Modelling resource availability in general hospitals design and implementation of a decision support model |
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| Affiliation: | 1. FSEGS, MODILS, Route de l''aéroport km4, Sfax 3018, Tunisia;2. Institut des Sciences et Techniques, Campus du Mont Houy, 59313 Valenciennes cedex 9;1. School of Health and Rehabilitation Sciences, The Ohio State University, Columbus, OH 43210, USA;2. Department of Integrated Systems Engineering, The Ohio State University, Columbus, OH 43210, USA;3. Department of Orthopedics, The Ohio State University, Columbus, OH 43210, USA;4. University of Louisville School of Nursing, Louisville, KY 40292, USA;5. College of Nursing, The Ohio State University, Columbus, OH 43210, USA;6. Ohio Supercomputer Center, Columbus, OH 43212, USA;1. European Centre of Excellence, NTIS New Technologies for Information Society, Faculty of Applied Sciences, University of West Bohemia in Pilsen, Univerzitní 22, 30614 Plzeň, Czech Republic;2. New Technologies Research Centre, University of West Bohemia in Pilsen, Univerzitní 8, 30614 Plzeň, Czech Republic;3. Université Paris-Est, ESIEE, Noisy-le-Grand Cedex, France;1. Department of Physical Medicine and Rehabilitation, University of Pittsburgh, USA;2. Center for Neuroscience, University of Pittsburgh, USA;3. Safar Center for Resuscitation Research, University of Pittsburgh, USA;4. Department of Critical Care Medicine, University of Pittsburgh, USA;5. Department of Obstetrics and Gynecology, Wake Forest University, USA;1. Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark;2. Danish Research Institute for Suicide Prevention, Mental Health Centre Copenhagen, Copenhagen, Denmark;3. Psychosis Research Unit, Aarhus University Hospital, Risskov, Denmark;4. National Centre for Register-based Research, Aarhus University, Aarhus, Denmark;5. Centre for Integrated Register-based Research at Aarhus University, Aarhus, Denmark;7. Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark;8. University of Maryland School of Medicine, Baltimore, Maryland;9. Rocky Mountain Mental Illness Research, Education and Clinical Center for Suicide Prevention, Denver, Colorado;10. The Military and Veteran Microbiome Consortium for Research and Education, Denver, Colorado;1. Carlson School of Management, University of Minnesota Twin Cities, 321 19th Ave S, Minneapolis, MN 55455, United States;2. Graduate School of Business, Stanford University, 655 Knight Way, Stanford, CA 94305, United States |
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| Abstract: | Admission planning in general hospitals means selecting elective patients from a waiting list in order to obtain optimal utilisation of the available beds, nursing staff and operating theatre facilities while taking into account emergency admissions. Also, a wide variety of other factors, often situational and not explicitly stated, play a part in this decision process. As such, it is a complex problem which is difficult to handle for any decision maker. In this paper a statistical model for the prediction of resource availability is presented. The model is first tested on empirical data. On the basis of the model a DSS was designed which is now in daily use in several hospitals. Problems encountered in that implementation process will be stated. The results obtained with the model show that such an approach based on statistical data provides sufficiently accurate results to be useful. |
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