Mosaic Equity Analysis Ken House, M.S. Director, Value Improvement Ken.house@mosaicmedical.org Marshall Greene, M.S. Manager, Data and Analytics Marshall.greene@mosaicmedical.org

5 Main Sites 10 Satellite Sites 21,080 74,800

Background Proud that Community Health Centers are a key part of our country’s solution to improve health equity. We say “Quality Care For All”…so do all Mosaic patients indeed receive similar quality of care? Are there disparities in care and/or outcomes among our own patients that we should focus on (internally or externally with community partners)? Let’s test our assumptions

Three Types of Data Staff perspective: Assessed our organization using “Culturally and Linguistically Appropriate Services (CLAS) standards Patient perspective: Surveyed patients using the CAHPS “Cultural Competence Item Set” Existing data: Dis-aggregated common metrics to compare across groups

Staff Perspective: CLAS Standards

Staff Perspective: CLAS Assessment Assessment Tool: http://www.culturecareconnection.org Conducted “Key informant interviews” representing all staff roles and locations Results presented in SWOT format Findings were encouraging and indicated Mosaic is aligned well with CLAS standards Opportunity: ongoing cultural sensitivity training/refreshers for staff and providers

Patient Perspective: Survey ~360 Mosaic patients surveyed using the CAHPS-CG “Cultural Competency” Supplemental Item Set From CAHPS Survey Guide: Culturally competent care is defined as: care that is responsive to diversity in the patient population and cultural factors that can affect health and health care, such as language, communication styles, beliefs, attitudes, and behaviors. To be culturally competent, health care providers have to employ various interpersonal and organizational strategies that bridge barriers to communication and understanding that stem from racial, ethnic, cultural, and linguistic differences.

Patient Perspective: Survey Patient-provider communication Complementary and alternative medicine Experiences of discrimination due to race/ethnicity, insurance, or language Experiences leading to trust or distrust, including level of trust, caring, and truth-telling Linguistic competency (access to language services)

Analyzing Existing Data Analyze quality measures, disease prevalence, access, patient experience*, and utilization of healthcare (claims data) Compare metrics across groups: race, ethnicity, gender, disability status, payor (future: SDoH) Identify statistically and meaningfully significant differences * Using CAHPS-CG + PCMH survey tool

Key Data Joins 1) Joined patient demographic details (available in OCHIN UDS Detail report) to clinical quality scores (available in Acuere and UDS quality reports) 2) Joined patient demographic details to claims data to compare utilization (patient-level file provided to us by our CCO, Pacific Source) Addition of confidence intervals / statistical validation

Summary of Findings Quality Disease Prevalence Access Self-Pay patients have meaningfully worse quality scores Latino patients do better on some quality measures, worse on others Disease Prevalence Meaningful differences in prevalence of some diseases by ethnicity Access No meaningful differences found Patient Experience (using CAHPS-CG+PCMH survey tool) Lower patient satisfaction in American Indian and Pacific Islander population Lower patient rating of ‘Comprehensiveness’ and ‘Self-Management among Latino population Women rate ‘Comprehensiveness’ higher than men, while rating ‘Access’ lower Healthcare Utilization/Spend (insured patients only) Slightly lower healthcare utilization by Latinos until age ~70. Amongst 70+, Latinos spend much less.

Clinical Quality Measures

Clinical Quality Measures by Ethnicity Addition of confidence intervals / statistical validation

Clinical Quality Measures by Ethnicity 2

Clinical Quality Measures by Ethnicity – Significantly Different KPIs and benchmarks Differences for these measures are statistically significant and are consistent with CCO patterns

Clinical Quality Measures by Race

Clinical Quality Measures by Race

Clinical Quality Measures by Race No meaningful differences found in quality measures by race

Clinical Quality Measures by Payor Self-Pay tends to perform worse, other payors mixed

Clinical Quality Measures by Payor

Clinical Quality Measures by Sex

Clinical Quality Measures by Disability Status

Chronic Disease Prevalence by Race and Ethnicity

Condition Prevalence by Race Non-White patients are less likely to be diagnosed with Hypertension and Obesity than other races Non-White patients are slightly more likely to be diagnosed with Diabetes Non-White patients are more likely to be diagnosed with PTSD

Condition Prevalence by Race 779 Patients included in Non-White and 21,383 in White

~25% of Hispanic patients are Self-Pay Hispanic patients are more likely to be Self-Pay ~25% of Hispanic patients are Self-Pay ~9% of Non-Hispanic patients are Self-Pay

Hispanic population is more likely to be diagnosed with Diabetes Condition Prevalence by Ethnicity Hispanic population is more likely to be diagnosed with Diabetes Hispanic population is more likely to be diagnosed with obesity, especially when looking at pediatric patients Hispanic population is less likely to be diagnosed with hypertension Hispanic population is less likely to be diagnosed with depression, especially when looking at male population

Top Conditions and Ethnicity Percent of Population with Condition by Ethnicity and Language (Top Ten) Top Conditions and Ethnicity Spanish English Condition Hispanic Non-Hispanic Difference DIABETES 14.2% 9.6% 32.1% OBESITY 18.0% 15.7% 13.2% LIVER_DISEASE 3.7% 6.1% -39.1% HTN 15.2% 25.6% -40.6% ARTHRITIS 9.1% 17.0% -46.3% ASTHMA 5.7% 11.0% -47.6% MIGRAINE 3.8% 7.4% -48.7% ANXIETY 5.1% 11.8% -56.8% BACK_PAIN 4.3% 10.2% -57.6% DEPRESSION 8.4% 22.0% -61.9% Hispanic population is more likely to be diagnosed with Obesity and Diabetes and less likely to be diagnosed with the other conditions, including depression and anxiety

Claims and Hospital Utilization Note: Mosaic has claims data only for patients insured by Pacific Source, hence uninsured/self-pay are not included here

Yearly Claims by Race (Insured Patients Only)

ER Utilization by Race

Inpatient Utilization by Race

Access Data

Access data shows no consistent bias by race or ethnicity Access Data showed no consistent bias by race or ethnicity If anything, access may be better for Hispanic patients even when controlling for clinic location Patterns emerged that self-pay patients may have quicker access to care and Medicare patients waited longer for appointments… Possibly due to a behavioral difference where Self-Pay more frequently seek same day appointments

Patient Experience

Patient Experience Survey Results by Ethnicity

Patient Experience Survey Results by Gender

Summary of Findings (repeated) Quality Self-Pay patients have meaningfully worse quality scores Latino patients do better on some quality measures, worse on others Disease Prevalence Meaningful differences in prevalence of some diseases by ethnicity Access No meaningful differences found Patient Experience Lower patient satisfaction in American Indian and Pacific Islander population Lower patient rating of ‘Comprehensiveness’ and ‘Self-Management among Latino population Women rate ‘Comprehensiveness’ higher than men, while rating ‘Access’ lower Healthcare Utilization Slightly lower healthcare utilization by Latinos until age ~70. Amongst 70+, Latinos spend much less

Next Steps Analyze cultural competency survey results Present to full Board for strategic planning Share with Patient Advisory Councils Share with providers and staff Focus groups? Develop a more robust population health management strategy specifically around the uninsured, including community partnerships Continue to improve capture of SDoH data

Thank you for your time! Ken House, M.S. Director, Value Improvement Ken.house@mosaicmedical.org Marshall Greene, M.S. Manager, Data and Analytics Marshall.greene@mosaicmedical.org