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Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework

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 Sign...

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Autores principales: Rossetti, Sarah Collins, Knaplund, Chris, Albers, Dave, Dykes, Patricia C, Kang, Min Jeoung, Korach, Tom Z, Zhou, Li, Schnock, Kumiko, Garcia, Jose, Schwartz, Jessica, Fu, Li-Heng, Klann, Jeffrey G, Lowenthal, Graham, Cato, Kenrick
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8200261/
https://www.ncbi.nlm.nih.gov/pubmed/33624765
http://dx.doi.org/10.1093/jamia/ocab006
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author Rossetti, Sarah Collins
Knaplund, Chris
Albers, Dave
Dykes, Patricia C
Kang, Min Jeoung
Korach, Tom Z
Zhou, Li
Schnock, Kumiko
Garcia, Jose
Schwartz, Jessica
Fu, Li-Heng
Klann, Jeffrey G
Lowenthal, Graham
Cato, Kenrick
author_facet Rossetti, Sarah Collins
Knaplund, Chris
Albers, Dave
Dykes, Patricia C
Kang, Min Jeoung
Korach, Tom Z
Zhou, Li
Schnock, Kumiko
Garcia, Jose
Schwartz, Jessica
Fu, Li-Heng
Klann, Jeffrey G
Lowenthal, Graham
Cato, Kenrick
author_sort Rossetti, Sarah Collins
collection PubMed
description 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.
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spelling pubmed-82002612021-06-14 Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework Rossetti, Sarah Collins Knaplund, Chris Albers, Dave Dykes, Patricia C Kang, Min Jeoung Korach, Tom Z Zhou, Li Schnock, Kumiko Garcia, Jose Schwartz, Jessica Fu, Li-Heng Klann, Jeffrey G Lowenthal, Graham Cato, Kenrick J Am Med Inform Assoc Research and Applications 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. Oxford University Press 2021-02-24 /pmc/articles/PMC8200261/ /pubmed/33624765 http://dx.doi.org/10.1093/jamia/ocab006 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permitsunrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research and Applications
Rossetti, Sarah Collins
Knaplund, Chris
Albers, Dave
Dykes, Patricia C
Kang, Min Jeoung
Korach, Tom Z
Zhou, Li
Schnock, Kumiko
Garcia, Jose
Schwartz, Jessica
Fu, Li-Heng
Klann, Jeffrey G
Lowenthal, Graham
Cato, Kenrick
Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework
title Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework
title_full Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework
title_fullStr Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework
title_full_unstemmed Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework
title_short Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework
title_sort healthcare process modeling to phenotype clinician behaviors for exploiting the signal gain of clinical expertise (hpm-expertsignals): development and evaluation of a conceptual framework
topic Research and Applications
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8200261/
https://www.ncbi.nlm.nih.gov/pubmed/33624765
http://dx.doi.org/10.1093/jamia/ocab006
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