Reducing Disparities: The Importance of Collecting Standardized Data on Patient Race, Ethnicity and Language Aligning Forces for Quality National Program Office

2 Why are we here? Understand the role of standardized R/E/L data collection in reducing disparities Identify and consider the key decision points to successfully implement standardized R/E/L data collection in your organization Obtain knowledge and tools to train staff on the standardized collection of R/E/L data

3 What will we cover today? Building blocks toward equitable care –National health care disparities –Increasing attention R/E/L data –Linking R/E/L data to quality –Using data to drive improvements Key Decision Points –Changes at the organizational level Nuts and Bolts –Tools to train your staff

4 What are disparities in health care quality? “Racial and ethnic minorities tend to receive a lower quality of healthcare than non-minorities” Less likely to receive: – Cancer screening – Cardiovascular therapy – Kidney dialysis – Transplants – Curative surgery for lung cancer – Hip and knee replacement – Pain medicines in the ER

5 Growing U.S. minority population Population Projections, 2010 to 2050 Source: U.S. Census Bureau, 2009 National Population Projections (Supplemental) 4. Projections of the Population by Sex, Race, and Hispanic Origin for the United States: 2010 to 2050

6 Increasing legislative and regulatory attention to R/E/L American Recovery and Reinvestment Act of 2009 – Hospitals and providers will need to collect R/E/L data to be eligible for “meaningful use” incentive payments – Race/Ethnicity follow Office of Management and Budget guidelines Patient Protection and Affordable Care Act of 2010 – Health programs receiving federal money are required to collect R/E/L data NCQA Patient-Centered Medical Home Standards – Points toward recognition earned by collecting and analyzing R/E/L data Revised Joint Commission standards – Expanded requirements related to the collection of patient language data – New requirement to collect patient-level data on race and ethnicity

7 Office of Management and Budget Race and ethnicity categories Race Black White Asian American Indian/Alaska Native Native Hawaiian/ Pacific Islander Ethnicity Hispanic Not Hispanic

8 Identifying and addressing disparities Three steps Standardized collection of self-reported R/E/L data –Categories are standardized –Patient self-reports Stratification and analysis of performance measures –Compare patients within an organization –Consolidate data to identify community-level trends Use of stratified data to identify and develop quality improvement interventions targeted to specific patient populations

9 National CABG rates Jha, NEJM, 2005 Rate per 1,000 Medicare enrollees

10 Diabetes Outcomes Better Health Greater Cleveland

11 Using R/E/L data to drive improvement Massachusetts General Hospital Chelsea Diabetes Project  Identified disparity between white and Latino patients in diabetes control and recommended care  Created culturally competent Diabetes Management Program  Improved mean HbA1c values for all patients, reduced gap between white and Latino patients  Increased overall number of patients with HbA1c test within past 9 months and eliminated disparity Source: Disparities Solution Center at MGH

12 How else can you use R/E/L data within your organization? Provide more patient-centered care Develop cultural competency training for staff Compare utilization of health services among different patients Compare patient satisfaction with care provided among different patients Target marketing materials to specific patient populations Capture changes in demographic trends

13 What needs to happen within your organization? Develop the capacity and infrastructure to collect standardized race, ethnicity and language information from all patients This will affect: – Registration system and processes – Staff training and workflow – Patient communications – How data are used to monitor quality

Key Decision Points in Standardizing Patient Race, Ethnicity and Language Data Collection

15 Key decision points to consider  Where are data currently collected?  Who needs to be engaged?  What registration system and IT modifications need to be made?  How will staff be trained?

16 Where are data collected? When scheduling/registering an office visit – Face-to-face – Written registration forms – Telephone Upon admission or registration at the hospital – Face-to-face – Telephone registration All points of entry (inpatient, outpatient, emergency department, cardiac catheterization lab, etc.) “Downstream effect” – Registries and other databases Source: HRET Toolkit, accessed on Sept 16, 2009http://

17 Who needs to be engaged? Senior Leadership Information Technology staff Registration/Admissions staff Quality Improvement Interpreter Services Clinicians Patient Advocacy/Diversity Team Community Relations/Marketing Source: HRET Toolkit, accessed on Sept 16, 2009http://

18 What registration system and IT modifications need to be made? Will you need add data fields to accommodate new categories? – Will you use granular categories? – Can patients choose more than one race? – Will you collect both spoken and written language? What is your system’s capacity to add a field? – Can the change be made ‘in-house’ and house-wide? – What departments need to be involved to make changes to the system? – Is there a need to create combined R/E categories? Will these fields be hard stops?

19 How will staff be trained? Who needs to be trained? Who will provide training? How will the training be implemented? – Role-playing? Handouts/scripts? Screen content? Will data be monitored after the training? How will you monitor staff? – Will feedback be given? – Will registrars see how data is used?

20 Anticipating staff concerns Patients will get angry It’s illegal Patients will get angry We don’t need to collect this information Patients will get angry I’m uncomfortable asking these questions Patients will get angry It will take too much time Patients will get angry

Nuts and Bolts of Collecting Patient Race, Ethnicity and Language Data: Staff Training

22 Purpose of this training We are implementing a standardized method of collecting race, ethnicity and language (R/E/L) data as self-reported by patients or their caregivers. You are key to ensuring that all data are collected consistently, accurately, professionally, and completely.

23 Learning Objectives After this training session you will be able to: – Describe the reasons for standardizing the collection of patient R/E/L – Use scripts to ask each patient to self-identify his/her R/E/L – Address patient questions and concerns

24 What is standardized data collection? Standardized categories across the organization Patient self-reports race, ethnicity and language – No more “eyeballing” the patient – Data is collected from all patients

25 Why collect standardized R/E/L data? We can ensure adequate interpreter services, patient information materials, cultural competency training for staff. We can link patient race, ethnicity and language data with clinical information to improve quality and examine any health care disparities. We can use quality improvement tools/techniques to address any health care disparities. By collecting this information, we can ensure that all patients receive high-quality care.

26 “….but we already collect this information!” That may be true, but studies examining R/E/L data collection in hospitals and ambulatory practices show: – In many organizations that currently collect R/E/L data, not everyone is doing a good job. – Many registrars collect the information by observing the patient and guessing. Allowing the patient to self-identify will lead to more accurate and reliable data.

27 Challenging assumptions – guess their race

28 How will the registration process change? 1.Letting your patients know 2.Ethnicity 3.Race 4.Preferred written and spoken language These are the recommended questions—your organization may choose to revise this list.

29 Recommended script for letting patients know “We want to make sure that all our patients get the best care possible. We would like you to tell us your racial/ethnic background and preferred language so that we can review the treatment that all patients receive and make sure that everyone gets the highest quality care.” Source: HRET Toolkit, accessed on Sept 16, 2009http:// This is a recommended script —your organization may choose to use a different or revised script.

30 Recommended script for ethnicity “First, do you consider yourself Hispanic or Latino?”  Yes  No  Declined  Unavailable Source: HRET Toolkit, accessed on Sept 16, 2009http:// This is a recommended script —your organization may choose to use a different or revised script, or use different categories. If applicable, you can include a screen shot that will show registration staff any changes to the computer screen that staff see during registration

31 Ethnicity definitions Hispanic or Latino: Person of Cuban, Mexican, Puerto Rican, South or Central American decent, regardless of race. Non-Hispanic or Latino: Person not of Hispanic or Latino ethnicity. Declined*: Patient is unwilling to provide an answer to the ethnicity question or cannot identify him/herself as Hispanic or Not Hispanic. Unavailable*: Patient is physically unable to respond. Source: HRET Toolkit, accessed on Sept 16, 2009http://

32 Recommended script for race “Which category best describes your race?”  American Indian/Alaska Native  Asian  Black/African American  Native Hawaiian/Other Pacific Islander  White  Declined  Unavailable  Some other race Source: HRET Toolkit, accessed on Sept 16, 2009http:// This is a recommended script —your organization may choose to use a different or revised script, or use different categories.

33 Race definitions American Indian or Alaska Native: Person having origins in any of the original peoples of North and South America (including Central America) and maintains tribal affiliation. Asian: Person having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent. Black or African American: Person having origins in any of the black racial groups of Africa. Native Hawaiian or Other Pacific Islander: Person having origins in any of the original peoples of Hawaii, Guam, Samoa, or other Pacific Islands. White: A person having origins in any of the original peoples of Europe, the Middle East, or North Africa. Source: HRET Toolkit, accessed on Sept 27, 2011http:// Some Other Race*: A person who does not self-identify with any of the OMB race categories. Declined*: Patient is unwilling to choose a race category or cannot identify him/herself with one of the listed races. Unavailable*: Patient is physically unable to respond. * This symbol indicates a modification we have made to the OMB recommendations

34 Recommended script for patient’s preferred language “What language do you feel most comfortable speaking with your doctor or nurse?”  English  Spanish  Other  Declined  Unavailable “What language do you feel most comfortable reading medical or health care instructions?”  English  Spanish  Other  Declined  Unavailable Source: HRET Toolkit, accessed on Sept 16, 2009http:// If your organization is going to use different questions, you can use that text on this slide.

35 Language definitions Preferred spoken: the language a patient feels most comfortable speaking with their doctor or nurse Preferred written: the language a patient feels most comfortable reading medical or health care instructions Declined: A person who is unwilling to state a language preference. Unavailable: Patient is physically unable to respond. If your organization will not ask preferred written language, you can remove that text from this slide.

36 “I Speak” Poster Source: Cambridge Health Alliance (Cambridge, MA)

37 What do patients think? Most patients (80%) think hospitals and clinics should be collecting data. Most patients (97%) also think it’s important for hospitals and clinics to examine differences in quality. Some patients are concerned about how the data will be used. Baker, DW, et al. Patients’ Attitudes toward Health Care Providers Collecting Information about Their Race and Ethnicity. Journal of General Internal Medicine. Volume 20 (10): 895 – 900. August 2005.

38 Letting your patients know Wall Posters Can be displayed in: Registration areas Waiting rooms

39 Addressing patient concerns Source: HRET Toolkit, accessed on Sept 16, 2009http:// Patient ResponseSuggested ResponseCode “I'm American." Would you like to use an additional term, or would you like me to just put American? Other or as specified "Can't you tell by looking at me?" Well, usually I can. But sometimes I'm wrong, so we think it is better to let people tell us. I don’t want to put in the wrong answer. I’m trained not to make any assumptions. As specified If using open-ended option: "I don’t know. What are the responses?” You can say White, Black or African American, Latino or Hispanic, Asian, American Indian or Alaska Native, Pacific Islander or Native Hawaiian, some other race, or any combination of these. You can also use more specific terms like Irish, Jamaican, Mexican As specified "I was born in Nigeria, but I've really lived here all my life. What should I say?" That is really up to you. You can use any term you like. It is fine to say that you are Nigerian. As specified

40 Addressing patient concerns Source: HRET Toolkit, accessed on Sept 16, 2009http:// Patient ResponseSuggested Response Code "I'm human." Is that your way of saying that you don’t want to answer the question? If so, I can just say that you didn't want to answer. Declined “It’s none of your business.”I'll just put down that you didn't want to answer, which is fine.Declined "Who looks at this?" The only people who see this information are registration staff, administrators for the hospital, and the people involved in quality improvement. "Are you trying to find out if I'm a US citizen?” No. Definitely not! Also, you should know that the confidentiality of what you say is protected by law, and we do not share this information with anyone.

41 Acknowledgements This presentation was adapted from the Health Research and Educational Trust Disparities Toolkit as part of the Aligning Forces for Quality (AF4Q) initiative, supported by the Robert Wood Johnson Foundation.