Jan 25 2023

Overcoming Obstacles to Data Sharing in Healthcare

Federal agency leaders discuss how data is transforming healthcare and how to foster data transparency.

“Health IT has such potential to reduce diagnostic error and tailor care through precision medicine to assist in the diagnosis of diseases and targeting of treatment to individual patients using real-time data,” said Nikki Bratcher-Bowman, COO and principal deputy assistant secretary for preparedness and response at the U.S. Department of Health and Human Services (HHS).

In her opening keynote at the AFCEA Bethesda Health IT Summit ’23, Bratcher-Bowman set the stage for much of the summit’s focus: how data analytics can improve healthcare research, public health and patient outcomes.

While the goal is clear across the federal government, siloed and unclean data as well as unstructured data are major obstacles to applying data analytics and artificial intelligence.

Throughout the conference, federal healthcare leaders discussed how the pandemic led to digital transformation in the data space, the potential of data analytics and AI to transform healthcare and public health, and ways organizations can begin to unlock patient data.

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How the Pandemic Transformed Data in Healthcare

The U.S. Food and Drug Administration worked with state and local governments in real time during the beginning of the pandemic to modernize their data collection and data analytics strategies. Dr. Sara Brenner, chief medical officer for in vitro diagnostics and associate director for medical affairs for the Center for Devices and Radiological Health at the FDA, said data was important in determining how well tests were detecting the virus and where cases were appearing.

“One of the big take-home messages from what we learned is that the data you have is not perfect or complete. The data won’t give an answer all the time, but can provide a window into what’s happening,” she said. “Being able to explain to the public that we have some information but not all of it is important for transparency and public trust. There’s a rush to say, ‘We have data, and it says this,’ rather than, ‘We have gathered data and our interpretation is XYZ, but here’s what we don’t know.’”

Nikolaos Ipiotis, chief data officer for HHS, explained that the organization used blockchain in its efforts to collect COVID-19 case data. A major difference in its approach compared with pre-pandemic approaches is that everyone in the department had access to the data and data visualizations.

“The intent is to keep it as a department asset,” he said. “We don’t want to create more silos.”

EXPLORE: The current state of AI in healthcare and where it's going in 2023.

The U.S. Centers for Disease Control and Prevention launched the Center for Forecasting and Outbreak Analytics in 2022 to use infectious disease modeling and analytics to improve outbreak response. It will also increase access to data for public health leaders. Dylan George, director of operations for the CFA, says the organization has been piloting innovative ways of bringing data scientists into the federal government.

“We’re excited about these new capabilities and look forward to working with the private sector,” he said. “Transparency is a key component of how we’re trying to do this. We want you to show us where we’re not meeting the mark to help us improve.”

Aloka Chakravarty, senior statistical adviser and director of data analytics in the Office of the Commissioner at the FDA, said that when the pandemic hit, the agency realized how fragmented its systems were. Its different departments needed to coordinate and unite to tackle an unprecedented pandemic with unprecedented data needs.

“We are at a transition point where we should look at data as a team sport, because you have to have data scientists, statisticians, and IT all talking together in a common language to understand each other’s needs,” Chakravarty said. “Moving from data to information isn’t a given. You have to work on it to get to the level where data becomes information, which becomes insight and then action.”

Aloka Chakravarty
Moving from data to information isn’t a given. You have to work on it to get to the level where data becomes information, which becomes insight and then action.”

Aloka Chakravarty Senior Statistical Adviser and Director of Data Analytics, Office of the Commissioner, FDA

Data Sharing Is the Future of Healthcare

Working with data isn’t new for federal health agencies, said Belinda Seto, deputy director of the Office of Data Science Strategy at the National Institutes of Health. What’s new is the volume, rate and complexity at which data is being generated. NIH has contracts with Google Cloud, Amazon Web Services and Microsoft Azure to put terabytes of data in the cloud. That paired with high performance computing is enabling research based on this influx of health data.

Still, NIH wants to go beyond data transparency to data sharing. Beginning this year, anyone who receives funding from NIH must share their data. The shared data must be of such quality that it is reusable and reproduceable, according to Seto.

However, many smaller organizations may have lesser-quality data, missing data or data in the wrong fields, said Ipiotis. This is something the National COVID Cohort Collaborative, part of NIH, has had to deal with in its efforts to collect and provide a large enclave of quality COVID-19 data to researchers.

Quality data is essential for health equity, according to Seto. Social determinants of health data can help clinicians think holistically about diagnosis and treatment, which can improve outcomes for Black patients and people of color who have disproportionately negative health outcomes. Applying artificial intelligence and machine learning to SDOH data can give clinicians even more insights to personalize healthcare.

“The government getting access to data is not the puck in the net; it’s a puck drop in the center of the ice,” added Brenner. “We still have a lot more challenges than solutions, but we’ve come a long way. We need to make more of the data we’re collecting available to academia and private industry.”

READ MORE: Federal officials urge healthcare industry’s adoption of FHIR for interoperability.

Making raw data sets public with the appropriate privacy and security safeguards is critical, she said. “It’s how we move science forward. We’ll make progress faster together if we’re more transparent.”

Interoperability using the Fast Healthcare Interoperability Resources standard can make it easier for organizations to share data. In addition, natural language processing can pull out unstructured data from medical charts to make data more accessible.

George said there needs to be a cultural shift in thinking about healthcare, from public health organizations to data organizations. The data enables public health outcomes.

“It’s easy to go on panels and talk about what needs to be done. There’s a lot of complexity to this landscape, but it’s time to roll up our sleeves. No matter where you’re at, you can champion these principles and take a step toward the future you want with data,” Brenner said. “Imagine a future where high-quality and useful data is made accessible and transparent. How much faster would we make progress together? Everyone has a role to play in that future.”

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