Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, Brisbane, QLD, 4059, Australia.
Princess Alexandra Hospital, Metro South Health, Woolloongabba, QLD, Australia.
BMC Med Inform Decis Mak. 2024 Sep 2;24(1):241. doi: 10.1186/s12911-024-02647-4.
Successful deployment of clinical prediction models for clinical deterioration relates not only to predictive performance but to integration into the decision making process. Models may demonstrate good discrimination and calibration, but fail to match the needs of practising acute care clinicians who receive, interpret, and act upon model outputs or alerts. We sought to understand how prediction models for clinical deterioration, also known as early warning scores (EWS), influence the decision-making of clinicians who regularly use them and elicit their perspectives on model design to guide future deterioration model development and implementation.
Nurses and doctors who regularly receive or respond to EWS alerts in two digital metropolitan hospitals were interviewed for up to one hour between February 2022 and March 2023 using semi-structured formats. We grouped interview data into sub-themes and then into general themes using reflexive thematic analysis. Themes were then mapped to a model of clinical decision making using deductive framework mapping to develop a set of practical recommendations for future deterioration model development and deployment.
Fifteen nurses (n = 8) and doctors (n = 7) were interviewed for a mean duration of 42 min. Participants emphasised the importance of using predictive tools for supporting rather than supplanting critical thinking, avoiding over-protocolising care, incorporating important contextual information and focusing on how clinicians generate, test, and select diagnostic hypotheses when managing deteriorating patients. These themes were incorporated into a conceptual model which informed recommendations that clinical deterioration prediction models demonstrate transparency and interactivity, generate outputs tailored to the tasks and responsibilities of end-users, avoid priming clinicians with potential diagnoses before patients were physically assessed, and support the process of deciding upon subsequent management.
Prediction models for deteriorating inpatients may be more impactful if they are designed in accordance with the decision-making processes of acute care clinicians. Models should produce actionable outputs that assist with, rather than supplant, critical thinking.
临床预测模型在临床恶化中的成功应用不仅与预测性能有关，还与模型整合到决策过程中有关。模型可能表现出良好的区分度和校准度，但无法满足接受、解释和根据模型输出或警报做出决策的急性护理临床医生的需求。我们试图了解临床恶化预测模型（也称为早期预警评分（EWS））如何影响经常使用这些模型的临床医生的决策，并征求他们对模型设计的看法，以指导未来的恶化模型开发和实施。
2022 年 2 月至 2023 年 3 月期间，我们对两家数字大都市医院中经常接收或响应 EWS 警报的护士和医生进行了长达一小时的半结构化访谈。我们将访谈数据分为子主题，然后使用反思性主题分析将其分为一般主题。然后，我们使用演绎框架映射将主题映射到临床决策模型中，以制定一套用于未来恶化模型开发和部署的实用建议。
共对 15 名护士（n=8）和医生（n=7）进行了访谈，平均访谈时间为 42 分钟。参与者强调使用预测工具来支持而不是替代批判性思维的重要性，避免过度规范护理，纳入重要的背景信息，并关注临床医生在管理恶化患者时如何生成、测试和选择诊断假设。这些主题被纳入一个概念模型中，该模型为以下建议提供了信息：临床恶化预测模型应具有透明度和交互性，生成针对最终用户任务和职责的定制化输出，避免在对患者进行体格检查之前向临床医生提示潜在诊断，并支持决策制定过程。
如果设计符合急性护理临床医生的决策过程，那么用于监测住院患者恶化的预测模型可能会更具影响力。模型应生成有助于批判性思维而不是替代批判性思维的可操作输出。
