Learning Centre: Ideas

Want to advance interoperability? Start with better patient record matching

The 21st Century Cures Act (Cures Act) was signed into law on December 13, 2016.  It is designed to help accelerate medical product development and bring new innovations and advances to patients with fewer bureaucratic and regulatory obstacles.  In the Spring of 2019, the Office of the National Coordinator for Health Information Technology (ONC)  released a proposal for improving health information interoperability in the United States.  Health and Human Services (HHS) has issued this challenge as a way to stimulate growth and innovation in the US Healthcare Industry.

IMT has worked with over 150 clients in the US, Canada, and around the world during the past 20+ years, with a mission of advancing patient record matching to support interoperability. Through hundreds of discussions, we have helped clients  undertake or advance a solution approach to patient record matching. These conversations invigorate, inform, and inspire our work — and make us very interested in the proposed HHS rule to improve the interoperability of health information.

As the ONC proposal for interoperability appropriately notes, no single activity or technology will solve this decades-old challenge. Rather, improving patient record matching requires a solution that unites people, process, and technology as wisely advocated by Sequoia Project, ECRI Institute, Pew Charitable Trust, and other private and public organizations.

We strongly believe that accurate patient matching starts within an organization.

Cross-organization record matching builds upon the processes and data created and nurtured within a single organization. Before an organization can pursue true interoperability with other organizations, its own patient data needs a strong foundation that can support a bottoms-up approach.

Specifically, as we look at the proposed HHS rule, that means:

  • We support standardization of data elements, and data itself, particularly in address and basic demographics. However, organizations can and should use additional data as it meets their unique strategic and operational goals. Additional trusted data will only further advance record matching.
  • The quest for better record matching for many institutions includes data elements like telephone numbers, maiden names, email addresses, and more. Our mobile society and evolving use of technology make email addresses and cell phone numbers particularly valuable in assisting record matching. These newer elements can supplant the previous value that the Social Security number represented, which is particularly important given the dramatic decline in this number’s capture over the past 20 years.
  • Many have advocated algorithm comparison; however, algorithms add the most value when used for a large “real-world” dataset (greater than 1M records) with historical data. The collapse of the CHIME Challenge and the apparent market dismissal of the ONC Patient Matching Algorithm Challenge illustrate the difficulty and weakness of relying on synthetic data. Only with a high record volume and depth will a third party such as ONC be able to truly evaluate algorithm differences. And perhaps it’s best for the marketplace to do this evaluation. Many clients conduct this assessment as part of their vendor evaluation prior to purchasing MDM/EMPI/record matching software.
  • Standard definitions for duplicates, duplicate rates, overlays, and the terms commonly used to describe MPIs are important. ONC need not start from scratch and could advance this cause by using and harmonizing what has been promulgated over the past few years by HIMSS, AHIMA, Sequoia Project, and others.
  • The goal of accurately capturing patient data at the point of access/registration should be a top priority. Biometrics hold promise to improve matching, but our clients share significant concerns about biometrics’ impact to consistent and timely patient intake processes, sanitation, and cultural acceptance. Rather than jumping to biometrics, the focus should include using better algorithms (probabilistic) when a patient encounter is created. This is the classic “an ounce of prevention is worth a pound of cure.”
  • Strong data governance is essential to improving patient record matching. This governance builds upon people, process, and technology to provide a solution approach to organizational efforts. Governance can’t be isolated; it must be incorporated into institutional governance in order to be effective.

Additionally, IMT supports the use of standards and APIs to allow greater data interoperability at a reduced cost. We are using FHIR with a limited number of clients and anticipate greater use in the near future. The latest balloted release of FHIR (not necessarily those specified in a regulation) provides for more adoption and flexibility.

We commend ONC (and CMS) for advancing the interoperability, patient engagement, and patient record matching conversation. We look forward to working with our clients to advance patient record matching to build the truly interoperable data that supports patient engagement.

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