What Healthcare Can Do to Address GAO's Concerns About Patient Matching

Can an enterprise master patient index help healthcare providers?

The Government Accountability Office (GAO) has just confirmed for the public what we in the healthcare industry have known and struggled with for years: how hard it is to correctly identify and match patients to their electronic health records. Whether records for different individuals are incorrectly matched or records for the same individual aren’t matched at all, patients and the organizations that care for them remain at risk because of poor EHR interoperability. The GAO, in a report published last week, found significant challenges to patient identification when dealing with EHRs.

>> READ: Inadequate Health Records Are Failing Mothers and Providers

They include:

  • Transcription errors when entering patient information
  • Incorrect or inconsistent information provided by the patient, including nicknames in place of formal names
  • Changes in demographic information, like address or phone number
  • Inconsistencies in IT systems’ collection of data
  • Inconsistencies in formatting records, either in the EHR or due to an organization’s policies
  • Some patient records are harder to match, such as those of newborns, who often have temporary names that aren’t updated; cultural differences that affect standardization of names; or transgender inaccuracies

The GAO stressed that these mismatches are not only a barrier to data sharing but can also adversely affect patient safety and privacy.

All seven of the providers interviewed by the GAO used manual medical record matching; six of them also used health IT tools to help them automatically identify and match records stored in their EHR and other data systems. The five health information exchanges (HIEs) interviewed also reported that they used a range of automated and manual approaches to match records.

The GAO suggested several ways to enhance data matching, such as improving the underlying data itself by standardizing it in EHRs, improving data accuracy by using smartphone apps and other tools to reduce manual errors and enable patients to update their data, sharing best practices, using national patient identifiers, public/private collaboration and using biometric data to verify information.

In its response to the GAO report, Pew Charitable Trusts agreed that there needs to be a multi-pronged approach to resolve the problem.

It also pointed out that two of the strategies highlighted in the GAO report­ — data standardization and biometrics — have previously been backed by Pew. It recommended that the Office of the National Coordinator of Health IT (ONC) take “immediate action” to standardize patient data in EHRs and for the private sector to “begin to lay the groundwork” on incorporating biometrics.

Health IT: The Most Practical Short-Term Solution

Unfortunately, some of the avenues to improve patient matching are not practicable today. For instance, even if ONC took immediate action to standardize these data elements, it would take months, if not years, to finalize the project.

Adoption of a nationwide matching strategy has been part of policy discussions since Congress dismissed the concept of a unique patient identifier system in 1998. While a coordinated approach to patient matching is needed, a universal identifier alone will never be enough to achieve complete integration of data across the continuum. And developing public-private collaboration to improve matching is hampered by lack of resources, according to the GAO report.

However, there are health IT tools that exist today that can step in now to help resolve this problem, which costs the U.S. healthcare system $6 billion annually.

An enterprise master patient index (EMPI) is one piece of technology already helping healthcare organizations accurately and automatically match patient data. Tired of EHR systems that only provide a limited view of a patient’s health history, progressive healthcare organizations are leveraging EMPIs as a strategic advantage to integrate multiple data sources more quickly and efficiency.

For many institutions, patient identification tools like EMPIs are rapidly transforming from a line of defense against duplicate medical records to the default approach for interoperability and enterprise-wide connectivity across care settings. Primary growth in EMPI investments are taking place in the cloud, which allows evolving organizations to scale with greater agility and integrate outside sources of information more easily, including social care and third-party data.

There’s a reason that most of the providers and all of the HIEs interviewed by the GAO augment their manual record matching with electronic assistance: It works. In an era of frequent, large-scale mergers and the transition away from fee-for-service, longitudinal medical records and robust interoperability will become competitive market advantages — and the need for EMPI technology as part of multi-pronged approach to patient matching will become progressively in demand for quality interventions and coordinated, patient-centered care.

Daniel Cidon is CTO and co-founder of NextGate, a global leader in healthcare identity management.

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