Aller au contenu principal
Accès ouvert déclaré 2025 article

The Digital Health Competencies in Medical Education Framework

148Citations signalées, ce qui n’est pas une note de qualité
71Institutions déclarées
42Pays d’affiliation déclarés

Rattachement africain : gb, sg, nl, au, si, hk, us, ae, vn, lb, cn, at, id, me, Rwanda, de, ph, Angola, ba, pl, in, iq, République démocratique du Congo, ge, Ghana, my, hr, dk, Nigéria, hn, it, Égypte, ie, om, pt, nz, kh, kr, tw, br, lk, ye. Niveau de preuve : code pays fourni par la source.

Le résumé fourni par la source

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.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.

Le contrôle bibliographique ouvert

DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.

Titre Crossref
The Digital Health Competencies in Medical Education Framework
Date Crossref
31/01/2025
Éditeur
American Medical Association (AMA)
Type
journal-article

Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.

Les institutions déclarées

King's College LondonImperial College LondonNanyang Technological UniversityOLVGUniversity of WollongongUniversity of LjubljanaChinese University of Hong KongMonash UniversityBeth Israel Deaconess Medical CenterFlorida International UniversityUniversity of California San DiegoUniversity of OxfordGulf Medical UniversityHue UniversityLebanese American UniversityPeking UniversityAustrian Research Institute for Artificial IntelligenceMedical University of ViennaUniversitas Gadjah MadaUniversity of MontenegroStanford Health CareStanford UniversityHarvard UniversityHarvard Global Health InstituteUniversity of Global Health EquityCharité - Universitätsmedizin BerlinPhilippine General HospitalUniversity of the Philippines ManilaUniversidade Katyavala BwilaUniversity of SarajevoThe University of SydneyJagiellonian UniversityAll India Institute of Medical SciencesLeiden University Medical CenterKoya UniversityUniversité Protestante au CongoPetre Shotadze Tbilisi Medical AcademyUniversity of Cape CoastTaylor's UniversityUniversity Hospital Centre ZagrebUniversity of RijekaAarhus UniversityUniversity of Hong KongBabcock UniversityCentral American Technological UniversityNew York UniversityUniversity of CagliariCairo UniversityChildren's Health Ireland at CrumlinUniversity of New EnglandAll India Institute of Medical Sciences, NagpurSultan Qaboos UniversityAustrian Institute for Health Technology Assessment GmbHUniversity of LisbonUniversidade Nova de LisboaNational University of SingaporeNational University HospitalUniversity of OtagoAmsterdam University Medical CentersUniversity of PuthisastraPusan National University Yangsan HospitalPusan National UniversityNetaji Subhash Chandra Bose Medical CollegeTaipei Medical UniversityUniversidade Estadual de Campinas (UNICAMP)University College LondonUniversity of AbujaUniversity Hospital DubravaUniversity of ColomboWestern Sydney UniversitySana'a University

Une affiliation ne permet pas de déduire la nationalité d’un auteur.

Les sujets associés

Mobile Health and mHealth ApplicationsTelemedicine and Telehealth ImplementationArtificial Intelligence in Healthcare and Education

BNTIC News n’est pas le producteur de ces données. Les publications sont interrogées à la demande dans Crossref, OpenAIRE, DOAJ, Europe PMC, HAL, DataCite, AfricArXiv, ROR et la Banque mondiale, sans clé d’accès. OpenAlex reste optionnel. Aucun service payant n’est nécessaire et aucune donnée externe n’est enregistrée en base. Consulter les sources et leurs limites.