Journal Article

Dynamic capacity allocation in a radiology service considering different types of patients, individual no-show probabilities, and overbooking

Krindges, André, Fogliatto, Flávio Sanson, da Silva, Rodolfo Benedito Zattar, dos Santos Cecconello, Moiseis

2021

Overview

Background We propose a mathematical model formulated as a finite-horizon Markov Decision Process (MDP) to allocate capacity in a radiology department that serves different types of patients. To the best of our knowledge, this is the first attempt at considering radiology resources with different capacities and individual no-show probabilities of ambulatory patients in an MDP model. To mitigate the negative impacts of no-show, overbooking rules are also investigated. Methods The model’s main objective is to identify an optimal policy for allocating the available capacity such that waiting, overtime, and penalty costs are minimized. Optimization is carried out using traditional dynamic programming (DP). The model was applied to real data from a radiology department of a large Brazilian public hospital. The optimal policy is compared with five alternative policies, one of which resembles the one currently used by the department. We identify among alternative policies the one that performs closest to the optimal. Results The optimal policy presented the best performance (smallest total daily cost) in the majority of analyzed scenarios (212 out of 216). Numerical analyses allowed us to recommend the use of the optimal policy for capacity allocation with a double overbooking rule and two resources available in overtime periods. An alternative policy in which outpatients are prioritized for service (rather than inpatients) displayed results closest to the optimal policy, being also recommended due to its easy implementation. Conclusions Based on such recommendation and observing the state of the system at any given period (representing the number of patients waiting for service), radiology department managers should be able to make a decision (i.e., define number and type of patients) that should be selected for service such that the system’s cost is minimized.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Analysis / Capacity allocation / Capacity management (Computers) / Decision making / Emergency medical care / Health Administration / Health care / Health care expenditures / Health Informatics / Health services / Hospitals / Markov analysis / Markov decision processes / Markov processes / Mathematical models / Medical imaging / Medicine / Medicine & Public Health / Methods / No-show / Numerical analysis / Nursing Research / Overbooking / Patients / Public Health / Quality / Radiology / Radiology services / Radiology, Medical / Research Article / safety and outcomes / Schedules / Services / Tomography