To get value out of health IT, ONC’s Donald Rucker turns the focus to patients, population health

Smartphone shopper
Smartphones are a key target for ONC to get medical data in the hands of patients, Rucker said during ONC's annual meeting.

Patient access to medical records will be central focus in the federal government’s quest to improve health IT usability and interoperability, according to the Office of the National Coordinator for Health IT's top official.

During his opening remarks at ONC’s annual meeting on Thursday, National Coordinator Donald Rucker, M.D., said patients are a “massive use case” for achieving interoperability, which remains a key priority for the agency. Part of that effort requires policymakers to figure out exactly what interfaces and tools will promote engagement and allow patients to easily bring their data to different providers.

“The smartphone is the device we need to target,” Rucker said.

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He added that the government hasn’t “quite gotten what we have paid for" when it comes to EHRs, referencing the tens of billions of dollars spent incentivizing providers adopt electronic platforms. Despite that funding, health IT has not yet empowered patients, lowered healthcare costs or improved interoperability. But a broad emphasis on usability and interoperability under the 21st Century Cures Act is the legislative force behind the agency’s priorities.

“Frankly, I often hear, ‘Well, there's not a business case for [interoperability],’” Rucker added. “You know, the brutal reality is that Congress has made the business case because it’s actually the law that we've collectively enacted.”


Rucker also advocated for leveraging “extraordinary computational tools” like artificial intelligence and machine learning to drive population health improvement, which will ultimately contribute to the shift towards value-based payment and hold providers accountable for the care they deliver.

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He pointed to the powerful analytics used by companies like Amazon to drive purchasing, arguing that same technology should be put to work in healthcare.

“If we can use the brilliant machine learning to help us spend money on Amazon and other online retailers, couldn't we use machine learning to help us collectively—in an automated fashion—get better medical care and learn from everybody who has come before us with the same illness?”