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Combating Health Inequities Through EHR Data Collection

A lack of standardization can lead to gaps and inaccuracies in EHR data collection, resulting in inequitable care delivery. But providers are finding ways to overcome this challenge to deliver more equitable care to patients.

Inequities in the healthcare system are abundant. From care delivery to health outcomes, there is a large divide in patient response and outcomes based on demographics such as gender, race and ethnicity, sexual identity, and even zip code.

The LGBTQ+ community has especially documented different and marginalizing experiences in healthcare and traditionally marginalized populations have historically received different care, from C-section rates to diabetes prevention. And while many of these key differences have flown under the radar for some providers, the COVID-19 pandemic is highlighting just how different types of populations experience healthcare.

But taking on the long list of care disparities in existence today can seem like a momentous take impossible to take on for a single provider, especially when there is no clear starting point.

“You need a really good idea about the patient’s journey through the medical system to be able to make informed decisions,” John Van Reenen, PhD, member of the MIT Task Force on the Work of the Future, digital fellow at MIT Initiative on the Digital Economy and professor at the London School of Economics told EHRIntelligence. “By having the information, you’ll be able to make much more accurate recommendations, which will lead to better health outcomes.”

But gathering this information is easier said than done as interoperability challenges within and between health systems can lead to disjointed and inaccurate data.

Addressing care disparities must first begin with clean, standardized data that allows providers and system administrators to identify the underlying problems. Only when these disparities are highlighted can steps be made to address these challenges.

“What data allows us to do is really understand not only where the inequality is but target the intervention,” added Laurie Zephyrin, MD, MBA, MPH vice president of delivery system reform at the Commonwealth Fund.

The Importance of Standardized Data

Two key elements of EHR data collection promote standardization: what data is collected and how that information is gathered.

“One of the biggest challenges around standardization is what’s being collected. One provider can interact in several different ways with your organization or within an organization so there are multiple ways data are collected,” explained Zephyrin.

Vice President of Delivery System Reform at Commonwealth Fund, Laurie Zephyrin, MD, MBA, MPH
Vice President of Delivery System Reform at Commonwealth Fund, Laurie Zephyrin, MD, MBA, MPH

Information from one clinic may not be accessible to the health system a patient is referred to. Whether the root cause is challenges to interoperability or a lack of gathering that information initially, it promotes discordance in care delivery. Providers may need to search for information that should be easily accessible or ask patients questions they have already answered.

Standardizing what information is captured in the EHR would allow providers within and between systems to better coordinate care as all the information needed to make care recommendations would be easily accessible.

“In the EHR it’s important to have the field and make sure the fields are available and can talk to each other,” Zephyrin said.

Once this information is accessible, providers and administrators can analyze it  and identify inequities occurring with their own communities and organization.

Information from the Veterans Health Administration, for example, indicated 89 percent of veterans’ records lacked gender identify data. Without access to this information, the VA health system was unable to understand if it was providing equitable care to this population.

So in July 2020, the system updated its Survey of Healthcare Experiences of Patients (SHEP) program to include veteran’s self-reported sexual orientation and identify. The survey captures information from recently discharged patients on their experiences and is used similarly to the national Hospital Consumer Assessment of Health Providers and Systems (HCAHPS) survey to capture information on provider communication, responsiveness of the hospital, and transition of care.

Now the VA captures and can analyze experiences based on sexual orientation and identity, understanding if it is delivering equitable care and targeting interventions when it is not.

But simply having the information is not enough. There should be standard collection methods as well.

A patient self-identifying race and ethnicity, for example, is a very different collection method than an administrator recording the patient’s race and ethnicity when she first walks into the clinic.

Important demographic information can only be ascertained when everyone involved in a patient’s care is assessing the same demographics and capturing them in a standardized method.

Top Down, Bottom Up Implementation

“With one electronic health record across many hospitals and clinics, it’s pretty standardized,” Zephyrin said in response to the Veterans Health Administration example.

A single EHR allows for a smoother standardization process across a health system as there is only one EHR that needs to be adjusted to capture the appropriate demographic information. But even making changes to a single system can be a challenge.

“A shock to the system involves a lot of changes, a lot of worries, and a lot of fears,” Van Reenen highlighted. “It’s very important to involve a lot of key members of staff in the hospital in very early stages. This way it’s not seen as something imposed on them. It’s something they work on together in order to understand what’s happening and make the transition process smooth.”

Administrators and executives are the ones to make the strategy and budget decisions that would allow for systematic changes in the EHR, but that does not mean frontline providers should not be involved in the process as well.

John Van Reenen, PhD, digital fellow at MIT Initiative on the Digital Economy and professor at the London School of Economics
John Van Reenen, PhD, digital fellow at MIT Initiative on the Digital Economy and professor at the London School of Economics

Providers are the ones who will be using the data and often inputting it into the system. When the intervention does not work for providers, it will not work for the health system.

“Healthcare professionals should be involved in a deeper way at an earlier stage,” Van Reenen furthered. “This will make it easier to roll out these technologies much more effectively and create the best productivity.”

Afterall, these technologies are meant to promote better care delivery.

“You need efficient systems and great data in systems to help providers be partners in care,” Zephyrin emphasized. “Armed with the data, providers can tailor their practices and really help improve their practice. They have a better sense of the care that they’re providing.”

Standardizing EHR data collection must be both a top down and bottom up approach. From the top, decision makers must make the investment in technology and resources that promote standardization in an attempt to identify inequities.

Then on the frontlines, providers must advocate for that data to be accessible and inform their practice.

“While systems and structures are being developed and tailored, people are still coming through the door,” continued Zephyrin. “The investments in technology and resources should not only collect data but also provide the systems to create that accountability for the care being provided.”

Taking Data One Step Further

Having easy access to information is not enough for meaningful change. After systems are in place to standardize data collection, that information must be made actionable.

“If one is focused on providing equitable care, the data must be disaggregated for analysis to determine if there are inequities in care. Otherwise, you’re operating blindly, and you don’t know where to target your information,” said Zephyrin. “It’s a key part of understanding.”

Disaggregating outcome and experience data by demographics highlights inequities. Armed with this information, health systems can more easily target interventions. Teams can track the interventions for effectiveness and lessen disparities over time.

“You have to then share the data and be transparent about it both within the system and beyond so that people have access to the data more broadly and can understand where the inequities are and what solutions and improvements are being made,” Zephyrin concluded.

Only when systems and providers are upfront about care disparities can they be tackled head on. And this begins with data to identify challenges.

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