Whole system quality: local benchmarking to improve workforce planning

As a team of workforce analysts and academics with an interest in workforce planning, we are aware that the data available to support primary care workforce planning are disorganised and overwhelming. This makes it difficult for General Practice to extract meaningful and relevant information. We deliver workforce planning workshops across England. Participants at our workshops regularly express their frustration with the quantity of information they are required to produce and the quality of information they receive from other parts of the system. We are dismayed at what we sense to be growing cynicism with data generation and information analysis and are interested in stimulating a conversation about what data matter and how primary care teams can extract data that are usefu

Emergency department attendances and GP patient satisfaction

Background: Despite invaluable national data, reasons for the relentless rise in England’s emergency department (ED) attendances remain elusive. Setting: All EDs and general practices in England. Question: Are rising ED attendances related to general practice patient satisfaction, i.e. if patients are unable to get a convenient appointment with their general practitioner (GP), then do they attend their local ED for diagnosis, treatment and care instead? Method: GP patient satisfaction and ED attendance data were extracted from national data warehouses and organised into two groups: (i) England clinical commissioning group (CCG) areas and (ii) a London CCG subset. Data from London CCGs were compared with CCGs outside London. Results: ED attendances were strongly correlated with GP patient satisfaction data in non-London CCGs, e.g. if patients said they had difficulty obtaining a convenient appointment at their general practice, then local ED attendances increased. Associations were repeated when other GP perception data were explored, e.g. if patients were satisfied with GPs and practice nurses, then they were less likely to attend their local EDs. However, these associations were not found in the London CCG subset despite lower satisfaction with London GP services. Discussion and Conclusions: Although our study generates valuable insights into ED attendances, the reasons why London general practice patient and ED attendance data don’t show the same associations found outside London warrants further study. Diverting patients from EDs to primary care services may not be straight forward as many would like to believe

A Methodology for Using Workforce Data to Decide Which Specialties and States to Target for Graduate Medical Education Expansion

OBJECTIVE: To outline a methodology for allocating graduate medical education (GME) training positions based on data from a workforce projection model. DATA SOURCES: Demand for visits is derived from the Medical Expenditure Panel Survey and Census data. Physician supply, retirements, and geographic mobility are estimated using concatenated AMA Masterfiles and ABMS certification data. The number and specialization behaviors of residents are derived from the AAMC's GMETrack survey. DESIGN: We show how the methodology could be used to allocate 3,000 new GME slots over 5 years-15,000 total positions-by state and specialty to address workforce shortages in 2026. EXTRACTION METHODS: We use the model to identify shortages for 19 types of health care services provided by 35 specialties in 50 states. PRINCIPAL FINDINGS: The new GME slots are allocated to nearly all specialties, but nine states and the District of Columbia do not receive any new positions. CONCLUSIONS: This analysis illustrates an objective, evidence-based methodology for allocating GME positions that could be used as the starting point for discussions about GME expansion or redistribution

Genomic epidemiology of SARS-CoV-2 in a UK university identifies dynamics of transmission

AbstractUnderstanding SARS-CoV-2 transmission in higher education settings is important to limit spread between students, and into at-risk populations. In this study, we sequenced 482 SARS-CoV-2 isolates from the University of Cambridge from 5 October to 6 December 2020. We perform a detailed phylogenetic comparison with 972 isolates from the surrounding community, complemented with epidemiological and contact tracing data, to determine transmission dynamics. We observe limited viral introductions into the university; the majority of student cases were linked to a single genetic cluster, likely following social gatherings at a venue outside the university. We identify considerable onward transmission associated with student accommodation and courses; this was effectively contained using local infection control measures and following a national lockdown. Transmission clusters were largely segregated within the university or the community. Our study highlights key determinants of SARS-CoV-2 transmission and effective interventions in a higher education setting that will inform public health policy during pandemics.</jats:p