Data science for winter pressures in primary care in the context of COVID-19 recovery: using data to detect local problems, mitigate risk, and understand the impact on patient outcomes
There is widespread concern about winter pressures on NHS services, particularly in the context of continued COVID recovery.
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People rely on primary care services for initial contact, diagnosis, treatment, and coordination of care. Where primary care is under pressure, it is likely that there will be a negative impact on patient care and the good functioning of the whole health service; for example, if someone cannot get an appointment with their GP, they might be more likely to present to A&E.
If we can identify early warning signs that an organisation is under pressure, plans could be put in place earlier to minimise any negative effect on patients. We want to investigate whether we can use patterns in primary care data to measure pressure on services (e.g., if a practice is overwhelmed, it might carry out less routine/non-urgent tasks) and how those measures of pressure vary between different practices and over time. We also want to investigate whether patients experience worse outcomes (e.g., presenting at A&E) if a practice does look to be overwhelmed.
Finally, we would like to developand test algorithms that can identify service pressure early on and alert primary care staff so that they can respond accordingly