A hybrid analytical model for an entire hospital resource optimisation

Abstract

Given the escalating healthcare costs around the world (more than 10% of the world's GDP) and increasing demand hospitals are under constant scrutiny in terms of managing services with limited resources and tighter budgets. Hospitals endeavour to find sustainable solutions for a variety of challenges ranging from productivity enhancements to resource allocation. For instance, in the UK, evidence suggests that hospitals are struggling due to increased delayed transfers of care, bed-occupancy rates well above the recommended levels of 85% and unmet A&E performance targets. In this paper, we present a hybrid forecasting-simulation-optimisation model for an NHS Foundation Trust in the UK. Using the Hospital Episode Statistics dataset for A&E, outpatient and inpatient services, we estimate the future patient demands for each speciality and model how it behaves with the forecasted activity in the future. Discrete event simulation is used to capture the entire hospital within a simulation environment, where the outputs is used as inputs into a multi-period integer linear programming (MILP) model to predict three vital resource requirements (on a monthly basis over a 1-year period), namely beds, physicians and nurses. We further carry out a sensitivity analysis to establish the robustness of solutions to changes in parameters, such as nurse-to-bed ratio. This type of modelling framework is developed for the first time to better plan the needs of hospitals now and into the future.

Healthcare services gap analysis: a supply capture and demand forecast modelling, Dubai 2018–2030

“…It is also recommended that at the micro level each hospital in the Emirate of Dubai conduct resource optimization exercise for better planning of hospital immediate and future needs. Similar practices for hospital resource optimization exist globally such as the National Health Services (NHS) hybrid forecasting-simulation–optimization model [ 27 ].…”

Can we accurately forecast non-elective bed occupancy and admissions in the NHS? A time-series MSARIMA analysis of longitudinal data from an NHS Trust

“…Further, Ordu et al 8 emphasise the importance of testing different modelling techniques for varying specialties, services and patients. More complex modelling strategies could prove to be more accurate, like the combination of different modelling techniques, such as hybrid forecasting–simulation–optimisation models,17 18 which can be used for not only A&E but inpatient and outpatient services as well.…”

“…Medicine and surgery may have different drivers in terms of admission to hospital, and longer lengths of stay tend to have different characteristics. Other work17 18 has modelled not only beds but nurse and physician availability in A&E, inpatient and outpatient services.…”

Modeling in Healthcare: Selection of Simulation Types

“…Even though DES is not a tool that will give the optimal solutions, "what if" scenarios could be analyzed in the model before modification or improvement is done to the real systems, especially in healthcare, which involves high risks or fatalities [20], [22]. Utilization of medical staff, including doctors and nurses, and waiting times and movements of patients are among the performance measures that are generally produced by DES [21]. DES usually comes together with queuing analysis since the performance measures of both techniques are similar [15], [23].…”

“…Usually, hybridization of simulation types is used to combine both strategic and operational issues. Ordu et al [21] combined DES with forecasting and optimization models, in a sequential flow. The outputs of forecasting were used as the inputs of the DES model, then the outputs from DES were used as inputs into the optimization model.…”