5 population health technology tips

August 4, 2014

How to collect, compile and present data

Ultimately, data will help healthcare shift from a focus on episodic care to prevention. For providers to ensure that they’re delivering high-quality care at the best cost, they must have a complete view of their patients’ medical histories. Provider organizations are in an ideal position to assist with population health data mining by combining claims and clinical data from multiple sources. 

Harness claims data

With all of the attention focused on combining claims and clinical data, it might seem reasonable to stick to the sidelines and wait for someone else to develop a combined data collection solution. However, claims data is readily available. 

Claims data can only tell you that care was received, not the value of that care. It might also be missing some information, such as free flu shots or prescription samples. Still, with all of its shortcomings, claims data is a critical component of gathering information on a large population.

“Claims data is not always good data, but it’s not worthless,” says Jack Lenhart, MD, medical director of Valley Preferred, a PPO based in Pennsylvania. “[It] gives you things that an EHR [electronic health record] doesn’t give you, as well as a broad universe of information that you may not be aware of in an EHR today. Claims data also gives you a global view of costs that you don’t get in an EHR.”

 


Cast a wide net

Even a small community of patients has multiple insurers, employers and care providers. Collecting data from any one of those sources leaves large gaps in your understanding of patient health.

“Provider systems can take claims data, put it onto one system and measure it together,” Lenhart says. “If we put all that claims data together-and here is the holy grail-add EHR data to it, now we get pretty good big data to understand claims cost and specifics. Putting that universe together is finally allowing us to answer the big question in medicine: What is the most cost effective care that produces the best outcome?”

 


Make it easy

According to a survey by the American Medical Association in conjunction with RAND Health, physicians agree with the goals of EHRs, but were not satisfied with current EHR systems’ “usability, time-consuming data entry, interference with face-to-face patient care, inefficient and less fulfilling work content, inability to exchange health information and degradation of clinical documentation.”

The age-old rule of information technology: “garbage in, garbage out,” definitely applies to EHR data collection. If a system doesn’t make data entry fast and easy, the results won’t live up to the full potential of an integrated data solution.

“We know providers are incredibly busy,” says Gregory Kile, vice president, Insurance and Payer Strategies for Lehigh Valley Health Network, one of the companies that founded the organization that owns Valley Preferred. “Our goal is to make it easier and more efficient for providers.”

 


Hire right

Making sense of all the data being collected requires people from various corners of the healthcare industry to best automate the data collection and interpret it.

“Having folks onboard with an insurance background is important to help bridge what insurers would do,” Kile says. “People with informatics competency are needed to interpret and push the analysis to care providers. It’s critical to how the merged data gets translated.”

 


Experiment

Valley Preferred began experimenting with preventive quality of care measures about 16 years ago when it began informing physicians about their performance and providing incentives.

“There were a number of tools available, even 15 years ago, that were focused on measuring financial performance,” Kile says. “But we wanted to measure clinical performance related to cost.” 

That led Valley Preferred to create its own analytics and reporting system to streamline quality measures. Now, with multiple vendors racing to merge claims data and payer data, the PPO has teamed up with one vendor’s research collaborative.