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Erschienen in: The European Journal of Health Economics 1/2017

29.12.2015 | Original Paper

An empirical comparison of Markov cohort modeling and discrete event simulation in a capacity-constrained health care setting

verfasst von: L. B. Standfield, T. A. Comans, P. A. Scuffham

Erschienen in: The European Journal of Health Economics | Ausgabe 1/2017

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Abstract

Objectives

To empirically compare Markov cohort modeling (MM) and discrete event simulation (DES) with and without dynamic queuing (DQ) for cost-effectiveness (CE) analysis of a novel method of health services delivery where capacity constraints predominate.

Methods

A common data-set comparing usual orthopedic care (UC) to an orthopedic physiotherapy screening clinic and multidisciplinary treatment service (OPSC) was used to develop a MM and a DES without (DES-no-DQ) and with DQ (DES-DQ). Model results were then compared in detail.

Results

The MM predicted an incremental CE ratio (ICER) of $495 per additional quality-adjusted life-year (QALY) for OPSC over UC. The DES-no-DQ showed OPSC dominating UC; the DES-DQ generated an ICER of $2342 per QALY.

Conclusions

The MM and DES-no-DQ ICER estimates differed due to the MM having implicit delays built into its structure as a result of having fixed cycle lengths, which are not a feature of DES. The non-DQ models assume that queues are at a steady state. Conversely, queues in the DES-DQ develop flexibly with supply and demand for resources, in this case, leading to different estimates of resource use and CE. The choice of MM or DES (with or without DQ) would not alter the reimbursement of OPSC as it was highly cost-effective compared to UC in all analyses. However, the modeling method may influence decisions where ICERs are closer to the CE acceptability threshold, or where capacity constraints and DQ are important features of the system. In these cases, DES-DQ would be the preferred modeling technique to avoid incorrect resource allocation decisions.
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Metadaten
Titel
An empirical comparison of Markov cohort modeling and discrete event simulation in a capacity-constrained health care setting
verfasst von
L. B. Standfield
T. A. Comans
P. A. Scuffham
Publikationsdatum
29.12.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
The European Journal of Health Economics / Ausgabe 1/2017
Print ISSN: 1618-7598
Elektronische ISSN: 1618-7601
DOI
https://doi.org/10.1007/s10198-015-0756-z

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