Electronic health records are making data more readily accessible to physicians and laying a foundation for the next big step: translating EHR data into actionable information for precision medicine.  

"Precision medicine is a great opportunity to start using behavioral health data, like activities and eating patterns," said Nancy McMillan, research leader of advanced analytics at Battelle.

McMillan will delve into the use of big data in healthcare at HIMSS16 in Las Vegas, beginning in late February.

By combining this type of information with a patient's EHR and genomic data, physicians will ultimately be able to predict health outcomes, she said.

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McMillan's presentation, “Enhancing Patient Outcomes with Big Data: Two Case Studies,” will provide a retrospective analysis of how hospital inpatient EHRs can identify patients. Her colleague, David A. Friedenberg , principal research statistician at Battelle, will add to the presentation by discussing his work on a neural bypass system.

Friedenberg and McMillian will present the pair of case studies to demonstrate ways institutions can improve population health. Her portion of the presentation will deal specifically with EHR collection and how hospitals can finally put that information to work.

"We're about to turn the corner in healthcare," McMillan said. "So much work has been done with the transition to EHRs, and there's going to be greater use with scientific analytics to make this data useful."

She said collecting data has been a "big burden" because it's such a major change, which is difficult for any large institution. But she's hopeful there will soon be progress.

Her presentation will also provide the processes Battelle has used to transform data into actionable datasets.

It's important for health systems to understand data isn't uniform in these systems, and taking into consideration how it's viewed at the ground level will affect how these key agents translate the information, she added.
The only way to use EHRs as a prediction tool for precision outcomes, McMilland said, is to validate the data using evidence-based research.

Her colleague, Friedenberg, will round out the presentation by discussing his neurolife work. He's found a method to use data from a cortical implant in a paralyzed person to decode imagined movements, bypass the damaged spinal cord injury and stimulate muscles in real-time with a neuromuscular electrical stimulation.

The HIMSS16 session “Enhancing Patient Outcomes with Big Data: Two Case Studies,” is scheduled for March 1, 2016, from 4:00-5:00 pm in the Sands Expo Convention Center Palazzo G.

Twitter: @JessiefDavis