Development of a Social Risk Score in the Electronic Health Record to Identify Social Needs Among Underserved Populations: Retrospective Study.

BACKGROUND: Patients with unmet social needs and social determinants of health (SDOH) challenges continue to face a disproportionate risk of increased prevalence of disease, health care use, higher health care costs, and worse outcomes. Some existing predictive models have used the available data on social needs and SDOH challenges to predict health-related social needs or the need for various social service referrals. Despite these one-off efforts, the work to date suggests that many technical and organizational challenges must be surmounted before SDOH-integrated solutions can be implemented on an ongoing, wide-scale basis within most US-based health care organizations. OBJECTIVE: We aimed to retrieve available information in the electronic health record (EHR) relevant to the identification of persons with social needs and to develop a social risk score for use within clinical practice to better identify patients at risk of having future social needs. METHODS: We conducted a retrospective study using EHR data (2016-2021) and data from the US Census American Community Survey. We developed a prospective model using current year-1 risk factors to predict future year-2 outcomes within four 2-year cohorts. Predictors of interest included demographics, previous health care use, comorbidity, previously identified social needs, and neighborhood characteristics as reflected by the area deprivation index. The outcome variable was a binary indicator reflecting the likelihood of the presence of a patient with social needs. We applied a generalized estimating equation approach, adjusting for patient-level risk factors, the possible effect of geographically clustered data, and the effect of multiple visits for each patient. RESULTS: The study population of 1,852,228 patients included middle-aged (mean age range 53.76-55.95 years), White (range 324,279/510,770, 63.49% to 290,688/488,666, 64.79%), and female (range 314,741/510,770, 61.62% to 278,488/448,666, 62.07%) patients from neighborhoods with high socioeconomic status (mean area deprivation index percentile range 28.76-30.31). Between 8.28% (37,137/448,666) and 11.55% (52,037/450,426) of patients across the study cohorts had at least 1 social need documented in their EHR, with safety issues and economic challenges (ie, financial resource strain, employment, and food insecurity) being the most common documented social needs (87,152/1,852,228, 4.71% and 58,242/1,852,228, 3.14% of overall patients, respectively). The model had an area under the curve of 0.702 (95% CI 0.699-0.705) in predicting prospective social needs in the overall study population. Previous social needs (odds ratio 3.285, 95% CI 3.237-3.335) and emergency department visits (odds ratio 1.659, 95% CI 1.634-1.684) were the strongest predictors of future social needs. CONCLUSIONS: Our model provides an opportunity to make use of available EHR data to help identify patients with high social needs. Our proposed social risk score could help identify the subset of patients who would most benefit from further social needs screening and data collection to avoid potentially more burdensome primary data collection on all patients in a target population of interest.

Barriers and facilitators to mobile health and active surveillance use among older adults with skin disease.

BACKGROUND: The COVID-19 pandemic has accelerated the adoption of telemedicine, including teledermatology. Monitoring skin lesions using teledermatology may become increasingly important for several skin diseases, including low-risk skin cancers. The purpose of this study was to describe the key factors that could serve as barriers or facilitators to skin disease monitoring using mobile health technology (mHealth) in older adults. METHODS: Older adult dermatology patients 65 years or older and their caregivers who have seen a dermatologist in the last 18 months were interviewed and surveyed between December 2019 and July 2020. The purpose of these interviews was to better understand attitudes, beliefs and behaviours that could serve as barriers and facilitators to the use of mHealth and active surveillance to monitor low-risk skin cancers. RESULTS: A total of 33 interviews leading to 6022 unique excerpts yielded 8 factors, or themes, that could serve as barriers, facilitators or both to mHealth and active surveillance. We propose an integrated conceptual framework that highlights the interaction of these themes at both the patient and provider level, including care environment, support systems and personal values. DISCUSSION AND CONCLUSIONS: These preliminary findings reveal factors influencing patient acceptance of active surveillance in dermatology, such as changes to the patient-provider interaction and alignment with personal values. These factors were also found to influence adoption of mHealth interventions. Given such overlap, it is essential to address barriers and facilitators from both domains when designing a new dermatology active surveillance approach with novel mHealth technology. PATIENT OR PUBLIC CONTRIBUTION: The patients included in this study were participants during the data collection process. Members of the Stanford Healthcare and Denver Tech Dermatology health-care teams aided in the recruitment phase of the data collection process.