Yoon Sungwon, Goh Hendra, Lee Phong Ching, Tan Hong Chang, Teh Ming Ming, Lim Dawn Shao Ting, Kwee Ann, Suresh Chandran, Carmody David, Swee Du Soon, Tan Sarah Ying Tse, Wong Andy Jun-Wei, Choo Charlotte Hui-Min, Wee Zongwen, Bee Yong Mong
Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore.
Centre for Population Health Research and Implementation, SingHealth Regional Health System, SingHealth, Singapore, Singapore.
JMIR Hum Factors. 2024 Jun 13;11:e50939. doi: 10.2196/50939.
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 (CDSSs) have proven to be effective in assisting clinicians with informed decision-making. Despite the merits of AI-driven CDSSs, a significant research gap exists concerning the early-stage implementation and adoption of AI-enabled CDSSs in T2DM management.
This study aimed to explore the perspectives of clinicians on the use and impact of the AI-enabled Prescription Advisory (APA) tool, developed using a multi-institution diabetes registry and implemented in specialist endocrinology clinics, and the challenges to its adoption and application.
We conducted focus group discussions using a semistructured interview guide with purposively selected endocrinologists from a tertiary hospital. The focus group discussions were audio-recorded and transcribed verbatim. Data were thematically analyzed.
A total of 13 clinicians participated in 4 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 comorbidities. 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 overreliance and automation bias, and issues surrounding accountability and liability hindered the adoption of the APA tool in clinical practice.
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.
由于临床实践指南不断演变以及可用药物种类日益增多，2型糖尿病（T2DM）的临床管理面临重大挑战。有证据表明，人工智能（AI）驱动的临床决策支持系统（CDSSs）已被证明在协助临床医生进行明智决策方面是有效的。尽管AI驱动的CDSSs有诸多优点，但在T2DM管理中，关于AI驱动的CDSSs的早期实施和采用仍存在重大研究空白。
本研究旨在探讨临床医生对使用人工智能处方咨询（APA）工具的看法及其影响，该工具利用多机构糖尿病登记系统开发，并在专科内分泌诊所实施，同时探讨其采用和应用所面临的挑战。
我们使用半结构化访谈指南，对一家三级医院中经过有目的选择的内分泌科医生进行焦点小组讨论。焦点小组讨论进行了录音，并逐字转录。对数据进行了主题分析。
共有13名临床医生参加了4次焦点小组讨论。我们的研究结果表明，APA工具提供了一些有用的功能，可协助临床医生有效管理T2DM。具体而言，临床医生认为人工智能生成的药物调整建议是一种很好的知识资源，有助于临床医生在护理点就药物调整做出决策，特别是对于患有合并症的患者。并发症风险预测被认为通过促进早期医患沟通和引发及时的临床反应，对患者护理产生积极影响。然而，风险评分的可解释性、对过度依赖和自动化偏差的担忧以及围绕问责制和责任的问题，阻碍了APA工具在临床实践中的采用。
尽管APA工具作为改善患者护理的宝贵资源具有巨大潜力，但仍需要进一步努力解决临床医生的担忧，并提高该工具在相关环境中的接受度和适用性。
