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.

Aligning care with the personal values of patients with complex care needs.

OBJECTIVE: To identify opportunities to align care with the personal values of patients from three distinct groups with complex medical, behavioral, and social needs. DATA SOURCES/STUDY SETTING: Between June and August 2019, we conducted semi-structured interviews with individuals with complex care needs in two integrated health care delivery systems. STUDY DESIGN: Qualitative study using semi-structured interviews. DATA COLLECTION METHODS: We interviewed three groups of patients at Kaiser Permanente Washington and Kaiser Permanente Colorado representing three distinct profiles of complex care needs: Group A ("obesity, opioid prescription, and low-resourced neighborhood"), Group B ("older, high medical morbidity, emergency department, and hospital use"), and Group C ("older, mental and physical health concerns, and low-resourced neighborhood"). These profiles were identified based on prior work and prioritized by internal primary care stakeholders. Interview transcripts were analyzed using thematic analysis. PRINCIPAL FINDINGS: Twenty-four patients participated; eight from each complex needs profile. Mean age across groups was 71 (range 48-86) years. We identified five themes common across the three groups that captured patients' views regarding values-aligned care. These themes focused on the importance of care teams exploring and acknowledging a patient's values, providing access to nonphysician providers who have different perspectives on care delivery, offering values-aligned mental health care, ensuring connection to community-based resources that support values and address needs, and providing care that supports the patient plus their family and caregivers. CONCLUSIONS: Our results suggest several opportunities to improve how care is delivered to patients with different complex medical, behavioral, and social needs. Future research is needed to better understand how to incorporate these opportunities into health care.

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.

Which Complex Patients Should Be Referred for Intensive Care Management? A Mixed-Methods Analysis.

BACKGROUND: A large and increasing proportion of health care costs are spent caring for a small segment of medically and socially complex patients. To date, it has been difficult to identify which patients are best served by intensive care management. OBJECTIVE: To characterize factors that best identify which complex patients are most suited for intensive care management. DESIGN: We conducted a mixed-methods study involving 35 care managers (CMs; 10 licensed social workers and 25 registered nurses) working in intensive care management programs within Kaiser Permanente Northern California (KPNC) outpatient medical centers. We asked CMs to review a randomly selected list of up to 50 patients referred to them in the prior year and to categorize each patient as either (1) "good candidates" for care management, (2) "not needing" intensive care management, or (3) "needing more" than traditional care management could provide. We then conducted semi-structured interviews to understand how CMs separated patients into these three groups. RESULTS: CMs assigned 1178 patients into the 3 referral categories. Less than two thirds (62%, n = 736) of referred patients were considered good candidates, with 18% (n = 216) categorized as not needing care management and 19% (n = 226) as needing more. Compared to the other two categories, good candidates were older (76.2 years vs. 73.2 for not needing and 69.8 for needing more, p < 0.001), prescribed more medications (p = 0.02) and had more prior year outpatient visits (p = 0.04), while the number of prior year hospital and emergency room admissions were greater than not needing but less than needing more (p < 0.001). A logistic regression model using available electronic record data predicted good candidate designation with a c statistic of 0.75. Several qualitative themes emerged that helped define appropriateness for referral, including availability of social support, patient motivation, non-medical transitions, recent trajectory of medical condition, and psychiatric or substance use issues. CONCLUSION: Many apparently complex patients are not good candidates for intensive care management. Current electronic medical records do not capture several of the most salient characteristics that determine appropriateness for care management. Our findings suggest that systematic collection of social support, patient motivation, and recent non-medically related life change information may help identify which complex patients are most likely to benefit from care management.

Pre-Visit Prioritization for complex patients with diabetes: Randomized trial design and implementation within an integrated health care system.

BACKGROUND/AIMS: Despite robust evidence to guide clinical care, most patients with diabetes do not meet all goals of risk factor control. Improved patient-provider communication during time-limited primary care visits may represent one strategy for improving diabetes care. METHODS: We designed a controlled, cluster-randomized, multi-site intervention (Pre-Visit Prioritization for Complex Patients with Diabetes) that enables patients with poorly controlled type 2 diabetes to identify their top priorities prior to a scheduled visit and sends these priorities to the primary care physician progress note in the electronic medical record. In this paper, we describe strategies to address challenges to implementing our health IT-based intervention study within a large health care system. RESULTS: This study is being conducted in 30 primary care practices within a large integrated care delivery system in Northern California. Over a 12-week period (3/1/2015-6/6/2015), 146 primary care physicians consented to enroll in the study (90.1%) and approved contact with 2496 of their patients (97.6%). Implementation challenges included: (1) navigating research vs. quality improvement requirements; (2) addressing informed consent considerations; and (3) introducing a new clinical tool into a highly time-constrained workflow. Strategies for successfully initiating this study included engagement with institutional leaders, Institutional Review Board members, and clinical stakeholders at multiple stages both before and after notice of Federal funding; flexibility by the research team in study design; and strong support from institutional leadership for "self-learning health system" research. CONCLUSIONS: By paying careful attention to identifying and collaborating with a wide range of key clinical stakeholders, we have shown that researchers embedded within a learning care system can successfully apply rigorous clinical trial methods to test new care innovations.