- Ensuring high levels of accuracy in EHR patient matching across healthcare organizations is increasingly critical as stakeholders work to improve interoperability, increase health information exchange (HIE) adoption, and promote health data exchange nationwide.
Federal initiatives and incentive programs including the Promoting Interoperability Program intend to encourage data sharing among providers for improved clinical decision-making. But patient matching errors can impede the effectiveness of health data exchange and lead to clinical, financial, and administrative consequences.
While a range of factors contribute to patient matching errors, Hawaii Health Information Exchange (HHIE) CEO Francis Chan posits the lack of demographic data standardization may be the most troublesome barrier to improved EHR patient matching.
“On the surface, matching a person with their records should be simple,” Chan told EHRIntelligence.com. “But when you have manual intervention, and you’re registering patients with different organizations, the person’s identity is often not recorded consistently across different systems.”
Lack of demographic data standardization across organizations leads to confusion in Hawaii, where patients often see several physicians at different health systems.
“In Hawaii, the healthcare system is pretty open,” said Chan. “Patients aren’t always going to a single facility or a single physician. So when a patient goes to three or four different facilities, each entity may register the person in a slightly different way.”
“Sometimes I tell people that I’m Francis T. Chan, sometimes I’m Francis T.C., and in some cases I’m Francis Chan. Sometimes I use my middle initial after Chan, depending on the context,” Chan added. “That in itself is pretty confusing.”
Inconsistencies in the way patient demographic data is recorded can lead to duplicative and mismatched health records, which can boost hospital spending, contribute to delays in care, and increase the risk of patient harm.
Chan and other HHIE leaders are taking steps to reduce instances of duplicate and mismatched records by implementing NextGate’s enterprise Patient Matching as a Service (PMaaS) and Provider Directory solutions. The enterprise matching patient index (EMPI) tools use demographic data such as patient name, birthdate, and Social Security number to match records to patients.
According to a 2018 Black Book survey, EMPI tools can enable healthcare organizations to improve matching accuracy rates by more than 60 percent.
“We need to improve on the current tools, and that’s why we chose this new EMPI,” explained Chan. “I know they’ve worked with some local facilities that have similar challenges, and they’ve developed algorithms that worked pretty well for us.”
In addition to implementing EMPI tools, stakeholders including RAND and Pew Charitable Trusts are exploring biometrics as a potential avenue for improving patient matching accuracy.
In a 2018 report, Pew Charitable Trusts found most patients support the use of biometrics as unique patient identifiers.
While Chan believes biometrics hold promise as a potential patient matching solution, he predicts attempting to gather and share patient biometric data could lead to a slew of new problems for HIEs and other entities.
“There are multiple issues,” said Chan. “With biometrics, number one, there are patient privacy concerns because of HIPAA. And different organizations often interpret patient privacy rules differently. We need to come to an agreement on how biometric data can be shared and used in patient matching.”
Chan also stated health IT may not yet be mature enough to facilitate the use of biometric data on a large scale.
“Biometrics is slightly ahead of its time at this point,” said Chan. “Maybe in two to three years the technology will be mature enough and the general consensus and agreement will be in place to allow that to be shared along with clinical data.”
At present, Chan suggested leveraging referential data may be more realistic.
“Some vendors now are advancing the concept of using referential matching, which involves using data from schools, credit reports, bills that a patient has to pay, credit cards and other things,” he explained. “Using this data helps with probabilistic matching.”
Overall, Chan stressed that stakeholders across the industry need to agree on common data and health IT standards to enhance patient matching and interoperability nationwide.
“We have to have a certain level of standardization and a certain level of technology to achieve interoperability,” said Chan.
Improving health data standardization for interoperability
According to Chan, organizations are still working to get on the same page about the minimum required data elements healthcare organizations should include when exchanging data.
“We need to know what data entities are looking for,” said Chan. “We need to identify certain data elements as key, and then be able to share them according to established or generally accepted standards.”
“The most effective way actually is for us to convene the community, whether it's task force or a steering committee, and have everybody put high pressure across the board to identify certain use cases, such as public health,” added Chan.
Inviting cross-industry stakeholders to address aspects of standardization such as minimum required data elements will help to establish a general agreement.
“We have everybody around the table,” said Chan. “Public health entities, specialists, emergency department physicians—everyone needs to be represented.”
ONC’s Trusted Exchange Framework and Common Agreement (TEFCA) presents an opportunity to proliferate the use of a set of agreed-upon standards on a nationwide scale.
“TEFCA is definitely needed,” said Chan. “When the HITECH Act came out, CMS promoted health information exchange. ONC was focusing on the technology part more than anything. And meaningful use focused on making sure electronic health record systems were in place to facilitate exchange of information.”
“Then as HIEs we identified a lot of issues that are mentioned in TEFCA, but because using certain standards is not mandated in the HITECH Act, it’s been a matter of convincing people we need standardization to facilitate interoperability,” he continued.
TEFCA is still currently in its draft phase. According to the framework as drafted, ONC will select a Recognized Coordinating Entity (RCE) from the private sector to build out the framework into a single set of guidelines and technical standards comprising the common agreement.
The RCE functions as a neutral coordinating entity that will be instrumental in developing guidelines for agreed-upon standards qualified health information networks (QHINs) and their participants will need to implement in order to engage in health data exchange.
“It would help facilitate at least the foundational pieces, and the fact that ONC will increase requirements incrementally—that helps,” said Chan.
“As HIEs, being able to point to the regulations that come from TEFCA will give us the muscle to get healthcare organizations to meet standards part of the common agreement.”