Clinician perspectives and recommendations regarding design of clinical prediction models for deteriorating patients in acute care.

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Robin Blythe, Sundresan Naicker, Nicole White, Raelene Donovan, Ian A Scott, Andrew McKelliget, Steven M McPhail
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Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

临床医生对急症护理中病情恶化患者临床预测模型设计的看法和建议。
背景:临床恶化预测模型的成功应用不仅与预测性能有关,还与决策过程的整合有关。模型可能表现出良好的辨别力和校准能力,但却无法满足急症护理临床医生的需求,他们需要接收、解释模型输出或警报,并根据模型输出或警报采取行动。我们试图了解临床病情恶化预测模型(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,以指导未来病情恶化模型的开发和实施:在 2022 年 2 月至 2023 年 3 月期间,我们采用半结构化形式对两家数字化都市医院中定期接收或响应 EWS 警报的护士和医生进行了长达一小时的访谈。我们采用反思性主题分析法将访谈数据归类为子主题,然后再归类为一般主题。然后使用演绎框架映射法将主题映射到临床决策模型中,从而为未来恶化模型的开发和部署提出一系列实用建议:15 名护士(n = 8)和医生(n = 7)接受了访谈,平均访谈时间为 42 分钟。参与者强调了使用预测工具支持而非取代批判性思维、避免过度协议化护理、纳入重要的背景信息以及关注临床医生在管理病情恶化患者时如何生成、测试和选择诊断假设的重要性。这些主题被纳入到一个概念模型中,该模型提出的建议包括:临床病情恶化预测模型应具有透明度和互动性,根据最终用户的任务和职责生成输出结果,避免在对患者进行身体评估之前向临床医生提供潜在诊断,以及支持决定后续管理的过程:针对病情恶化的住院患者的预测模型如果能根据急诊临床医生的决策过程进行设计,可能会产生更大的影响。模型应产生可操作的输出结果,协助而非取代批判性思维。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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