• A large, south east London multi-site general practice wanted to understand the characteristics of persistent frequent attenders to their services.
  • Frequent users (those attending on average every 2 weeks; ie 26 times a year) were more likely to be old, female, have one or multiple long-term conditions, and have attended an accident and emergency department multiple times in the past year.

Valentine Health, a GP practice in south east London with two sites and more than 25,000 registered patients, had a problem similar to many practices: it had some patients who were using the practice more frequently than others over a sustained period of time. The practice thought that if it understood more about these patients (those attending on average every 2 weeks; ie 26 times a year), that it could design more personalised care. This would improve both the quality of services and reduce the number of GP appointments needed by these patients. While these patients made up less than 0.5% of the population, they were using more than 5% of all appointments.

Working with Valentine Health GPs, the in-house data analytics team at the Health Foundation extracted the pseudonymised medical records for all patients. Analysis of these medical records found the following differences between the clinical and personal characteristics of the frequent attenders at the general practice and the rest of the practice population.

  • Frequent attenders were nearly seven times more likely to have multiple long-term conditions than the rest of the patient population (69% vs 11%), and over four times more likely to have depression (43% vs 9%).
  • Nearly half of all frequent users lived alone, compared to fewer than a quarter of the rest of the practice population.
  • 33% of frequent users were aged 60 and older, compared to just 8% of non-frequent users.
  • 53% of frequent attenders had visited an accident and emergency department multiple times in the last year, compared to 6% of patients in the rest of the general practice.

While more work would be needed to understand which characteristics were predictive of more frequent utilisation, Rebecca Rosen, a GP from the practice, was able to use this descriptive analysis to identify patient groups who might benefit from redesigned services. She describes what they did next to co-design services with these groups of patients, and what more complex modelling could do in the future in this interview.

Further reading