A collaboration team to build social service partnerships within a safety-net health system.

BACKGROUND: To facilitate safety-net healthcare system partnerships with community social service providers, the Los Angeles County Department of Health Services (LAC DHS) created a new collaboration team to spur cross-agency social and medical referral networks and engage communities affected by health disparities as part of a Sect. 1115 Medicaid waiver in Los Angeles County entitled Whole Person Care-Los Angeles (WPC-LA). METHODS: This observational research reviews three years of collaboration team implementation (2018-2020) through Medicaid-reportable engagement reports, a collaboration team qualitative survey on challenges, facilitators, and recommendations for community engagement. Member reflections for survey findings were conducted with the collaboration team and LAC DHS WPC-LA leadership. RESULTS: Collaboration team Medicaid engagement reports (n = 144) reported > 2,700 events, reaching > 70,000 individuals through cross-agency and community-partnered meetings. The collaboration team survey (n = 9) and member reflection sessions portrayed engagement processes through outreach, service assessments, and facilitation of service partnerships. The collaboration team facilitated community engagement processes through countywide workgroups on justice-system diversion and African American infant and maternal health. Recommendations for future safety net health system engagement processes included assessing health system readiness for community engagement and identifying strategies to build mutually beneficial social service partnerships. CONCLUSIONS: A dedicated collaboration team allowed for bi-directional knowledge exchange between county services, populations with lived experience, and social services, identifying service gaps and recommendations. Engagement with communities affected by health disparities resulted in health system policy recommendations and changes.

Community-based organizations' perspectives on improving health and social service integration.

BACKGROUND: Collaborations between health systems and community-based organizations (CBOs) are increasingly common mechanisms to address the unmet health-related social needs of high-risk populations. However, there is limited evidence on how to develop, manage, and sustain these partnerships, and implementation rarely incorporates perspectives of community social service organizations. To address these gaps, we elicited CBOs' perspectives on service delivery for clients, the impact of the Whole Person Care-Los Angeles (WPC-LA) initiative to integrate health and social care, and their suggestions for improving health system partnerships. METHODS: Using stakeholder engaged principles and a qualitative Rapid Assessment Process, we conducted brief surveys and in-depth semi-structured interviews with 65 key informants from 36 CBOs working with WPC-LA. RESULTS: Major themes identified by CBOs included: 1) the importance of a holistic, client-centered, continuously engaged approach that is reliant on regional partnerships; 2) benefits of WPC-LA expanding capacity and networks; 3) concerns about communication and redundancy hindering WPC-LA; and 4) a need for more equitable partnerships incorporating their approaches. CONCLUSIONS: CBOs value opportunities for integration with health systems, bring critical expertise to these partnerships, and seek to strengthen cross-sector collaborations. Early, equitable, and inclusive participation in the development and implementation of these partnerships may enhance their effectiveness, but requires policy that prioritizes and incentivizes sustainable and mutually beneficial partnerships.

Defining Team Effort Involved in Patient Care from the Primary Care Physician's Perspective.

BACKGROUND: A better understanding of the attributes of patients who require more effort to manage may improve risk adjustment approaches and lead to more efficient resource allocation, improved patient care and health outcomes, and reduced burnout in primary care clinicians. OBJECTIVE: To identify and characterize high-effort patients from the physician's perspective. DESIGN: Cohort study. PARTICIPANTS: Ninety-nine primary care physicians in an academic primary care network. MAIN MEASURES: From a list of 100 randomly selected patients in their panels, PCPs identified patients who required a high level of team-based effort and patients they considered complex. For high-effort patients, PCPs indicated which factors influenced their decision: medical/care coordination, behavioral health, and/or socioeconomic factors. We examined differences in patient characteristics based on PCP-defined effort and complexity. KEY RESULTS: Among 9594 eligible patients, PCPs classified 2277 (23.7 %) as high-effort and 2676 (27.9 %) as complex. Behavioral health issues were the major driver of effort in younger patients, while medical/care coordination issues predominated in older patients. Compared to low-effort patients, high-effort patients were significantly (P < 0.01 for all) more likely to have higher rates of medical (e.g. 23.2 % vs. 6.3 % for diabetes) and behavioral health problems (e.g. 9.8 % vs. 2.9 % for substance use disorder), more frequent primary care visits (10.9 vs. 6.0 visits), and higher acute care utilization rates (25.8 % vs. 7.7 % for emergency department [ED] visits and 15.0 % vs. 3.9 % for hospitalization). Almost one in five (18 %) patients who were considered high-effort were not deemed complex by the same PCPs. CONCLUSIONS: Patients defined as high-effort by their primary care physicians, not all of whom were medically complex, appear to have a high burden of psychosocial issues that may not be accounted for in current chronic disease-focused risk adjustment approaches.

Targeted Reminder Phone Calls to Patients at High Risk of No-Show for Primary Care Appointment: A Randomized Trial.

BACKGROUND: No-shows, or missed appointments, are a problem for many medical practices. They result in fragmented care and reduce access for all patients. OBJECTIVE: To determine whether telephone reminder calls targeted to patients at high risk of no-show can reduce no-show rates. DESIGN: Single-center randomized controlled trial. PARTICIPANTS: A total of 2247 primary care patients in a hospital-based primary care clinic at high risk of no-show (>15 % risk) for their appointment in 7 days. INTERVENTION: Seven days prior to their appointment, intervention arm patients were placed in a calling queue to receive a reminder phone call from a patient service coordinator. Coordinators were trained to engage patients in concrete planning. All patients received an automated phone call (usual care). MAIN MEASURES: Primary outcome was no-show rate. Secondary outcomes included arrival rate, cancellation rate, reschedule rate, time to cancellation, and change in revenue. KEY RESULTS: The no-show rate in the intervention arm (22.8 %) was significantly lower (absolute risk difference -6.4 %, p < 0.01, 95 % CI [-9.8 to -3.0 %]) than that in the control arm (29.2 %). Arrival, cancellation, and reschedule rates did not differ significantly. In the intervention arm, rescheduling and cancellations occurred further in advance of the appointment (mean difference, 0.35 days; 95 % CI [0.07-0.64]; p = 0.01). Reimbursement did not differ significantly. CONCLUSIONS: A phone call 7 days prior to an appointment led to a significant reduction in no-shows and increased reimbursement among patients at high risk of no-show. The use of targeted interventions may be of interest to practices taking on increased accountability for population health.

Evaluating a Model to Predict Primary Care Physician-Defined Complexity in a Large Academic Primary Care Practice-Based Research Network.

BACKGROUND: Improving the ability to risk-stratify patients is critical for efficiently allocating resources within healthcare systems. OBJECTIVE: The purpose of this study was to evaluate a physician-defined complexity prediction model against outpatient Charlson score (OCS) and a commercial risk predictor (CRP). DESIGN: Using a cohort in which primary care physicians reviewed 4302 of their adult patients, we developed a predictive model for estimated physician-defined complexity (ePDC) and categorized our population using ePDC, OCS and CRP. PARTICIPANTS: 143,372 primary care patients in a practice-based research network participated in the study. MAIN MEASURES: For all patients categorized as complex in 2007 by one or more risk-stratification method, we calculated the percentage of total person time from 2008-2011 for which eligible cancer screening was incomplete, HbA1c was ≥ 9 %, and LDL was ≥ 130 mg/dl (in patients with cardiovascular disease). We also calculated the number of emergency department (ED) visits and hospital admissions per person year (ppy). KEY RESULTS: There was modest agreement among individuals classified as complex using ePDC compared with OCS (36.7 %) and CRP (39.6 %). Over 4 follow-up years, eligible ePDC-complex patients had higher proportions (p < 0.001) of time with: incomplete cervical (17.8 % vs. 13.3 % for OCS; 19.4 % vs. 11.2 % for CRP), breast (21.4 % vs. 14.9 % for OCS; 22.7 % vs. 15.0 % for CRP), and colon (25.9 % vs. 18.7 % for OCS; 27.0 % vs. 18.2 % for CRP) cancer screening; HbA1c ≥ 9 % (15.6 % vs. 8.1 % for OCS; 15.9 % vs. 6.9 % for CRP); and LDL ≥ 130 mg/dl (12.4 % vs. 7.9 % for OCS; 11.8 % vs 9.0 % for CRP). ePDC-complex patients had higher rates (p < 0.003) of: ED visits (0.21 vs. 0.11 ppy for OCS; 0.17 vs. 0.15 ppy for CRP), and admissions in patients 45-64 and ≥ 65 years old (0.11 vs. 0.10 ppy AND 0.24 vs. 0.21 ppy for OCS). CONCLUSION: Our measure for estimated physician-defined complexity compared favorably to commonly used risk-prediction approaches in identifying future suboptimal quality and utilization outcomes.

Appointment "no-shows" are an independent predictor of subsequent quality of care and resource utilization outcomes.

BACKGROUND: Identifying individuals at high risk for suboptimal outcomes is an important goal of healthcare delivery systems. Appointment no-shows may be an important risk predictor. OBJECTIVES: To test the hypothesis that patients with a high propensity to "no-show" for appointments will have worse clinical and acute care utilization outcomes compared to patients with a lower propensity. DESIGN: We calculated the no-show propensity factor (NSPF) for patients of a large academic primary care network using 5 years of outpatient appointment data. NSPF corrects for patients with fewer appointments to avoid over-weighting of no-show visits in such patients. We divided patients into three NSPF risk groups and evaluated the association between NSPF and clinical and acute care utilization outcomes after adjusting for baseline patient characteristics. PARTICIPANTS: A total of 140,947 patients who visited a network practice from January 1, 2007, through December 31, 2009, and were either connected to a primary care physician or to a primary care practice, based on a previously validated algorithm. MAIN MEASURES: Outcomes of interest were incomplete colorectal, cervical, and breast cancer screening, and above-goal hemoglobin A1c (HbA1c) and low-density lipoprotein (LDL) levels at 1-year follow-up, and hospitalizations and emergency department visits in the subsequent 3 years. KEY RESULTS: Compared to patients in the low NSPF group, patients in the high NSPF group (n=14,081) were significantly more likely to have incomplete preventive cancer screening (aOR 2.41 [2.19-.66] for colorectal, aOR 1.85 [1.65-.08] for cervical, aOR 2.93 [2.62-3.28] for breast cancer), above-goal chronic disease control measures (aOR 2.64 [2.22-3.14] for HbA1c, aOR 1.39 [1.15-1.67] for LDL], and increased rates of acute care utilization (aRR 1.37 [1.31-1.44] for hospitalization, aRR 1.39 [1.35-1.43] for emergency department visits). CONCLUSIONS: NSPF is an independent predictor of suboptimal primary care outcomes and acute care utilization. NSPF may play an important role in helping healthcare systems identify high-risk patients.

Caring for high-need, high-cost patients: what makes for a successful care management program?

Provider groups taking on risk for the overall costs of care in accountable care organizations are developing care management programs to improve care and thereby control costs. Many such programs target "high-need, high-cost" patients: those with multiple or complex conditions, often combined with behavioral health problems or socioeconomic challenges. In this study we compared the operational approaches of 18 successful complex care management programs in order to offer guidance to providers, payers, and policymakers on best practices for complex care management. We found that effective programs customize their approach to their local contexts and caseloads; use a combination of qualitative and quantitative methods to identify patients; consider care coordination one of their key roles; focus on building trusting relationships with patients as well as their primary care providers; match team composition and interventions to patient needs; offer specialized training for team members; and use technology to bolster their efforts.

Neighborhood incarceration rate and asthma prevalence in New York City: a multilevel approach.

OBJECTIVES: We examined the association between neighborhood incarceration rate and asthma prevalence and morbidity among New York City adults. METHODS: We used multilevel modeling techniques and data from the New York City Community Health Survey (2004) to analyze the association between neighborhood incarceration rate and asthma prevalence, adjusting for individual-level sociodemographic, behavioral, and environmental characteristics. We examined interactions between neighborhood incarceration rate, respondent incarceration history, and race/ethnicity. RESULTS: The mean neighborhood rate of incarceration was 5.4% (range = 2.1%-12.8%). Neighborhood incarceration rate was associated with individual-level asthma prevalence (odds ratio [OR] = 1.06; 95% confidence interval [CI] = 1.03, 1.10) in unadjusted models but not after adjustment for sociodemographic characteristics (OR = 1.01; 95% CI = 0.98, 1.04). This association did not differ according to respondent race/ethnicity. CONCLUSIONS: Among New York City adults, the association between neighborhood incarceration rate and asthma prevalence is explained by the sociodemographic composition of neighborhoods and disparities in asthma prevalence at the individual level. Public health practitioners should further engage with criminal justice professionals and correctional health care providers to target asthma outreach efforts toward both correctional facilities and neighborhoods with high rates of incarceration.

Engaging individuals recently released from prison into primary care: a randomized trial.

OBJECTIVES: Individuals released from prison have high rates of chronic conditions but minimal engagement in primary care. We compared 2 interventions designed to improve primary care engagement and reduce acute care utilization: Transitions Clinic, a primary care-based care management program with a community health worker, versus expedited primary care. METHODS: We performed a randomized controlled trial from 2007 to 2009 among 200 recently released prisoners who had a chronic medical condition or were older than 50 years. We abstracted 12-month outcomes from an electronic repository available from the safety-net health care system. Main outcomes were (1) primary care utilization (2 or more visits to the assigned primary care clinic) and (2) emergency department (ED) utilization (the proportion of participants making any ED visit). RESULTS: Both groups had similar rates of primary care utilization (37.7% vs 47.1%; P = .18). Transitions Clinic participants had lower rates of ED utilization (25.5% vs 39.2%; P = .04). CONCLUSIONS: Chronically ill patients leaving prison will engage in primary care if provided early access. The addition of a primary care-based care management program tailored for returning prisoners reduces ED utilization over expedited primary care.

Defining patient complexity from the primary care physician's perspective: a cohort study.

BACKGROUND: Patients with complex health needs are increasingly the focus of health system redesign. OBJECTIVE: To characterize complex patients, as defined by their primary care physicians (PCPs), and to compare this definition with other commonly used algorithms. DESIGN: Cohort study. SETTING: 1 hospital-based practice, 4 community health centers, and 7 private practices in a primary care network in the United States. PARTICIPANTS: 40 physicians who reviewed a random sample of 120 of their own patients. MEASUREMENTS: After excluding patients for whom they were not directly responsible, PCPs indicated which of their patients they considered complex. These patients were characterized, independent predictors of complexity were identified, and PCP-defined complexity was compared with 3 comorbidity-based methods (Charlson score, Higashi score, and a proprietary Centers for Medicare & Medicaid Services algorithm). RESULTS: Physicians identified 1126 of their 4302 eligible patients (26.2%) as complex and assigned a mean of 2.2 domains of complexity per patient (median, 2.0 [interquartile range, 1 to 3]). Mental health and substance use were identified as major issues in younger complex patients, whereas medical decision making and care coordination predominated in older patients (P<0.001 for trends by decade). Major independent predictors of PCP-defined complexity (P<0.001) included age (probability of complexity increased from 14.8% to 19.8% with age increasing from 55 to 65 years), poorly controlled diabetes (from 12.7% to 47.6% if hemoglobin A1c level≥9%), use of antipsychotics (from 12.7% to 31.8%), alcohol-related diagnoses (from 12.9% to 27.4%), and inadequate insurance (from 12.5% to 19.2%). Classification agreement for complex patients ranged from 26.2% to 56.0% when PCP assignment was compared with each of the other methods. LIMITATION: Results may not be generalizable to other primary care settings. CONCLUSION: Primary care physicians identified approximately one quarter of their patients as complex. Medical, social, and behavioral factors all contributed to PCP-defined complexity. Physician-defined complexity had only modest agreement with 3 comorbidity-based algorithms. PRIMARY FUNDING SOURCE: Partners Community Healthcare, Inc.

Transitions clinic: creating a community-based model of health care for recently released California prisoners.

Most California prisoners experience discontinuity of health care upon return to the community. In January 2006, physicians working with community organizations and representatives of the San Francisco Department of Public Health's safety-net health system opened the Transitions Clinic (TC) to provide transitional and primary care as well as case management for prisoners returning to San Francisco. This article provides a complete description of TC, including an illustrative case, and reports information about the recently released individuals who participated in the program. From January 2006 to October 2007, TC saw 185 patients with chronic medical conditions. TC patients are socially and economically disenfranchised; 86% belong to ethnic minority groups and 38% are homeless. Eighty-nine percent of patients did not have a primary care provider prior to their incarceration. Preliminary findings demonstrate that a community-based model of care tailored to this disenfranchised population successfully engages them in seeking health care.

Relationship between patient panel characteristics and primary care physician clinical performance rankings.

CONTEXT: Physicians have increasingly become the focus of clinical performance measurement. OBJECTIVE: To investigate the relationship between patient panel characteristics and relative physician clinical performance rankings within a large academic primary care network. DESIGN, SETTING, AND PARTICIPANTS: Cohort study using data from 125,303 adult patients who had visited any of the 9 hospital-affiliated practices or 4 community health centers between January 1, 2003, and December 31, 2005, (162 primary care physicians in 1 physician organization linked by a common electronic medical record system in Eastern Massachusetts) to determine changes in physician quality ranking based on an aggregate of Health Plan Employer and Data Information Set (HEDIS) measures after adjusting for practice site, visit frequency, and patient panel characteristics. MAIN OUTCOME MEASURES: Composite physician clinical performance score based on 9 HEDIS quality measures (reported by percentile, with lower scores indicating higher quality). RESULTS: Patients of primary care physicians in the top quality performance tertile compared with patients of primary care physicians in the bottom quality tertile were older (51.1 years [95% confidence interval {CI}, 49.6-52.6 years] vs 46.6 years [95% CI, 43.8-49.5 years], respectively; P < .001), had a higher number of comorbidities (0.91 [95% CI, 0.83-0.98] vs 0.80 [95% CI, 0.66-0.95]; P = .008), and made more frequent primary care practice visits (71.0% [95% CI, 68.5%-73.5%] vs 61.8% [95% CI, 57.3%-66.3%] with >3 visits/year; P = .003). Top tertile primary care physicians compared with the bottom tertile physicians had fewer minority patients (13.7% [95% CI, 10.6%-16.7%] vs 25.6% [95% CI, 20.2%-31.1%], respectively; P < .001), non-English-speaking patients (3.2% [95% CI, 0.7%-5.6%] vs 10.2% [95% CI, 5.5%-14.9%]; P <.001), and patients with Medicaid coverage or without insurance (9.6% [95% CI, 7.5%-11.7%] vs 17.2% [95% CI, 13.5%-21.0%]; P <.001). After accounting for practice site and visit frequency differences, adjusting for patient panel factors resulted in a relative mean change in physician rankings of 7.6 percentiles (95% CI, 6.6-8.7 percentiles) per primary care physician, with more than one-third (36%) of primary care physicians (59/162) reclassified into different quality tertiles. CONCLUSION: Among primary care physicians practicing within the same large academic primary care system, patient panels with greater proportions of underinsured, minority, and non-English-speaking patients were associated with lower quality rankings for primary care physicians.

Development and preliminary testing of the health in community survey.

OBJECTIVES: Develop and pilot test the Health in Community Survey (HCS), to collect patients' perceptions of care integration between traditional care providers and community-based services that address social determinants of health. RESEARCH DESIGN: Interviews of working-age, English-or Spanish-speaking patients with multiple chronic conditions to identify survey domains; cognitive interviews and pilot test of HCS Version 1. RESULTS: Preliminary interview subjects identified diverse care concerns. From these findings, we designed six HCS domains. Cognitive interviews identified problems relating to patients' perceptions about limited function and disability. Nearly one-third of pilot test subjects reported they did not definitely have enough resources for food, transportation, doctor and hospital bills, and medications; 41.6% said their clinicians only sometimes or never knew about their resource problems. CONCLUSIONS: Although it requires further validation, the HCS offers insights into patients' perceptions of care integration between traditional health care providers and services addressing social determinants of health.