Workforce predictive risk modelling: development of a model to identify general practices at risk of a supply−demand imbalance. Issue 1 (23rd January 2020)

• Record Type: Journal Article
• Authors: Abel, Gary A; Gomez-Cano, Mayam; Mustafee, Navonil; Smart, Andi; Fletcher, Emily; Salisbury, Chris; Chilvers, Rupa; Dean, Sarah Gerard; Richards, Suzanne H; Warren, F; Campbell, John L
• Is Part Of: BMJ open. Volume 10:Issue 1(2020)
• Publication Date: 2020-01-23
• Subjects: primary care -- workforce -- supply-demand -- organisation of health services; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2018-027934
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Objective: This study aimed to develop a risk prediction model identifying general practices at risk of workforce supply–demand imbalance. Design: This is a secondary analysis of routine data on general practice workforce, patient experience and registered populations (2012 to 2016), combined with a census of general practitioners' (GPs') career intentions (2016). Setting/Participants: A hybrid approach was used to develop a model to predict workforce supply–demand imbalance based on practice factors using historical data (2012–2016) on all general practices in England (with over 1000 registered patients n=6398). The model was applied to current data (2016) to explore future risk for practices in South West England (n=368). Primary outcome measure: The primary outcome was a practice being in a state of workforce supply–demand imbalance operationally defined as being in the lowest third nationally of access scores according to the General Practice Patient Survey and the highest third nationally according to list size per full-time equivalent GP (weighted to the demographic distribution of registered patients and adjusted for deprivation). Results: Based on historical data, the predictive model had fair to good discriminatory ability to predict which practices faced supply–demand imbalance (area under receiver operating characteristic curve=0.755). Predictions using current data suggested that, on average, practices at highest risk of future supply–demand imbalance are currently characterised by having larger patient lists, employing more nurses, serving more deprived and younger populations, and having considerably worse patient experience ratings when compared with other practices. Incorporating findings from a survey of GP's career intentions made little difference to predictions of future supply–demand risk status when compared with expected future workforce projections based only on routinely available data on GPs' gender and age. Conclusions: It is possible to make reasonable predictions of an individual general practice's future risk of undersupply of GP workforce with respect to its patient population. However, the predictions are inherently limited by the data available.