Background

To explore attitudes about artificial intelligence (AI) among staff who utilized AI-based clinical decision support (CDS).

Methods

A survey was designed to assess staff attitudes about AI-based CDS tools. The survey was anonymously and voluntarily completed by clinical staff in three primary care outpatient clinics before and after implementation of an AI-based CDS system aimed to improve glycemic control in patients with diabetes as part of a quality improvement project. The CDS identified patients at risk for poor glycemic control and generated intervention recommendations intended to reduce patients’ risk.

Results

Staff completed 45 surveys pre-intervention and 38 post-intervention. Following implementation, staff felt that care was better coordinated (11 favorable responses, 14 unfavorable responses pre-intervention; 21 favorable responses, 3 unfavorable responses post-intervention; p < 0.01). However, only 14% of users would recommend the AI-based CDS. Staff feedback revealed that the most favorable aspect of the CDS was that it promoted team dialog about patient needs (N = 14, 52%), and the least favorable aspect was inadequacy of the interventions recommended by the CDS.

Conclusions

AI-based CDS tools that are perceived negatively by staff may reduce staff excitement about AI technology, and hands-on experience with AI may lead to more realistic expectations about the technology’s capabilities. In our setting, although AI-based CDS prompted an interdisciplinary discussion about the needs of patients at high risk for poor glycemic control, the interventions recommended by the CDS were often perceived to be poorly tailored, inappropriate, or not useful. Developers should carefully consider tasks that are best performed by AI and those best performed by the patient’s care team.

中文翻译：

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实施中的一个教训：对提供者基于人工智能的临床决策支持经验的事前研究。

背景

探索利用基于AI的临床决策支持（CDS）的员工对人工智能（AI）的态度。

方法

一项调查旨在评估员工对基于AI的CDS工具的态度。这项调查是在实施基于AI的CDS系统之前和之后由三名初级保健门诊的临床工作人员匿名自愿完成的，该系统旨在改善糖尿病患者的血糖控制，这是一项质量改进项目的一部分。CDS确定了血糖控制不良的风险患者，并提出了旨在降低患者风险的干预建议。

结果

工作人员在干预前完成了45项调查，在干预后完成了38项调查。实施后，工作人员认为护理得到更好的协调（干预前有11例，干预前有14例；干预后21例，有3例； p <0.01）。但是，只有14％的用户会推荐基于AI的CDS。员工反馈显示，CDS最有利的方面是它促进了有关患者需求的团队对话（N = 14，52％），而最不满意的方面是CDS建议的干预措施不足。

结论

员工认为基于AI的CDS工具可能会减少员工对AI技术的兴奋度，而动手实践AI经验可能会导致对该技术功能的更加现实的期望。在我们的环境中，尽管基于AI的CDS引发了关于血糖控制不良高风险患者需求的跨学科讨论，但CDS推荐的干预措施通常被认为是针对性较差，不合适或无用的。开发人员应仔细考虑由AI最佳执行的任务和由患者护理团队最佳执行的任务。