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2
Experiences of using artificial intelligence in healthcare: a qualitative study of UK clinician and key stakeholder perspectives.在医疗保健中使用人工智能的体验:英国临床医生和主要利益相关者观点的定性研究。
BMJ Open. 2023 Dec 11;13(12):e076950. doi: 10.1136/bmjopen-2023-076950.
3
Advances in artificial intelligence (AI)-based diagnosis in clinical practice-correspondence.临床实践中基于人工智能(AI)诊断的进展——通信
Ann Med Surg (Lond). 2023 Jun 16;85(7):3757-3758. doi: 10.1097/MS9.0000000000000959. eCollection 2023 Jul.
4
Epic Sepsis Model Inpatient Predictive Analytic Tool: A Validation Study.重症脓毒症模型住院患者预测分析工具:一项验证研究。
Crit Care Explor. 2023 Jun 30;5(7):e0941. doi: 10.1097/CCE.0000000000000941. eCollection 2023 Jul.
5
Deployment of machine learning algorithms to predict sepsis: systematic review and application of the SALIENT clinical AI implementation framework.部署机器学习算法预测脓毒症:SALIENT 临床人工智能实施框架的系统评价与应用。
J Am Med Inform Assoc. 2023 Jun 20;30(7):1349-1361. doi: 10.1093/jamia/ocad075.
6
A Sociotechnical Systems Framework for the Application of Artificial Intelligence in Health Care Delivery.一种用于在医疗保健服务中应用人工智能的社会技术系统框架。
J Cogn Eng Decis Mak. 2022 Dec;16(4):194-206. doi: 10.1177/15553434221097357. Epub 2022 May 11.
7
Desired Characteristics of a Clinical Decision Support System for Early Sepsis Recognition: Interview Study Among Hospital-Based Clinicians.用于早期脓毒症识别的临床决策支持系统的理想特征:基于医院的临床医生访谈研究
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Factors Influencing Clinician Trust in Predictive Clinical Decision Support Systems for In-Hospital Deterioration: Qualitative Descriptive Study.影响临床医生对院内病情恶化预测性临床决策支持系统信任度的因素:定性描述性研究
JMIR Hum Factors. 2022 May 12;9(2):e33960. doi: 10.2196/33960.
9
Diagnostic Challenges in Sepsis.脓毒症的诊断挑战
Curr Infect Dis Rep. 2021;23(12):22. doi: 10.1007/s11908-021-00765-y. Epub 2021 Oct 25.
10
Workflow integration analysis of a human factors-based clinical decision support in the emergency department.基于人为因素的临床决策支持在急诊科的工作流程整合分析。
Appl Ergon. 2021 Nov;97:103498. doi: 10.1016/j.apergo.2021.103498. Epub 2021 Jun 26.

一种广泛使用的基于人工智能的脓毒症系统的终端用户体验。

End user experience of a widely used artificial intelligence based sepsis system.

作者信息

Owoyemi Ayomide, Okpara Ebere, Salwei Megan, Boyd Andrew

机构信息

Department of Biomedical and Health Informatics, University of Illinois at Chicago, Chicago, IL 60612, United States.

Department of Pharmacy Systems, Outcomes and Policy, University of Illinois at Chicago, Chicago, IL 60612, United States.

出版信息

JAMIA Open. 2024 Oct 7;7(4):ooae096. doi: 10.1093/jamiaopen/ooae096. eCollection 2024 Dec.

DOI:10.1093/jamiaopen/ooae096
PMID:39386065
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11458550/
Abstract

OBJECTIVES

Research on the Epic Sepsis System (ESS) has predominantly focused on technical accuracy, neglecting the user experience of healthcare professionals. Understanding these experiences is crucial for the design of Artificial Intelligence (AI) systems in clinical settings. This study aims to explore the socio-technical dynamics affecting ESS adoption and use, based on user perceptions and experiences.

MATERIALS AND METHODS

Resident doctors and nurses with recent ESS interaction were interviewed using purposive sampling until data saturation. A content analysis was conducted using Dedoose software, with codes generated from Sittig and Singh's and Salwei and Carayon's frameworks, supplemented by inductive coding for emerging themes.

RESULTS

Interviews with 10 healthcare providers revealed mixed but generally positive or neutral perceptions of the ESS. Key discussion points included its workflow integration and usability. Findings were organized into 2 main domains: workflow fit, and usability and utility, highlighting the system's seamless electronic health record integration and identifying design gaps.

DISCUSSION

This study offers insights into clinicians' experiences with the ESS, emphasizing the socio-technical factors that influence its adoption and effective use. The positive reception was tempered by identified design issues, with clinician perceptions varying by their professional experience and frequency of ESS interaction.

CONCLUSION

The findings highlight the need for ongoing ESS refinement, emphasizing a balance between technological advancement and clinical practicality. This research contributes to the understanding of AI system adoption in healthcare, suggesting improvements for future clinical AI tools.

摘要

目的

对重症脓毒症系统(ESS)的研究主要集中在技术准确性上,而忽视了医疗保健专业人员的用户体验。了解这些体验对于临床环境中人工智能(AI)系统的设计至关重要。本研究旨在基于用户的认知和体验,探索影响ESS采用和使用的社会技术动态。

材料与方法

采用目的抽样法对近期与ESS有交互的住院医生和护士进行访谈,直至数据饱和。使用Dedoose软件进行内容分析,代码来自西蒂格和辛格以及萨尔韦和卡拉扬的框架,并辅以对新出现主题的归纳编码。

结果

对10名医疗保健提供者的访谈显示,他们对ESS的看法不一,但总体上是积极或中性的。关键讨论点包括其工作流程整合和可用性。研究结果分为两个主要领域:工作流程适配性以及可用性和实用性,突出了该系统无缝的电子健康记录整合,并识别出设计差距。

讨论

本研究深入了解了临床医生使用ESS的体验,强调了影响其采用和有效使用的社会技术因素。已识别出的设计问题削弱了积极的反馈,临床医生的认知因其专业经验和与ESS交互的频率而异。

结论

研究结果凸显了持续改进ESS的必要性,强调了技术进步与临床实用性之间的平衡。这项研究有助于理解医疗保健领域中AI系统的采用情况,为未来临床AI工具的改进提供了建议。