3 Consequences of Patient Matching, Health Record Issues

Mismatched data results in patient harm

Mismatched patient data and incomplete medical history can lead to potentially fatal consequences, experts assert.

“Any time you lack complete information to make the best decision possible, there's an opportunity for error,” said Grannis. “Patient matching is a safety issue. Patient identification is paramount to making sure that patients receive appropriate, safe care.”

Mismatched data could result in incorrect or unnecessary medical care.

For example, Grannis noted a woman whose mammogram was assigned to another woman’s chart. With the mammogram nowhere to be found, her diagnosis was delayed until the clinicians learned of the incorrect patient matching, thus delaying her treatment.

A deadly example occurs when drug prescribing is the topic. If a patient is not matched to the correct record, a drug could be prescribed to the wrong patient; if the clinician does not know of the patient’s drug history, it could result in a potentially fatal outcome.  

“The fact that they’re on all of their medication may not be present to the current provider, so particular drug interactions may not be obvious,” Grannis said.

Mismatched data puts a financial strain on health systems

Patient safety is always a major priority, but there is also a significant cost burden on the health system if there is a patient matching error.

“A recent study identified that it costs a system somewhere in the area of $1 million a year to disambiguate and identify patients,” explained Grannis. “It's not a cheap thing to do, and the approaches are different. When patients move from system to system, there’s no guarantee that a patient’s data will follow them.”

According to a 2018 Black Book survey, duplicate patient EHRs cost hospitals an average of $1,950 per patient. The survey also found that roughly 33 percent of denied claims are due to mismatched or incorrect patient information.

“As data sharing grows and challenges in connectivity are tackled, resolving patient record matching issues has become more urgent and complex,” said Doug Brown, Black Book research managing partner.

“Despite the increases in record sharing among providers, increased risk and cost from redundant medical tests and procedures because of fragmented data trapped in siloes makes tracking patients especially difficult,” said Brown.

Results showed an average of 18 percent of patient health records were discovered to be duplicates. These duplicate records cost health systems over $1,950 per inpatient stay and over $800 per emergency department (ED) visit.

Denied claims cost hospitals an average of $1.5 million in 2017 and $6 billion annually for the healthcare system as a whole.

“Ultimately, the real challenge of identity management and parsing together a longitudinal health record has to do with integration and interoperability,” Brown stated. “Many systems still do not communicate and store data in disjointed architectures and an upsurge of identifiers continue to be created.”

The survey also found that for larger hospitals that hold 150 or more beds and hundreds of thousands of patient records, the average data cleanup per organization averages longer than 5 months. This cleanup includes data cleansing, normalization, and data validity checking.

Potential solutions with security consequences

Researchers and stakeholders are working on ways to enhance patient matching, but there are potential drawbacks with these ideas.

Most recently, Ben Moscovitch, project director of health information technology at Pew Charitable Trusts, told EHRInteligence that the ONC interoperability rule could be the answer.

Moscovitch explained that the ONC can take additional steps to improve patient matching by way of a patient’s mailing address.

“As part of the final rule, ONC assures that additional data will be available to match records,” he said. “They've added previous addresses to what needs to be made available for matching and email addresses, which were not previously made available.”

“Adding those data elements will further facilitate greater matching,” he continued. “For example, we know that email addresses in already more than half of patient records were typically not used for matching. These final rules accelerate the case for that purpose.”

Moscovitch explained how standardizing the address in the US Postal Service format would improve match rates by approximately 3 percent. Using that format, ONC can further improve patient matching.

A common solution is a national patient identifier. An NPI is similar to a Social Security number, where a number code would be used across all providers to identify individual patients. This would replace the current system that uses a name, address, or date of birth.

Both methods possess obvious positives to the situation, but there are potential patient security and mismatching hazards.

If the NPI number is compromised, the patient’s entire health record would be easily accessible in one location. Proper security measures would have to be implemented to promote safety and security.

If two patients were accidently given the same number, serious medical issues could occur. Or even if the clinician were to type in the incorrect number or add a medical record to the wrong number, there could be fatal consequences.

The patient matching issue is and will always be debated. However, the importance of finding solutions is clear.

“If we want to standardize data, then we need to get people to agree,” explained Shaun Grannis, MD, MS Regenstrief Institute vice president of data and analytics. “Whatever it's going to be, we need a consistent approach to make sure that we do the best job possible identifying patients. Without a consistent approach, there are going to be gaps in the system, and information is going to be missed. Patients will continue to receive less than optimal care.”