A Cross-Cutting Approach to Understanding Disparities in Care

Among the regional quality improvement collaboratives that participate in Aligning Forces for Quality, Better Health Greater Cleveland is unique in the diversity of health care organizations and practice sites that share their medical records-based clinical information. In particular, the broad use of electronic health records (EHRs) and the active participation of all of the region’s federally qualified health centers (FQHCs) and other safety-net practices side-by-side with more affluent systems and patients enables Better Health to provide a more representative picture of the region’s patients and to compare its performance among patients of different socioeconomic and insurance status.

We sat down with Thomas E. Love, PhD, director of Better Health Greater Cleveland’s Data Center, to learn more.

Q: How did you get involved with the work of Better Health and initiatives related to the Aligning Forces for Quality program?

I started out as a sort of typical statistician, and I fell into public health and health systems research because one of the first people who wanted to collaborate with me was Randall D. Cebul, MD, director of the Case Western Reserve University-MetroHealth System Center for Health Care Research and Policy, and later director of Better Health. Randy had a project that he wanted to do and after a while it became clear that he had a lot of interesting projects. When the opportunity to work on AF4Q came about, I was skeptical at first, but it became clear pretty quickly that there was going to be a lot of work that was not what a statistician typically did. In some cases, this work was simpler than what I was used to, in others it was more difficult. It was much harder in the sense that there were far more varied data to wrangle and a lot more political coalition-building to do. It also gave me the opportunity to play more of a leadership role and to carve out a niche that was useful and meaningful.

My local network in Cleveland is now vastly larger than it would be had I not been involved in AF4Q. I’ve gotten better at communicating ideas to people and motivating people to do things. I now have a reputation as someone who can do coalition building. I’ve met and been inspired by people in all parts of health care, from people on the informatics and analysis side to government, health system and provider leaders, and the nurses, doctors, and technicians who are actually gathering data on the ground.

Q: Tell us a bit about how Better Health has measured issues of equity and disparity. It goes beyond race and ethnicity, correct?

We did see issues of equity and disparity in ways that went beyond race and ethnicity from the start. There were two catalyzing factors. One, it was clear when we first saw the Robert Wood Johnson Foundation’s request for proposals for AF4Q that it was talking about measuring performance in terms of claims data rather than clinical data. It referred to “covered lives,” so the approach referred only to people who were insured. MetroHealth, where we are located, is one of the largest providers of care for the uninsured in the Cleveland area. We also have the largest population of Medicaid patients. So we were very interested in the impact of insurance and its role as a critical variable in disparities issues when we submitted our proposal.

The second galvanizing moment came just after we received the AF4Q grant. We had a meeting with interested parties and shared some basic data we pulled and analyzed from MetroHealth’s EHRs. One of our potential partners, the head of a local FQHC, was overwhelmed by how quickly we could put something together based on EHRs, and it helped confirm for her how desperately her site needed to get an EHR system in place. It really opened our eyes to the enormous differences between systems with EHRs and those without. Without EHRs, you couldn’t answer basic questions: How many patients with diabetes are we seeing? How many are being treated to standard? How are they doing? The “on-the-ground” impact of data in addressing disparities was not something I had thought much about. What we saw right away were not huge racial and ethnic disparities, but disparities between patients who had insurance and patients who did not, and especially disparities between patients in practices that had EHRs and those that did not. Practices with EHRs were doing better on things that mattered to patients, such as controlling blood sugar and keeping people out of the hospital.

Q: What kinds of demographic information are you gathering in addition to race, ethnicity, and insurance coverage?

From the start, we knew that location mattered. And we knew about the social determinants of health. What goes into health doesn’t always have a lot to do with what happens at the doctor’s office. A lot is determined by environment, and we tried to capture that in different ways. What we wanted to know was not just where you lived, but how that reflected your socioeconomic status. We used tools to link census data to patient addresses to get a sense of a patient’s estimated income and estimated educational level. This has been part of our reports from the beginning. As time went by, we added the patient’s preferred language.

Currently, we’re adding even more geography data to our new report, and looking at the gaps that exist between people who live in the city of Cleveland, in Cuyahoga County, and outside of the county. We know a much higher percentage of diabetes patients inside the city are smoking. We also know that blood pressure control is poorer among those who live in the city. One area in which location does not seem to have an impact is obesity: two thirds of patients in our region are obese, no matter where they live.

Q: Are there concrete examples of these data improving care for racial and ethnic minorities?

One good example of this is our work to improve blood pressure control in African American patients. Local Kaiser Permanente (now HealthSpan) practices put a program in place that included culturally sensitive patient education and an algorithm that doctors could use to choose among the large number of medications available to treat high blood pressure. My role was to help develop an approach to data collection, aggregation, and analysis that maximized our chances of getting good information without disrupting the workflow of people taking care of patients.

The primary impact of our reporting and analyses is the identification of best practices, which seem for the most part not to reduce racial and ethnic disparities, but to improve care across the board. The gaps—though they are not always large—remain. What we’re seeing is the “rising tide lifts all boats” phenomenon. The one area in which we have seen gaps narrowing is in measures of blood pressure control and cholesterol control among Hispanic patients. We’ve also noted that race stratification is more pronounced in diabetes and high blood pressure patients than in heart failure patients. This may be because disparities narrow as people age into the Medicare population, and older people are more likely to suffer heart failure. The bottom line is that race can be a proxy for so many things—location, lifestyle, genetics. We are trying to take baby steps to tease these things out.

Q: What kinds of training and education are necessary to produce this kind of information at the clinic and system level?

Since we started with three systems that had existing EHRs, our main task was to identify what we could get from these existing systems consistently. Over time, we have become more aggressive about adding things; for example, we have added measures of screening for and treating depression.

Q: How have you made data available in the region?

We have a fairly new public-facing data website, betterhealthcleveland.org/data. Anecdotally, people are saying that it’s a good way to get the message across. It’s designed for the general public and for people who are interested in policy issues. We also have a protected, members-only site that allows the eight systems and all of their providers to access information about their performance and how it compares with their peers’. Some of these systems, such as the Cleveland Clinic, have very sophisticated systems of their own, but our site is key for members such as FQHCs. We also have semi-annual learning collaboratives during which we share much of this information.

Q: Do you expect to see changes in reporting based on patients who are newly insured as a result of the Affordable Care Act?

We already have. MetroHealth is the largest Medicaid provider in the state and had a big role in moving thousands of uninsured patients to Medicaid before Medicaid expansion through a Medicaid waiver it obtained. Our next report will show an enormous increase in the number of Medicaid patients locally, and a drop in the number of uninsured among patients with chronic disease. We are looking at a 75 percent increase in Medicaid patients with diabetes, for instance. The question we haven’t quite answered yet is, what are the characteristics of these newly insured people? How are they different from the Medicaid or uninsured patients of the past? Like other parts of the country, we anticipate a possible increase in emergency department visits for these new Medicaid patients in the short term until we can get people into true medical homes. My suspicion is that the new, larger pool of Medicaid patients in our region may be a little wealthier and slightly more White than those we saw with Medicaid in the past, as it now includes those who were previously among the poorest of the uninsured, but not poor enough to qualify for Medicaid in the past.

Q: What advice would you have for others seeking to understand disparities in this more cross-cutting way?

Looking at social determinants of health and a broader array of disparities than was required of us is one of the most important things we have done. I would encourage others to do it as well. One important thing is to include additional modes of measurement or ways of describing patients from the start. Do it in the beginning, because it’s clear that once a recording system is in place, it’s very hard to add to it. It’s tough to go back to people with additional requests. People want to change care, not data collection systems.