Health care management science
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Health Care Manag Sci · Jun 2016
A markov decision process model for the optimal dispatch of military medical evacuation assets.
We develop a Markov decision process (MDP) model to examine aerial military medical evacuation (MEDEVAC) dispatch policies in a combat environment. The problem of deciding which aeromedical asset to dispatch to each service request is complicated by the threat conditions at the service locations and the priority class of each casualty event. We assume requests for MEDEVAC support arrive sequentially, with the location and the priority of each casualty known upon initiation of the request. ⋯ The utility gained from servicing a specific request depends on the number of casualties, the priority class for each of the casualties, and the locations of both the servicing ambulatory helicopter and casualty event. Instances of the dispatching problem are solved using a relative value iteration dynamic programming algorithm. Computational examples are used to investigate optimal dispatch policies under different threat situations and armed escort delays; the examples are based on combat scenarios in which United States Army MEDEVAC units support ground operations in Afghanistan.
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Health Care Manag Sci · Mar 2016
Reducing Emergency Medical Service response time via the reallocation of ambulance bases.
The demand for highly efficient and effective services and consumer goods is an essential prerequisite for modern organizations. In healthcare, efficiency and effectiveness mean reducing disabilities and maintaining human life. One challenge is guaranteeing rapid Emergency Medical Service (EMS) response. ⋯ A simulation of this proposed configuration is run to analyze the dynamic behavior of the system. The main assumption is that optimizing the ambulance base locations can improve the system response time. Feasible solutions were found and the current system may be improved while considering economic and operational changes.
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Health Care Manag Sci · Dec 2015
Allocating operating room block time using historical caseload variability.
Operating room (OR) allocation and planning is one of the most important strategic decisions that OR managers face. The number of ORs that a hospital opens depends on the number of blocks that are allocated to the surgical groups, services, or individual surgeons, combined with the amount of open posting time (i.e., first come, first serve posting) that the hospital wants to provide. By allocating too few ORs, a hospital may turn away surgery demand whereas opening too many ORs could prove to be a costly decision. ⋯ This algorithm could be used to adjust existing blocks or to assign new blocks to surgeons that did not previously have a block. We also have studied the effect of turnover time on the number of ORs that needs to be allocated. Numerical experiments based on real data from a large health-care provider indicate the opportunity to achieve over 2,900 hours of OR time savings through improved block allocations.
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In this paper, we consider two hospitals with different perceived quality of care competing to capture a fraction of the total market demand. Patients select the hospital that provides the highest utility, which is a function of price and the patient's perceived quality of life during their life expectancy. ⋯ We also show that in a monopoly market (a market with a single hospital) the optimal demand captured by the hospital is independent of the perceived quality of care. We investigate the effects of different parameters including the market demand, hospitals' capacities, and perceived quality of care on the fraction of the demand that each hospital captures using some numerical examples.
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Resource rationalization and reduction of waiting lists for surgery are two main guidelines for hospital units outlined in the Portuguese National Health Plan. This work is dedicated to an elective surgery scheduling problem arising in a Lisbon public hospital. In order to increase the surgical suite's efficiency and to reduce the waiting lists for surgery, two objectives are considered: maximize surgical suite occupation and maximize the number of surgeries scheduled. ⋯ This study shows that by using the bicriteria approach presented here it is possible to build surgical plans with very good performance levels. This method can be used within an interactive approach with the decision maker. It can also be easily adapted to other hospitals with similar scheduling conditions.