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Helping Hospitals Reduce Disparities

If efforts to improve the quality of health care received by minority Americans are to succeed, hospitals and other providers must routinely collect and analyze data not only on quality measures, but on patients' race, ethnicity, and language. Hospitals that currently track quality of care--under national initiatives like the Hospital Quality Alliance (HQA)--do not necessarily examine their data using these demographic factors. With support from The Commonwealth Fund, researchers at George Washington (GW) University have been working with hospitals serving large minority populations to help them analyze their HQA data by race, ethnicity, and language. Recently, we spoke with GW's Bruce Siegel, M.D., M.P.H., about the challenges involved in collecting and using such data, and how hospitals can begin to effect sustainable change.

HQA was established to collect data on 10 quality measures related to treatment of patients with acute myocardial infarction, heart failure, and pneumonia. How difficult was it to adapt the HQA framework to include information about race and ethnicity?
Bruce Siegel: There were definitely challenges. One problem is that we're not quite sure HQA measures are the right measures for looking at disparities. HQA measures tend to look at very narrow episodes of care and very narrow events--for example, did you get an aspirin after a heart attack? The things that lead to disparities may be more complex than that. So, for instance, all the patients may be getting beta blockers, but minority patients are being readmitted with heart failure a lot more often. That's an indicator--the readmission rate for heart failure--that is much more sensitive to disparities in care. The danger is that providers will look at a few measures and declare themselves "disparity-free," when that is not really the case.

What kinds of race and ethnicity categories did you incorporate?
Siegel: We used whatever the hospitals were using. Most had black or African American as a category. But once you get beyond that, there was no standardization. Some hospitals used Oriental, some Asian and Pacific Islander. Some hospitals collect ethnicity separately from race, most did not. But they were all collecting these data. The interesting thing, though, is that none had looked at quality by race or ethnicity before. Here they are collecting this huge amount of demographic data, which could be very useful in understanding the quality of care they're rendering and in understanding their patients' needs, but they were making very little use of it.

How were they using these racial/ethnic classifications?
Siegel: Some were using the information for understanding the markets they served and where their patients came from. But in terms of taking it to the next step and using it to understand how they performed vis-à-vis different groups of patients--they weren't going there yet.

Were any hospitals receiving incentives to collect that information?
Siegel: No. That's one of the great challenges we have. Today, hospitals in America have to record patients' language. That's a Joint Commission [on Accreditation of Healthcare Organizations] requirement. They may have fields for race in the patient registration system, but there's really no mandate for collecting high-quality data on patients' race, ethnicity, or language.

It's really fascinating. Corporate America is spending billions of dollars every year, trying to determine--with greater and greater accuracy and intensity--who its customers are, where they live, what languages they speak, what their preferences are, how they vote. The hospital and health care industry seems so far behind.

What kind of obstacles or resistance did you meet from the hospitals with which you worked?
Siegel: For some people, at some hospitals, there's this belief that disparities do not and cannot exist in the care they provide. Then, when you ask them, 'How do you know? Have you looked at data by race, ethnicity, language?' They say 'No, we never have.' It's assumed equity without any data to back it up. That's a very common pattern. We also asked, 'What's your stance on reporting the HQA measures by race or ethnicity?' About half of the respondents in the interviews were comfortable with that. They thought it was the right next step. The rest were very uncomfortable. They were unsure what that would show, and they were nervous about it. Another recurring theme was, 'We wouldn't know what to do about it. Nobody's giving us tools or strategies to deal this.' And they're right.

Also, just getting the data was a big obstacle. They were all able to get it, but it took a lot longer than they expected. Most HQA data are collected and analyzed using third-party vendor systems. Running the data by race and ethnicity required a customized analysis, which often meant going to their vendor and asking--and paying--for it. What we thought would be a simple data request often took them three or four months to complete. But for most hospitals to take equity seriously, they will have to analyze these data routinely.

How can these data be used? How can they help hospitals reduce racial and ethnic disparities in care?
Siegel: Even before you get to tools, the data are critical for just beginning the dialogue. These are very sensitive topics; clinicians don't like the term 'disparities.' They believe it has pejorative connotations, that it impugns behavior. We try to say that disparities arise not from people being bad, but because of systems that have developed over years. Our question is: How do we change systems? Initially, somebody has to create an opportunity for change, and the data can show that. Secondly, leadership needs to take the data and send a powerful message that says, 'We can talk about this. We're not going to push this under the rug. We're not going to punish people or point fingers.' Before you get to tools, the biggest obstacle is just having an honest discussion. And unless leadership at the very top creates an environment where that discussion can happen, it will not happen.

Does the protocol you've developed have potential to be replicated on a national basis?
Siegel: What we did, any hospital in America could do itself. My hope is that while there are still some kinks to work out, hospitals will start to run their data by race and by ethnicity. And they'll start to see, for instance, whether their performance is different for their Spanish-speaking patients versus their English-speaking patients, like we found in some instances. They'll start to find what the opportunities are and what gaps there may be. There are certainly challenges and obstacles, but it's not impossible. What it takes is somebody in leadership to say, 'This is important. We want to know about this.'

July 2006

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