Department of Biomedical Informatics, Columbia University, New York, New York, USA.
School of Nursing, Columbia University, New York, New York, USA.
J Am Med Inform Assoc. 2021 Jun 12;28(6):1242-1251. doi: 10.1093/jamia/ocab006.
There are signals of clinicians' expert and knowledge-driven behaviors within clinical information systems (CIS) that can be exploited to support clinical prediction. Describe development of the Healthcare Process Modeling Framework to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals).
We employed an iterative framework development approach that combined data-driven modeling and simulation testing to define and refine a process for phenotyping clinician behaviors. Our framework was developed and evaluated based on the Communicating Narrative Concerns Entered by Registered Nurses (CONCERN) predictive model to detect and leverage signals of clinician expertise for prediction of patient trajectories.
Seven themes-identified during development and simulation testing of the CONCERN model-informed framework development. The HPM-ExpertSignals conceptual framework includes a 3-step modeling technique: (1) identify patterns of clinical behaviors from user interaction with CIS; (2) interpret patterns as proxies of an individual's decisions, knowledge, and expertise; and (3) use patterns in predictive models for associations with outcomes. The CONCERN model differentiated at risk patients earlier than other early warning scores, lending confidence to the HPM-ExpertSignals framework.
The HPM-ExpertSignals framework moves beyond transactional data analytics to model clinical knowledge, decision making, and CIS interactions, which can support predictive modeling with a focus on the rapid and frequent patient surveillance cycle.
We propose this framework as an approach to embed clinicians' knowledge-driven behaviors in predictions and inferences to facilitate capture of healthcare processes that are activated independently, and sometimes well before, physiological changes are apparent.
临床信息系统（CIS）中存在着临床医生专业知识驱动行为的信号，可以利用这些信号来支持临床预测。描述开发医疗保健过程建模框架以表型化临床医生行为以利用临床专业知识信号增益（HPM-ExpertSignals）的过程。
我们采用了迭代框架开发方法，该方法结合了数据驱动建模和模拟测试，以定义和完善一种表型化临床医生行为的过程。我们的框架是基于“护士输入的沟通式叙事关注点（CONCERN）”预测模型来开发和评估的，该模型用于检测和利用临床医生专业知识的信号来预测患者轨迹。
在 CONCERN 模型的开发和模拟测试中确定了七个主题，这些主题为框架开发提供了信息。HPM-ExpertSignals 概念框架包括三个建模步骤：（1）从用户与 CIS 的交互中识别临床行为模式；（2）将模式解释为个人决策、知识和专业技能的代表；（3）在预测模型中使用模式与结果相关联。CONCERN 模型比其他早期预警评分更早地区分高危患者，这为 HPM-ExpertSignals 框架提供了信心。
HPM-ExpertSignals 框架超越了事务数据分析，而是对临床知识、决策和 CIS 交互进行建模，这可以支持以快速频繁的患者监测周期为重点的预测建模。
我们提出了这个框架作为一种将临床医生知识驱动行为嵌入预测和推理中的方法，以促进捕获独立激活的医疗保健流程，有时甚至在生理变化明显之前就已经激活。
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