Seeking a solution for patient record matching
Ensuring providers accurately match electronic health records with the right patient has emerged as a hot-button issue for the health information technology industry as its provider customers come under increasing pressure to freely share records with other providers.
Many providers’ EHR systems, often purchased from different vendors, are incapable of accurately matching electronic patient medical records without costly and time-consuming human intervention to resolve confusion over patients with similar names. The result is a failure to maximize the clinical care and patient-safety benefits of EHRs. It also limits future research opportunities, experts say.
According to surveys and published reports, the error rates for automatic patient matching range from 1% to over 50%, depending on whether a requesting provider is querying patient records within an organization, from another provider or from a health information exchange.
For decades, some health IT proponents have pushed for a national patient identifier system as the best solution. But public opposition and a lack of support in Washington, D.C., has kept establishment of an NPI on the backburner until recently.
“People realize how the lack of an identification solution that’s universal is compounding the problems with electronic health information exchange,” said Russell Branzell, CEO of the College of Healthcare Information Management Executives, a professional association for hospital and healthcare system chief information officers. “It’s probably as big an issue as we have out there.”
This year’s Healthcare Information and Management Systems Society’s annual conference, Feb. 29 through March 4 in Las Vegas, will feature at least four formal educational sessions on patient-matching technology and best practices. Presenters will include Adam Culbertson, the HIMSS-sponsored “innovator in residence” at HHS’ Idea Lab, who will address matching problems, best practices to solve them and why matching well is more of an art than a science.
Tom Leary, vice president of government relations for the Chicago-based HIMSS, said future research—particularly in the highly touted field of precision medicine— will depend heavily on getting matching right. “If you’re going to have a million people participating in a cohort, and gain the learning that’s going to occur from that million-person cohort, you have to be able to figure out where all their data is,” Leary said. But the most important reasons for fixing the matching problem involve maximizing patient safety and improving clinical decisionmaking in an era of payment reform. “In terms of value-based care, interoperability is key—and we’re not going to get to where we need to get to without this patient matching” being addressed, said Albert Oriol, CIO of Rady Children’s Hospital, San Diego.
The record-matching challenge at Rady is most acute with newborns, particularly children of a multiple birth who may need extended or intensive care. “In many cases, when the baby is born, the birth hospital doesn’t even have
a name yet,” Oriol said. “It may be ‘baby girl A’ or ‘baby girl B.’ By the time the parents come up with a name, that baby is already in our” neonatal intensive-care unit.
Hospitals handle it internally by issuing patients an in-house patient identifier. But that’s of little help when that patient’s information needs to be matched with records from another organization later in life. “It follows them from birth,” Oriol said. “They may be John David Smith and John Daniel Smith. So they end up with the same first and last names, same initials, same parents, same address and same data of birth.”
To ensure accurate matching, the system has to have personnel review about 1% or 2% of its records, Oriol said. But with a health information exchange, “we’re up to 9% with these things that require people to intervene and match. … It’s not ideal.”
Moreover, sometimes by the time the matching question is resolved, the physician has already seen the patient and lost the opportunity to incorporate the external information into their decisionmaking. “We’d like to have that information flowing like when you open a tap at your house,” he said.
Katherine Lusk, chief health information management and exchange officer at Children’s Health, Dallas, said her hospital has had a quality assurance and data governance program for patient matching for many years. Children’s Health is one of 11 organizations participating in a pilot by the American Health Information Management Association to create a national information governance model, which will include guidance on patient matching.
The model, scheduled for public release this year, will include a tool to identify gaps in an organization’s patient-matching approach, such as inadequate training, Lusk said.
Cost is also another reason to fix the matching problem, said Marc Probst, vice president and CIO at Intermountain Healthcare, Salt Lake City. The integrated delivery system studied it a few years ago and estimated it could save between $4 million to $5 million a year simply by doing a better job of matching records.
Intermountain recently announced results of a patient-matching improvement project with its statewide HIE, the Utah Health Information Network, which includes University of Utah Health Care, its chief rival. “The University of Utah came up with a pretty good algorithm that got us closer,” Probst said. But much of the improvements came by virtue of participants reaching consensus on doing basic things the same way, such as agreeing on a larger than normal number of data fields to use for matching.
It included not only the first and last names, sex and dates of birth, but also Social Security numbers, home phone numbers, race and home addresses. The project standardized how those attributes are recorded, which improved the rate of automatic matching across the exchange to 95% from 10%.
Dan Chavez, executive director of San Diego Health Connect, that Southern California community’s local health information exchange, is scheduled to talk at HIMSS about patient matching and how his HIE overcame “the largest obstacle to EHR exchange.”
Chavez estimates that 30% of EHRs have basic data on patient identities such as names, addresses or Social Security numbers that are old, incomplete or incorrect and therefore can’t be matched across providers without manual intervention. The San Diego HIE cut its manual effort by 75% and doubled its matching accuracy using third-party data.
Earlier this month, CHIME kicked off a National Patient ID Challenge with HeroX, an online platform to promote challenges linked to tech innovations. They’re offering $1 million in prize money—to be raised by CHIME and other sponsors—to encourage developers to address patient matching on a national scale with 100% accuracy.
Physician informaticist Dr. Barry Heib said his notfor-profit organization, Global Patient Identifiers, based in Tucson, Ariz., will be a CHIME prize competitor. The GPI approach is to generate and store unique patient identifiers, but not store patient medical records themselves. A GPI database will keep track of those providers that have records for each stored identifier. Users of the system will be charged a few cents each time they query it for the whereabouts of a patient’s records.
Improving patient matching will take far more than a technology upgrade, important as that may be, said Dr. Charles Jaffe, CEO of Health Level 7, a healthcare standards development organization.
“However good patient-matching algorithms are, I would never argue that you have the perfect algorithm,” Jaffe said. “It’s really contingent on the quality of the demographic data you’ve collected and that tends to be error-filled. Even if we do get a patient identifier, we’ll still have problem with data entry in the identifier.”
Hospitals handle it internally by issuing patients an in-house patient identifier. But that’s of little help when that patient’s information needs to be matched with records from another organization later in life.