Datica Blog

Avoid Inaccurately Matching Patients’ Health Records

Marcia Noyes

Marcia Noyes

Director of Communications

December 9, 2015   Company Interoperability

If you haven’t been following along on our blog, we have been taking a dive into the Government Accountability Office’s (GAO) recently released report, Nonfederal Effort to Help Achieve Health Information Interoperability. Specifically, we have been exploring the five barriers this report found to achieving true nation-wide interoperability:

  1. Insufficiencies in health data standards
  2. Variation in state privacy rules
  3. Accurately matching patients’ health records
  4. Costs associated with interoperability
  5. The need for governance and trust among entities

For the fourth entry, we would like to explore the difficulties in accurately matching patients’ health records. What most people probably do not realize is that traditional data management and patient index tools only automatically match approximately 70 percent of records. The other 30 percent will fall into a queue of “potential matches” – records that are similar but not exact so they cannot be considered non-matches.

As is well known, the exchange of health data across care settings is a central and necessary constituent of a patient-centric model of care that can improve quality and reduce costs. So in order to do this, enablement of a clinician to view an encompassing picture of the patient is required for efficiency and accurate matching of patient health records across these care settings. On the flip side, incorrectly matching a patient to a health record may have privacy and security implications, such as wrongful disclosure, in addition to treatment based on another patient’s health information.

Data standardization must be the solution!

Yes, possibly. And no, probably.

The lack of standardization in the data attributes or fields used for matching, increases error rates, as well as significant burdens and costs within the health care systems. Standardization is one potential solution, as many of the interoperability initiative representatives reported that when trying to become interoperable with other entities, the process gets held up due to incongruence in data sets and matching variables.

So what’s the alternative?

A few alternative solutions exist on the market. One of the more well-known comes from a company called Verato. This solution surpasses traditional matching solutions. The company has created an innovative matching engine that can match up to 98% of patient records by associating each patient record to an identity in its proprietary reference database, trademarked as CARBON.

Know of any other solutions on the market that can accurately match patient records? We’d love to hear about them! Tweet or email us.

For further reading:

  1. Why Interoperability?
  2. Need for Governance and Trust Among Entities
  3. Variation in State Privacy Laws

Earlier

Need for Governance and Trust Among Entities

In a day and age where data breaches and cyber terrorism are at an all time high, it is no surprise that our healthcare system lacks trust, especially when pertaining to patient data.

Next Post

EHR Costs Hinder Interoperability

Value-based care finally provides the incentives for enhanced interoperability, but who is going to pay the up front bill to get started? Vendors? Doctors? Healthcare organizations? Payers?