The new HHS “Innovator in Residence faces some tough issues on patient identification and data matching.
On the heels of the recent announcement by HIMSS and the Department of Health and Human Services to hire an “Innovator in Residence” and make progress on the establishment of a nationwide patient data matching strategy, we thought it would be pertinent to outline some of the issues this person will face that require careful consideration. If the end goal is to establish a more consistent, industry standard approach that redefines patient identification and data matching accuracy, this new leader faces some tough challenges on the road ahead. Matching the right patient to the right data requires almost heroic efforts across an extremely disparate healthcare network and is the cornerstone of any viable health information exchange (HIE). Here are our top 5 issues that the new HIMSS/HHS “Innovator in Residence” must address:
1. Cost – Any new patient identification and data matching initiative will likely involve assessing the potential financial impact to healthcare facilities since any solution will most likely involve incorporating accurate matching algorithms into certified EHRs plus making changes to fields that capture soon to be standardized patient identifying attributes. With the recent changes that the HITECH Act and Meaningful Use requirements brought to the industry and the amount of dollars already shelled out for health IT, investment weary healthcare providers may balk at any solution that requires additional funds allocated to EHR resources to completely replace a system.
The Office of the National Coordinator for Health Information Technology (ONC) recently released results from a study on developing an open source algorithm “to test the accuracy of their patient matching algorithms or be utilized by vendors that do not currently have patient matching capabilities built into their systems.” Their results indicated:
“During the environmental scan, many indicated that replacing their current systems would be cost prohibitive. As such, it is not suggested that a standardized patient matching algorithm be developed or required. In a more limited way, however, there is value in developing an open source algorithm or updating and supporting an existing open source algorithm that EHR vendors may choose to utilize in their products.”
2. Patient buy-in and accountability – As noble as the healthcare industry’s efforts to establish more accurate patient identification and data matching standards, the entire initiative is moot unless the new Innovator in Residence forges best practices and policies to encourage patients to keep their demographic information up-to-date and accurate. The new Innovator in Residence would be wise to capitalize on the patient engagement momentum spurred by Meaningful Use Stage 2 and extend the patient engagement initiative to include patient accountability for demographic information accuracy. Without patient buy-in and involvement, the industry can’t reasonably expect any worthwhile patient identification and data matching initiative to lift it’s wheels of the ground.
3. Technology – Incorporating non-traditional data attributes to improve patient matching is a great example of a “wish list” item by industry advocates pushing for stricter patient identification and data matching but currently, most EHR systems do not support the collection of this information in a standardized field format. Any legitimate effort to standardize patient identifiers and substantially increase data matching will most likely require new technologies or modifications of existing ones to meet these goals. On the surface, requests to add demographic fields to existing EHR interfaces or incorporate standardized deterministic or probabilistic algorithms may seem like small changes that don’t require a lot of effort, but in reality even the simplest of changes require health IT vendors to make significant investments in upgrading or completely replacing existing technology.
4. Rekindling the national patient identifier debate – Did you know that it’s been 14 years since Congress placed a moratorium on funding research and implementation of a national patient identifier (NPI)? 14 years. Sure to be rekindled as a debate topic that closely coincides with the industry’s push to standardize patient demographic data, the idea of establishing a NPI needs to be addressed now and the new Innovator in Residence should be standing behind the healthcare industry podium leading the discussion. Sure, there are lingering questions on the privacy and security implications of creating a NPI, issues surrounding who will manage and have access to any databases created, but ultimately the topic deserves to be put back on the table and expectations are that the Innovator in Residence will spearhead the efforts. Many people believe that an NPI is no different than the plethora of other personal identifiers we deal with in our everyday lives – social security numbers, employee IDs, and driver’s licenses numbers just to name a few. Why should the NPI be treated any differently? We surmise that the new Innovator in Residence will have to address a NPI sooner rather than later.
5. The validity of health information exchanges (HIEs) – Although there are myriad reasons to develop HIEs, the bottom line is that their existence is meant to facilitate the fluid exchange of health information between disparate systems in order to improve individual and population health. What often seems to often be left out in the conversation about HIEs is the introduction of a foolproof patient identification technology that can uniquely tie together a patient with their electronic health record in a standardized data format to help ensure high levels of data integrity. After all, what good is developing an integrated HIE without a back end patient identification system that prevents the creation of duplicate medical records and overlays?
The new HIMSS/HHS Innovator in Residence faces some tough challenges to help tie together and incorporate a nationwide patient identification and data matching initiatives. What points would you add to our list that are critical for this new position to address?