ONC Patient Identification and Matching Report: The journey continues

RHIA,Global Healthcare Industry Ambassador, IBM Information Management

Patient matching requires a solution that includes people, process and technology. There is no silver bullet such as a national patient identifier. The ONC Patient Identification and Matching Final Report reflected this long-held belief. Enjoy some highlights from this report below:

Standardize data attributes and formats. Standardizing attributes, as well as their formats, was a clear priority in the December 16 meeting, and in the final report. None of the attributes proposed were a radical shift from what is already captured to facilitate registration, payment or patient care. Standardization would particularly help those who do deterministic matching today, the byte for byte comparison of data characters. The best probabilistic algorithms, such as IBM’s MDM, won’t benefit from this, as they assume dirty, inconsistent data is the norm. Thus IBM’s MDM product development has long focused on applying data routines (tokenization, transposition, phonetics, concatenation and so on) to compensate and address lack of data quality. Interestingly, the report also included a viewpoint from some stakeholders that deterministic matching does such a poor job it should be banned. 

Certification. Once agreement can be reached on the data attributes and formats, certification is the logical next step.  I suspect this would take the form of requiring certified EHRs to capture and format the data as prescribed to meet Stage 3 Meaningful Use.  Significant work would be required to validate the benefit of standardization, otherwise vendors and constituents will likely feel this is a burdensome, resource0-intensive effort that lacks commensurate value. 

Algorithms. While initially there was discussion about the government writing an open source, probabilistic algorithm, it appears this withered after more review. There are several open source algorithms already, with the CDC’s algorithm being a good example, so perhaps is why this faded from consideration. 

Reports. Requiring reports that list the potential duplicate records is a recommendation. But, we still struggle with this recommendation being part of certification, given the variety of options that exist for integrating a probabilistic algorithm. The algorithm may be invoked at registration and be from a third party such as IBM, embedded into the EHR or run passively after the ADT transaction has been executed. Love the recommendation, don’t agree with the certification. 

Case Studies. Rich case studies are in the appendices, and well worth a read. We particularly liked Group Health, Kaiser Permanente, HealthInfoNet and Mayo Clinic as they embraced the need for strong multi-disciplinary governance of patient identification and matching. 

Some are undoubtedly unhappy that the report didn’t squarely promote biometrics (we heard that during the stakeholder meeting December 16, 2013), but the lack of mature, scalable and adopted biometric solutions likely lead to this exclusion. Others are displeased that consumer engagement and control is not front-and-center in this journey. However, the reality is consumers (authors included) sign agreements that allow our data to be used for treatment, payments and operations, and we don’t believe a consumer wants to get countless emails, phone calls or text messages as their data is used for care delivery, claims processing or care coordination. 

We’re looking forward to further progress on the journey, particularly around studying the value of adding and standardizing data attributes. Here’s to better patient identification and patient matching solutions that will improve care coordination, save precious human and financial resources and underpin safer patient care.

Co-Author Michele O'Connor

micheleoconnor.jpg Michele O’Connor is an international and domestic thought leader, accomplished author and respected public speaker on topics related to technology’s role in healthcare transformation. Michele is a Fellow in the American Health Information Management Associations (AHIMA).  Michele has also served as President for the New Jersey Health Information Management Association and received their “Distinguished Member” award. She has published numerous articles and blogs, and presented domestically and internationally on using information technology for the improvement of individual and population health, and creating a single view of citizen to enable seamless linking of patients with their records for health and social services. Michele is expanding industry and customer views on the value of big data and analytics, and the importance of information integration and governance as healthcare embraces big data.