Sungwon Yoon, Hendra Goh, Phong Ching Lee, Hong Chang Tan, Ming Ming Teh, Dawn Shao Ting Lim, Ann Kwee, Chandran Suresh, David Carmody, Du Soon Swee, Sarah Ying Tse Tan, Andy Jun-Wei Wong, Charlotte Hui-Min Choo, Zongwen Wee, Yong Mong Bee
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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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"11 ","pages":"e50939"},"PeriodicalIF":2.6000,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11211700/pdf/","citationCount":"0","resultStr":"{\"title\":\"Assessing the Utility, Impact, and Adoption Challenges of an Artificial Intelligence-Enabled Prescription Advisory Tool for Type 2 Diabetes Management: Qualitative Study.\",\"authors\":\"Sungwon Yoon, Hendra Goh, Phong Ching Lee, Hong Chang Tan, Ming Ming Teh, Dawn Shao Ting Lim, Ann Kwee, Chandran Suresh, David Carmody, Du Soon Swee, Sarah Ying Tse Tan, Andy Jun-Wei Wong, Charlotte Hui-Min Choo, Zongwen Wee, Yong Mong Bee\",\"doi\":\"10.2196/50939\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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. 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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.</p><p><strong>Conclusions: </strong>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.</p>\",\"PeriodicalId\":36351,\"journal\":{\"name\":\"JMIR Human Factors\",\"volume\":\"11 \",\"pages\":\"e50939\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-06-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11211700/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JMIR Human Factors\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2196/50939\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JMIR Human Factors","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2196/50939","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0
摘要
背景:2 型糖尿病(T2DM)的临床管理是一项重大挑战,因为临床实践指南不断变化,可供选择的药物种类也越来越多。有证据表明,人工智能(AI)驱动的临床决策支持系统(CDSS)已被证明能有效协助临床医生做出知情决策。尽管人工智能驱动的临床决策支持系统有很多优点,但在 T2DM 管理中早期实施和采用人工智能驱动的临床决策支持系统方面仍存在很大的研究空白:本研究旨在探讨临床医生对人工智能处方咨询(APA)工具的使用和影响的看法,该工具是利用多机构糖尿病登记处开发的,并在内分泌专科诊所实施,同时还探讨了采用和应用该工具所面临的挑战:方法:我们采用半结构化访谈指南,与一家三甲医院特意挑选的内分泌科医生进行了焦点小组讨论。对焦点小组讨论进行了录音和逐字记录。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,可帮助临床医生有效管理 T2DM。具体来说,临床医生认为人工智能生成的药物改变是一个很好的知识资源,可以支持临床医生在治疗过程中做出药物调整的决策,尤其是对有合并症的患者。并发症风险预测促进了医患之间的早期沟通,并启动了及时的临床反应,因此被视为对患者护理产生了积极影响。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与问责制和责任相关的问题阻碍了 APA 工具在临床实践中的应用:尽管 APA 工具作为改善患者护理的宝贵资源具有巨大潜力,但仍需进一步努力消除临床医生的顾虑,提高该工具在相关情况下的接受度和适用性。
Assessing the Utility, Impact, and Adoption Challenges of an Artificial Intelligence-Enabled Prescription Advisory Tool for Type 2 Diabetes Management: Qualitative Study.
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 (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.
Objective: 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.
Methods: 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.
Results: 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.
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.