School of Health and Welfare, Halmstad University, Box 823, Halmstad, 301 18, Sweden.
BMC Prim Care. 2024 Jul 24;25(1):268. doi: 10.1186/s12875-024-02516-z.
Artificial intelligence (AI) holds significant promise for enhancing the efficiency and safety of medical history-taking and triage within primary care. However, there remains a dearth of knowledge concerning the practical implementation of AI systems for these purposes, particularly in the context of healthcare leadership. This study explores the experiences of healthcare leaders regarding the barriers to implementing an AI application for automating medical history-taking and triage in Swedish primary care, as well as the actions they took to overcome these barriers. Furthermore, the study seeks to provide insights that can inform the development of AI implementation strategies for healthcare.
We adopted an inductive qualitative approach, conducting semi-structured interviews with 13 healthcare leaders representing seven primary care units across three regions in Sweden. The collected data were subsequently analysed utilizing thematic analysis. Our study adhered to the Consolidated Criteria for Reporting Qualitative Research to ensure transparent and comprehensive reporting.
The study identified implementation barriers encountered by healthcare leaders across three domains: (1) healthcare professionals, (2) organization, and (3) technology. The first domain involved professional scepticism and resistance, the second involved adapting traditional units for digital care, and the third inadequacies in AI application functionality and system integration. To navigate around these barriers, the leaders took steps to (1) address inexperience and fear and reduce professional scepticism, (2) align implementation with digital maturity and guide patients towards digital care, and (3) refine and improve the AI application and adapt to the current state of AI application development.
The study provides valuable empirical insights into the implementation of AI for automating medical history-taking and triage in primary care as experienced by healthcare leaders. It identifies the barriers to this implementation and how healthcare leaders aligned their actions to overcome them. While progress was evident in overcoming professional-related and organizational-related barriers, unresolved technical complexities highlight the importance of AI implementation strategies that consider how leaders handle AI implementation in situ based on practical wisdom and tacit understanding. This underscores the necessity of a holistic approach for the successful implementation of AI in healthcare.
人工智能（AI）在提高初级保健中的病史采集和分诊效率和安全性方面具有重要意义。然而，对于这些目的的 AI 系统的实际实施，特别是在医疗保健领导力方面，仍然缺乏知识。本研究探讨了医疗保健领导者在实施用于自动采集病史和分诊的 AI 应用程序方面所面临的障碍，以及他们为克服这些障碍而采取的行动。此外，该研究旨在为医疗保健中 AI 实施策略的制定提供参考。
我们采用了一种归纳性的定性方法，对来自瑞典三个地区的七个初级保健单位的 13 名医疗保健领导者进行了半结构化访谈。随后使用主题分析对收集到的数据进行分析。我们的研究遵循了定性研究报告的综合标准，以确保透明和全面的报告。
该研究在三个领域发现了医疗保健领导者所面临的实施障碍：（1）医疗保健专业人员，（2）组织，和（3）技术。第一个领域涉及专业怀疑和抵制，第二个领域涉及为数字护理调整传统单位，第三个领域涉及 AI 应用功能和系统集成方面的不足。为了克服这些障碍，领导者采取了以下步骤：（1）解决经验不足和恐惧问题，减少专业怀疑；（2）使实施与数字成熟度保持一致，并引导患者接受数字护理；（3）完善和改进 AI 应用程序，并适应当前 AI 应用程序开发的状态。
该研究为医疗保健领导者在初级保健中实施 AI 以实现病史采集和分诊自动化提供了有价值的经验见解。它确定了实施这些障碍的原因，以及医疗保健领导者如何调整行动以克服这些障碍。虽然在克服专业相关和组织相关障碍方面取得了进展，但未解决的技术复杂性突显了 AI 实施策略的重要性，这些策略考虑了领导者如何根据实际智慧和默会理解在现场处理 AI 实施。这强调了在医疗保健中成功实施 AI 需要采取整体方法。
