PT  - JOURNAL ARTICLE
AU  - Adam Jeyes
AU  - Laura Pugh
TI  - Implementation of social prescribing to reduce frequent attender consultation rates in primary care
AID  - 10.3399/bjgp19X703109
DP  - 2019 Jun 01
TA  - British Journal of General Practice
PG  - bjgp19X703109
VI  - 69
IP  - suppl 1
4099  - http://bjgp.org/content/69/suppl_1/bjgp19X703109.short
4100  - http://bjgp.org/content/69/suppl_1/bjgp19X703109.full
SO  - Br J Gen Pract2019 Jun 01; 69
AB  - Background The impact of social determinants on health has been established. Evidence shows that addressing social needs through link workers can improve wellbeing and consultation rates. This is of importance since demand on primary care appointments is high and access to primary care is pivotal to the NHS as a whole.Aim To evaluate whether frequently-attending patients who might benefit from social prescribing, can be recognised through a computer search of risks for isolation, loneliness, or social pressures and whether a social intervention has an effect on wellbeing and consultation rate.Method Patients highlighted as frequent attenders (≥20 GP/nurse practitioner [NP] appointments in past 12 months) were screened for appropriateness of referral to link worker. A social risk tool was applied to select patients most at risk of social isolation. Patients who agreed had a pre- and post-intervention wellness score calculated. Number of appointments pre- and post-intervention were also recorded and matched by month. Post-intervention questionnaires allowed collation of qualitative data analysing patient opinions of the scheme.Results There was an average increase in wellbeing score post-intervention of 0.8/5. The average reduction in appointments for GPs and NPs combined was 5.1 appointment/patient (37% reduction) at 6 months and 12.9 appointments/patient at 1 year (53% reduction).Conclusion The numbers in this project are small, but it supports the growing evidence that social prescribing can improve patient well-being and sustained reduced demand for GP and NP appointments. It supports the suggestion that computer searches can delineate a high-risk population.