Objectives - Primary Care Networks (PCNs) are a new organisational hierarchy with wide-ranging responsibilities introduced in the National Health Service (NHS) Long Term Plan. The vision is that they represent ‘natural’ communities of general practices (GP practices) working together at scale and covering a geography that make sense to practices, other healthcare providers and local communities. Our study aims to identify natural communities of GP practices based on patient registration patterns using Markov Multiscale Community Detection, an unsupervised network-based clustering technique to create catchments for these communities.

Design - Retrospective observational study using Hospital Episode Statistics – patient-level administrative records of inpatient, outpatient and emergency department attendances to hospital.

Setting – General practices in the 32 Clinical Commissioning Groups of Greater London

Participants - All adult patients resident in and registered to a GP practices in Greater London that had one or more outpatient encounters at NHS hospital trusts between 1st April 2017 and 31st March 2018.

Main outcome measures
The allocation of GP practices in Greater London to PCNs based on the registrations of patients resident in each Lower Super Output Area (LSOA) of Greater London. The population size and coverage of each proposed PCN.

Results - 3,428,322 unique patients attended 1,334 GPs in 4,835 LSOAs in Greater London. Our model grouped 1,291 GPs (96.8%) and 4,721 LSOAs (97.6%), into 165 mutually exclusive PCNs. The median PCN list size was 53,490, with a lower quartile of 38,079 patients and an upper quartile of 72,982 patients. A median of 70.1% of patients attended a GP within their allocated PCN, ranging from 44.6% to 91.4%.

Conclusions - With PCNs expected to take a role in population health management and with community providers expected to reconfigure around them, it is vital we recognise how PCNs represent their communities. Our method may be used by policy-makers to understand the populations and geography shared between networks.