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Computerization of the Work of General Practitioners: Mixed Methods Survey of Final-Year Medical Students in Ireland

BACKGROUND: The potential for digital health technologies, including machine learning (ML)–enabled tools, to disrupt the medical profession is the subject of ongoing debate within biomedical informatics. OBJECTIVE: We aimed to describe the opinions of final-year medical students in Ireland regarding...

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Autores principales: Blease, Charlotte, Kharko, Anna, Bernstein, Michael, Bradley, Colin, Houston, Muiris, Walsh, Ian, D Mandl, Kenneth
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10131917/
https://www.ncbi.nlm.nih.gov/pubmed/36939809
http://dx.doi.org/10.2196/42639
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author Blease, Charlotte
Kharko, Anna
Bernstein, Michael
Bradley, Colin
Houston, Muiris
Walsh, Ian
D Mandl, Kenneth
author_facet Blease, Charlotte
Kharko, Anna
Bernstein, Michael
Bradley, Colin
Houston, Muiris
Walsh, Ian
D Mandl, Kenneth
author_sort Blease, Charlotte
collection PubMed
description BACKGROUND: The potential for digital health technologies, including machine learning (ML)–enabled tools, to disrupt the medical profession is the subject of ongoing debate within biomedical informatics. OBJECTIVE: We aimed to describe the opinions of final-year medical students in Ireland regarding the potential of future technology to replace or work alongside general practitioners (GPs) in performing key tasks. METHODS: Between March 2019 and April 2020, using a convenience sample, we conducted a mixed methods paper-based survey of final-year medical students. The survey was administered at 4 out of 7 medical schools in Ireland across each of the 4 provinces in the country. Quantitative data were analyzed using descriptive statistics and nonparametric tests. We used thematic content analysis to investigate free-text responses. RESULTS: In total, 43.1% (252/585) of the final-year students at 3 medical schools responded, and data collection at 1 medical school was terminated due to disruptions associated with the COVID-19 pandemic. With regard to forecasting the potential impact of artificial intelligence (AI)/ML on primary care 25 years from now, around half (127/246, 51.6%) of all surveyed students believed the work of GPs will change minimally or not at all. Notably, students who did not intend to enter primary care predicted that AI/ML will have a great impact on the work of GPs. CONCLUSIONS: We caution that without a firm curricular foundation on advances in AI/ML, students may rely on extreme perspectives involving self-preserving optimism biases that demote the impact of advances in technology on primary care on the one hand and technohype on the other. Ultimately, these biases may lead to negative consequences in health care. Improvements in medical education could help prepare tomorrow’s doctors to optimize and lead the ethical and evidence-based implementation of AI/ML-enabled tools in medicine for enhancing the care of tomorrow’s patients.
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spelling pubmed-101319172023-04-27 Computerization of the Work of General Practitioners: Mixed Methods Survey of Final-Year Medical Students in Ireland Blease, Charlotte Kharko, Anna Bernstein, Michael Bradley, Colin Houston, Muiris Walsh, Ian D Mandl, Kenneth JMIR Med Educ Original Paper BACKGROUND: The potential for digital health technologies, including machine learning (ML)–enabled tools, to disrupt the medical profession is the subject of ongoing debate within biomedical informatics. OBJECTIVE: We aimed to describe the opinions of final-year medical students in Ireland regarding the potential of future technology to replace or work alongside general practitioners (GPs) in performing key tasks. METHODS: Between March 2019 and April 2020, using a convenience sample, we conducted a mixed methods paper-based survey of final-year medical students. The survey was administered at 4 out of 7 medical schools in Ireland across each of the 4 provinces in the country. Quantitative data were analyzed using descriptive statistics and nonparametric tests. We used thematic content analysis to investigate free-text responses. RESULTS: In total, 43.1% (252/585) of the final-year students at 3 medical schools responded, and data collection at 1 medical school was terminated due to disruptions associated with the COVID-19 pandemic. With regard to forecasting the potential impact of artificial intelligence (AI)/ML on primary care 25 years from now, around half (127/246, 51.6%) of all surveyed students believed the work of GPs will change minimally or not at all. Notably, students who did not intend to enter primary care predicted that AI/ML will have a great impact on the work of GPs. CONCLUSIONS: We caution that without a firm curricular foundation on advances in AI/ML, students may rely on extreme perspectives involving self-preserving optimism biases that demote the impact of advances in technology on primary care on the one hand and technohype on the other. Ultimately, these biases may lead to negative consequences in health care. Improvements in medical education could help prepare tomorrow’s doctors to optimize and lead the ethical and evidence-based implementation of AI/ML-enabled tools in medicine for enhancing the care of tomorrow’s patients. JMIR Publications 2023-03-20 /pmc/articles/PMC10131917/ /pubmed/36939809 http://dx.doi.org/10.2196/42639 Text en ©Charlotte Blease, Anna Kharko, Michael Bernstein, Colin Bradley, Muiris Houston, Ian Walsh, Kenneth D Mandl. Originally published in JMIR Medical Education (https://mededu.jmir.org), 20.03.2023. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Education, is properly cited. The complete bibliographic information, a link to the original publication on https://mededu.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Blease, Charlotte
Kharko, Anna
Bernstein, Michael
Bradley, Colin
Houston, Muiris
Walsh, Ian
D Mandl, Kenneth
Computerization of the Work of General Practitioners: Mixed Methods Survey of Final-Year Medical Students in Ireland
title Computerization of the Work of General Practitioners: Mixed Methods Survey of Final-Year Medical Students in Ireland
title_full Computerization of the Work of General Practitioners: Mixed Methods Survey of Final-Year Medical Students in Ireland
title_fullStr Computerization of the Work of General Practitioners: Mixed Methods Survey of Final-Year Medical Students in Ireland
title_full_unstemmed Computerization of the Work of General Practitioners: Mixed Methods Survey of Final-Year Medical Students in Ireland
title_short Computerization of the Work of General Practitioners: Mixed Methods Survey of Final-Year Medical Students in Ireland
title_sort computerization of the work of general practitioners: mixed methods survey of final-year medical students in ireland
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10131917/
https://www.ncbi.nlm.nih.gov/pubmed/36939809
http://dx.doi.org/10.2196/42639
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