A stochastic model of acute-care decisions based on patient and provider heterogeneity

Muge Capan (), Julie S. Ivy (), James R. Wilson () and Jeanne M. Huddleston ()
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Muge Capan: John H. Ammon Medical Education Center
Julie S. Ivy: North Carolina State University
James R. Wilson: North Carolina State University
Jeanne M. Huddleston: Mayo Clinic

Health Care Management Science, 2017, vol. 20, issue 2, No 3, 187-206

Abstract: Abstract The primary cause of preventable death in many hospitals is the failure to recognize and/or rescue patients from acute physiologic deterioration (APD). APD affects all hospitalized patients, potentially causing cardiac arrest and death. Identifying APD is difficult, and response timing is critical - delays in response represent a significant and modifiable patient safety issue. Hospitals have instituted rapid response systems or teams (RRT) to provide timely critical care for APD, with thresholds that trigger the involvement of critical care expertise. The National Early Warning Score (NEWS) was developed to define these thresholds. However, current triggers are inconsistent and ignore patient-specific factors. Further, acute care is delivered by providers with different clinical experience, resulting in quality-of-care variation. This article documents a semi-Markov decision process model of APD that incorporates patient and provider heterogeneity. The model allows for stochastically changing health states, while determining patient subpopulation-specific RRT-activation thresholds. The objective function minimizes the total time associated with patient deterioration and stabilization; and the relative values of nursing and RRT times can be modified. A case study from January 2011 to December 2012 identified six subpopulations. RRT activation was optimal for patients in “slightly concerning” health states (NEWS > 0) for all subpopulations, except surgical patients with low risk of deterioration for whom RRT was activated in “concerning” states (NEWS > 4). Clustering methods identified provider clusters considering RRT-activation preferences and estimation of stabilization-related resource needs. Providers with conservative resource estimates preferred waiting over activating RRT. This study provides simple practical rules for personalized acute care delivery.

Keywords: Acute physiological deterioration; Early warning scores; Semi-Markov decision process model; Cluster analysis; 60J28; (Applications; of; continuous-time; Markov; processes; on; discrete; state; spaces) (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10729-015-9347-x

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