Bai Enze, Zhang Zhan, Xu Yincao, Luo Xiao, Adelgais Kathleen
School of Computer Science and Information Systems, Pace University, New York City, NY, USA.
School of Business, Oklahoma State University, Stillwater, OK, USA.
BMC Med Inform Decis Mak. 2025 Jan 17;25(1):31. doi: 10.1186/s12911-024-02844-1.
In prehospital emergency care, providers face significant challenges in making informed decisions due to factors such as limited cognitive support, high-stress environments, and lack of experience with certain patient conditions. Effective Clinical Decision Support Systems (CDSS) have great potential to alleviate these challenges. However, such systems have not yet been widely adopted in real-world practice and have been found to cause workflow disruptions and usability issues. Therefore, it is critical to investigate how to design CDSS that meet the needs of prehospital providers while accounting for the unique characteristics of prehospital workflows.
We conducted semi-structured interviews with 20 prehospital providers recruited from four Emergency Medical Services (EMS) agencies in an urban area in the northeastern U.S. The interviews focused on the decision-making challenges faced by prehospital providers, their technological needs for decision support, and key considerations for the design and implementation of a CDSS that can seamlessly integrate into prehospital care workflows. The data were analyzed using content analysis to identify common themes.
Our qualitative study identified several challenges in prehospital decision-making, including limited access to diagnostic tools, insufficient experience with certain critical patient conditions, and a lack of cognitive support. Participants highlighted several desired features to make CDSS more effective in the dynamic, hands-busy, and cognitively demanding prehospital context, such as automatic prompts for possible patient conditions and treatment options, alerts for critical patient safety events, AI-powered medication identification, and easy retrieval of protocols using hands-free methods (e.g., voice commands). Key considerations for successful CDSS adoption included balancing the frequency and urgency of alerts to reduce alarm fatigue and workflow disruptions, facilitating real-time data collection and documentation to enable decision generation, and ensuring trust and accountability while preventing over-reliance when using CDSS.
This study provides empirical insights into the challenges and user needs in prehospital decision-making and offers practical and system design implications for addressing these issues.
在院前急救护理中，由于认知支持有限、高压力环境以及对某些患者病情缺乏经验等因素，急救人员在做出明智决策时面临重大挑战。有效的临床决策支持系统（CDSS）有极大潜力缓解这些挑战。然而，此类系统尚未在实际应用中广泛采用，且已发现会导致工作流程中断和可用性问题。因此，研究如何设计既能满足院前急救人员需求又能兼顾院前工作流程独特特征的CDSS至关重要。
我们对从美国东北部一个城市地区的四个紧急医疗服务（EMS）机构招募的20名院前急救人员进行了半结构化访谈。访谈聚焦于院前急救人员面临的决策挑战、他们对决策支持的技术需求，以及设计和实施能无缝融入院前护理工作流程的CDSS的关键考虑因素。使用内容分析法对数据进行分析以识别共同主题。
我们的定性研究确定了院前决策中的若干挑战，包括诊断工具获取有限、对某些危急患者病情经验不足以及缺乏认知支持。参与者强调了几个期望的功能，以使CDSS在动态、手部忙碌且认知要求高的院前环境中更有效，例如针对可能的患者病情和治疗选项的自动提示、危急患者安全事件警报、人工智能驱动的药物识别，以及使用免提方法（如语音命令）轻松检索协议。成功采用CDSS的关键考虑因素包括平衡警报的频率和紧迫性以减少警报疲劳和工作流程中断，促进实时数据收集和记录以支持决策生成，以及在使用CDSS时确保信任和问责制同时防止过度依赖。
本研究为院前决策中的挑战和用户需求提供了实证见解，并为解决这些问题提供了实际和系统设计方面的启示。
