Regenstrief to tackle problem of patient identification
The Agency for Healthcare Research and Quality has awarded a five-year, $1.7 million grant to the Regenstrief Institute’s Center for Biomedical Informatics to support the development and real-world testing of automated identification solutions to match patients to their electronic health information.
Accurately matching electronic health records to the correct patient is critical to medical care and safety by reducing preventable errors. However, as many as half of EHRs are mismatched when data is transferred between healthcare systems contends Shaun Grannis, MD, director of the Center for Biomedical Informatics and principal investigator for the new AHRQ grant.
“We’re increasingly reliant on the disparate systems to bring data together, and so patient identification is becoming an increasingly important topic,” says Grannis. “We as a nation have not established any clear, comprehensive and ubiquitous guidelines for how we identify patients.”
Under the grant, Regenstrief’s CBMI will formally evaluate and test evidence-based best practice recommendations from the Office of the National Coordinator for Health Information Technology on how to solve the patient identification problem.
“We’re going to look at thousands of datasets and analyze each of the specific recommendations from ONC,” observes Grannis. “We want to answer the question: Which of these recommendations actually shows good evidence for usefulness in improving accuracy and efficiency?”
In addition, the center is going to look at improvements to some commonly used patient matching algorithms to see if they can result in better accuracy. Regenstrief investigators will evaluate the newly developed algorithms by leveraging the Indiana Network for Patient Care (INPC), the country’s largest inter-organizational clinical data repository, which will serve as a testing ground for their work.
“We’re using our health information exchange in the state of Indiana,” adds Grannis. “We have nearly 30 million unique patient registrations in the system and it goes back over 40 years, so we have a lot of data to study this problem with.”
Developed by CBMI and now operated by the Indiana Heath Information Exchange, INPC provides an ideal environment for developing and testing enhancements to broadly used patient matching algorithms, Grannis contends, adding that the software that’s developed will be open source and publicly available.
He notes that current patient matching algorithms are capable of a little more than 90 percent accuracy on average.
“Fundamentally, this area is going to go in one of two directions,” Grannis concludes. “Either we will ultimately end up with a national unique patient identifier—like many countries do—or we will find that algorithm and data science are sufficient to solve this problem. It’s still an open question and we want to help better understand and answer that question.”