Analysing Out-patient Demand and Forecasting Theatre Requirements in a Teaching Hospital

Understanding demand on healthcare services is critical to inform resourcing decisions for service demands. We ask two questions: 1) Can out-patient (OPD) demand for the plastic and reconstructive services be forecast? 2) Can we predict theatre requirements in terms of volume, type or complexity? The use of Time Series Analysis (TSA), simulation modelling, data-driven methods including data mining are reviewed to address the questions. Starting with a knowledge-discovery in databases methodology, Autoregressive integrated moving average (ARIMA) TSA is applied to forecast OPD referral demand. Monte Carlo simulation (MCs) is used to forecast the theatre requirements in terms of type, complexity, volume, and duration. The ARIMA modelling forecasts 4,151 OPD referrals in the coming 12 months, which results in the requirement for 499 theatre sessions with intensive care facilities (total of 671 surgical intervention procedures); 301 minor theatre sessions (total of 1,836 procedures) and 206 theatre sessions (total of 761 procedures). Surgical intervention (procedure) types and theatre requirements form the research output that predicts an increase in theatre capacity is required to keep pace with demand in the short term. The insight provided into issues allows informed strategy development and decision-making. Our methodology can be easily adapted and applied to other surgical specialities with similar datasets.