Telephone-based social health screening by pharmacists in the nonadherent Medicare population.

BACKGROUND: Unmet social health needs are associated with medication nonadherence. Although pharmacists are well positioned to address medication nonadherence, there is limited experience with screening for and addressing social health needs. OBJECTIVES: To compare the prevalence of social health needs among Medicare patients with higher vs lower social health risk using a predictive model. To also evaluate pre-post changes in medication adherence and health care use following a pharmacist-initiated social health screening. METHODS: A social health screening workflow was implemented into a routine pharmacist adherence program at an integrated health care delivery system. The social health screening was conducted during medication adherence outreach phone calls with Medicare members who were overdue for statin, blood pressure, or diabetes medications. We developed a social health need predictive algorithm to flag higher-risk patients and tested this algorithm against a random subset of lower-risk patients. Screening conversations were guided by a focus group that developed open-ended questions to identify social health needs. Comparisons in social health needs were made between higher- and lower-risk patients. Use and adherence outcomes were compared pre and post for patients who accepted a referral to social health resources and patients who declined a referral. RESULTS: 1,217 patients were contacted and screened for social health needs by pharmacists. Patients flagged by the social risk algorithm were more likely to report social health needs (28.7% vs 12.7% in the unflagged group; P < 0.01). Commonly reported needs included transportation (43%), finances (34%), caregiving (22%), mental health (11%), and food access (10%). 221 patients accepted a referral to a central resource website and call center that connected patients to local services. One year after screening dates, patients who did not accept a referral spent more time in the hospital (mean change +0.7 days, SD = 7.3, P < 0.01), had fewer primary care visits (mean change -0.5 visits, SD = 6.5, P < 0.01), and had a shorter length of membership (mean change -0.4 months, SD = 1.9, P < 0.01). Patients who accepted a referral had increased statin adherence (62.3% adherent pre vs 74.7% post, P = 0.02). CONCLUSIONS: We implemented a workflow for pharmacists to screen for social health needs. The social health need prediction model doubled the identification rate of patients who have needs. Intervening on social health needs during these calls may improve statin adherence and may have no adverse effect on health care utilization or health plan membership. DISCLOSURES: Social health risk predictive model development and validation was funded by the Agency for Healthcare Research and Quality (AHRQ R18HS027343).

Associations Between Different Self-reported Social Risks and Neighborhood-level Resources in Medicaid Patients.

BACKGROUND: Adverse social conditions are a key contributor to health disparities. Improved understanding of how social risk factors interact with each other and with neighborhood characteristics may inform efforts to reduce health disparities. DATA: A questionnaire of 29,281 patients was collected through the enrollment of Medicaid beneficiaries in a large Northern California integrated health care delivery system between May 2016 and February 2020. EXPOSURES: Living in the least resourced quartile of neighborhoods as measured by a census-tract level Neighborhood Deprivation Index score. MAIN OUTCOMES: Five self-reported social risk factors: financial need, food insecurity, housing barriers, transportation barriers, and functional limitations. RESULTS: Nearly half (42.0%) of patients reported at least 1 social risk factor; 22.4% reported 2 or more. Mean correlation coefficient between social risk factors was ρ=0.30. Multivariable logistic models controlling for age, race/ethnicity, sex, count of chronic conditions, and insurance source estimated that living in the least resourced neighborhoods was associated with greater odds of food insecurity (adjusted odds ratio=1.07, 95% confidence interval: 1.00-1.13) and transportation barriers (adjusted odds ratio=1.20, 95% confidence interval: 1.11-1.30), but not financial stress, housing barriers, or functional limitations. CONCLUSIONS AND RELEVANCE: We found that among 5 commonly associated social risk factors, Medicaid patients in a large Northern California health system typically reported only a single factor and that these factors did not correlate strongly with each other. We found only modestly greater social risk reported by patients in the least resourced neighborhoods. These results suggest that individual-level interventions should be targeted to specific needs whereas community-level interventions may be similarly important across diverse neighborhoods.

Use of Latent Class Analysis and k-Means Clustering to Identify Complex Patient Profiles.

Importance: Medically complex patients are a heterogeneous group that contribute to a substantial proportion of health care costs. Coordinated efforts to improve care and reduce costs for this patient population have had limited success to date. Objective: To define distinct patient clinical profiles among the most medically complex patients through clinical interpretation of analytically derived patient clusters. Design, Setting, and Participants: This cohort study analyzed the most medically complex patients within Kaiser Permanente Northern California, a large integrated health care delivery system, based on comorbidity score, prior emergency department admissions, and predicted likelihood of hospitalization, from July 18, 2018, to July 15, 2019. From a starting point of over 5000 clinical variables, we used both clinical judgment and analytic methods to reduce to the 97 most informative covariates. Patients were then grouped using 2 methods (latent class analysis, generalized low-rank models, with k-means clustering). Results were interpreted by a panel of clinical stakeholders to define clinically meaningful patient profiles. Main Outcomes and Measures: Complex patient profiles, 1-year health care utilization, and mortality outcomes by profile. Results: The analysis included 104 869 individuals representing 3.3% of the adult population (mean [SD] age, 70.7 [14.5] years; 52.4% women; 39% non-White race/ethnicity). Latent class analysis resulted in a 7-class solution. Stakeholders defined the following complex patient profiles (prevalence): high acuity (9.4%), older patients with cardiovascular complications (15.9%), frail elderly (12.5%), pain management (12.3%), psychiatric illness (12.0%), cancer treatment (7.6%), and less engaged (27%). Patients in these groups had significantly different 1-year mortality rates (ranging from 3.0% for psychiatric illness profile to 23.4% for frail elderly profile; risk ratio, 7.9 [95% CI, 7.1-8.8], P < .001). Repeating the analysis using k-means clustering resulted in qualitatively similar groupings. Each clinical profile suggested a distinct collaborative care strategy to optimize management. Conclusions and Relevance: The findings suggest that highly medically complex patient populations may be categorized into distinct patient profiles that are amenable to varying strategies for resource allocation and coordinated care interventions.