Federal health tech leaders want to extract data for greater equity

The Biden administration has repeatedly called for equity considerations to be incorporated into new policies or programs, and more federal leaders are grasping...

Designing technology infrastructure with its target communities in mind increases the chances that those groups adopt it. That, in turn, fosters greater equity in health care systems — a recurring message at last week’s Health Innovation Summit 22 from ACT-IAC.

The Biden administration has repeatedly called for equity considerations to be incorporated into new policies or programs, and more federal leaders are grasping what that means for their particular agencies.

For the Center for Medicare and Medicaid Innovation at the Department of Health and Human Services, equity is “the attainment of the highest level of health for all people,” according to its Healthy People 2030 strategic refresh. One of the refresh’s metrics for success is stronger data collection and intersectional analyses for populations defined by race, ethnicity, language, geography and disability, in order to identify gaps in care and develop interventions to address them. To do that, CMMI said it would require participants in all new models to collect and report data to identify and monitor impacts on health and the reduction of disparities, while existing models are incentivized to do the same.

All new models will also include patients from historically underserved populations and safety net providers, such as community health centers and disproportionate share hospitals — facilities that serve a significantly disproportionate number of low-income patients and receive payments from the Centers for Medicaid and Medicare Services to cover care to uninsured patients.

CMMI Deputy Director Arrah Tabe-Bedward said we’ve spent a lot of time really just trying to understand and start to collect data to understand what is the Center’s reach and how it can get more providers to participate.

“We think that there are incredible opportunities to do that in advanced primary care models and [accountable care organization] models. That sort of a structure, of course, requires that there is ample opportunity for information to be exchanged efficiently and effectively across providers and across care settings, in order to optimize the patient experience,” she said. “And so being able to get that right, as we are driving towards the very ambitious goal that we’ve set out for ourselves for 2030, to ensure that all of our Medicare beneficiaries, and the vast majority of those who are in Medicaid, are in those sorts of aligned care relationships with providers.”

Tabe-Bedward said CMMI wants to make sure there is technology to support those relationships and to ensure that the care being optimized.

Some communities lack experience with AI and machine learning, or actively distrust the technology, thus hampering its implementation. Susan Gregurick, associate director for Data Science at the National Institutes of Health, said this very problem was observed by NIH’s Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) Program.

“In this case, federated learning is the right approach. And I think that’s one of my messages is that there’s no one hammer for all the many use cases … we really have to adopt and adapt our technologies for the communities and the research programs that we really want to address,” she said.

As the White House Presidential Innovation fellow at the Technology Transformation Services, Nina Walia is passionate about accessible data. For health care data in particular, the troves of PDFs and documents used by providers keep valuable information trapped, leading to a lot of redundant data entry.

“If we started to just mass and bulk and all adopting the idea of optical character recognition compared with computer vision, we could start to actually extract this data in an automated process so that this data can be machine readable,” she said.

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