Earlier this summer, experts from leading healthcare organizations highlighted ways big data can improve population health management.
To improve the health management of the population, vendors need to use big data to understand how they can best meet the needs of the community.In a panel convened by HealthITAnalytics, IT executives from Stanford Children’s Health, Jefferson Health, and Geisinger discussed the big Data and its role in Improving population health management. Abdul Tariq, director of machine learning at Geisinger, stated that the standard definition of big data focuses on three V’s: velocity, volume and variety. When designing big data, however, Tariq primarily focuses on variety and volume.
“When we talk about machine learning and population health, we’re very interested in not just utilizing our electronic medical record only, or our claims, or the surveys that we conduct. We want to utilize all of this in a single cohesive framework,” Tariq said.
It is also vital to analyzing how companies handle, manage, and store big data. By collecting big data, companies can assess their patients’ needs and fill gaps in care. Big data is often used to address population health concerns to help large communities of people. Before you start collecting big data, however, you need to understand patient metrics and how risk scores are evolving.
“Some of the things that we’re trying to start figuring out how to use is things like risk scores that might pull a number of different metrics from all over the system. Basics like age, gender, insurance, and more complicated things like certain past medical history and lab values. We can then pull that all into a more broad overview of the patient, with the idea being that you can then target your outreach,” said Jefferson Health’s Medical Information Officer Bracken Babula.
Big data and population health management are increasingly being used in working with social determinants of health. At Stanford Children, researchers collect patient data to better understand environmental factors that could affect a person’s health.
“The one huge aspect of this that we’re looking at Stanford Children’s is around the social determinants of health. Understanding what are the conditions, beyond just the typical things you collect in a physician visit. Is there domestic violence or food insecurities, or things like that, that really would ultimately affect the patient’s health down the road and may have different interventions than a typical physician visit?” revealed Stanford Children’s Chief Analytics Officer Brendan Watkins.
Jefferson Hospital also looked at the social determinants of health related to the COVID19 vaccine. The hospital used metrics called the Social Vulnerability Index and Community Need Index to assess and determine where vaccines should be used. For the future of big data in population health management, Tariq said she sees consumer health informatics not only in the healthcare sector, but also in the world of technologies and providers.
“As more and more people get these wearable devices like Fitbit, Apple Watch, that data will start getting captured, then there will be a market that will open up where technology companies will start providing some of these insights that traditional health systems have provided,” Tariq said.
“With that regulation, I’m sure there will be policy enactments that will change how providers deliver care. Then, eventually, the policy will shift how providers, systems, get into this space, and what that means.”
As research continues to build big data capabilities, companies can learn more about how to use artificial intelligence and machine learning to ensure better patient care and better health management for the population.
“We’re just still scratching the surface of machine learning with lots of the different things like the wearables or radiological studies or other things. There’s a lot more we can do. We can get a lot more sophisticated than that,” Watkins said.
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