Workforce predictive risk modelling: development of a model to identify general practices at risk of a supply−demand imbalance
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 ce...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2020-01-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/10/1/e027934.full |
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| author | Gary A Abel John L Campbell Sarah Gerard Dean Chris Salisbury Emily Fletcher Mayam Gomez-Cano Navonil Mustafee Andi Smart Rupa Chilvers Suzanne H Richards F Warren |
| author_facet | Gary A Abel John L Campbell Sarah Gerard Dean Chris Salisbury Emily Fletcher Mayam Gomez-Cano Navonil Mustafee Andi Smart Rupa Chilvers Suzanne H Richards F Warren |
| author_sort | Gary A Abel |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-dc482156014e4d3c8af14d9ab78850e8 |
| institution | OA Journals |
| issn | 2044-6055 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-dc482156014e4d3c8af14d9ab78850e82025-08-20T02:38:30ZengBMJ Publishing GroupBMJ Open2044-60552020-01-0110110.1136/bmjopen-2018-027934Workforce predictive risk modelling: development of a model to identify general practices at risk of a supply−demand imbalanceGary A Abel0John L Campbell1Sarah Gerard Dean2Chris Salisbury3Emily Fletcher4Mayam Gomez-Cano5Navonil Mustafee6Andi Smart7Rupa Chilvers8Suzanne H Richards9F Warren10University of Exeter Medical School (Primary Care), University of Exeter, Exeter, UK2 Medical School, University of Exeter, Exeter, UK2 Medical School, University of Exeter, Exeter, UKCentre for Academic Primary Care, NIHR School for Primary Care Research, School of Socialand Community Medicine, University of Bristol, Bristol, UKHospital Medicine, Duke University School of Medicine, Durham, North Carolina, USA1 Medical School (Primary Care), University of Exeter, Exeter, UKUniversity of Exeter Business School, Exeter, UKUniversity of Exeter Business School, Exeter, UKTangerine Bee, Exeter, UK9 Leeds Institute of Health Sciences, University of Leeds, Leeds, UKUniversity of Exeter Medical School (Primary Care), University of Exeter, Exeter, UKObjective 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.https://bmjopen.bmj.com/content/10/1/e027934.full |
| spellingShingle | Gary A Abel John L Campbell Sarah Gerard Dean Chris Salisbury Emily Fletcher Mayam Gomez-Cano Navonil Mustafee Andi Smart Rupa Chilvers Suzanne H Richards F Warren Workforce predictive risk modelling: development of a model to identify general practices at risk of a supply−demand imbalance BMJ Open |
| title | Workforce predictive risk modelling: development of a model to identify general practices at risk of a supply−demand imbalance |
| title_full | Workforce predictive risk modelling: development of a model to identify general practices at risk of a supply−demand imbalance |
| title_fullStr | Workforce predictive risk modelling: development of a model to identify general practices at risk of a supply−demand imbalance |
| title_full_unstemmed | Workforce predictive risk modelling: development of a model to identify general practices at risk of a supply−demand imbalance |
| title_short | Workforce predictive risk modelling: development of a model to identify general practices at risk of a supply−demand imbalance |
| title_sort | workforce predictive risk modelling development of a model to identify general practices at risk of a supply demand imbalance |
| url | https://bmjopen.bmj.com/content/10/1/e027934.full |
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