Monitoring policy in the context of preventive treatment of cardiovascular disease.

Autor: Otero-Leon DF; Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, 48109, USA. dfotero@umich.edu., Lavieri MS; Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, 48109, USA., Denton BT; Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, 48109, USA., Sussman J; Department of Internal Medicine, University of Michigan, Ann Arbor, MI, 48109, USA., Hayward RA; Department of Internal Medicine, University of Michigan, Ann Arbor, MI, 48109, USA.
Jazyk: angličtina
Zdroj: Health care management science [Health Care Manag Sci] 2023 Mar; Vol. 26 (1), pp. 93-116. Date of Electronic Publication: 2022 Oct 26.
DOI: 10.1007/s10729-022-09621-4
Abstrakt: Preventing chronic diseases is an essential aspect of medical care. To prevent chronic diseases, physicians focus on monitoring their risk factors and prescribing the necessary medication. The optimal monitoring policy depends on the patient's risk factors and demographics. Monitoring too frequently may be unnecessary and costly; on the other hand, monitoring the patient infrequently means the patient may forgo needed treatment and experience adverse events related to the disease. We propose a finite horizon and finite-state Markov decision process to define monitoring policies. To build our Markov decision process, we estimate stochastic models based on longitudinal observational data from electronic health records for a large cohort of patients seen in the national U.S. Veterans Affairs health system. We use our model to study policies for whether or when to assess the need for cholesterol-lowering medications. We further use our model to investigate the role of gender and race on optimal monitoring policies.
(© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
Databáze: MEDLINE
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