We provide a comprehensive overview of the typical decisions to be made in resource capacity planning and control in health care, and a structured review of relevant articles from the field of Operations Research and Management Sciences (OR/MS) for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making.

/pdf/taxonomic-classification-of-planning-decisions-in-health-2eisyi4lp1.pdf

Taxonomic classification of planning decisions in health care: a structured review of the state of the art in OR/MS

In this survey we review quantitative health care models to illustrate the extent to which they encompass multiple hospital departments. The paper provides general overviews of the relationships that exists between major hospital departments and describes how these relationships are accounted for by researchers. We find the atomistic view of hospitals often taken by researchers is partially due to the ambiguity of patient care trajectories. To this end clinical pathways literature is reviewed to illustrate its potential for clarifying patient flows and for providing a holistic hospital perspective.

/pdf/a-survey-of-health-care-models-that-encompass-multiple-42tk6v2rpl.pdf

A Survey of Health Care Models that Encompass Multiple Departments

In recent years, Operations Research/Management (OR/OM) has had a significant impact on improving the performance of hospital Emergency Departments (EDs). This includes improving a wide range of processes involving patient flow from the initial call to the ED through disposition, discharge home, or admission to the hospital. We review approximately 350 related papers to (i) demonstrate the influence of OR/OM in EDs, and (ii) assist both researchers and practitioners with the OR/OM techniques already available to optimize ED patient flow. In addition, we elaborate on some practical challenges yet to be addressed. By shedding light on some less studied aspects that can have significant impacts on ED operations, we also discuss important possibilities for future OR/OM researchers.

/pdf/operations-research-management-contributions-to-emergency-1zxan2yfmd.pdf

Operations research/management contributions to emergency department patient flow optimization: Review and research prospects

Rationale: Intensive care unit (ICU) beds are a scarce resource, and patients denied intensive care only because the unit is full may be at increased risk of death.Objective: To compare mortality after first ICU referral in admitted patients and in patients denied admission because the unit was full.Methods: Prospective observational multicenter cohort study of consecutive patients referred for ICU admission during two 45-day periods, conducted in 10 ICUs.Measurements and Main Results: Of 1,762 patients, 430 were excluded from the study, 116 with previously denied admission to another ICU and 270 because they were deemed too sick or too well to benefit from ICU admission. Of the remaining 1,332 patients, 1,139 were admitted, and 193 were denied admission because the unit was full (65 were never admitted, 39 were admitted after bumping of another patient, and 89 were admitted on subsequent referral). Crude Day 28 and Day 60 mortality rates in the nonadmitted and admitted groups were 30.1 versus 24.3% (P = ...

https://www.atsjournals.org/doi/pdf/10.1164/rccm.201104-0729OC

Refusal of intensive care unit admission due to a full unit: impact on mortality.

Objective:To determine the relationship between hospital size and changes in the number of critical care medicine (CCM) beds, proportion of hospital beds allocated to CCM, and CCM occupancy in acute care hospitals in the United States from 1985 to 2000.Design:A 16-yr (1985 to 2000) retrospective ana

Changes in Critical Care Beds and Occupancy in the United States 1985-2000: Differences Attributable to Hospital Size

A simple method to optimize hospital beds capacity.

Objective
Most methods used to estimate ICU bed needs rely either on simple formulas that do not consider the actual needs of the population or on simulations that are too specific to be applicable to all hospitals. We sought to develop a universally applicable nonparametric method.

/pdf/a-universal-method-for-determining-intensive-care-unit-bed-3l8yre63ri.pdf

A universal method for determining intensive care unit bed requirements.

Objectives: To propose an objective approach in order to determine the number of beds required for a hospital department by considering how recruitment fluctuates over time. To compare this approach with classical bed capacity planning techniques. Methods: A simulated data-based evaluation of the impact that the variability in hospital department activity produces upon the performance of methods used for determining the number of beds required. The evaluation criteria included productive efficiency measured by the bed occupancy rate, accessibility measured by the transfer rate of patients due to lack of available beds and a proxy of clinical effectiveness, by the proportion of days during which there is no possibility for unscheduled admission. Results: When the variability of the number of daily patients increases, the Target Occupancy Rate favors productive efficiency at the expense of accessibility and proxy clinical effectiveness. On the contrary, when the variability of the department activity is marginal, the Target Activity Rate penalizes the proxy of clinical effectiveness, and the Target Occupancy Rate underoptimizes productive efficiency. The method we propose led to a superior performance in terms of accessibility and proxy of clinical effectiveness at the expense of productive efficiency. Such a situation is suitable for intensive care units. In the case of other departments, a weighting procedure should be used to improve productive efficiency. Conclusions: This approach could be considered as the first step of a family of methods for quantitative healthcare planning.

An objective method for bed capacity planning in a hospital department - a comparison with target ratio methods.

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A simple method to optimize hospital beds capacity.

A universal method for determining intensive care unit bed requirements.

An objective method for bed capacity planning in a hospital department - a comparison with target ratio methods.