Health data science course for clinicians: Time to bridge the skills gap?
Background: Data science skills are highly relevant for clinicians working in an era of big data in healthcare. However, these skills are not routinely taught, representing a growing unmet educational need. This education report presents a structured short course that was run to teach clinicians dat...
| Publicado en: | Perfusion Vol. 40; no. 5; pp. 1237 - 1243 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Sage Publications, Ltd.
Jul2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=186210741&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 186210741 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02676591 31J jtl: Perfusion issn: 02676591 maglogo: N pubinfo: dt: Jul2025 vid: 40 iid: 5 pid: 33180 pub: Sage Publications, Ltd. place: <Blank> artinfo: ui: 186210741 180197005 186210741 186210741 10.1177/02676591241291946 186210741 ppf: 1237 ppct: 6 formats: tig: atl: Health data science course for clinicians: Time to bridge the skills gap? aug: au: Naderi, Hafiz Yang, Yu-Hsuen Munroe, Patricia B Petersen, Steffen E Westwood, Mark Aung, Nay affil: William Harvey Research Institute, NIHR Barts Biomedical Research Centre, 4617 Queen Mary University of London, London, UK sug: subj: Physicians Education Data Science Education Time Human Funding Source Information Needs Health Care Delivery Tertiary Health Care Hospitals United Kingdom United Kingdom Feedback Summated Rating Scaling Statistics Education Cardiology Education Online Education Data Analysis Software Descriptive Statistics Confidence ab: Background: Data science skills are highly relevant for clinicians working in an era of big data in healthcare. However, these skills are not routinely taught, representing a growing unmet educational need. This education report presents a structured short course that was run to teach clinicians data science and the lessons learnt. Methods: A 1-day introductory course was conducted within a tertiary hospital in London. It consisted of lectures followed by facilitated pair programming exercises in R, an object-oriented programming language. Feedback was collated and participant responses were graded using a Likert scale. Results: The course was attended by 20 participants. The majority of participants (69%) were in higher speciality cardiology training. While more than half of the participants (56%) received prior training in statistics either through formal taught programmes (e.g., a Master's degree) or online courses, the participants reported several barriers to expanding their skills in data science due to limited programming skills, lack of dedicated time, training opportunities and awareness. After the short course, there was a significant increase in participants' self-rated confidence in using R for data analysis (mean response; before the course: 1.69 ± 1.0, after the course: 3.2 ± 0.9, p =.0005) and awareness of the capabilities of R (mean response; before the course: 2.1 ± 0.9, after the course: 3.6 ± 0.7, p =.0001, on a 5-point Likert scale). Conclusion: This proof-of-concept study demonstrates that a structured short course can effectively introduce data science skills to clinicians and supports future educational initiatives to integrate data science teaching into medical education. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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