University of Exeter Medical School (Primary Care), University of Exeter, Exeter, UK
University of Exeter Medical School (Primary Care), University of Exeter, Exeter, UK.
BMJ Open. 2020 Jan 23;10(1):e027934. doi: 10.1136/bmjopen-2018-027934.
This study aimed to develop a risk prediction model identifying general practices at risk of workforce supply-demand imbalance.
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).
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).
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.
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.
本研究旨在开发一种风险预测模型，以识别存在劳动力供需失衡风险的全科医生诊所。
这是对全科医生劳动力、患者体验和注册人群（2012 年至 2016 年）的常规数据进行二次分析，并结合全科医生职业意向（2016 年）普查的结果。
地点/参与者：采用混合方法，基于历史数据（2012-2016 年）中英格兰所有全科医生（拥有 1000 名以上注册患者的 n=6398）的实践因素，开发一种预测劳动力供需失衡的模型。该模型应用于当前数据（2016 年），以探索英格兰西南部实践的未来风险（n=368）。
主要结局是根据全科医生患者调查的就诊机会得分和根据每位全职等效全科医生的名单大小（按注册患者的人口分布加权并调整为贫困程度）的全国最高三分之一，定义为处于劳动力供需失衡状态的实践。
基于历史数据，预测模型对预测面临供需失衡的实践具有良好到中等的区分能力（接收者操作特征曲线下面积=0.755）。使用当前数据进行预测表明，平均而言，未来供需失衡风险最高的实践目前的特点是患者名单较大，雇用更多的护士，为更多贫困和年轻的人群服务，并且与其他实践相比，患者体验评分明显较差。与仅基于全科医生性别和年龄的常规数据预测未来劳动力情况相比，将全科医生职业意向调查的结果纳入预测未来供需风险状况几乎没有差异。
可以对个体全科医生在其患者人群中未来的全科医生劳动力供应不足风险做出合理的预测。然而，预测结果受到可用数据的固有限制。
