SpringerPlus最新文献

Provider Perspectives on Artificial Intelligence-Guided Screening for Low Ejection Fraction in Primary Care: Qualitative Study. 医疗服务提供者对人工智能引导的初级医疗低射血分数筛查的看法：定性研究。

SpringerPlus Pub Date : 2022-10-14 DOI: 10.2196/41940

Barbara Barry, Xuan Zhu, Emma Behnken, Jonathan Inselman, Karen Schaepe, Rozalina McCoy, David Rushlow, Peter Noseworthy, Jordan Richardson, Susan Curtis, Richard Sharp, Artika Misra, Abdulla Akfaly, Paul Molling, Matthew Bernard, Xiaoxi Yao

{"title":"Provider Perspectives on Artificial Intelligence-Guided Screening for Low Ejection Fraction in Primary Care: Qualitative Study.","authors":"Barbara Barry, Xuan Zhu, Emma Behnken, Jonathan Inselman, Karen Schaepe, Rozalina McCoy, David Rushlow, Peter Noseworthy, Jordan Richardson, Susan Curtis, Richard Sharp, Artika Misra, Abdulla Akfaly, Paul Molling, Matthew Bernard, Xiaoxi Yao","doi":"10.2196/41940","DOIUrl":"10.2196/41940","url":null,"abstract":"Background: The promise of artificial intelligence (AI) to transform health care is threatened by a tangle of challenges that emerge as new AI tools are introduced into clinical practice. AI tools with high accuracy, especially those that detect asymptomatic cases, may be hindered by barriers to adoption. Understanding provider needs and concerns is critical to inform implementation strategies that improve provider buy-in and adoption of AI tools in medicine.Objective: This study aimed to describe provider perspectives on the adoption of an AI-enabled screening tool in primary care to inform effective integration and sustained use.Methods: A qualitative study was conducted between December 2019 and February 2020 as part of a pragmatic randomized controlled trial at a large academic medical center in the United States. In all, 29 primary care providers were purposively sampled using a positive deviance approach for participation in semistructured focus groups after their use of the AI tool in the randomized controlled trial was complete. Focus group data were analyzed using a grounded theory approach; iterative analysis was conducted to identify codes and themes, which were synthesized into findings.Results: Our findings revealed that providers understood the purpose and functionality of the AI tool and saw potential value for more accurate and faster diagnoses. However, successful adoption into routine patient care requires the smooth integration of the tool with clinical decision-making and existing workflow to address provider needs and preferences during implementation. To fulfill the AI tool's promise of clinical value, providers identified areas for improvement including integration with clinical decision-making, cost-effectiveness and resource allocation, provider training, workflow integration, care pathway coordination, and provider-patient communication.Conclusions: The implementation of AI-enabled tools in medicine can benefit from sensitivity to the nuanced context of care and provider needs to enable the useful adoption of AI tools at the point of care.Trial registration: ClinicalTrials.gov NCT04000087; https://clinicaltrials.gov/ct2/show/NCT04000087.","PeriodicalId":21923,"journal":{"name":"SpringerPlus","volume":"4 1","pages":"e41940"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11041436/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88211505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}