Scaling-up social needs screening in practice: A retrospective, cross-sectional analysis of data from electronic health records from Bronx county, New York, USA

Objectives: There has been renewed focus on health systems integrating social care to improve health outcomes with relatively less related research focusing on ‘real-world’ practice. This study describes a health system’s experience from 2018 to 2020, following the successful pilot in 2017, to scale social needs screening of patients within a large urban primary care ambulatory network. Setting: Academic medical centre with an ambulatory network of 18 primary care practices located in an urban county in New York City (USA). Participants: This retrospective, cross-sectional study used electronic health records of 244 764 patients who had a clinical visit between 10 April 2018 and 8 December 2019 across any one of 18 primary care practices. Methods: We organised measures using the RE-AIM framework domains of reach and adoption to ascertain the number of patients who were screened and the number of providers who adopted screening and associated documentation, respectively. We used descriptive statistics to summarise factors comparing patients screened versus those not screened, the prevalence of social needs screening and adoption across 18 practices. Results: Between April 2018 and December 2019, 53 093 patients were screened for social needs, representing approximately 21.7% of the patients seen. Almost one-fifth (19.6%) of patients reported at least one unmet social need. The percentage of screened patients varied by both practice location (range 1.6%–81.6%) and specialty within practices. 51.8% of providers (n=1316) screened at least one patient. Conclusions: These findings demonstrate both the potential and challenges of integrating social care in practice. We observed significant variability in uptake across the health system. More research is needed to better understand factors driving adoption and may include harmonising workflows, establishing unified targets and using data to drive improvement.