Simulation applications on operations management in hospitals are frequently published and claim to support decision-making on operations management subjects. However, the reported implementation rates of recommendations are low and the actual impact of the changes recommended by the modeler has hardly been examined. This paper examines: 1) the execution rate of simulation study recommendations, 2) the research methods used to evaluate implementation of recommendations, 3) factors contributing to implementation, and 4) the differences regarding implementation between literature and practice.

Discharge planning is integral to patient flow as delays can lead to hospital-wide congestion. Because a structured discharge plan can reduce hospital length of stay while enhancing patient satisfaction, this topic has caught the interest of many healthcare professionals and researchers. Predicting discharge outcomes, such as destination and time, is crucial in discharge planning by helping healthcare providers anticipate patient needs and resource requirements. This article examines the literature on the prediction of various discharge outcomes. Our review discovered papers that explore the use of prediction models to forecast the time, volume, and destination of discharged patients. Of the 101 reviewed papers, 49.5% looked at the prediction with machine learning tools, and 50.5% focused on prediction with statistical methods. The fact that knowing discharge outcomes in advance affects operational, tactical, medical, and administrative aspects is a frequent theme in the papers studied. Furthermore, conducting system-wide optimization, predicting the time and destination of patients after discharge, and addressing the primary causes of discharge delay in the process are among the recommendations for further research in this field.

Mixed registration type clinics accept both walk-in and scheduled patients. Such clinics provide patients with an additional option for how they access care while patient bookings help providers ensure a full workday. The model described in this paper determines how many patients to schedule (and when) in mixed registration type clinics. The methodology, simulation optimisation allows stochastic features found in such clinic to be modelled and provides optimisation techniques to identify solutions. A general simulation optimisation formulation for mixed registration type clinics is presented. Furthermore, the methodology is applied to a case study of a collaborative emergency centre in Nova Scotia, Canada. We demonstrate how the model can be used in clinics with multiple providers and multiple objectives. We compare the simulation optimisation generated schedule with existing schedules and show the advantages the collaborative emergency centre can expect when using schedules developed with the presented methods.