A framework for making predictive models useful in practice. (22nd December 2020)

• Record Type: Journal Article
• Authors: Jung, Kenneth; Kashyap, Sehj; Avati, Anand; Harman, Stephanie; Shaw, Heather; Li, Ron; Smith, Margaret; Shum, Kenny; Javitz, Jacob; Vetteth, Yohan; Seto, Tina; Bagley, Steven C; Shah, Nigam H
• Is Part Of: Journal of the American Medical Informatics Association. Volume 28:Number 6(2021)
• Page Start: 1149
• Page End: 1158
• Publication Date: 2020-12-22
• Subjects: machine -- learning, evaluation, utility assessment, workflow simulation, advanced care planning; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocaa318
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: To analyze the impact of factors in healthcare delivery on the net benefit of triggering an Advanced Care Planning (ACP) workflow based on predictions of 12-month mortality. Materials and Methods: We built a predictive model of 12-month mortality using electronic health record data and evaluated the impact of healthcare delivery factors on the net benefit of triggering an ACP workflow based on the models' predictions. Factors included nonclinical reasons that make ACP inappropriate: limited capacity for ACP, inability to follow up due to patient discharge, and availability of an outpatient workflow to follow up on missed cases. We also quantified the relative benefits of increasing capacity for inpatient ACP versus outpatient ACP. Results: Work capacity constraints and discharge timing can significantly reduce the net benefit of triggering the ACP workflow based on a model's predictions. However, the reduction can be mitigated by creating an outpatient ACP workflow. Given limited resources to either add capacity for inpatient ACP versus developing outpatient ACP capability, the latter is likely to provide more benefit to patient care. Discussion: The benefit of using a predictive model for identifying patients for interventions is highly dependent on the capacity to execute the workflow triggered by the model. We provide a framework for quantifying the impact of healthcare delivery factors and work capacity constraints on achieved benefit. Conclusion: An analysis of the sensitivity of the net benefit realized by a predictive model triggered clinical workflow to various healthcare delivery factors is necessary for making predictive models useful in practice.