BMC Med Inform Decis Mak - Decision support for hospital bed management using adaptable individual length of stay estimations and shared resources.


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CKGROUND: 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.

Resumo Limpo

ckground elect patient admiss assign plan import task strateg oper manag hospit earli becam central topic clinic oper research manag hospit bed import subtask various approach propos involv comput effici assign regard patient condit necess treatment patient prefer howev approach most base static unadapt estim length stay thus take account uncertainti patient recoveri furthermor effect aggreg bed capac investig context comput support bed manag combin adapt length stay estim treatment share resourc aggreg bed capac yet suffici investig aim work defin cost function patient admiss take account adapt length stay estim aggreg resourc defin mathemat program formal model assign problem architectur decis support investig four algorithm methodolog address assign problem one baselin approach evalu methodolog wrt cost outcom perform dismiss ratiomethod expect free ward capac calcul base individu length stay estim introduc bernoulli distribut random variabl ward occup state approxim probabl densiti assign problem repres binari integ program four strategi solv problem appli compar exact approach use mix integ program solver scip three heurist strategi name longest expect process time shortest expect process time random choic baselin approach serv compar optim strategi simpl model status quo approach evalu realist discret event simul outcom ratio success assign dismiss comput time model cost factorsresult discret event simul case show reduct dismiss rate compar baselin percentag point mean dismiss ratio compar status quo optim strategi optim strategi lead improv assign exact approach margin advantag heurist strategi model cost factor moreoverthi margin advantag achiev price comput time fifti time heurist model averag comput time s use exact method vs s heurist strategyconclus term perform qualiti solut heurist strategi rand prefer method bed assign case share resourc futur research need investig whether equal mark improv can achiev larg scale clinic applic studi ideal one compris depart involv admiss assign plan

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