How Do Curricula for Medical Students and Physician Learners Address the Social Determinants of Health? A Scoping Review.

PURPOSE: Curricula about social determinants (drivers) of health (SDOH) are becoming more common in medical education, reflecting increasing expectations from payers and accreditors that health care systems do more to address health-related social needs and close pervasive health equity gaps. Few previous reviews have addressed the content of SDOH-related curricula. This review examines the scope and focus of medical education on SDOH and adjacent concepts. METHOD: The authors screened 2,442 articles describing curricula delivered in undergraduate, graduate, and continuing medical education settings between 2010 and 2023 using PubMed and 2 field-specific databases, yielding 289 articles. Data on course duration, pedagogic approach, assessment methods, and curricular content were extracted and analyzed. Curricular content was categorized using the National Academies of Science, Engineering, and Medicine's (NASEM's) 5As framework, which recommends 5 key activities health care can undertake to mitigate social risk (awareness, adjustment, assistance, alignment, and advocacy). RESULTS: A total of 289 articles were included in this review. Curricula covering SDOH-related concepts have increased over time. Of the included articles, 190 (65.7%) referenced at least 1 of NASEM's 5 key activities. Training on social risk screening and other awareness activities were noted most frequently (123 [42.6%]), followed by curricula on helping patients get social care (assistance; 86 [29.8%]) and providing social risk-adjusted health care (adjustment; 81 [28.0%]). Curricula on system- and policy-level activities, including alignment of health care and social care organizations (alignment), and advocacy (advocacy) were described less frequently (43 [14.9%] and 49 [17.0%], respectively). Ninety-four articles (32.5%) referenced only general information about SDOH without describing specific actions to adjust care or reduce social adversity. CONCLUSIONS: NASEM's 5As framework provides a useful construct for characterizing SDOH-related curricula. Medical educators should teach not only the prevalence and pathophysiology of SDOH but also what physicians can do to address these factors.

Comparison of two propensity score-based methods for balancing covariates: the overlap weighting and fine stratification methods in real-world claims data.

BACKGROUND: Two propensity score (PS) based balancing covariate methods, the overlap weighting method (OW) and the fine stratification method (FS), produce superb covariate balance. OW has been compared with various weighting methods while FS has been compared with the traditional stratification method and various matching methods. However, no study has yet compared OW and FS. In addition, OW has not yet been evaluated in large claims data with low prevalence exposure and with low frequency outcomes, a context in which optimal use of balancing methods is critical. In the study, we aimed to compare OW and FS using real-world data and simulations with low prevalence exposure and with low frequency outcomes. METHODS: We used the Texas State Medicaid claims data on adult beneficiaries with diabetes in 2012 as an empirical example (N = 42,628). Based on its real-world research question, we estimated an average treatment effect of health center vs. non-health center attendance in the total population. We also performed simulations to evaluate their relative performance. To preserve associations between covariates, we used the plasmode approach to simulate outcomes and/or exposures with N = 4,000. We simulated both homogeneous and heterogeneous treatment effects with various outcome risks (1-30% or observed: 27.75%) and/or exposure prevalence (2.5-30% or observed:10.55%). We used a weighted generalized linear model to estimate the exposure effect and the cluster-robust standard error (SE) method to estimate its SE. RESULTS: In the empirical example, we found that OW had smaller standardized mean differences in all covariates (range: OW: 0.0-0.02 vs. FS: 0.22-3.26) and Mahalanobis balance distance (MB) (< 0.001 vs. > 0.049) than FS. In simulations, OW also achieved smaller MB (homogeneity: <0.04 vs. > 0.04; heterogeneity: 0.0-0.11 vs. 0.07-0.29), relative bias (homogeneity: 4.04-56.20 vs. 20-61.63; heterogeneity: 7.85-57.6 vs. 15.0-60.4), square root of mean squared error (homogeneity: 0.332-1.308 vs. 0.385-1.365; heterogeneity: 0.263-0.526 vs 0.313-0.620), and coverage probability (homogeneity: 0.0-80.4% vs. 0.0-69.8%; heterogeneity: 0.0-97.6% vs. 0.0-92.8%), than FS, in most cases. CONCLUSIONS: These findings suggest that OW can yield nearly perfect covariate balance and therefore enhance the accuracy of average treatment effect estimation in the total population.

Utilization, quality, and spending for pediatric Medicaid enrollees with primary care in health centers vs non-health centers.

BACKGROUND: Limited research has explored the performance of health centers (HCs) compared to other primary care settings among children in the United States. We evaluated utilization, quality, and expenditures for pediatric Medicaid enrollees receiving care in HCs versus non-HCs. METHODS: This national cross-sectional study utilized 2012 Medicaid Analytic eXtract (MAX) claims to examine children 0-17 years with a primary care visit, stratified by whether majority (> 50%) of primary care visits were at HCs or non-HCs. Outcome measures include utilization (primary care visits, non-primary care outpatient visits, prescription claims, Emergency Department (ED) visits, hospitalizations) and quality (well-child visits, avoidable ED visits, avoidable hospitalizations). For children enrolled in fee-for-service Medicaid, we also measured expenditures. Propensity score-based overlap weighting was used to balance covariates. RESULTS: A total of 2,383,270 Medicaid-enrolled children received the majority of their primary care at HCs, while 18,540,743 did at non-HCs. In adjusted analyses, HC patients had 20% more primary care visits, 15% less non-primary care outpatient visits, and 21% less prescription claims than non-HC patients. ED visits were similar across the two groups, while HC patients had 7% lower chance of hospitalization than non-HC. Quality of care outcomes favored HC patients in main analyses, but results were less robust when excluding managed care beneficiaries. Total expenditures among the fee-for-service subpopulation were lower by $239 (8%) for HC patients. CONCLUSIONS: In this study of nationwide claims data to evaluate healthcare utilization, quality, and spending among Medicaid-enrolled children who receive primary care at HCs versus non-HCs, findings suggest primary care delivery in HCs may be associated with a more cost-effective model of healthcare for children.

Health Centers and Value-Based Payment: A Framework for Health Center Payment Reform and Early Experiences in Medicaid Value-Based Payment in Seven States.

Policy Points As essential access points to primary care for almost 29 million US patients, of whom 47% are Medicaid enrollees, health centers are positioned to implement the population health management necessary in value-based payment (VBP) contracts. Primary care payment reform requires multiple payment methodologies used together to provide flexibility to care providers, encourage investments in infrastructure and new services, and offer incentives for achieving better health outcomes. State policy and significant financial incentives from Medicaid agencies and Medicaid managed care plans will likely be required to increase health center participation in VBP, which is consistent with broader state efforts to expand investment in primary care. CONTEXT: Efforts are ongoing to advance value-based payment (VBP), and health centers serve as essential access points to comprehensive primary care services for almost 29 million people in the United States. Therefore, it is important to assess the levels of health center participation in VBP, types of VBP contracts, characteristics of health centers participating in VBP, and variations in state policy environments that influence VBP participation. METHODS: This mixed methods study combined qualitative research on state policy environments and health center participation in VBP with quantitative analysis of Uniform Data System and health center financial data in seven vanguard states: Oregon, Washington, California, Colorado, New York, Hawaii, and Kentucky. VBP contracts were classified into three layers: base payments being transformed from visit-based to population-based (Layer 1), infrastructure and care coordination payments (Layer 2), and performance incentive payments (Layer 3). FINDINGS: Health centers in all seven states participated in Layer 2 and Layer 3 VBP, with VBP participation growing from 35% to 58% of all health centers in these states from 2013 to 2017. Among participating health centers, the average percentage of Medicaid revenue received as Layer 2 and Layer 3 VBP rose from 6.4% in 2013 to 9.1% in 2017. Oregon and Washington health centers participating in Layer 1 payment reforms received most of their Medicaid revenue in VBP. In 2017, VBP participation was associated with larger health center size in four states (P <.05), and higher average number of days cash on hand (P <.05) in three states. CONCLUSIONS: A multilayer payment model is useful for implementing and monitoring VBP adoption among health centers. State policy, financial incentives from Medicaid agencies and Medicaid managed plans, and health center-Medicaid collaboration under strong primary care association and health center leadership will likely be required to increase health center participation in VBP.

Quality of Care for US Adults With Medicaid Insurance and Type 2 Diabetes in Federally Qualified Health Centers Compared With Other Primary Care Settings.

OBJECTIVE: The objective of this study was to evaluate indicators of diabetes quality of care for US nonelderly, adult Medicaid enrollees with type 2 diabetes and compare federally qualified health centers (FQHCs) versus non-FQHCs. RESEARCH DESIGN AND METHODS: We analyzed diabetes process measures and acute health services utilization with 2012 US fee-for-service and managed care Medicaid claims in all 50 states and DC. We compared FQHC (N=121,977) to non-FQHC patients (N=700,401) using propensity scores to balance covariates and generalized estimating equation models. RESULTS: Overall, laboratory-based process measures occurred more frequently (range, 65.7%-76.6%) than measures requiring specialty referrals (retinal examinations, 33.3%; diabetes education, 3.4%). Compared with non-FQHC patients, FQHC patients had about 3 percentage point lower rates of each process measure, except for higher rates of diabetes education [relative risk=1.09, 95% confidence interval (CI): 1.03-1.16]. FQHC patients had fewer overall [incident rate ratio (IRR)=0.87, 95% CI: 0.86-0.88] and diabetes-related hospitalizations (IRR=0.79, 95% CI: 0.77-0.81), but more overall (IRR=1.06, 95% CI: 1.05-1.07) and diabetes-related emergency department visits (IRR=1.10, 95% CI: 1.08-1.13). CONCLUSIONS: This national analysis identified opportunities to improve diabetes management among Medicaid enrollees with type 2 diabetes, especially for retinal examinations or diabetes education. Overall, we found slightly lower rates of most diabetes care process measures for FQHC patients versus non-FQHC patients. Despite having higher rates of emergency department visits, FQHC patients were significantly less likely to be hospitalized than non-FQHC patients. These findings emphasize the need to identify innovative, effective approaches to improve diabetes care for Medicaid enrollees, especially in FQHC settings.

Early experience of the quality improvement award program in federally funded health centers.

OBJECTIVES: To describe the Health Resources and Services Administration's Quality Improvement Award (QIA) program, award patterns, and early lessons learned. STUDY SETTING: 1413 health centers were eligible for QIA from 2014 to 2018. STUDY DESIGN: We assessed cumulative QIA funding earned and modified funding excluding payments for per-patient bonuses, electronic health record (EHR) use, patient-centered medical home (PCMH) accreditation, and health information technology. We compared health centers on rural/urban location, PCMH accreditation, EHR reporting, and size. DATA COLLECTION: Organizational and quality measures are reported in the Uniform Data System, QIA program data. PRINCIPAL FINDINGS: Average cumulative funding was higher for health centers that were not rural (USD 380,387 [± USD 233,467] vs. USD 303,526 [± USD 164,272]), had PCMH accreditation (USD 401,675 [± USD 218,246] vs. USD 250,784 [± USD 144,404]), used their EHR for quality reporting (USD 374,214 (± USD 222,866) vs. USD 331,150 (± USD 198,689)), and were large (USD 435,473 (± USD 238,193) vs. USD 270,681 (± USD 114,484) an USD 231,917 (± USD 97,847) for small and medium centers, respectively). There were similar patterns, with smaller differences, for average modified payments. CONCLUSIONS: QIA is an important feasible initiative to introduce value-based payment principles to health centers. Early lessons for program design include announcing award criteria in advance and focusing on a smaller number of priority targets.

Teaching Intersectionality of Sexual Orientation, Gender Identity, and Race/Ethnicity in a Health Disparities Course.

Introduction: Intersectionality considers how different identities simultaneously affect an individual's experiences. Those of multiple minority statuses may experience effects of intersecting systems of oppression. Most health disparities curricula do not focus on intersectionality. We studied the impact of an innovative module teaching intersectionality of sexual orientation, gender identity, and race/ethnicity issues in the required Pritzker School of Medicine course Health Care Disparities: Equity and Advocacy. Methods: A short lecture reviewed sexual and gender minority (SGM) health disparities, intersectionality, minority stress, and shared decision making (SDM) to establish shared language among 83 first-year medical students. Students then viewed four videos of SGM patients of color (POC) describing their health care experiences, each followed by moderated discussion about how compounded minority stress affects lived experiences and health and how to improve SDM for SGM POC. One video interviewee attended the session and answered students' questions. Evaluation was performed using pre- and postsurveys. Results: Feeling somewhat/completely confident in defining intersectionality increased from 57% to 96%. Prior to the session, 62% of respondents reported feeling somewhat/completely confident in identifying barriers to care for SGM patients, and 92% after. Thirty-three percent felt somewhat/completely confident in asking SGM patients about their identities before the session, and 81% after. Eighty-four percent rated the session as very good or excellent. Discussion: The session was well received, improved student knowledge of intersectionality, and improved confidence in communicating with and caring for SGM patients. Future iterations could include condensing the lecture and including a patient panel and/or small-group discussion.

Diabetes Management in Community Health Centers: a Review of Policies and Programs.

PURPOSE OF REVIEW: Community health centers (CHCs) provide care to millions of vulnerable patients in the USA, including a disproportionate number with diabetes. Policies affecting diabetes management in CHCs therefore have broad implications for clinical practice and patient outcomes nationwide. We describe prior policies that have influenced diabetes management in CHCs, discuss current policies and programs, as well as present emerging innovations and future directions for diabetes care in this setting. RECENT FINDINGS: Domains for current diabetes policies and programs in CHCs include coverage requirements, quality reporting and incentives, prescription discounts, healthy behavior incentives, and team-based care. Policies in these areas affect the management of diabetes at multiple levels, from organizations that support CHCs to individual health centers, and the providers and patients based there. Several domains of interrelated policies and programs impact CHC diabetes management at multiple levels. Stakeholders' understanding of these policies and programs may identify opportunities to improve diabetes care.

Provider and Staff Morale, Job Satisfaction, and Burnout over a 4-Year Medical Home Intervention.

BACKGROUND: The patient-centered medical home (PCMH) is a widely adopted primary care model. However, it is unclear whether changes in provider and staff perceptions of clinic PCMH capability are associated with changes in provider and staff morale, job satisfaction, and burnout in safety net clinics. OBJECTIVE: To determine how provider and staff PCMH ratings changed under a multi-year PCMH transformation initiative and assess whether changes in provider and staff PCMH ratings were associated with changes in morale, job satisfaction, and burnout. DESIGN: Comparison of baseline (2010) and post-intervention (2013-2014) surveys. SETTING: Sixty clinics in five states. PARTICIPANTS: Five hundred thirty-six (78.2%) providers and staff at baseline and 589 (78.3%) post-intervention. INTERVENTION: Collaborative learning sessions and on-site coaching to implement PCMH over 4 years. MEASUREMENTS: Provider and staff PCMH ratings on 0 (worst) to 100 (best) scales; percent of providers and staff reporting good or better morale, job satisfaction, and freedom from burnout. RESULTS: Almost half of safety net clinics improved PCMH capabilities from the perspective of providers (28 out of 59, 47%) and staff (25 out of 59, 42%). Over the same period, clinics saw a decrease in the percentage of providers reporting high job satisfaction (- 12.3% points, p = .009) and freedom from burnout (- 10.4% points, p = .006). Worsened satisfaction was concentrated among clinics that had decreased PCMH rating, with those clinics seeing far fewer providers report high job satisfaction (- 38.1% points, p < 0.001). LIMITATIONS: Control clinics were not used. Individual-level longitudinal survey administration was not feasible. CONCLUSION: If clinics pursue PCMH transformation and providers do not perceive improvement, they may risk significantly worsened job satisfaction. Clinics should be aware of this potential risk of PCMH transformation and ensure that providers are aware of PCMH improvements.

Development and Testing of the Provider and Staff Perceptions of Integrated Care (PSPIC) Survey.

This article discusses development and testing of the Provider and Staff Perceptions of Integrated Care Survey, a 21-item questionnaire, informed by Singer and colleagues' seven-construct framework. Questionnaires were sent to 2,936 providers and staff at 100 federally qualified health centers and other safety net clinics in 10 Midwestern U.S. states; 332 were ineligible, leaving 2,604 potential participants. Following 4 mailings, 781 (30%) responded from 97 health centers. Item analyses, exploratory factor analysis, and confirmatory factor analysis were undertaken. Exploratory factor analysis suggests four latent factors: Teams and Care Continuity, Patient Centeredness, Coordination with External Providers, and Coordination with Community Resources. Confirmatory factor analysis confirmed these factor groupings. For the total sample, Cronbach's alpha exceeded 0.7 for each latent factor. Descriptive responses to each of the 21 Provider and Staff Perceptions of Integrated Care questions appear to have potential in identifying areas that providers and staff recognize as care integration strengths, and areas that may warrant improvement.

Patient Experience of Chronic Illness Care and Medical Home Transformation in Safety Net Clinics.

OBJECTIVE: To examine the relationship between medical home transformation and patient experience of chronic illness care. STUDY SETTING: Thirteen safety net clinics located in five states enrolled in the Safety Net Medical Home Initiative. STUDY DESIGN: Repeated cross-sectional surveys of randomly selected adult patients were completed at baseline (n = 303) and postintervention (n = 271). DATA COLLECTION METHODS: Questions from the Patient Assessment of Chronic Illness Care (PACIC) (100-point scale) were used to capture patient experience of chronic illness care. Generalized estimating equation methods were used to (i) estimate how differential improvement in patient-centered medical home (PCMH) capability affected differences in modified PACIC scores between baseline and postintervention, and (ii) to examine cross-sectional associations between PCMH capability and modified PACIC scores for patients at completion of the intervention. PRINCIPAL FINDINGS: In adjusted analyses, high PCMH improvement (above median) was only marginally associated with a larger increase in total modified PACIC score (adjusted ß = 7.7, 95 percent confidence interval [CI]: -1.1 to 16.5). At completion of the intervention, a 10-point higher PCMH capability score was associated with an 8.9-point higher total modified PACIC score (95 percent CI: 3.1-14.7) and higher scores in four of five subdomains (patient activation, delivery system design, contextual care, and follow-up/coordination). CONCLUSIONS: We report that sustained, 5-year medical home transformation may be associated with modest improvement in patient experience of chronic illness care for vulnerable populations in safety net clinics.

Development and Validation of a Short-Form Safety Net Medical Home Scale.

OBJECTIVE: To develop a short-form Safety Net Medical Home Scale (SNMHS) for assessing patient-centered medical home (PCMH) capability in safety net clinics. DATA SOURCES/STUDY SETTING: National surveys of federally qualified health centers (FQHCs). Interviews with FQHC directors. STUDY DESIGN: We constructed three short-form SNMHS versions and examined correlations with full SNMHS and related primary care assessments. We tested usability with FQHC directors and reviewed scale development with an advisory group. DATA COLLECTION: Federally qualified health center surveys were administered in 2009 and 2013, by mail and online. Usability testing was conducted through telephone interviews with FQHC directors in 2013. PRINCIPAL FINDINGS: Six-, 12-, and 18-question short-form SNMHS versions had Pearson correlations with full scale of 0.84, 0.92, and 0.96, respectively. All versions showed a level of convergent validity with other primary care assessment scales comparable to the full SNMHS. User testers found short forms to be low-burden, though missing some PCMH concepts. Advisory group members expressed caution over missing concepts and appropriate use of short-form self-assessments. CONCLUSIONS: Short-form versions of SNMHS showed strong correlations with full scale and may be useful for brief assessment of safety net PCMH capability. Each short-form SNMHS version may be appropriate for different research, quality improvement, and assessment purposes.

Solving Disparities Through Payment And Delivery System Reform: A Program To Achieve Health Equity.

Payment systems generally do not directly encourage or support the reduction of health disparities. In 2013 the Finding Answers: Solving Disparities through Payment and Delivery System Reform program of the Robert Wood Johnson Foundation sought to understand how alternative payment models might intentionally incorporate a disparities-reduction component to promote health equity. A qualitative analysis of forty proposals to the program revealed that applicants generally did not link payment reform tightly to disparities reduction. Most proposed general pay-for-performance, global payment, or shared savings plans, combined with multicomponent system interventions. None of the applicants proposed making any financial payments contingent on having successfully reduced disparities. Most applicants did not address how they would optimize providers' intrinsic and extrinsic motivation to reduce disparities. A better understanding of how payment and care delivery models might be designed and implemented to reduce health disparities is essential.

What Factors Are Associated With Medicaid Patients' Use of Health Centers?

OBJECTIVE: To identify patient and neighborhood factors associated with health center (HC) use. METHODS: A cross-sectional study of Medicaid fee-for-service claims in 2009 comparing HC users and nonusers. RESULTS: Dually eligible patients (odds ratio [OR] 95% CI = [0.60, 0.61]) and those with high chronic disease burden (OR 95% CI = [0.73, 0.74]) had lower odds of HC use. Temporary Assistance for Needy Families participants (OR 95% CI = [1.20, 1.24]), black (OR 95% CI = [1.33, 1.36]) and Hispanic (OR 95% CI = [1.22, 1.25]) beneficiaries had higher odds. Local HC presence predicted higher HC use (OR 95% CI = [2.63, 2.70]). CONCLUSION: Findings may be useful in steering HC policies affecting critical access for Medicaid beneficiaries.

Medical Home Characteristics and Quality of Diabetes Care in Safety Net Clinics.

We examined associations between patient-centered medical home (PCMH) characteristics and quality of diabetes care in 15 safety net clinics in five states. Surveys among clinic directors assessed PCMH characteristics using the Safety Net Medical Home Scale. Chart audits among 864 patients assessed diabetes process and outcome measures. We modeled the odds of the patient receiving performance measures as a function of total PCMH score and of PCMH subscales and covariates. PCMH characteristics had mixed, inconsistent associations with the quality of diabetes care. The PCMH model may require refinement in design and implementation to improve diabetes care among vulnerable populations.

Health Care Use and Spending for Medicaid Enrollees in Federally Qualified Health Centers Versus Other Primary Care Settings.

OBJECTIVES: To compare health care use and spending of Medicaid enrollees seen at federally qualified health centers versus non-health center settings in a context of significant growth. METHODS: Using fee-for-service Medicaid claims from 13 states in 2009, we compared patients receiving the majority of their primary care in federally qualified health centers with propensity score-matched comparison groups receiving primary care in other settings. RESULTS: We found that health center patients had lower use and spending than did non-health center patients across all services, with 22% fewer visits and 33% lower spending on specialty care and 25% fewer admissions and 27% lower spending on inpatient care. Total spending was 24% lower for health center patients. CONCLUSIONS: Our analysis of 2009 Medicaid claims, which includes the largest sample of states and more recent data than do previous multistate claims studies, demonstrates that the health center program has provided a cost-efficient setting for primary care for Medicaid enrollees.

Characteristics Associated with Patient-Centered Medical Home Capability in Health Centers: A Cross-Sectional Analysis.

BACKGROUND: The patient-centered medical home (PCMH) model is being implemented in health centers (HCs) that provide comprehensive primary care to vulnerable populations. OBJECTIVE: To identify characteristics associated with HCs' PCMH capability. DESIGN: Cross-sectional analysis of a national dataset of Federally Qualified Health Centers (FQHCs) in 2009. Data for PCMH capability, HC, patient, neighborhood, and regional characteristics were combined from multiple sources. PARTICIPANTS: A total of 706 (70 %) of 1014 FQHCs from the Health Resources and Services Administration Community Health Center Program, representing all 50 states and the District of Columbia. MAIN MEASURES: PCMH capability was scored via the Commonwealth Fund National Survey of FQHCs through the Safety Net Medical Home Scale (0 [worst] to 100 [best]). HC, patient, neighborhood, and regional characteristics (all analyzed at the HC level) were measured from the Commonwealth survey, Uniform Data System, American Community Survey, American Medical Association physician data, and National Academy for State Health Policy data. KEY RESULTS: Independent correlates of high PCMH capability included having an electronic health record (EHR) (11.7-point [95 % confidence interval, CI 10.2-13.3]), more types of financial performance incentives (0.7-point [95 % CI 0.2-1.1] higher total score per one additional type, maximum possible = 10), more types of hospital-HC affiliations (1.6-point [95 % CI 1.1-2.1] higher total score per one additional type, maximum possible = 6), and location in certain US census divisions. Among HCs with an EHR, location in a state with state-supported PCMH initiatives and PCMH payments was associated with high PCMH capability (2.8-point, 95 % CI 0.2-5.5). Other characteristics had small effect size based on the measure unit (e.g. 0.04-point [95 % CI 0-0.08] lower total score per one percentage point more minority patients), but the effects could be practically large at the extremes. CONCLUSIONS: EHR adoption likely played a role in HCs' improvement in PCMH capability. Factors that appear to hold promise for supporting PCMH capability include a greater number of types of financial performance incentives, more types of hospital-HC affiliations, and state-level support and payment for PCMH activities.

The Costs of Participating in a Diabetes Quality Improvement Collaborative: Variation Among Five Clinics.

BACKGROUND: Quality improvement collaboratives (QICs) support rapid testing and implementation of interventions through the collective experience of participating organizations to improve care quality and reduce costs. Although QICs have been societally cost-effective in improving the care of chronic diseases, they may not be adopted by outpatient clinics if their costs are high. Diabetes QICs warrant reexamination as secular trends in the quality of diabetes care, new care guidelines for diabetes, and evolving strategies for quality improvement may have altered implementation costs. METHODS: The costs over the first four years-from June 2009 through May 2013-of an ongoing diabetes QIC were characterized by activities and over time. The QIC, linking six clinics on Chicago's South Side, tailored interventions to minority populations and built community partnerships. Costs were calculated from clinic surveys regarding activities, labor, and purchases. RESULTS: Data were obtained from five of the six participating clinics. Cost/diabetic patient/year ranged across clinic sites from $6 (largest clinic) to $68 (smallest clinic). Clinics spent 62%-88% of their total QIC costs on labor. The cost/diabetic patient/year changed over time from Year 1 (range across clinics, $5-$51), Year 2 ($11-$84), Year 3 ($4-$57), to Year 4 ($4-$80), with costs peaking at Year 2 for all clinics except Clinic 4, where costs peaked at Year 4. DISCUSSION: Cost experiences of QICs in clinics were di- verse over time and setting. High per-patient costs may stem from small clinic size, a sicker patient population, and variation in personnel type used. Cost decreases over time may represent increasing organizational learning and efficiency. Sharing resources may have achieved additional cost savings. This practical information can help administrators and policy makers predict, manage, and support costs of QICs as payers increasingly seek high-value health care.

Comparing the Cost of Caring for Medicare Beneficiaries in Federally Funded Health Centers to Other Care Settings.

OBJECTIVE: To compare total annual costs for Medicare beneficiaries receiving primary care in federally funded health centers (HCs) to Medicare beneficiaries in physician offices and outpatient clinics. DATA SOURCES/STUDY SETTINGS: Part A and B fee-for-service Medicare claims from 14 geographically diverse states. The sample was restricted to beneficiaries residing within primary care service areas (PCSAs) with at least one HC. STUDY DESIGN: We modeled separately total annual costs, annual primary care costs, and annual nonprimary care costs as a function of patient characteristics and PCSA fixed effects. DATA COLLECTION: Data were obtained from the Centers for Medicare & Medicaid Services. PRINCIPAL FINDINGS: Total median annual costs (at $2,370) for HC Medicare patients were lower by 10 percent compared to patients in physician offices ($2,667) and by 30 percent compared to patients in outpatient clinics ($3,580). This was due to lower nonprimary care costs in HCs, despite higher primary care costs. CONCLUSIONS: HCs may offer lower total cost practice style to the Centers for Medicare & Medicaid Services, which administers Medicare. Future research should examine whether these lower costs reflect better management by HC practitioners or more limited access to specialty care by HC patients.

Patient-centered Medical Home capability and clinical performance in HRSA-supported health centers.

OBJECTIVES: To evaluate the relationship between Patient-centered Medical Home (PCMH) model adoption in health centers (HCs) and clinical performance measures and to determine if adoption of PCMH characteristics is associated with better clinical performance. RESEARCH DESIGN: Data came from the Health Resources and Services Administration's 2009 Uniform Data System and the 2009 Commonwealth Fund National Survey of Federally Qualified Health Centers. Clinical performance measures included 2 process measures (childhood immunization and cervical cancer screening) and 2 outcome measures (hypertension control and diabetes control). Total and subscale PCMH scores were regressed on the clinical performance measures, adjusting for patient, provider, financial, and institutional characteristics. RESULTS: The findings showed different directional relationships, with some PCMH domains (care management, test/referral tracking, quality improvement, and external coordination) showing little or no effect on outcome measures of interest, 1 domain (access/communication) associated with improved outcomes, and 1 domain (patient tracking/registry) associated with worse outcomes. CONCLUSIONS: This study is among the first to examine the association between PCMH transformation and clinical performance in HCs, providing an understanding of the impact of PCMH adoption within safety-net settings. The mixed results highlight the importance of examining relationships between specific PCMH domains and specific clinical quality measures, in addition to analyzing overall PCMH scores which could yield distorted findings.