The healthcare IT industry is at a crossroads.
On the one hand, there have been tremendous advancements made in health technology over the past decade: wide adoption of electronic health records, telehealth, remote monitoring tools, and the broadening effort to enable secure health data exchange. On the other hand, many problems lurk, including one with nationwide, massive implications: patient matching.
As patient data skyrocket and the need for data sharing increases, matching patient records among systems becomes critical. Yet patient matching remains a formidable challenge. Not only is it an issue of patient safety, it’s also a very expensive and time-consuming problem for healthcare providers.
Recently, 25 healthcare organizations – the American Medical Association, the Healthcare Information and Management Systems Society, the College of Healthcare Information Management Executives and others – advocated in a letter to Congress that a unique national patient identifier (UPID) be considered as an option to improve patient matching. In early October, five US senators wrote a letter to the Government Accountability Office (GAO) urging it “to consider how ONC could improve patient matching by considering the application of a national patient matching strategy.”
I fully understand why these requests were made but don’t believe this is the best approach moving forward. Let me explain why.
The Problem of Patient Misidentification
Medical errors caused by an inability to match patients to their correct record cost the average healthcare facility $17.4 million a year, according to the 2016 National Patient Misidentification Report, a recent survey of healthcare executives. The same report cites that 86% of respondents say they witnessed or know of a medical error that was the result of patient misidentification.
All of this is prompting the resurgence of a call for a UPID. Supporters claim it’s the most accurate way to identify the same person across the entire continuum of care – across every provider, lab, health system, health plan, and other healthcare-related entities where a person’s health information is stored. They argue that achieving this goal would decrease unnecessary costs, reduce errors, improve data sharing, enhance care quality, and most importantly, improve patient safety.
Clearly something needs to happen…and quickly. But a UPID not only raises significant privacy and security concerns, it simply isn’t feasible or practical.
It’s unrealistic to think the Office of the National Coordinator for Health IT and CMS could distribute and manage 320 million UPIDs. And even if they somehow were able to, this would presume the existence of a perfect matching system on the backend to keep track of changes, losses and updates. Further, UPIDs wouldn’t eliminate existing duplicate health records, and just like Social Security cards, people would find ways to steal or abuse unique patient IDs.
And let’s not forget the costs. When Medicare recently undertook the process of changing patient identifiers on their cards from a Social Security number to a unique ID, a seemingly simple change, the cost for this action alone is expected to be north of $800 million, not including the rollout costs for states and healthcare organizations.
So What’s Next?
I believe we’re at an interesting inflection point – one where changes in industry collaboration and advances in technology are finally poised to help us tackle this pervasive problem.
I’ve read the Sequoia Project’s whitepaper recommending improvements to how organizations use their existing systems to better achieve patient matching. And recently, ONC suspended its Patient Matching Algorithm Challenge to spur the industry to tackle this problem head on. But these approaches will help achieve only incremental improvements.
We need more than just incremental improvements. We need new technologies that show real promise in addressing the industry’s patient identification and matching challenges.
Many industries, including retail and banking, already leverage “referential matching” to provide greater accuracy and precision in identifying people. Referential matching solutions compare customer records to a reference database that contains commercially available demographic data about each person in the U.S.–data that include the full name, name changes and variations due to things like marriage, old addresses, etc. This allows the referential matching solution to determine if two records belong to the same person, even if those records contain discrepancies in their demographic data. Two records with very different data can still yield a match.
This type of technology is why most retail and consumer markets already have confidence in who I am, what I buy, what my hobbies are, and who I live with.
When applied to the healthcare domain, the results are just as impressive. New offerings yield match rates up to 98%, a rate never before achieved. This seems like a more realistic, practical approach than rolling out unique IDs.
We should encourage innovation like referential matching because better matching equates to improved care. We must do better. We need to exhaustively explore promising technologies because it’s too costly to stay on the same course. It’s time to veer off in a better direction.
Anita Samarth is the CEO and co-founder of Clinovations Government + Health, a consulting firm offering strategic, clinical, and health IT advisory and management services to the government and stakeholders in the public sector, provider, interoperability, and technology domains. Anita brings extensive on-the-ground experience in implementation of EHR, HIE, PACS, and data analytics technologies in the health system, hospital, and ambulatory practice settings at over 100 organizations.