Live From eHealth Initiative: Why You Should Care About Patient-Matching Algorithms | David Raths | Healthcare Blogs Skip to content Skip to navigation

Live From eHealth Initiative: Why You Should Care About Patient-Matching Algorithms

February 4, 2015
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Will current algorithms for patient matching scale up?

How important is accurate patient matching to the interoperability of health data? Here’s the problem in a nutshell: In Texas, the Harris County Hospital District’s database has medical records on almost 2,500 people named Maria Garcia and 231 of them have the same birth date. 

At the eHealth Initiative annual meeting in Washington, D.C., on Feb. 4, Shaun Grannis, M.D., an associate professor of family medicine in the Indiana University School of Medicine and a medical informatics research scientist at the Regenstrief Institute, talked about the increasing importance of the algorithms HIEs and enterprise master patient index vendors rely upon. 

The Indiana Health Information Exchange (IHIE) has researched how fragmented data is in the Hoosier state, he said. Twenty-five percent of labs in the IHIE are performed outside the home health system of the patient, and 40 percent of emergency department patients have data residing in other hospital emergency departments. Each health system has created its own identifier for the patient. So how do HIEs match up these patient records?

They rely on algorithms that have about 92 percent accuracy, Grannis said. Since the country currently lacks the political will to adopt unique patient identifiers, it is important to understand whether the kind of algorithms used today will scale up to handle the needs of ACOs, larger HIEs and research networks. “Billion-dollar bets are being made that if we invest in integrated medical data, good things will happen,” he said. “Will current algorithms for patient matching scale?” he asked. “It is an unanswered question today. If they do, we need to make sure best practices are crystal clear because we are betting big. If it doesn’t work, then we need to begin strategizing about weaknesses in the system.”

Lee Stevens, policy director of ONC’s State HIE program, talked about some of the things ONC is working on to help with patient-matching algorithms, including work on standardizing the attributes used such as date of birth, names and historical addresses, and through certification how EHRs capture this data. ONC has innovators in residence working on an algorithm that can be made available on healthit.gov for health systems to use or test against.

Congress has prohibited HHS from using funding to look at unique patient identifiers. It is a social and political problem defined as a technology problem, Grannis said, adding that in countries that use unique identifiers, such as Canada, they work well (although they are not perfect). He noted that Indiana studied the possibility of assigning a unique state identifier to all patients in the state. They estimated it would cost $250 million, and that would only raise the accuracy another five to seven percent. It was a non-starter, he said. “Show me the politician that is going to invest in that,” he said.

 

 

 

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