It’s mid-January, 36o F, and nary a flake of snow is visible on my front yard. I remind myself this is not the typical winter we had anticipated in Montana. My husband grouses that he’s only used his new toy (snowplow) twice.
I’ve made my customary resolutions for the new year, including to be more engaged in the dialogue and activities around patient identification and record matching. 2018 proved to be a tough year for my work. We moved from California to Montana, which required three trips as we bid California adieu and settled into Montana. Several life events also took big chunks of time. Now I’m energized to devote 2019 to discussing, brainstorming – and hopefully advancing – patient/consumer record matching.
Consumer Engagement. The RAND Corporation report (which I covered in a September blog) was the first major discussion about how to engage consumers to address the decades-old challenge of matching patients/consumers to their records. Pilot studies, which I hope to see launch in 2019, will need to explore how to engage the “high flyer” consumers who are most apt to reap benefits from better record matching. Organizations and vendors will need to explore the policy challenges, begin to solve the workflow implications, and build smartphone applications, all while promoting the long-term potential. This is obviously a significant, complex, multi-faceted endeavor that could dramatically change the landscape.
Data Standardization. I am hopeful that vendors and organizations will fully test the data standardization recommendations Dr. Shaun Grannis shared in the October Pew webcast. Dr. Grannis explained that his research has shown data standardization (of both data elements and formats) can improve the record match rate by up to 10%. Data standardization has also been recommended by the Sequoia Project and the ONC 2014 patient matching report, so it’s time to put this into play.
Referential Matching. The October Pew report that I discussed in a blog included a recommendation that referential matching be a key focus for advancing the patient identification and record matching journey. I’ve talked with organizations that have quickly adopted this approach and seen huge gains in their record matching. I hope a third party will fund the research proposed in the Pew report.
Costs and Inertia. Many reports, articles, and blogs have been written by vendors, organizations, and individuals about the cost of correcting record matching errors. Sadly, there is no published research about the total cost of today’s poor record matching. The implications are vast, but some examples would include:
- redundant testing due to lack of historical information in multiple medical records
- medical errors or patient safety events due to lack of access to historical records
- time wasted by the caregivers in searching for all components of a patient health record
- consumer costs as deductibles are paid, travel is required, and appointments are rescheduled due to not having a complete picture of the patient’s medical history
I hope an organization will step forward and conduct such research, even if it is not perfect, as I believe this cost is staggering – perhaps staggering enough to draw support and funding for change. I know this will not be an easy undertaking, as we discussed this when I was working on the HIMSS Patient Identity Integrity workgroup way back in 2008-2012. But I believe this is an essential to overcome inertia and prioritize the solution development necessary to resolve this long-standing problem.
While my January Montana weather report is unseasonably mild, I suspect February or March will see some blizzard activity. Here’s hoping 2019 produces a blizzard of activity that advances the patient identification and record matching solution.