Accepted for/Published in: JMIR Human Factors
Date Submitted: Jul 17, 2023
Date Accepted: May 5, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Assessing utility, impact and adoption challenges of AI-enabled prescription advisory tool for type 2 diabetes management: perspectives from endocrinologists in a tertiary hospital
ABSTRACT
Background:
The clinical management of type 2 diabetes mellitus (T2DM) presents a significant challenge due to the constantly evolving clinical practice guidelines and growing array of drug classes available. Evidence suggests that artificial intelligence (AI)-enabled clinical decision support systems (CDSS) have proven to be effective in assisting clinicians with informed decision-making. Despite the merits of AI-driven CDSS, a significant research gap exists concerning the early-stage implementation and adoption of AI-enabled CDSS in T2DM management.
Objective:
This study aimed to explore the perspectives of clinicians on the utility and impact of an AI-enabled prescription advisory (APA) tool, developed using a multi-institutions diabetes registry and implemented in specialist endocrinology clinics, and the challenges to its adoption and application.
Methods:
We conducted focus group discussions using a semi-structured interview guide with purposively selected endocrinologists from a tertiary hospital. The focus group discussions were audio recorded and transcribed verbatim. Data were thematically analyzed.
Results:
A total of 13 clinicians participated in four focus group discussions. Our findings suggest that the APA tool offered several useful features to assist clinicians in effectively managing T2DM. Specifically, clinicians viewed the AI-generated medication alterations as a good knowledge resource in supporting the clinician's decision-making on drug modifications at the point of care, particularly for patients with co-morbidities. The complication risk prediction was seen as positively impacting patient care by facilitating early doctor-patient communication and initiating prompt clinical responses. However, the interpretability of the risk scores, concerns about over-reliance and automation bias, and issues surrounding accountability and liability hindered the adoption of the APA tool in clinical practice.
Conclusions:
Although the APA tool holds great potential as a valuable resource for improving patient care, further efforts are required to address clinicians' concerns and improve the tool's acceptance and applicability in relevant contexts. Clinical Trial: N/A
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