Using Time Series Analysis to forecast future RACGP OSCE capacity

Abstract

Problem: The Australian government increased the number of first year general practice (GP) trainee places from 600 in 2010 to 1,200 in 2014 and again to 1,500 in 2015. No extra funding was provided to assess the clinical competency of the trainees to ensure they meet unsupervised Australian General Practice standards, that is, for trainees to be workforce ready. Objective: To forecast the timing and number of GP trainees requiring clinical assessment. The focus is the Objective Structured Clinical Examination (OSCE) at the Royal Australian College of General Practitioners (RACGP). This research will encourage a proactive approach to capacity planning and reduce potential GP workforce delays. Participants/Data: Historical aggregate RACGP and General Practice Education and Training (GPET) data were linked for the first time to produce time-series forecasts. Ethics approval was not required. Individuals could not be identified as only aggregate data was used. Results: The best forecast model from over 30 models was adjusted using GPET data. Based on two OSCEs a year, figures suggest a potential steady increase of candidates from 2014 semester 2 (2014.2) to 2016.2, with figures expected to reach about 900 and 1,100 respectively. In 2018.2, candidates are expected to peak to approximately 1,200 per semester. For perspective, to assess 600 candidates, around 1,000 FRACGP GPs are required as examiners in a day. Current capacity is stretched to assess 800 candidates. More needs to be done to meet future expected candidates. Conclusion: The forecasts show how many candidates are expected to present for future OSCEs and their respective timing. This forecast can enhance current and future education and training capacity planning by planning additional exams, updating policies and continuing collaboration between organisations. Benefits of this research extend beyond the RACGP, medical workforce planning and continual professional development bodies. These forecasts could be applied in other health areas other than education. For example modelling patients, hospital resources required costs of services over time, etc.