Tekin Aysun, Herasevich Svetlana, Minteer Sarah A, Gajic Ognjen, Barwise Amelia K
Division of Nephrology and Hypertension, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, United States.
Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, Minnesota, United States.
Appl Clin Inform. 2025 Jan;16(1):223-233. doi: 10.1055/a-2451-9046. Epub 2024 Oct 25.
This study aimed to evaluate critical care provider perspectives about diagnostic practices for rare and atypical infections and the potential for using artificial intelligence (AI) as a decision support system (DSS).
We conducted an anonymous web-based survey among critical care providers at Mayo Clinic Rochester between November 25, 2023, and January 15, 2024, to evaluate their experience with rare and atypical infection diagnostic processes and AI-based DSSs. We also assessed the perceived usefulness of AI-based DSSs, their potential impact on improving diagnostic practices for rare and atypical infections, and the perceived risks and benefits of their use.
A total of 47/143 providers completed the survey. Thirty-eight out of 47 agreed that there was a delay in diagnosing rare and atypical infections. Among those who agreed, limited assessment of specific patient factors and failure to consider them were the most frequently cited important contributing factors (33/38). Thirty-eight out of 47 reported familiarity with the AI-based DSS applications available to critical care providers. Less than half (18/38) thought AI-based DSSs often provided valuable insights into patient care, but almost three-quarters (34/47) thought AI-based DDSs often provided valuable insight when specifically asked about their ability to improve the diagnosis of rare and atypical infections. All respondents rated reliability as important in enhancing the perceived utility of AI-based DSSs (47/47) and almost all rated interpretability and integration into the workflow as important (45/47). The primary concern about implementing an AI-based DSS in this context was alert fatigue (44/47).
Most critical care providers perceive that there are delays in diagnosing rare infections, indicating inadequate assessment and consideration of the diagnosis as the major contributors. Reliability, interpretability, workflow integration, and alert fatigue emerged as key factors impacting the usability of AI-based DSS. These findings will inform the development and implementation of an AI-based diagnostic algorithm to aid in identifying rare and atypical infections.
本研究旨在评估重症监护医护人员对罕见和非典型感染诊断方法的看法，以及使用人工智能（AI）作为决策支持系统（DSS）的可能性。
2023年11月25日至2024年1月15日，我们在罗切斯特梅奥诊所对重症监护医护人员进行了一项匿名的基于网络的调查，以评估他们在罕见和非典型感染诊断过程以及基于AI的DSS方面的经验。我们还评估了基于AI的DSS的感知有用性、其对改善罕见和非典型感染诊断方法的潜在影响，以及使用它们的感知风险和益处。
共有47/143名医护人员完成了调查。47名受访者中有38人认为在诊断罕见和非典型感染方面存在延迟。在这些认同的人中，对特定患者因素的评估有限以及未能考虑这些因素是最常被提及的重要促成因素（33/38）。47名受访者中有38人表示熟悉重症监护医护人员可用的基于AI的DSS应用程序。不到一半（18/38）的人认为基于AI的DSS通常能为患者护理提供有价值的见解，但当特别询问其改善罕见和非典型感染诊断的能力时，近四分之三（34/47）的人认为基于AI的DDS通常能提供有价值的见解。所有受访者都认为可靠性对于提高基于AI的DSS的感知效用很重要（47/47），几乎所有人都认为可解释性和融入工作流程很重要（45/47）。在这种情况下，实施基于AI的DSS的主要担忧是警报疲劳（44/47）。
大多数重症监护医护人员认为在诊断罕见感染方面存在延迟，表明评估不足和对诊断的考虑是主要原因。可靠性、可解释性、工作流程整合和警报疲劳是影响基于AI的DSS可用性的关键因素。这些发现将为开发和实施基于AI的诊断算法提供参考，以帮助识别罕见和非典型感染。
