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IHI Leader: Data Key Component in Addressing Health Equity

Analysis  |  By Christopher Cheney  
   September 28, 2021

To eliminate health disparities, health systems must be able to capture how patients self-identify and how they are experiencing health outcomes differently.

To address health equity, health systems need to improve the collection, storage, and processing of patient data, according to Kedar Mate, MD, president and CEO of the Institute for Healthcare Improvement.

Health equity has emerged as a pressing issue in U.S. healthcare during the coronavirus pandemic. In particular, there have been COVID-19 health disparities for many racial and ethnic groups that have been at higher risk of getting sick and experiencing relatively high mortality rates.

Mate has made addressing health inequities a top priority at the Boston-based Institute for Healthcare Improvement (IHI). He recently discussed the importance of data in the effort to ameliorate health inequities with HealthLeaders. The following is a lightly edited transcript of that conversation.

HealthLeaders: Why is data a key component of addressing health equity?

Kedar Mate, MD: In any kind of improvement work, it is difficult to improve what you cannot measure. That holds as much for equity concerns as it does for other areas of quality. Local data on disparity and inequity is often gathered poorly.

For example, there is a recent Government Accountability Office report about the understanding that we have about the race and ethnicity of the people who are affected by COVID and those who have received a single dose of the vaccine. We only have the data on about 47% for each of those two measures—the data on the race and ethnicity of the individuals who have been infected or received a single dose of the vaccine. For the majority of the records that we have around this incredible public health emergency, we still only have less than half of the available information about how this pandemic has been affecting our communities and how we have been responding to them.

HL: What kind of data is critical for addressing health equity?

Mate: The data that are important are the characteristics that people self-identify that matter to the health concern that we are talking about. In certain circumstances, the race of a patient might matter. There may be Black versus White differences or Hispanic versus White differences that matter. In other circumstances, rural versus urban residence of the patient might matter. In other circumstances, whether the patient has a disability might matter.

HL: Why do health systems need a data infrastructure to address health inequity?

Mate: Health systems need a data infrastructure to address health equity, but it is a broader consideration than that. Health systems need data infrastructure to create improvements in health and health outcomes. Health systems have known this for a long time, and they have been operationalizing a data infrastructure to help improve health, healthcare, and healthcare quality for many years. We are adding to that a layer of understanding of the individual that is deeper and richer than we have had historically.

We have often taken in information and understanding about the system itself and the system properties and outcomes. We are adding to that an understanding of how people experience their care differently and how people are experiencing health outcomes differently. We are not seeking wholesale changes of the data infrastructure, nor I am suggesting that we must add a tremendous amount of change at a time when health systems are incredibly stressed. But we must leverage the existing data infrastructure and coach it to collect more information to help understand who is affected and how they are affected differentially by our healthcare and our health systems.

HL: How can health systems go about creating this new layer of data infrastructure?

Mate: A lot of what IHI has learned in our Pursuing Equity program is there is a tremendous amount of work that health systems can do to improve their information technology data collection systems to help guide them to capture the necessary information. A big part of this is prioritization. Healthcare informatics teams are probably some of the most stressed teams inside of a health system. They are constantly being asked to attend to myriad concerns and considerations.

If a health system makes health equity a strategic priority, then we must ensure that healthcare informatics teams are rating the work on health equity appropriately on the long list of the things that they face. If the health system makes it a priority, it should be a priority for the data teams. We must give our data teams and clinicians the tools to ask patients how they self-identify such as race, ethnicity, language, and gender identity. We must give our clinicians and the other people who are meeting patients the necessary tools to ask patients how they self-identify.

We must have a place for clinicians to easily report the answer to these questions. So, when someone is asked a question about how they self-identify, it must be placed in a prominent location in the record, so that people can see it, understand it, and use that information.

Lastly, we must have teams within the organization that are prepared to use the data to take action. One of the observations that we made in our Pursuing Equity work is that the teams that tended to respond to the information the best were either the quality improvement team or the population health team. They are responsible to take action. They need to look at the data, process the data, pick some priorities, then take some action.

Related: Health Equity Top Priority for Leader of Institute for Healthcare Improvement

Christopher Cheney is the senior clinical care​ editor at HealthLeaders.


KEY TAKEAWAYS

Local data on disparity and inequity is often gathered poorly.

Improving health equity requires an understanding of the individual patient that is richer and deeper than has been gleaned historically.

If health systems make health equity a strategic priority, they must ensure that their data teams are committed to the effort.


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