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.

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.