalised medicine, and operational efficiency. However, if these AI solutions are to be embedded in clinical practice, then at least three issues need to be considered: the technical possibilities and limitations; the ethical, regulatory and legal framework; and the governance framework. In this article, we report on the results of a systematic analysis designed to provide a clear overview of the second of these elements: the ethical, regulatory and legal framework. We find that ethical issues arise at six levels of abstraction (individual, interpersonal, group, institutional, sectoral, and societal) and can be categorised as epistemic, normative, or overarching. We conclude by stressing how important it is that the ethical challenges raised by implementing AI in healthcare settings are tackled proactively rather than reactively and map the key considerations for policymakers to each of the ethical concerns highlighted.

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Digital health: meeting the ethical and policy challenges

Cited by 104 publications

References 0 publications

A Digitally Competent Health Workforce: Scoping Review of Educational Frameworks

A Digitally Competent Health Workforce: Scoping Review of Educational Frameworks

The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review

The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review

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