Cited by 156Innovations in Medical EducationHealth and Medical EducationDigital literacy in education

The DOI matches its Crossref record; 74 of the 74 references it cites were found in a registry or an index; 16 of the 51 sources its claims cite were checked for support.

The Digital Health Competencies in Medical Education Framework

Open access
Car, Josip, Ong, Qi Chwen, Erlikh Fox, Tatiana, Leightley, Daniel, Kemp, Sandra J., Švab, Igor, Tsoi, Kelvin K. F., Sam, Amir H., Kent, Fiona M., Hertelendy, Attila J., Longhurst, Christopher A., Powell, John, Hamdy, Hossam, Nguyen, Huy V.Q., Aoun Bahous, Sola, Wang, Mai, Baumgartner, Martin, Mahendradhata, Yodi, Popovic, Natasa, Khong, Andy W. H., Prober, Charles G., Atun, Rifat, Digital Health Systems Collaborative, Bekele Zerihun, Abebe, Poncette, Akira-Sebastian, Molina, Al Joseph R., Ferreira, Albano V.L., Fajkić, Almir, Kaushal, Amit, Farmer, Andrew J., Lane, Andrew S., Kononowicz, Andrzej A., Bhongir, Aparna V., Alayande, Barnabas T., Bene, Benard Ayaka, Dameff, Christian J., Hallensleben, Cynthia, Back, David A., Hawezy, Dawan J., Tulantched, Dieudonné Steve M., Kldiashvili, Ekaterina, Achampong, Emmanuel K., Ramachandran, Ganesh, Hauser, Goran, Grove, Jakob, Cheung, Jason P. Y., Imaralu, John O., Sotunsa, John O., Bulnes Vides, Juan P., Lawrence, Katharine S., Agha-Mir-Salim, Louis, Saba, Luca, Zhang, Luxia, Elfiky, Mahmoud M. A., Hesseling, Markus W., Guppy, Michelle P., Phatak, Mrunal S., Al Saadoon, Muna A. A., Lai, Nai Ming, Chavannes, Niels H., Kimberger, Oliver, Povoa, Pedro, Goh, Poh-Sun, Grainger, Rebecca, Nannan Panday, Rishi S., Forsyth, Rowena, Vento, Sandro, Lee, Sang Yeoup, Yadav, Sanjay Kumar, Syed-Abdul, Shabbir, Appenzeller, Simone, Denaxas, Spiros, Garba, Stephen Ekundayo, Flügge, Tabea, Bokun, Tomislav, Dissanayake, Vajira H. W., Ho, Vincent, Obadiel, Yasser A.·2025·JAMA Network Open

Abstract

Importance: Rapid digitalization of health care and a dearth of digital health education for medical students and junior physicians worldwide means there is an imperative for more training in this dynamic and evolving field. Objective: To develop an evidence-informed, consensus-guided, adaptable digital health competencies framework for the design and development of digital health curricula in medical institutions globally. Evidence Review: A core group was assembled to oversee the development of the Digital Health Competencies in Medical Education (DECODE) framework. First, an initial list was created based on findings from a scoping review and expert consultations. A multidisciplinary and geographically diverse panel of 211 experts from 79 countries and territories was convened for a 2-round, modified Delphi survey conducted between December 2022 and July 2023, with an a priori consensus level of 70%. The framework structure, wordings, and learning outcomes with marginal percentage of agreement were discussed and determined in a consensus meeting organized on September 8, 2023, and subsequent postmeeting qualitative feedback. In total, 211 experts participated in round 1, 149 participated in round 2, 12 participated in the consensus meeting, and 58 participated in postmeeting feedback. Findings: The DECODE framework uses 3 main terminologies: domain, competency, and learning outcome. Competencies were grouped into 4 domains: professionalism in digital health, patient and population digital health, health information systems, and health data science. Each competency is accompanied by a set of learning outcomes that are either mandatory or discretionary. The final framework comprises 4 domains, 19 competencies, and 33 mandatory and 145 discretionary learning outcomes, with descriptions for each domain and competency. Six highlighted areas of considerations for medical educators are the variations in nomenclature, the distinctiveness of digital health, the concept of digital health literacy, curriculum space and implementation, the inclusion of discretionary learning outcomes, and socioeconomic inequities in digital health education. Conclusions and Relevance: This evidence-informed and consensus-guided framework will play an important role in enabling medical institutions to better prepare future physicians for the ongoing digital transformation in health care. Medical schools are encouraged to adopt and adapt this framework to align with their needs, resources, and circumstances.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

The DECODE framework was developed through a scoping review, expert consultations, a 2-round modified Delphi survey, a consensus meeting, and postmeeting feedback to define digital health competencies for medical education globally. The final framework comprises 4 domains, 19 competencies, and 178 learning outcomes (33 mandatory and 145 discretionary).

Key findings

  • The final DECODE framework contains 4 domains—professionalism in digital health, patient and population digital health, health information systems, and health data science—with 19 competencies, 33 mandatory learning outcomes, and 145 discretionary learning outcomes.
  • The Delphi survey involved 211 experts from 79 countries and territories in round 1 and 149 in round 2 (70.6% retention); the a priori consensus threshold was 70%, and all newly added round 2 items reached consensus.
  • Of the 178 learning outcomes, 119 (66.8%) related to knowledge, 35 (19.7%) to skill, and 24 (13.5%) to behavior; 13 of 19 competencies have both mandatory and discretionary learning outcomes, while 6 have only discretionary outcomes.
  • Postmeeting feedback from 58 respondents showed 29 (50.0%) expressed willingness to adopt the DECODE framework at their medical schools, 6 (10.3%) would consider adoption with conditions, 7 (12.1%) mentioned a need for further discussion, 6 (10.3%) indicated barriers or were noncommittal, and 10 (17.2%) did not directly respond.

Methods

  • Five stages: mapping based on a scoping review, iterative expert consultations and piloting, a 2-round modified Delphi survey (December 2022–July 2023), a consensus meeting on September 8, 2023, and postmeeting qualitative feedback.
  • Participants: 211 experts in round 1, 149 in round 2, 12 in the consensus meeting, and 58 in postmeeting feedback; round 1 included 79 countries and territories, round 2 included 68 countries, with representation from all 6 WHO regions and all 4 World Bank country income groups.
  • Consensus was defined a priori as at least 70% combined agreement (agree and strongly agree, excluding blank votes and abstentions); round 2 also asked experts to classify learning outcomes as mandatory, elective, or supplementary, with qualitative free-text analysis and ACCORD reporting.

Limitations

  • Stated: the preliminary scoping review included only English-language literature; the study was conducted in English, limiting participation to English speakers; the Likert scale lacked a neutral option, possibly introducing response bias; and the Delphi approach carries potential selection bias from purposive sampling and may not fully capture all country-specific contexts.
  • Stated: experts were confined to voting on a predetermined list, so exhaustiveness of the initial item list could not be guaranteed, although free-text responses were allowed to propose additional items.
  • Evident: the framework has not yet been implemented or validated in medical schools; the authors state that future work may include an implementation guide, assessment methods, and evaluation of the framework’s impact on student competencies and patient care.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1001/jamanetworkopen.2024.53131

Cites

74 of 74 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

26 Own interpretation · 25 Own method · 15 Own result · 14 No basis given · 9 No full text of the sources · 6 Couldn’t confirm · 6 Sources not found · 3 Partly supported · 3 Not checked yet · 1 Supported · 1 Not supported · accuracy not yet measured

Show all 23 claims

  • It may state two separate claims as one. 44
  • Probably not a substantial claim. 62
  • Not yet classified. 1
86 of 86
  • The paper relies on this without support.

    The expert panel's diversity and consensus are sufficient to ensure the framework's applicability and relevance to a global audience.

    “The consensus process garnered an international panel of experts with diverse professional backgrounds, leadership roles in medical education, and geographical locations to ensure the framework's applicability and relevance to a global audience.”
  • The paper relies on this without support.

    The initial list of domains, competencies, and learning outcomes derived from the scoping review and expert consultations is comprehensive enough to cover all relevant digital health competencies.

    “The initial list of domains, competencies, and learning outcomes was based on findings from a 2020 scoping review of relevant international medical literature, 29 which analyzed existing DHC frameworks used by health care professionals”
  • The paper relies on this without support.

    The use of terms such as competencies, outcomes, and objectives is widespread, but the way these terms are defined, used, and understood varies around the world.

    “The use of terms such as competencies, outcomes, and objectives is widespread, but the way these terms are defined, used, and understood varies around the world.”
  • The paper relies on this without support.

    Some fundamental principles, such as data privacy and security, have already existed in the predigital era and are not exclusive to digital health.

    “This emphasis arises from the recognition that some fundamental principles, such as data privacy and security, have already existed in the predigital era and are not exclusive to digital health.”
4 of 4

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

1 of 1

Citations

Cites

Research works printed but not linked: 2

  1. Australian Medical Council Limited. Review of accreditation standards for primary medical programs (medical school standards review). July 2023. Accessed November 20, 2024. https://www.amc.org.au/accredited-organisations/review-of-accreditation-standards-for-primary-medical-programs/
  2. Honeyman? M, Maguire? D, Evans? H, Davies? A. Digital technology and health inequalities: a scoping review (Wales). Health Inequalities. Published 2020. Accessed November 20, 2024. https://health-inequalities.eu/jwddb/digital-technology-and-health-inequalities-a-scoping-review-wales/

Cited by

Sorted by citations

See all 156

Metadata

Authors
Car, J., Ong, Q. C., Erlikh Fox, T., Leightley, D., Kemp, S. J., Švab, I., Tsoi, K. K. F., Sam, A. H., Kent, F. M., Hertelendy, A. J., Longhurst, C. A., Powell, J., Hamdy, H., Nguyen, H. V. Q., Aoun Bahous, S., Wang, M., Baumgartner, M., Mahendradhata, Y., Popovic, N., Khong, A. W. H., Prober, C. G., Atun, R., Collaborative, D. H. S., Bekele Zerihun, A., Poncette, A.-S., Molina, A. J. R., Ferreira, A. V. L., Fajkić, A., Kaushal, A., Farmer, A. J., Lane, A. S., Kononowicz, A. A., Bhongir, A. V., Alayande, B. T., Bene, B. A., Dameff, C. J., Hallensleben, C., Back, D. A., Hawezy, D. J., Tulantched, D. S. M., Kldiashvili, E., Achampong, E. K., Ramachandran, G., Hauser, G., Grove, J., Cheung, J. P. Y., Imaralu, J. O., Sotunsa, J. O., Bulnes Vides, J. P., Lawrence, K. S., Agha-Mir-Salim, L., Saba, L., Zhang, L., Elfiky, M. M. A., Hesseling, M. W., Guppy, M. P., Phatak, M. S., Al Saadoon, M. A. A., Lai, N. M., Chavannes, N. H., Kimberger, O., Povoa, P., Goh, P.-S., Grainger, R., Nannan Panday, R. S., Forsyth, R., Vento, S., Lee, S. Y., Yadav, S. K., Syed-Abdul, S., Appenzeller, S., Denaxas, S., Garba, S. E., Flügge, T., Bokun, T., Dissanayake, V. H. W., Ho, V., & Obadiel, Y. A.
Affiliations
  • King's College London (GB)
  • Nanyang Technological University (SG)
  • Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
  • University of Wollongong (AU)
  • University of Ljubljana (SI)
  • Chinese University of Hong Kong (HK)
  • Imperial College London (GB)
  • Monash University (AU)
  • Beth Israel Deaconess Medical Center (US)
  • Department of Biomedical Informatics, University of California San Diego Health, La Jolla
  • University of Oxford (GB)
  • Gulf Medical University (AE)
  • Department of Obstetrics and Gynecology, Hue University of Medicine and Pharmacy, Hue University, Hue, Vietnam
  • Lebanese American University School of Medicine, Byblos, Lebanon
  • Peking University (CN)
  • Medical University of Vienna (AT)
  • Universitas Gadjah Mada (ID)
  • Faculty of Medicine, University of Montenegro, Podgorica, Montenegro
  • Stanford University (US)
  • Harvard University (US)
  • University of Global Health Equity (RW)
  • University of Global Health Equity, Kigali, Rwanda
  • Philippine General Hospital (PH)
  • University of Sarajevo (BA)
  • The University of Sydney (AU)
  • Sydney Medical School, The University of Sydney, Sydney, New South Wales, Australia
  • Jagiellonian University (PL)
  • University of California San Diego (US)
  • Leiden University Medical Center (NL)
  • Koya University (IQ)
  • Université Protestante au Congo (CD)
  • Department of Family Medicine, Université Protestante au Congo, Kinshasa, Democratic Republic of the Congo
  • University of Cape Coast (GH)
  • University of Rijeka (HR)
  • Aarhus University (DK)
  • Babcock University (NG)
  • Central American Technological University, Tegucigalpa, Honduras
  • New York University (US)
  • University of Cagliari (IT)
  • Cairo University (EG)
  • Children's Health Ireland at Crumlin (IE)
  • All India Institute of Medical Sciences, Nagpur (IN)
  • Department of Child Health, College of Medicine and Health Sciences, Sultan Qaboos University, Seeb, Oman
  • Ludwig Boltzmann Institute Digital Health and Patient Safety (AT)
  • Nova Medical School, Nova University of Lisbon, Lisbon, Portugal
  • National University of Singapore (SG)
  • Amsterdam University Medical Centers (NL)
  • Amsterdam University Medical Center, Amsterdam, the Netherlands
  • University of Puthisastra (KH)
  • Netaji Subhash Chandra Bose Medical College (IN)
  • Universidade Estadual de Campinas (UNICAMP) (BR)
  • University College London (GB)
  • Charité - Universitätsmedizin Berlin (DE)
  • University Hospital Dubrava (HR)
  • Western Sydney University (AU)
  • Sana'a University (YE)
Corresponding author
  • Car, Josip
  • Ong, Qi Chwen
Venue
JAMA Network Open
Published
2025-01-31
Volume
8
Issue
1
Pages
e2453131
ISSN
2574-3805 (ISSN-L)
OpenAlex
W4407010033
Cited by
155
References
69
Citations by year
2026: 112 · 2025: 42
FWCI
127.89
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JAMA Network Open (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DR-NTU (Nanyang Technological University) (publishedVersion, cc-by)
  • Repository of the University of Ljubljana (University of Ljubljana) (submittedVersion, cc-by)
  • dCOBISS.SI Digital Repository (submittedVersion, cc-by)
  • The HKU Scholars Hub (University of Hong Kong) (submittedVersion, cc-by-nc-nd)
  • UNICA IRIS Institutional Research Information System (University of Cagliari) (submittedVersion, other-oa)
  • Pure Amsterdam UMC (submittedVersion, other-oa)
  • New University of Lisbon's Repository (New University of Lisbon) (submittedVersion)
Funders
  • Harvard University
  • Florida International University
  • Harvard T.H. Chan School of Public Health
  • Gulf Medical University
  • Lebanese American University
  • University of Oxford
  • Nanyang Technological University
  • Universität Wien
  • Medizinische Universität Wien
  • Peking University
  • Universitas Gadjah Mada
  • Đại học Huế
  • University of California, San Diego
  • Department of Global Health and Population, Harvard T.H. Chan School of Public Health
Topics
  • Social Sciences › Education › Innovations in Medical Education
  • Health Sciences › Health Professions › General Health Professions › Health and Medical Education
  • Social Sciences › Education › Digital literacy in education
Keywords
digital health competencies, medical education, competency framework, learning outcomes, digital health literacy, health information systems, health data science, digitalization of healthcare
MeSH terms
Digital Health, Clinical Competence, Curriculum, Delphi Technique, Education, Medical, Humans, Consensus

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

A language model drew the claims from the paper on 2026-10-15, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).

A second model labelled each claim on 2026-10-15 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).

Cited sources are checked with a language model’s help (gpt-6-sol).

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review