Big data has steadily moved to the forefront of healthcare as providers look for better ways to manage population health and produce improved medical outcomes for patients. After years of collecting pertinent healthcare data, health systems are beginning to realize the potential benefits behind mining EMRs for vital insights into population health.

At the Ohio State University’s Wexner Medical Center, researchers have developed the Oncology Research Information Exchange Network, also known as ORIEN.¹ The scientists are looking to examine cancer at the molecular level to better understand its primary components by collecting tissue samples and clinical data from more than 100,000 consenting patients. ORIEN will utilize Total Cancer Care, a comprehensive collaborative tool that partners patients, doctors and researchers to establish precise treatment strategies.

“[ORIEN] uses deidentified patient information paired with genetic analyses,” said Steve Gabbe, M.D., CEO of OSU Wexner Medical Center. “Researchers can examine the genetic composition of a tumor and determine the course of attack, identifying treatments and patients’ responses.”

Wexner Medical had already been recognized for leveraging big data analytics from EMRs, finding that heart patients skip rehabilitation appointments due to comorbidities or lack of motivation.² Researchers used this information to create a trial program that had patients’ families and friends send positive text messages encouraging them to continue going to their rehab sessions.

How big data can impact population health
The ability to access and analyze medical data holds the key to providing more efficient and better quality of care while allowing for expedited clinical research on new treatment strategies.³ But using it can come with its own host of challenges, since a large amount of big data is unstructured, pulling from documents such as physician notes, lab reports and national databases to glean meaningful information. Providers have to focus on exploring data-driven care solutions to mitigate population health.⁴ They can achieve success through three different media:

1. Predictive analytics
2. Clinical identifying algorithms
3. Ambulatory care centers.

In a nearby community hospital in Columbus, the Wexner Medical Center recognized that heart disease and cancer were prevalent chronic conditions in the area. To address these issues, the hospital established an ambulatory clinic that included cardiology and oncology specialists. Doing so resulted in reduced readmissions, increased savings, improvements in patient safety and more focused and personalized care.

“Using data to focus on each patient’s needs allows us to deliver well care, not sick care. We can identify [clinical] markers to get ahead of conditions and diseases, which leads to [custom] care plans,” Gabbe explained.

By leveraging big data analytics, developing algorithms that identify patients who may experience readmissions and establishing ambulatory clinics in troubled areas, Wexner is able to cut down on spending and boost its profitability. While the program is still in its adolescence, the hospital has already experienced fewer emergency department visits. Other health organizations can benefit from similar models, improving Medicare reimbursements and patient satisfaction with personalized care that aims to not only treat diseases and conditions, but prevent them.

¹The Ohio State University Comprehensive Cancer Center: http://cancer.osu.edu/mediaroom/releases/Pages/ORIEN.aspx

²Fierce Health IT: http://www.fiercehealthit.com/story/3-ways-healthcare-orgs-use-big-data/2013-11-01

³Association of American Medical Colleges: https://www.aamc.org/newsroom/reporter/january2014/366338/big-data.html

⁴Advance Healthcare Network Executive Insight: http://healthcare-executive-insight.advanceweb.com/Features/Articles/Separating-Fact-from-Fiction-Around-Big-Data.aspx