Strategic room type allocation for nursing wards through Markov chain modeling

Abstract

7Providing patients with the best possible care is the most essential function of any hospital. In an 8 increasing number of countries, hospitals are governed by the number of patients they are able to attract 9 and the corresponding services they provide for patients. One such service, which is often of significant 10 importance for patients, is the option to choose their room type. 11Hospital decision makers would benefit from a strategic method for optimizing the configuration of room 12 types among nursing wards by distinguishing between patients who prefer private rooms and those who 13 have no preference concerning whether they are assigned to a private or shared room. Such a decision 14 support method is currently non-existent, therefore the goal of this study is to provide a methodology for 15 hospital management. Specifically, a mixed modeling approach is proposed which evaluates the patient 16 flow behavior by applying a Continuous-Time Markov Chain within a heuristic search procedure. This 17 procedure recursively improves a configuration of rooms among the wards by sampling from a gradually 18 improved interpolation of an objective function. 19 Based on patient data obtained from both a Danish and Belgian hospital, the performance and robustness 20 of the proposed approach is validated through various numerical experiments, demonstrating that solutions 21 within a relative gap of 1% from the optimum are attained in most cases.22

Optimisation and control of the supply of blood bags in hemotherapic centres via Markov decision process with discounted arrival rate

“…In this paper, we model blood inventory as a Markov decision process -MDP. MDPs are designed for sequential decision making under uncertainty (Puterman, 2014), hence their frequent use in medical applications (e.g., Andersen et al, 2019;Arruda et al, 2019;Steimle and Denton, 2017;Bennett and Hauser, 2013;Alagoz et al, 2010) and in blood inventory problems (e.g., Attari et al, 2019b;Hosseinifard et al, 2019). The use of an MDP ensures more flexibility as it allows an optimal decision for each available state, while also ensuring optimality in the long run.…”

Discrete simulation applied to the improvement of patient flows in hospital institutions

“…It allows analyzing different scenarios and percentages of use based on objectives clearly set by the management. Other tools, such as the integration of machine learning models and linear programming [40] and Markovian modeling [41], that allow resource planning in the hospital context are evidenced in the literature.…”