Decision support for hospital bed management using adaptable individual length of stay estimations and shared resources.
Background: Elective patient admission and assignment planning is an important task of the strategic and operational management of a hospital and early on became a central topic of clinical operations research. The management of hospital beds is an important subtask. Various approaches have been pro...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 13; no. 1; pp. 3 - 4 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104269581&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104269581 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 2013 vid: 13 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 104269581 104269581 NLM23289448 2012080881 10.1186/1472-6947-13-3 NLM23289448 PMC3621822 104269581 ppf: 3 ppct: 1 formats: tig: atl: Decision support for hospital bed management using adaptable individual length of stay estimations and shared resources. aug: au: Schmidt, Robert Geisler, Sandra Spreckelsen, Cord affil: Institute for Medical Informatics, RWTH Aachen University, Pauwelsstr, 30, Aachen, 52074, Germany. robert.schmidt@rwth-aachen.de. sug: subj: Decision Support Systems, Clinical Emergency Service Administration Bed Occupancy Shared Services, Health Care Administration Length of Stay Statistics and Numerical Data Case Management Decision Making, Computer Assisted Diagnosis-Related Groups Organizational Efficiency Germany Health Resource Allocation Shared Services, Health Care Economics Shared Services, Health Care Labor Supply Human Employee Orientation Interviews Models, Statistical Outcomes (Health Care) Classification Qualitative Studies Quality Improvement ab: Background: Elective patient admission and assignment planning is an important task of the strategic and operational management of a hospital and early on became a central topic of clinical operations research. The management of hospital beds is an important subtask. Various approaches have been proposed, involving the computation of efficient assignments with regard to the patients' condition, the necessity of the treatment, and the patients' preferences. However, these approaches are mostly based on static, unadaptable estimates of the length of stay and, thus, do not take into account the uncertainty of the patient's recovery. Furthermore, the effect of aggregated bed capacities have not been investigated in this context. Computer supported bed management, combining an adaptable length of stay estimation with the treatment of shared resources (aggregated bed capacities) has not yet been sufficiently investigated. The aim of our work is: 1) to define a cost function for patient admission taking into account adaptable length of stay estimations and aggregated resources, 2) to define a mathematical program formally modeling the assignment problem and an architecture for decision support, 3) to investigate four algorithmic methodologies addressing the assignment problem and one base-line approach, and 4) to evaluate these methodologies w.r.t. cost outcome, performance, and dismissal ratio.Methods: The expected free ward capacity is calculated based on individual length of stay estimates, introducing Bernoulli distributed random variables for the ward occupation states and approximating the probability densities. The assignment problem is represented as a binary integer program. Four strategies for solving the problem are applied and compared: an exact approach, using the mixed integer programming solver SCIP; and three heuristic strategies, namely the longest expected processing time, the shortest expected processing time, and random choice. A baseline approach serves to compare these optimization strategies with a simple model of the status quo. All the approaches are evaluated by a realistic discrete event simulation: the outcomes are the ratio of successful assignments and dismissals, the computation time, and the model's cost factors.Results: A discrete event simulation of 226,000 cases shows a reduction of the dismissal rate compared to the baseline by more than 30 percentage points (from a mean dismissal ratio of 74.7% to 40.06% comparing the status quo with the optimization strategies). Each of the optimization strategies leads to an improved assignment. The exact approach has only a marginal advantage over the heuristic strategies in the model's cost factors (≤3%). Moreover,this marginal advantage was only achieved at the price of a computational time fifty times that of the heuristic models (an average computing time of 141 s using the exact method, vs. 2.6 s for the heuristic strategy).Conclusions: In terms of its performance and the quality of its solution, the heuristic strategy RAND is the preferred method for bed assignment in the case of shared resources. Future research is needed to investigate whether an equally marked improvement can be achieved in a large scale clinical application study, ideally one comprising all the departments involved in admission and assignment planning. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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