Mosaic Equity Analysis Ken House, M.S. Director, Value

Mosaic Equity Analysis Ken House, M.S. Director, Value

Mosaic Equity Analysis Ken House, M.S. Director, Value Improvement [email protected] Marshall Greene, M.S. Manager, Data and Analytics [email protected] 5 Main Sites

10 Satellite Sites 21,080 74,800 Background Proud that Community Health Centers are a key part

of our countrys solution to improve health equity. We say Quality Care For Allso 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)? Lets 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 (available in OCHIN UDS Detail report)

to claims data to compare utilization (patient-level file provided to us by our CCO, Pacific Source) Summary of Findings 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 (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

Clinical Quality Measures by Ethnicity 2 Clinical Quality Measures by Ethnicity Significantly Different KPIs and benchmarks Adolescent Well Checks Hispanic Non Hispanic 55.5%

45.2% Mosaic 47.2% 39.6% CCO Cervical Cancer Screening

Effective Contraception (ages 15-17) 66.8% 58.3% 52.0% 40.4% 51.3% 23.1%

Mosaic CCO Mosaic Differences for these measures are statistically significant and are consistent with CCO patterns 37.2%

18.0% CCO 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 Condition Prevalence by Race Non-White White 25% 20%

15% 10% 5% 0% N HT PR DE

S ES N IO TY SI E OB

AR S TI RI H T ET XI

N A Y A HM T AS O

OH C AL RU LD G B K

AC IN PA E AB DI TE

S A HE RT SE DI E AS

RA IG M 779 Patients included in Non-White and 21,383 in White E IN R

CE N CA E LIV R SE DI

AS E T U RA A M

J IN UR Y S PT

D Hispanic patients are more likely to be Self-Pay ~25% of Hispanic patients are SelfPay ~9% of NonHispanic patients are Self-Pay 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 Percent of Population with Condition by Ethnicity and Language (Top Ten)

Top Conditions and Ethnicity Condition DIABETES OBESITY LIVER_DISEASE HTN ARTHRITIS ASTHMA MIGRAINE ANXIETY BACK_PAIN

DEPRESSION Spanish Hispanic 14.2% 18.0% 3.7% 15.2% 9.1% 5.7% 3.8%

5.1% 4.3% 8.4% English Non-Hispanic 9.6% 15.7% 6.1% 25.6% 17.0%

11.0% 7.4% 11.8% 10.2% 22.0% Difference 32.1% 13.2% -39.1% -40.6%

-46.3% -47.6% -48.7% -56.8% -57.6% -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 [email protected] Marshall Greene, M.S. Manager, Data and Analytics [email protected]

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