Improving patient matching has been a top priority for the Office of the National Coordinator for Health IT (ONC) albeit one facing multiple challenges. A one-size-fits-all solution is unlikely but better matching is needed for effective care coordination and information exchange.

The best way to proceed with patient matching, says Lee Stevens, policy director of ONC’s state health information exchange program, is determining what’s working today. To that end, ONC launched a literature review and environmental scan and discussed the results at a December 13 meeting. “We want something implementable in the near term—a first step toward meaningful improvement in patient matching.”  

Improvements will be multifaceted and incremental with no single solution or step that is final, he says. “Potential improvements should apply to all sizes and types of provider settings. Patient safety is the overarching principle.”

Tackling obstacles

The initial findings uncovered several obstacles preventing accurate patient matching that served as the basis for the following recommendations:

• Require standardized patient identifying attributes in the relevant exchange transactions. “A lack of data attributes that are populated consistently and in a standardized format has been identified by the industry as a major impediment to more accurate patient matching,” says Scott Afzal, principal of Audacious Inquiry, the consulting firm charged with researching the issue.
• Introduce certification criteria that require certified EHR technology (CEHRT) to capture the data attributes that would be required in the standardized patient identifying attributes.
• Study the ability of additional, nontraditional data attributes to improve patient matching. Statistical analysis can be performed with these data attributes to assess their ability to improve patient matching, Afzal says.
• Develop or support an open source algorithm that could be used by vendors to test the accuracy of their patient matching algorithms or be used by vendors that do not currently have patient matching capabilities built into their systems. “We found a huge range of capabilities out there,” he says. “Smaller practices and hospitals don’t have a focus on identity management, however, nor the tools to help them do well with it.”
• Introduce certification that requires CEHRT that performs patient matching to demonstrate the ability to detect potential duplicate patient records. CEHRT should clearly define for users the process for correcting duplicate records which typically requires the merging of records, Afzal says. “Identifying duplicate records is important to ensuring accurate matching. Not all EHR systems currently provide these reports to their users. You can’t measure what isn’t defined.”
• Build on the initial best practices from the environmental scan by developing a more formal structure for establishing best practices for the matching process and data governance. “As we talked to healthcare systems, we found that those doing the best put a focus on it, developing programs internally.”
• Develop policies to encourage consumers to keep their health information accurate and up-to-date. “Patient engagement efforts have not yet evolved to ensure that consumers can routinely access t heir demographic information to inform and update it, either with the help of staff or independently via patient portal,” says Afzal.

Multi-pronged approach

ONC has a range of efforts underway to further improve patient matching. Some may wonder why the nation doesn’t have a unique patient identifier (UPI) system. Joy Pritts, JD, ONC’s chief privacy officer, explains that it is out of ONC’s scope, even though “we know it’s on a lot of people’s wish list.”

When HIPAA was first enacted, it included a statute prohibiting ONC from developing standards for a UPI. “That doesn’t mean the private sector can’t have organizations that have their own patient identifier. It just means the federal government isn’t going to do so.” Pritts points out, however, that it’s not the panacea many consider it to be. “Large organizations with a UPI system still have patient matching problems.”

Healthcare can learn a lot from other industries, says Bryan Sivak, ONC chief technology officer. He plans to bring external people into the Department of Health and Human Services (HHS) to work with internal teams and solve these kinds of challenging problems. A new program, Innovator in Residence (IIR), will involve nonprofit organizations sponsoring at HHS for one year who will then go to the nonprofit for the following year to work on a project of his or her choosing.

The IIR also will assess the longer-term applicability of identity management methods, processes and technologies currently in use in healthcare and other sectors, and create a framework for innovative technology and policy solutions to help provide consistent matching of patient health records and patient identification.

“We have to consider the difference between structured and unstructured data” and not just string matching but pattern matching, says Doug Fridsma, MD, PhD, ONC’s chief science officer. “There are opportunities to think about this in a different way.”

And, standards are not enough, he says. Perfect standards cannot make up for poor-quality data

Considering the entire care continuum is important as well, he says. “There are different needs across the continuum but there may be some commonality.”

When considering algorithms, “it’s important to recognize that the kind of data you need is dependent on the kind of algorithm you use.” Also, testing is needed to answer the question of “did we get it right?”

Because of the long list of issues, patient matching changes have to be done incrementally, Fridsma says. “We can’t rip and replace.”

Considering strategies

Participants of the December ONC meeting included a discussion on potential strategies. “We feel stuck in yesterday,” says Deborah C. Peel, MD, founder of Patient Privacy Rights, a leading patient privacy rights advocacy organization.

The patient matching conundrum is the result of taking the patient’s right of consent out of the HIPAA regulations, she says. “Most of the problems with data integrity come from disassociating patients from direct involvement. We need to match what we can for now but we really need to change this paradigm because it pushes patients away and disengages them.”

Others suggested using Social Security numbers. However, 40 million Social Security numbers are used by two or more people in the United States, said Adrian Gropper, MD, Patient Privacy Rights’ chief technology officer. “Using the last four digits is suicide. The system needs to give patients anonymity.”

“I love this concept of being able to do some type of validation on algorithms,” says Beth Just, MBA, CEO and founder of Beth Just Associates. Benchmarking and relative measures require valid datasets, she says. She also says the idea of open source algorithms is great, rather than proprietary solutions.

Another meeting attendee points out that the industry needs reports that show the true cost of identification issues because executives don’t understand the cost and risk to their organizations.

All these considerations and more will factor in as patient matching initiatives progress.