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EHR Demographic Data Standards Could Improve Patient Matching

Implementing standards for demographic data in EHRs could improve patient matching among providers.

EHR demographic data standards could improve patient matching

Source: Thinkstock

By Jessica Kent

- Healthcare organizations should work to adopt more standardized methods of inputting demographic data into the EHR to ensure accurate patient matching, according to a recent report from the Government Accountability Office (GAO).

While matching patient records is an essential component of coordinated, effective care, many entities still struggle to accurately match patients to their health data, especially when exchanging this information among providers from different organizations. In fact, GAO cited a 2014 study which found that only half of records are accurately matched when organizations exchange information.

“Accurate record matching can help ensure that providers have current information about patients’ laboratory or other diagnostic test results; their medications; their diagnosed medical conditions, such as allergies; and their family medical histories,” GAO said in its report.

“In contrast, inaccurate patient record matching can adversely affect the care patients receive as well as their privacy.”

To determine how organizations are effectively improving patient matching, GAO reviewed published reports and conducted interviews with physician practices and hospitals and health information exchange (HIE) organizations.

READ MORE: Pew: Better Patient Matching, APIs Will Enhance Interoperability

Many of the representatives GAO interviewed said that patient demographic data has a significant impact on patient matching accuracy.

“Representatives from providers, HIE organizations, and the other stakeholders we interviewed emphasized the importance of using quality patient demographic data when matching patients’ medical records,” the report said.  

“These stakeholders noted that inaccurate, incomplete, or inconsistently formatted demographic information in patients’ medical records can make it challenging to identify and match all the records belonging to a single patient.”

The interviewees said that inconsistencies in demographic data occur because providers collect inaccurate information from patients, or because the patient information isn’t consistently updated.

Additionally, demographic data can be inconsistent across disparate organizations because providers collect different information from their patients, and because health IT systems allow users to input this data differently.

READ MORE: ONC Focuses on Data Governance with Patient Matching Framework

To improve the consistency of demographic data, interviewees said they implemented common standards for data collection and formatting. For example, in Texas, 23 providers were able to agree on standards for how staff should record patient names, addresses, and other information to facilitate health information exchange.

Representatives that were part of this effort reported that it allowed providers to accurately match patient records automatically, without having to manually review the records. The group estimated that their organizations experienced a 90 percent decrease in manual review time to solve patient matching issues.

Interviewees also described their efforts to boost patients’ ability to share their health information with their providers. Two stakeholders said that they have used smartphone apps and other tools that enable patients to share their demographic data with their various providers.  

Representatives from the Workgroup for Electronic Data Interchange (WEDI) initiated a “virtual clipboard” project that would automate the transmission of demographic, insurance, and clinical information to providers.

Additionally, respondents from the Pew Charitable Trusts described a solution in which patients could verify their mobile phone number and other identifying information with their providers, and then share this information with other providers through a smartphone app.

READ MORE: Half of HIM Pros Face Patient Matching, Data Integrity Issues

While the interviewees said that these tools could improve the consistency and accuracy of demographic data, they also emphasized that organizations and developers should evaluate the possible challenges that could come with using these technologies.

“Representatives from both WEDI and Pew told us that, when developing these types of tools, it is important to consider the practical implications for the providers that would need to be able to accept data in this way,” GAO wrote.

“For example, Pew representatives said that it would be important to understand whether these tools present any workflow challenges in provider settings, such as with any IT tools that providers would need to access the data stored via smartphone applications, or with the steps needed to incorporate that data into their EHR systems.”

GAO also noted that for some patient populations, record matching can be particularly challenging. Medical records for newborns often contain temporary names and aren’t updated when an official name is given, which can make it difficult to locate these records. Twins also present record matching challenges, as they often have the same date of birth, address, and similar names.

Several representatives described their efforts to improve these challenges, including a children’s hospital in California that implemented a standard for recording a temporary name at birth.

“According to representatives from this hospital, after implementing this standard, clinical staff are able to more easily match patients’ records and therefore have access to real-time information on the care newborns received in other hospitals,” GAO said.

Another children’s hospital has implemented indicators in their EHR that highlight when a newborn has a twin or additional multiple-birth siblings.

“Representatives said that this helps prevent medical records from one child being incorrectly matched with the medical records of a sibling,” GAO said.

“In 2017, this hospital began working with its health IT vendor to explore the broader use of a multiple birth indicator to improve the probability of accurate matching for the multiple birth population between different vendors’ EHRs.”

By presenting these solutions, GAO expects to help organizations improve the quality of patient demographic data, and in turn, patient matching accuracy.

“The ability to accurately match patient medical records across different providers is a critical part of effective health information exchange, which can benefit patient care. Quality demographic data is important for effectively matching patients’ medical records,” GAO concluded.


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