Developing a platform for comparison of hospital admission systems: An illustration

Vissers, Jmh Jan; Adan, Ijbf Ivo; Dellaert, Nico

doi:10.1016/j.ejor.2006.04.034

Cited by 57 publications

(40 citation statements)

References 14 publications

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“…Some of these [5,[9][10][11][12][13] are queueing models, others [14][15][16][17][18][19] utilise simulation or population/ ratio based approaches which use average lengths of stay to quantify the number of patients requiring care resources, for example [20][21][22][23][24]. However, some of these methods, which are mostly stochastic or simulation based, are computationally very complex and require substantial data and logistics [25].…”

Section: Introductionmentioning

confidence: 99%

A non-homogeneous discrete time Markov model for admission scheduling and resource planning in a cost or capacity constrained healthcare system

Garg

McClean

Meenan

et al. 2009

Health Care Manag Sci

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Healthcare resource planners need to develop policies that ensure optimal allocation of scarce healthcare resources. This goal can be achieved by forecasting daily resource requirements for a given admission policy. If resources are limited, admission should be scheduled according to the resource availability. Such resource availability or demand can change with time. We here model patient flow through the care system as a discrete time Markov chain. In order to have a more realistic representation, a nonhomogeneous model is developed which incorporates timedependent covariates, namely a patient's present age and the present calendar year. The model presented in this paper can be used for admission scheduling, resource requirement forecasting and resource allocation, so as to satisfy the demand or resource constraints or to meet the expansion or contraction plans in a hospital and community based integrated care system. Such a model can be used with both fixed and variable numbers of admissions per day and should prove to be a useful tool for care managers and policy makers who require to make strategic management decisions. We also describe an application of the model to an elderly care system, using a historical dataset from the geriatric department of a London hospital.

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Section: Introductionmentioning

confidence: 99%

A non-homogeneous discrete time Markov model for admission scheduling and resource planning in a cost or capacity constrained healthcare system

Garg

McClean

Meenan

et al. 2009

Health Care Manag Sci

View full text Add to dashboard Cite

show abstract

“…To compare different approaches to hospital admission planning Vissers, Adan and Dellaert [2] use a framework that distinguishes different levels within admission planning: the admission service concept at strategic level (what is the philosophy behind and what are the objectives for admission planning), the admission policy at tactical level (what is the mix of patients to be admitted and what amount of resources is required), and admission scheduling rules at operational level (what is the best scheduling of individual patients).…”

Section: Introductionmentioning

confidence: 99%

Patient mix optimisation and stochastic resource requirements: A case study in cardiothoracic surgery planning

Adan

Bekkers

Dellaert

et al. 2008

Health Care Manag Sci

109

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Cardiothoracic surgery planning involves different resources such as operating theatre time, beds, IC beds and nursing staff. In the daily practice of the Thorax Centre case study setting, the planning focuses on optimal use of operating theatre time, though the performance of the Thorax Centre as a whole is often more limited by other resources. For operating theatres a master surgical schedule is used to allocate operating theatre resources at tactical level for a longer period. Operational schedules at weekly level are derived from this master schedule. Within cardiothoracic surgery different categories of patients can be distinguished based on their requirement of resources. The mix of patients operated is, therefore, an important decision variable for the Thorax Centre to manage the use of these resources. In this paper we will consider the planning problem at the tactical level to generate a master surgical schedule that realises a given target of patient throughput and optimises an objective function for the utilisation of resources. The problem can be mathematically approached by mixed integer linear programming, which we already demonstrated in a previous paper. The specific topic of the current paper is to investigate the influence of using a stochastic instead of a deterministic length of stay. We will discuss the new mathematical model developed for this planning problem. The results obtained by the model indicate that we can generate master surgical schedules with a better performance on target utilization levels of resources by considering the stochastic length of stay.

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“…The challenge is to cope with variability in patient arrivals and length of stay. Smoothing patient inflow, and thus workload at nursing wards, prevents large differences between peak and non-peak periods, and so realizes a more efficient use of resources [4,238,502].…”

Section: Tactical Planningmentioning

confidence: 99%

“…For many inpatient care services the authority on admission control is limited due to the high dependency on the operating room schedule (see surgical care services). By adjusting the surgical schedule, extremely busy and slack periods can be avoided [4,26,152,156,170,210,217,238,469,492,493,501,502,524] and cancellation of elective surgeries can be avoided [291]. In practice, the operating room planning is generally done under the assumption that a free bed is available for postoperative care [293], which may result in surgery cancellations.…”

Section: Tactical Planningmentioning

confidence: 99%

“…Methods: computer simulation [3,156,170,210,238,243,291,293,347,415,439,469,478,502,524], heuristics [26,501], Markov processes [46,167,249,251,296,492,493], mathematical programming [3,4,30,469], queueing theory [30,152,189,217,270,285,355,443,481] Staff-shift scheduling Shifts are hospital duties with a start and end time [74]. Shift scheduling deals with the problem of selecting what shifts are to be worked and how many employees should be assigned to each shift to meet patient demand [166,289].…”

Section: Tactical Planningmentioning

confidence: 99%

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Integrated decision making in healthcare : an operations research and management science perspective

Hulshof¹

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Developing a platform for comparison of hospital admission systems: An illustration

Cited by 57 publications

References 14 publications

A non-homogeneous discrete time Markov model for admission scheduling and resource planning in a cost or capacity constrained healthcare system

A non-homogeneous discrete time Markov model for admission scheduling and resource planning in a cost or capacity constrained healthcare system

Patient mix optimisation and stochastic resource requirements: A case study in cardiothoracic surgery planning

Integrated decision making in healthcare : an operations research and management science perspective

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