Keith Hurst1. 1. Nuffield Health and Social Care Policy Group, Health Sciences and Public Health Research Institute, Leeds University, Leeds, UK. k.hurst@leeds.ac.uk
Abstract
AIM: This article reports a study that provided primary and community care managers with information, allowing them to: (a) evaluate the size and mix of their workforce; and (b) develop knowledgeable and skilled teams to meet the demands of growing and changing services. BACKGROUND: Primary and community care services are growing in the United Kingdom, but workforce planning and development, despite their wide-ranging cost and quality implications, have not received the same attention. Indeed, most primary and community care workforce planning and development issues are universal. Demand 1-1 side workforce planning is concerned not only with the number, but also with staff mix; but how these autonomous and isolated practitioners spend their time is unique. The other side of the equation, workforce supply, raises many recruitment and retention challenges for managers in many countries. Any country's main workforce planning methods apply equally well to primary care, but each is flawed. A second, main problem is that the methods lead to fragmented services, whereas modern workforce planning methods should be multidisciplinary. Consequently, it has never been more important for managers to have data and algorithms to develop appropriate care teams. METHOD: A large and versatile workforce database, profiling 304 English primary care trusts using demographic, socio-economic, mortality, morbidity, staffing and performance workforce-related variables, compiled in 2002 and updated yearly, is described. Data were supplemented with a systematic literature review leading to a 340-item annotated bibliography; and qualitative interviews with managers. RESULTS: Workforce size and mix are historical and irrational at best. Moreover, the number of variables that influence staffing is growing, thereby complicating workforce planning. CONCLUSION: Evaluating and adjusting the size and mix of teams using empirically determined community demand and performance variables based on the area's socio-economic characteristics is feasible.
AIM: This article reports a study that provided primary and community care managers with information, allowing them to: (a) evaluate the size and mix of their workforce; and (b) develop knowledgeable and skilled teams to meet the demands of growing and changing services. BACKGROUND: Primary and community care services are growing in the United Kingdom, but workforce planning and development, despite their wide-ranging cost and quality implications, have not received the same attention. Indeed, most primary and community care workforce planning and development issues are universal. Demand 1-1 side workforce planning is concerned not only with the number, but also with staff mix; but how these autonomous and isolated practitioners spend their time is unique. The other side of the equation, workforce supply, raises many recruitment and retention challenges for managers in many countries. Any country's main workforce planning methods apply equally well to primary care, but each is flawed. A second, main problem is that the methods lead to fragmented services, whereas modern workforce planning methods should be multidisciplinary. Consequently, it has never been more important for managers to have data and algorithms to develop appropriate care teams. METHOD: A large and versatile workforce database, profiling 304 English primary care trusts using demographic, socio-economic, mortality, morbidity, staffing and performance workforce-related variables, compiled in 2002 and updated yearly, is described. Data were supplemented with a systematic literature review leading to a 340-item annotated bibliography; and qualitative interviews with managers. RESULTS: Workforce size and mix are historical and irrational at best. Moreover, the number of variables that influence staffing is growing, thereby complicating workforce planning. CONCLUSION: Evaluating and adjusting the size and mix of teams using empirically determined community demand and performance variables based on the area's socio-economic characteristics is feasible.