Getting Started: Building a Foundation to Address Disparities Through Data Collection

Transcription

1 Getting Started: Building a Foundation to Address Disparities Through Data Collection October 16, :00pm 2:30pm EST

2 A Toolkit for Collecting Race, Ethnicity, and Primary Language Information From Patients Romana Hasnain-Wynia, PhD Vice President, Research Health Research and Educational Trust

3 Goals Assist hospitals, health systems, community health centers, health plans and other potential users in understanding the importance of: Accurate data collection Assessing organizational capacity to collect data Implementing a framework designed specifically for obtaining information from patients/enrollees about their race, ethnicity and primary language efficiently, effectively, and respectfully

4 Goals Addressing disparities in healthcare within a quality of care framework Highlighting the importance of systematically collecting race, and ethnicity to improve quality of care Linking race and ethnicity data to quality of care measures to develop targeted interventions

5 Why Collect Data On Patient Race/Ethnicity and Language? Valid and reliable data are fundamental building blocks for identifying differences in care and developing targeted interventions Being responsive to communities: Pressing community health problems such as disparities in care can be addressed more effectively if health care organizations and health professionals build the trust of the community by documenting accomplishments

6 Why Collect Data? (Continued) Link race and ethnicity information to quality measures to examine disparities and undertake targeted interventions Ensure the adequacy of interpreter services, patient information materials, and cultural competency training for staff

7 Why Collect Data? (Continued) External Factors Reporting to the Joint Commission on Accreditation of Healthcare Organizations National Committee Quality Assurance Reporting to Centers for Medicare & Medicaid Services (payer, purchaser regulator, insurer, works through QIOs) State mandates

8 What We Know About Data Collection in Hospitals Main Findings All the hospitals collected race but not ethnicity or primary language Categories varied across all the sites Staff mostly collected through observation Staff at some sites had been trained to not ask Most of the hospitals indicated that they did not use these data for quality improvement

9 Barriers To Collecting Data Validity and reliability of data Legal concerns System/organizational barriers Profiling Time-Consuming Appropriate categories Patients /enrollees perceptions about why this information is being collected Discomfort in explicitly asking patients/enrollees to provide this information

10 Nuts and Bolts of Data Collection First get organizational buy-in from leadership and front-line staff Address discomfort Categories Staff training Start the dialogue with the community before implementing systematic data collection on race/ethnicity/language

11 Recommendations For Standardization Who provides the information should always be patients or their caretakers; should never be done by observation alone When to collect upon admission or patient registration to ensure appropriate fields are completed when patient begins treatment (for plans, at enrollment) What racial and ethnic categories should be used--- start with the U.S. Census categories. Hospitals can provide more fine-grained categories if needed

12 Recommendations (Continued) Where should data be stored --- in a standard format for easy linking to clinical data Patient concerns --- should be addressed up front and clearly prior to obtaining information Staff training --- need to provide on-going training and evaluation

13 Are Categories a Problem? Patients asked to state race/ethnicity in terms of their choice. Asked standard 2-part Race/Ethnicity questions (OMB Categories) Latino/Hispanic? What is your race? (7 options read) Asked preference between two methods

14 Race Which category best describes your race? American Indian/Alaska Native Asian Black or African American Native Hawaiian/Other Pacific Islander White Multiracial Declined Unavailable

15 If Using OMB Categories and Not Wanting to Split Race/Ethnicity African American/Black Asian Caucasian/White Hispanic/Latino/White Hispanic/Latino/Black Hispanic/Latino/ Declined Native American Native Hawaiian/Pacific Islander Multiracial Declined Unavailable/Unknown

16 Related Publications Hasnain-Wynia, R., Pierce, D. and Pittman, M. Who, When and How: The Current State of Race, Ethnicity, and Primary Language Data Collection in Hospitals. May, The Commonwealth Fund. Baker DW, Cameron KA, Feinglass J, Georgas P, Foster S, Pierce D, Thompson J., Hasnain-Wynia R. Patients Attitudes Toward Health Care Providers Collecting Information About Their Race And Ethnicity. J Gen Intern Med. Vol 20 (10). October Baker DW, Cameron KA, Feinglass J, Georgas P, Foster S, Pierce D, Thompson J, Hasnain-Wynia R. Development and Testing of a System to Rapidly and Accurately Collecting Patients Race And Ethnicity. Am J Public Health. Vol 96. no Hasnain-Wynia, R and Baker D.W. Obtaining Data on Patient Race, Ethnicity, and Primary Language in Health Care Organizations: Current Challenges and Proposed Solutions. Health Services Research Vol 41 Number 4, August 2006)

17 HRET Websites (toolkit) (HRET website)

18 Data as Building Blocks for Change Carmella Bocchino, RN, MBA Executive Vice President, Clinical Affairs and Strategic Planning America s Health Insurance Plans (AHIP)

19 Plans Collect Racial and Ethnic Data to Reduce Disparities and Improve Quality And Communications Support language and culturally appropriate communication to enrollees Base quality improvement efforts to reduce disparities identified in quality measures Identify enrollees with risk factors for certain conditions Assess variation in quality measures (such as HEDIS measures) by racial and ethnic groups Identify need for translation of materials

20 100% The The Disparities Solutions Individuals are Enrolled in Plans that Voluntarily Collect or Obtain Racial and Ethnic Data 80% 74.3% 78.2% 60% 40% 50.9% 53.5% 20% 0% Commercial (n=58) Medicare (n=33) Medicaid (n=46) All Plans (n=137) Data is weighted by enrollment

21 Methods Used by Plans to Voluntarily Collect or Obtain Racial and Ethnic Information About Enrollees Direct methods Self-identified during enrollment 74.1% Through participation in special programs (e.g., Disease management, health education) 35.4% Satisfaction surveys 8.7% Collected by health plan after enrollment 5.4% Indirect methods Linked files w/ external sources (e.g., Federal and state agencies) 40.1% Geocoding 38.5% Data is weighted by enrollment

22 Racial and Ethnic Categories Included in Health Insurance Plan Data Collection Efforts 100% 94.7% 93.4% 93.4% 93.1% 92.9% 88.0% 80% 60% 40% 20% 22.3% 0% Hispanic African American White Native American Asian American Pacific Islander Multiple Race/Ethnicity Data is weighted by enrollment

23 Concerns Affecting a Plan s Decision Not to Collect Racial and Ethnic Data on Enrollees Enrollees reactions No good/reliable method for data collection Perceived federal laws/regulations inhibit collection of this data Providers/employers are reluctant to supply this data Collection of this data is not common in health insurance plan s market (s)

24 Primary Language Data of Enrollees is Most Commonly Collected or Obtained by Medicaid Plans 100% 90.5% 80% 60% 55.0% 52.1% 56.4% 40% 20% 0% Commercial Medicare Medicaid All Plans Data is weighted by enrollment

25 Spanish and Asian Languages of Enrollees Are Most Commonly Identified by Health Insurance Plans 100% 96.7% 80% 76.2% 72.8% 60% 40% 49.1% 43.1% 20% 0% Spanish Chinese Korean Vietnamese Slavic Languages Data is weighted by enrollment

26 Enrollees Are Most Likely to Self-Identify Their Primary Language Rather than Choose from a Defined List 100% 80% 60% 71.5% 68.5% 59.6% 72.5% 40% 38.5% 39.9% 34.7% 37.9% 20% 0% Fill in "free text" field Choose from defined list of languages Commercial Medicare Medicaid All Plans Data is weighted by enrollment

27 Tools to Address Disparities in Health Available on AHIP s Website Highlights of AHIP/RWJF Quantitative and Qualitative Research Data as Building Blocks for Change, a data collection toolkit Quality Interactions: A Patient-Based Approach to Cross-Cultural Care, a one hour CME course for physicians Communications Resources to Close the Gap, a compendium of tools and resources for health insurance plans, providers, and health care organizations Available at:

28 Conclusions and Challenges Health insurance plans use data on race, ethnicity, and primary language of their enrollees to: Enhance quality of care Design culturally and linguistically appropriate programs for diverse populations Improve language appropriate services and resources for individuals with limited English proficiency Results of the 2006 AHIP-RWJF follow-up survey highlights progress on collecting data on race, ethnicity, and primary language for addressing disparities and closing the gap in care.

29 Conclusions and Challenges Ability and Intent for Data Collection Resides with: Willingness and perception Standardization of categories Uniform methodology for collecting data Cultural Competency Need to expand training of practicing physicians and as a part of a medical school curriculum Learning from Experiences/Best Practices Adoption of workable solutions Stimulating further innovations

30 Collection of Race and Ethnicity Data in a Complex Healthcare Organization Rohit Bhalla, MD, MPH Montefiore Medical Center Bronx, NY

31 Total population Population below age 18 Individuals below poverty level Foreign born The The Disparities Solutions The Bronx, New York Speak a language other than English at home (population > 5 years old) Hispanic or Latino ethnicity Black or African American race Bronx 1.4 million 30% 29% 32% 57% 52% 32% U.S.A. 300 million 25% 13% 12% 19% 15% 12% Source: U.S. Census Bureau, 2005 American Community Survey,

32 Montefiore Medical Center An Integrated Delivery System Ambulatory Care Emergency Dept. Hospital-Based Amb. Care Medical Group Managed Care Infrastructure Montefiore Inpatient Care Moses Div 726 Beds Adult M/S and Psych CHAM Einstein Div. 396 beds Post-Acute & Long term Care Homecare Certified HHA LTHHP Rehabilitation Unit Ambulatory Specialty Care

33 CMO CHCC World Wide Web BARNES AVE WAKEFIELD AIDS MENTAL HEALTH MARBLE HILL EDI to Outside Entities 3550 JEROME BURKE AVE SOUTH BRONX CHILDRENS HEALTH MAMARONECK 2005 JEROME ASTOR YONKERS DATA CENTER JOHNSON AVE. MEDICAL ARTS PAVILION BAINBRIDGE NURSING HOME MOSES DIVISION HARTSDALE MONTENET 80 registration areas 650 registrars CASTLE HILL MARAN PLACE PARKCHESTER LARCHMONT UNIVERSITY CO-OP CITY WEILER DIVISION BUHRE AVE ST. LAWRENCE WEST FARMS NORWOOD WHITE PLAINS HENRY HUDSON PKWY CROSS COUNTY 4514 BAINBRIDGE CFCC WILLIAMSBRIDGE DOBBS FERRY HOSPITAL JEROME AVE FAMILY PRACTICE BOSTON POST RD HARRISON FORDHAM FAMILY 1982/1894 EASTCHESTER PROSPECT WEST FORDHAM ROAD FORDHAM PLAZA KINGSBRIDGE FORDHAM HEALTH CENTER MONTEFIORE MEDICAL PARK AGING IN AMERICA BULLARD AVE METHADONE MARBLE HILL FAMILY HEALTH CENTER LEGEND SONET HIGH SPEED SERVICE FRAME RELAY 56K SERVICE FRAME RELAY T1 SERVICE MMC FIBER EXTENSION

34 Factors We Considered Management Registration Quality Unit Expertise Process: EHIT Content: HRET, RWJF Data use Optimal categories Recoding old data to new Interfaces Monitoring Workflow Field order Number of categories Specific issues Other Patient not present Patient refusal Hard vs. Soft stops Different care settings Education Staff training Questions from patients and families

35 Feedback sessions Leadership Staff HRET Helped define Concerns to be responded to Key deliverables Presented back To leadership and staff With multiple disciplines Implementation Training and education components Policy context Revised policies New fields Screens Leadership-staff materials Staff scripts Patient FAQs and potential answers Specific scenarios Staff questions Monitoring

36 Fields and Categories Process Required fields Ethnicity first Ethnicity Hispanic or Latino Not Hispanic or Latino Declined Patient unavailable Preferred language [Numerous] Declined Other Patient unavailable Race American Indian or Alaskan Native Asian Black or African American Native Hawaiian or Other Pacific Islander White Multiracial: Asian/Black-African American Multiracial: Asian/White Multiracial: Black-African American/White Multiracial: Other combination Declined Patient unavailable

37 Next Steps Monitoring data on category use By field By care setting Review feedback from staff and patients Refining workflow in specific settings Additional materials Patients Staff Stratified QI data

38 Addressing Racial And Ethnic Disparities In Health Care: Aetna s Data Collection Experience Maisha Cobb, Ph.D. Research Consultant Aetna, Inc.

39 Initiative Formation IOM Report Unequal Treatment, March 2002 Aetna Chairman Authorizes Data Gathering The Aetna Foundation, Present Advisory Committee Convenes November, 2002 Data Collection Begins October, 2002 Aetna Task Force Assembled, September 2002

40 Data Collection: Numbers Over Time 2,669,712 19,085 70, , ,744 1,294,781 2,242,425 3,675,290 3,786,300 3,903,112 4,000,000 3,200,000 2,400,000 1,600, ,000 0 Current Members Sharing Data Dec '02 Jun '03 Dec '03 Jun '04 Dec '04 Jun '05 Dec '05 Jun '06 Jul '06 Aug '06

41 Data Collection: Enrollment Forms

42 Data Collection: Aetna Navigator Website Online benefits portal for members Provides multiple opportunities to reach member Eliminate enrollment form challenges NJ mandates use of State enrollment forms NH won t allow data collection through enrollment Large clients complexities

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47 Data Collection: Numbers Over Time 2,669,712 Navigator 4,000,000 3,200,000 19,085 70, , ,744 1,294,781 2,242,425 3,675,290 3,786,300 3,903,112 2,400,000 1,600, ,000 0 Current Members Sharing Data Dec '02 Jun '03 Dec '03 Jun '04 Dec '04 Jun '05 Dec '05 Jun '06 Jul '06 Aug '06

48 Data Collection: Enhancements to Aetna Navigator Race/Ethnicity Data Collection Created separate categories for Asian and Pacific Islander Developed the ability to report biracial up to two categories for race/ethnicity for each member

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50 Use of Member Race/Ethnicity Data Reporting of Data Annual comprehensive report Standardized HEDIS/Market reports Ad hoc requests Data Use Member Interventions Breast Health Diabetes

51 Our next free web seminar is on Tuesday, October 31 st, :00pm 3:30pm EST Getting it Right: Navigating the Complexities of Collecting Race/Ethnicity Data To register for this web seminar please visit our website To sign up for our mailing list and to receive information on future events and web seminars us at: