Identifying Research Priorities in Digital Education for Health Care: Umbrella Review and Modified Delphi Method Study.

Alison Potter, Chris Munsch, Elaine Watson, Emily Hopkins, Sofia Kitromili, Iain Cameron O'Neill, Judy Larbie, Essi Niittymaki, Catriona Ramsay, Joshua Burke, Neil Ralph
Author Information
  1. Alison Potter: Technology Enhanced Learning, NHS England, Southampton, United Kingdom. ORCID
  2. Chris Munsch: Technology Enhanced Learning, NHS England, Leeds, United Kingdom. ORCID
  3. Elaine Watson: Technology Enhanced Learning, NHS England, Oxford, United Kingdom. ORCID
  4. Emily Hopkins: Knowledge Management Service, NHS England, Manchester, United Kingdom. ORCID
  5. Sofia Kitromili: Technology Enhanced Learning, NHS England, Southampton, United Kingdom. ORCID
  6. Iain Cameron O'Neill: Technology Enhanced Learning, NHS England, London, United Kingdom. ORCID
  7. Judy Larbie: Technology Enhanced Learning, NHS England, London, United Kingdom. ORCID
  8. Essi Niittymaki: Technology Enhanced Learning, NHS England, London, United Kingdom. ORCID
  9. Catriona Ramsay: Technology Enhanced Learning, NHS England, Newcastle upon Tyne, United Kingdom. ORCID
  10. Joshua Burke: Manchester Foundation Trust, Manchester, United Kingdom. ORCID
  11. Neil Ralph: Technology Enhanced Learning, NHS England, London, United Kingdom. ORCID

Abstract

BACKGROUND: In recent years, the use of digital technology in the education of health care professionals has surged, partly driven by the COVID-19 pandemic. However, there is still a need for focused research to establish evidence of its effectiveness.
OBJECTIVE: This study aimed to define the gaps in the evidence for the efficacy of digital education and to identify priority areas where future research has the potential to contribute to our understanding and use of digital education.
METHODS: We used a 2-stage approach to identify research priorities. First, an umbrella review of the recent literature (published between 2020 and 2023) was performed to identify and build on existing work. Second, expert consensus on the priority research questions was obtained using a modified Delphi method.
RESULTS: A total of 8857 potentially relevant papers were identified. Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, we included 217 papers for full review. All papers were either systematic reviews or meta-analyses. A total of 151 research recommendations were extracted from the 217 papers. These were analyzed, recategorized, and consolidated to create a final list of 63 questions. From these, a modified Delphi process with 42 experts was used to produce the top-five rated research priorities: (1) How do we measure the learning transfer from digital education into the clinical setting? (2) How can we optimize the use of artificial intelligence, machine learning, and deep learning to facilitate education and training? (3) What are the methodological requirements for high-quality rigorous studies assessing the outcomes of digital health education? (4) How does the design of digital education interventions (eg, format and modality) in health professionals' education and training curriculum affect learning outcomes? and (5) How should learning outcomes in the field of health professions' digital education be defined and standardized?
CONCLUSIONS: This review provides a prioritized list of research gaps in digital education in health care, which will be of use to researchers, educators, education providers, and funding agencies. Additional proposals are discussed regarding the next steps needed to advance this agenda, aiming to promote meaningful and practical research on the use of digital technologies and drive excellence in health care education.

Keywords

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MeSH Term

Delphi Technique
Humans
COVID-19
Education, Distance
Health Personnel
Research
Digital Technology

Word Cloud

Created with Highcharts 10.0.0educationdigitalresearchhealthuselearningreviewDelphipaperscareidentifyrecentevidencegapspriorityusedprioritiesumbrellaquestionsmodifiedtotal217listartificialintelligenceoutcomesBACKGROUND:yearstechnologyprofessionalssurgedpartlydrivenCOVID-19pandemicHoweverstillneedfocusedestablisheffectivenessOBJECTIVE:studyaimeddefineefficacyareasfuturepotentialcontributeunderstandingMETHODS:2-stageapproachFirstliteraturepublished20202023performedbuildexistingworkSecondexpertconsensusobtainedusingmethodRESULTS:8857potentiallyrelevantidentifiedUsingPRISMAPreferredReportingItemsSystematicReviewsMeta-Analysesmethodologyincludedfulleithersystematicreviewsmeta-analyses151recommendationsextractedanalyzedrecategorizedconsolidatedcreatefinal63process42expertsproducetop-fiveratedpriorities:1measuretransferclinicalsetting?2canoptimizemachinedeepfacilitatetraining?3methodologicalrequirementshigh-qualityrigorousstudiesassessingeducation?4designinterventionsegformatmodalityprofessionals'trainingcurriculumaffectoutcomes?5fieldprofessions'definedstandardized?CONCLUSIONS:providesprioritizedwillresearcherseducatorsprovidersfundingagenciesAdditionalproposalsdiscussedregardingnextstepsneededadvanceagendaaimingpromotemeaningfulpracticaltechnologiesdriveexcellenceIdentifyingResearchPrioritiesDigitalEducationHealthCare:UmbrellaReviewModifiedMethodStudyAIprofessions

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