A simple method to optimize hospital beds capacity.

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.

Objectives: Benchmarking statements provide a mechanism for making academic standards explicit within a subject area. They allow comparisons between courses to be based on learning outcomes rather than by defining a curriculum. No such statement has been produced for informatics. In the absence of any established benchmarking statements for informatics a new biomedical informatics course at St. George’s has developed a first benchmarking statement – which defines the skills knowledge and understanding a biomedical informatics student should acquire by the time they complete the course. Methods: Review of national biomedical science and computing subject benchmarking statements and academic educational objectives and national occupational competencies in informatics. Results: We have developed a twenty-item benchmarking statement and this is available on-line at: http://www.gpinformatics.org/benchmark2006/. This benchmarking statement includes a definition and justification for all twenty statements. We found international educational objectives and national informatics competencies useful and these are mapped to each one. National subject benchmarks for computing and biomedical science were less useful and have not been systematically mapped. Conclusions: Benchmarking the skills, knowledge and understanding that a student should acquire during their course of study may be more useful than setting a standard curriculum. This benchmarking statement is a first step towards defining the learning outcomes and competencies a student of this discipline should acquire. The international informatics community should consider moving from a standard curriculum to an agreed subject benchmarking statement for medical, health and biomedical informatics.

https://core.ac.uk/download/pdf/6200204.pdf

An informatics benchmarking statement.

/pdf/compartmental-flow-modelling-of-acute-care-hospital-bed-4tw0uy10ce.pdf

Compartmental Flow Modelling of Acute Care Hospital Bed Occupancy for Strategic Decision-Making

The determination of the number of beds needed for a hospital department is a complex problem that try to take into account efficiency, forecasting of needs, appropriateness of stays. Health authority used methods based on ratios that do not take into account local specificities and use rather to support an economic decision. On the other side, the models developed are too specific to be applied to all type of hospital department. Moreover, all the solutions depend on the LoS (Length of Stay). We have developed a non parametric method to solve this problem. This modelisation was successfully tested in teaching and non teaching hospitals, for an Intensive Care Unit, two Internal Medicine and a surgical departments. A software easy to use was developed, working on Windows available on our website www.sante.univ-nantes.fr/med/stat/.

Beds Simulator 1.0: a software for the modelisation of the number of beds required for a hospital department.

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Beds Simulator 1.0: a software for the modelisation of the number of beds required for a hospital department.