Open AccessHealthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework
Author(s) -
Sarah Collins Rossetti,
Chris Knaplund,
David J. Albers,
Patricia C. Dykes,
Min Jeoung Kang,
Tom Z. Korach,
Li Zhou,
Kumiko O. Schnock,
José Pérez García,
James H. Schwartz,
Li-Heng Fu,
Jeffrey G. Klann,
Graham Lowenthal,
Kenrick Cato
Publication year - 2021
Publication title -
journal of the american medical informatics association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.614
H-Index - 150
eISSN - 1527-974X
pISSN - 1067-5027
DOI - 10.1093/jamia/ocab006
Subject(s) - computer science , leverage (statistics) , conceptual framework , process (computing) , health care , predictive analytics , data science , machine learning , artificial intelligence , knowledge management , process management , philosophy , business , epistemology , economics , economic growth , operating system
Objective 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). Materials and Methods 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. Results 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. Discussion 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. Conclusions 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.