1. Mosaic Equity Analysis
Ken House, M.S.
Director, Value Improvement
Marshall Greene, M.S.
Manager, Data and Analytics

2. 5
Main Sites 10
Satellite Sites
21,080
74,800

3. 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

4. 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

5. Staff Perspective: CLAS Standards

6. Staff Perspective: CLAS Assessment
Assessment Tool:
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

7. 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.

8. 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)

13. 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

14. 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

15. 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.

16. Clinical Quality Measures

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

18. Clinical Quality Measures by Ethnicity2

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

20. Clinical Quality Measures by Race

21. Clinical Quality Measures by Race

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

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

24. Clinical Quality Measures by Payor

25. Clinical Quality Measures by Sex

26. Clinical Quality Measures by Disability Status

27. Chronic Disease Prevalence by Race and Ethnicity

28. 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

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

30. ~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

32. 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

33. 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

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

35. Yearly Claims by Race (Insured Patients Only)

36. ER Utilization by Race

37. Inpatient Utilization by Race

38. Access Data

39. 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

40. Patient Experience

43. Patient Experience Survey Results by Ethnicity

45. Patient Experience Survey Results by Gender

46. 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

47. 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

48. Thank you for your time! Ken House, M.S. Director, Value Improvement
Marshall Greene, M.S.
Manager, Data and Analytics