Workforce predictive risk modelling: development of a model to identify general practices at risk of a supply−demand imbalance

Abstract

ObjectiveThis study aimed to develop a risk prediction model identifying general practices at risk of workforce supply–demand imbalance.DesignThis 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/ParticipantsA 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 measureThe 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).ResultsBased 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.ConclusionsIt 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.

Inequalities in the distribution of the general practice workforce in England: a practice-level longitudinal analysis

“…The finding that GP workforce shortages disproportionately affect practices in areas of higher deprivation, an inequality that has widened since 2015, is consistent with recent cross-sectional analyses of workforce inequalities. [15][16][17][18][19] Moreover, the present findings are comparable with a recent longitudinal report by The Health Foundation, which plotted general practice workforce supply over time by IMD quintiles; while this report did not include significance testing, the observed trends align with the results presented in this article. 47 The most recent longitudinal SII analysis of workforce inequalities reported a slight pro-poor, although not statistically significant, SII estimate in 2013-2014.…”

“…5 Evidence suggests that these workforce shortages are not equally distributed across England; areas with higher levels of deprivation tend to have fewer GPs relative to the patient population despite a greater burden of chronic disease. 6,7,10,[15][16][17][18][19] Moreover, GP supply has been linked to health-outcome measures, including mortality rates, self-reported health, and life expectancy, even after controlling for sociodemographic measures. [20][21][22][23][24][25][26][27] The NHS Long Term Plan, published in 2019, emphasised the importance of general practice services and established PCNs, or groups of neighbouring practices that enable economies of scale and integrated care.…”

An app for predicting nurse intention to quit the job using artificial neural networks (ANNs) in Microsoft Excel

“…Although the intention to leave does not always lead to action (or behavior), [51] predicting nurses' intention to leave is an essential and necessary approach to establish an early warning mechanism from the perspective of human resource manage- ment [38] when considering the constant shortage of nursing staff. [1][2][3] In the current study, we verified that the ANN could improve the prediction accuracy on intention-to-leave classification, which is modern and innovative, where predictions are made without asking direct questions of quitting the job. [38] 4.3.…”

“…[16] All of these findings indicate that the phenomenon of nurses' intention to quit their jobs deteriorates the supply of nurses in healthcare settings. [3] Lack of nursing staff may lead to increased medical incidence because of negligence, involving infection, falls, medication errors, tube dislodgement, pressure sores, and, most seriously, death. [17,18] Nurses overloaded with a high number of patients may experience increased work stress, which has been identified as a key factor in increasing nurse turnover.…”

“…The World Health Organization predicts increased global demand for health and social care staff with the creation of 40 million new jobs by 2030 [1] and estimates a shortage of >4 million doctors, nurses, midwives, and others, [2] far from meeting the rising demands for health care manpower, particularly for nurses in healthcare settings. [3] We observed that more than half of nurses (52%) are considering quitting the National Health Service (NHS) as work according to an exclusive survey by Nursing Standard and the Sunday Mirror. [4,5] Approximately 21% of nurses (n = 44,082) reported intention to leave in the United States.…”