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. 2008 Feb;3(3):75-88.

Reducing Wait Times through Operations Research: Optimizing the Use of Surge Capacity

Jonathan Patrick  1 , Martin L Puterman
Affiliations

Reducing Wait Times through Operations Research: Optimizing the Use of Surge Capacity

Jonathan Patrick et al. Healthc Policy. 2008 Feb.

Abstract
in English, French

Widespread public demand for improved access, political pressure for shorter wait times, a stretched workforce, an aging population and overutilized equipment and facilities challenge healthcare leaders to adopt new management approaches. This paper highlights the significant benefits that can be achieved by applying operations research (OR) methods to healthcare management. It shows how queuing theory provides managers with insights into the causes for excessive wait times and the relationship between wait times and capacity. It provides a case study of the use of several OR methods, including Markov decision processes, linear programming and simulation, to optimize the scheduling of patients with multiple priorities. The study shows that by applying this approach, wait time targets can be attained with the judicious use of surge capacity in the form of overtime. It concludes with some policy insights.

Les demandes du public concernant l'accès amélioré, les pressions exercées sur les décideurs en vue de réduire les temps d'attente, une main-d'œuvre poussée au-delà de ses capacités, une population vieillissante et du matériel et des installations surutilisés obligent les dirigeants du domaine de la santé à adopter de nouvelles méthodes de gestion. Le présent article met l'accent sur les avantages significatifs que procure l'application des méthodes de recherche opérationnelle (RO) à la gestion des soins de santé. Il montre comment la théorie des files d'attente permet aux gestionnaires de mieux comprendre les causes des temps d'attente excessifs et la relation entre les temps d'attente et les capacités. Il fournit une étude de cas sur l'utilisation des méthodes de RO, y compris les processus décisionnels de Markov, la programmation linéaire et la simulation pour optimiser l'établissement des horaires des patients avec des priorités multiples. L'étude montre qu'en appliquant cette méthode, on peut atteindre les temps d'attente cibles grâce à une utilisation judicieuse des capacités en cas de hausse subite de la demande sous forme d'heures supplémentaires. L'article se termine par quelques remarques générales sur les politiques.

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Figures

FIGURE 1.
FIGURE 1.
Theoretical relationship between wait time targets and idle capacity (calculations based on a single server exponential queueing model with arrival rate of 10 patients per week, service rate varies between 10 to 16 patients per week and a target of one week service time)
FIGURE 2.
FIGURE 2.
Waiting times by priority class for the OS policy (based on a simulation of 450,000 patients)
FIGURE 3.
FIGURE 3.
Waiting times by priority class for the OTLR policy (based on a simulation of 150,000 patients)
See this image and copyright information in PMC

References

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