Posted: 09 May 2023 5 min. read

Intentional data collection could help advance health equity

By Kulleni Gebreyes, M.D., chief health equity officer for health care consulting, Deloitte Consulting LLP

Imagine taking a picture of the landscape and focusing on a stand of trees. Although the photo might accurately depict those trees, it would not be representative of the entire forest. And that photo might completely miss the beautiful beach that lies just beyond. Similarly, medical data often fails to provide a complete picture of a patient or a disease because it is often incomplete, biased, or both. Everyone tends to view the world a bit differently. In health care, those differing perspectives can influence the type of data that is collected and how it is used.

I recently led a panel at ViVE’s second annual conference in Nashville where I discussed some of the challenges in collecting and thoroughly analyzing/interrogating data in health care and in clinical trials. The panel also explained how they are able to probe data to help make health more equitable. (See Dr. Jay Bhatt’s blog for an overview of his ViVE presentation on health equity.)

Alisahah Jackson, president of the Lloyd H. Dean Institute for Human Kindness & Health Justice at CommonSpirit Health, described some of her team’s experiences in trying to improve COVID-19 vaccination rates within historically marginalized communities (see Overcoming access, trust, hesitancy , and other barriers). The health system collaborated with the Deloitte Health Equity Institute (DHEI) and Get Well to help boost vaccination rates in two geographically distinct locations (see Multi-sector collaboration leads to successful vaccination-outreach program). The program tapped into public health tools to identify people who had not been vaccinated and used texting to connect to individuals.

She said there was an early assumption that some people decided not to get vaccinated because they didn’t trust the vaccines or the medical community. In some cases, that was true, but there were also people who wanted the vaccine but could not get to the vaccination site during business hours. “This information challenged some of our assumptions and our biases as to why some people had not been vaccinated,” she said, adding “never underestimate the power of collaboration.”

Too much data could cause more challenges

Biases in data, or incomplete data, can lead to imprecise or incorrect conclusions that result in poor treatment decisions. But too much data can also be problematic. When I was an emergency medicine physician, electronic health record (EHR) companies would often give clinicians far more data than we needed. It was sometimes overwhelming and difficult to know how to use it appropriately when making patient care decisions. Being able to take pieces of accessible data, interrogate it, and convert it into actual insight can be far more valuable than having access to all of a patient’s health information.

Shannon West, chief product officer at Datavant, agreed that too much data can cause challenges. For example, she explained that when a patient shows up in the emergency room with a head injury, the clinical team might want to see the complete medical record. “But that’s not what they actually need,” she told attendees. “For a head injury, the doctors should have very specific information, such as whether the patient is on heparin” (a drug that prevents blood clotting). Shannon’s example illustrates the importance of being intentional about the use of data in clinical practice. Datavant is a health information technology company that develops digital ecosystems for health data.

Building diverse clinical trials can be a challenge

Much of the presentation focused on the challenges in making clinical trials more representative. The data collected, and the narrative that is created, can be heavily influenced by the design of the program. When designing a clinical trial, the sponsor should identify the type of data that will be needed to answer a specific, well-defined research question. This fit-for-purpose data should be both unbiased and representative of the problem that is being addressed. It is also important to identify some of the drivers of health that could influence the data (e.g., the patient’s diet, financial stability, and homelife).  Moreover, attracting a diverse patient pool might not be enough. Study sponsors should also consider strategies that help to ensure participants stay engaged throughout the trial.

Clinical outcomes can vary by gender, race, ethnicity, and background, said John Kraus, M.D., Ph.D., chief medical officer at Otsuka America Pharmaceutical, Inc. He acknowledged that clinical trials tend to lack diversity and noted that enrolling a representative patient population is an ongoing challenge. “As we develop a clinical trial, we need try to understand the epidemiology of the disease to be sure the right population is represented,” he explained. Access challenges, he said, can limit diversity in clinical trials and impact the data. He recalled a clinical trial several years ago for a therapy developed to treat a rare disease in children. To attract a diverse cohort of patients, trial sites were set up in urban areas and in socioeconomically disadvantaged neighborhoods. However, nearly all of kids who participated in the trial were white, he said.

John said the lack of diversity was not surprising. He explained that the study required a full day of assessments and at least one parent needed to be on-site the entire time. This meant parents likely needed to take time off work. They might have also needed childcare if they had other children at home. While racial and ethnic groups were not intentionally excluded from participation, the complexity of the trial and unforeseen access barriers may have—at least in part—kept them from participating, he said.

Early in the process, clinical trial sponsors and drug manufacturers often make assumptions about what a successful trial should look like. But those assumptions might limit the clinical trial before it even begins. Incorporating an equity agenda from the planning stages could help remove potential biases and ensure a more representative trial. As clinical trials are being designed, considering the patient’s perspective could help to identify potential barriers that could keep some people from participating.

Education, alternative sites of care could improve access

While diversity has become a priority in clinical research, underrepresented populations are often left out (see Increasing clinical trial diversity). Some retail pharmacy chains are challenging the idea that clinical trials can only be conducted in academic settings. Most of the U.S. population has never participated in, nor been invited to participate in, a clinical trial, said Ramita Tandon, chief clinical trials officer at Walgreens. She noted that trust and familiarity can be important factors when trying to recruit clinical trial participants. “We want patients, at minimum, to be educated about clinical trials,” she said. “We are trying to use the trust and the relationships our pharmacists have with their patients to help educate customers about clinical trials and the potential benefits of participation.” The retail pharmacy chain has nearly 9,000 stores and pharmacies across the U.S. and serves nearly 10 million customers a day. Ramita said the company is working to convert a subset of its stores into clinical trial centers. That could help reduce some access issues. Ramita noted that roughly 78% of the U.S. population is within five miles of a Walgreens pharmacy.

Data collection should be intentional

I reflected on some of the insights the panelists shared and discussed them with my colleague, David Rabinowitz. He said that being intentional about the type of data that is being collected—and being able to interrogate it—could help clinicians, clinical trial sponsors, and pharmaceutical companies advance health equity. But it is most likely more than just the data. Choices, intention, and humility can also be important, he told me.

We reflected and aligned on these key takeaways: Data may be at the heart of health care, but not all data is created equal. Humans generally decide how data is generated and most humans have implicit biases. However, acknowledging those biases might make it easier to identify and remove them so that the data can be used to create an accurate and unbiased picture. Part of the challenge can be to disrupt traditional thinking and existing structures that might be based on inaccurate mental models. The first step might be to acknowledge that the traditional ways of doing things might not be the right way. Care models that are built around health equity could prove to be more efficient and less expensive than trying to build equity into existing models. In addition, challenging existing orthodoxies could help to unlock the promise of data.

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