TY - JOUR
T1 - Artificial intelligence for secondary prevention of myocardial infarction
T2 - A qualitative study of patient and health professional perspectives
AU - Pelly, Melissa
AU - Fatehi, Farhad
AU - Liew, Danny
AU - Verdejo-Garcia, Antonio
N1 - Funding Information:
This work was supported by the Digital Health CRC Limited (“DHCRC”). DHCRC is funded under the Commonwealth's Cooperative Research Centres (CRC). (Grant No. DHCRC1.1/237886587).
Publisher Copyright:
© 2023 The Author(s)
PY - 2023/5
Y1 - 2023/5
N2 - Background: Artificial intelligence (AI) has potential to improve self-management of several chronic conditions. However, the perspective of patients and healthcare professionals regarding AI-enabled health management programs, which are key to successful implementation, remains poorly understood. Purpose: To explore the opinions of people with a history of myocardial infarction (PHMI) and health professionals on the use of AI for secondary prevention of MI. Procedure: Three rounds of focus groups were conducted via videoconferencing with 38 participants: 22 PHMI and 16 health professionals. Findings: We identified 21 concepts stemming from participants' views, which we classified into five categories: Trust; Expected Functions; Adoption; Concerns; and Perceived Benefits. Trust covered the credibility of information and safety to believe health advice. Expected Functions covered tailored feedback and personalised advice. Adoption included usability features and overall interest in AI. Concerns originated from previous negative experience with AI. Perceived Benefits included the usefulness of AI to provide advice when regular contact with healthcare services is not feasible. Health professionals were more optimistic than PHMI about the usefulness of AI for improving health behaviour. Conclusions: Altogether, our findings provide key insights from end-users to improve the likelihood of successful implementation and adoption of AI-enabled systems in the context of MI, as an exemplar of broader applications in chronic disease management.
AB - Background: Artificial intelligence (AI) has potential to improve self-management of several chronic conditions. However, the perspective of patients and healthcare professionals regarding AI-enabled health management programs, which are key to successful implementation, remains poorly understood. Purpose: To explore the opinions of people with a history of myocardial infarction (PHMI) and health professionals on the use of AI for secondary prevention of MI. Procedure: Three rounds of focus groups were conducted via videoconferencing with 38 participants: 22 PHMI and 16 health professionals. Findings: We identified 21 concepts stemming from participants' views, which we classified into five categories: Trust; Expected Functions; Adoption; Concerns; and Perceived Benefits. Trust covered the credibility of information and safety to believe health advice. Expected Functions covered tailored feedback and personalised advice. Adoption included usability features and overall interest in AI. Concerns originated from previous negative experience with AI. Perceived Benefits included the usefulness of AI to provide advice when regular contact with healthcare services is not feasible. Health professionals were more optimistic than PHMI about the usefulness of AI for improving health behaviour. Conclusions: Altogether, our findings provide key insights from end-users to improve the likelihood of successful implementation and adoption of AI-enabled systems in the context of MI, as an exemplar of broader applications in chronic disease management.
KW - Artificial intelligence
KW - Co-design
KW - Myocardial infarction
KW - Perspectives
KW - Qualitative study
KW - Secondary prevention
UR - http://www.scopus.com/inward/record.url?scp=85150285176&partnerID=8YFLogxK
U2 - 10.1016/j.ijmedinf.2023.105041
DO - 10.1016/j.ijmedinf.2023.105041
M3 - Article
C2 - 36934609
AN - SCOPUS:85150285176
SN - 1386-5056
VL - 173
JO - International Journal of Medical Informatics
JF - International Journal of Medical Informatics
M1 - 105041
ER -