Background Community healthcare is a diverse sector consisting a wide range of different services, where patients commonly use a range of services to treat any number of co-morbidities. With a policy focus towards increased care for patients with multiple long term illnesses and increased community provision, the management and planning of these services is a key priority. In this project we worked alongside the North East London Foundation Trust (NELFT) to better understand referrals and patient use within community services for elderly patients, based in Havering. Of interest was how patients concurrently used services, whether common patterns of referrals existed and how sequences of referrals occurred over time. To this end we developed a range of novel maps in collaboration with NELFT clinicians to aid in the design and implementation of a single point of access (SPA) for referrals into community services. Methods Using operational research methods, we attempt to better understand the structure of NELFT's community provision. From a non-identifiable patient level dataset we constructed a series of maps exploring patient referrals. We first created a network depiction of referrals where nodes represented services and edges represented a referral between them. In support of this, we plotted the time distribution of concurrent patient referrals at a population level – looking at how long patients remained in one, two, three, four, five and six or greater referrals at the same time as time progressed. Developing further, we analysed common sequences and patterns of referrals. We found common mixtures and orders of services first used by patients, looking at sequences of three, four, five and six referrals. Finally, using a timeline plot, we analysed how subsequent referrals develop after a first referral to a given service. Results The network map highlights the intensity and frequency of patient activity within the system as well as its complexity and vastness. The time distribution of patient discharges shows how the number of patients requiring multiple treatments evolves over time and how subsequent referrals overlap. Insight into groups of services with high activity and correlation of referrals is gained by finding common sequences and patterns of referrals, whilst the timeline of subsequent referrals shows how these sequences develop over time. Implications In applying these methods to NELFT services we helped to inform the design of their SPA using a ``what if'' analysis. This analysis provided information about how referrals may be streamlined to improve access, particularly in light of both areas of high activity formed of multiple services, and the large volume of low use referral paths. These methods highlighted important dynamics of patient flow and referrals within community care to be considered in planning services, and visually depicted them to communicate valuable insight into NELFT community referrals.