Even as you move throughout a single healthcare system’s various facilities–hospitals, physician practices, outpatient surgical centers, imaging centers, rehab centers, etc.–you are required to fill out identification forms before receiving care. This can result in both duplicate records (where one patient is identified with multiple health records) and overlays (where one patient’s data is mistakenly overwritten with another). Now imagine the problem created when one patient moves across different healthcare systems, especially, for example, in the case of an emergency. Any inaccurate data that influences a crucial medical decision could cause severe consequences for the patient, even death. Here, the integrity of patient identity–the consistent access to a single uniform patient identity for each patient–is absolutely vital.
Interoperability of Electronic Health Records (EHRs) within and across health information systems is hardly a new problem, of course. But as our technologies and healthcare standards continue to evolve in the public and private spheres, the need for addressing interoperability in each of its myriad components will only become more pressing.
What complicates the integrity of patient identity?
The American Health Information Management Association (AHIMA) asserts that this complexity “stems from many factors including variability in practices of authentication, data collection, technology, and the historical silo approach to patient identification.” AHIMA lists the following as common factors that contribute to the lack of integrity in patient identity:
- Proof of ID isn’t always required at “the time of data capture” (when information is entered into a system), which often causes duplicates and overlays.
- In emergency situations, the accuracy of patient information takes a back seat to care delivery, which also contributes to duplicate and overlays.
- “Registration is a high staff turnover area where entry-level employees typically do not have adequate education and training.”
- “High-volume registration areas such as scheduling have a much higher risk of duplicate creation and overlays due to the lack of direct patient contact.”
- Specimen registration often lacks specific enough information to help locate patient information.
- The source systems which provide information to an exchange network are not always kept in sync across the entire network.
- The variety of IT solutions used in and across health systems often create or magnify inaccuracies.
AHIMA goes on to say that, even when proof of ID is required, the requirements themselves vary too much across different provider organizations. Some organizations even except proof of ID without photo evidence, to which AHIMA suggests that government-issued ID become the universal standard.
How can we improve the integrity of patient identity?
The American Recovery and Reinvestment Act of 2009 (ARRA) instituted Health Information Organizations (HIO), which Technopedia defines as “U.S. government-led non-profit health organizations that provide information about the ARRA as it pertains to EHRS development for incentive payments.” At the federal, state, and local level, HIOs help to inform and instruct regarding interoperability and EHRs exchange between healthcare facilities (Technopedia).
HIOs, however, are by no means a cure-all. Because each participant in an EHR exchange will use its own unique identifiers to authenticate a patient identity, HIOs must, in the absence of a universal identifier, “rely on sophisticated matching technology” (AHIMA). This essentially means algorithm-based solutions.
Here’s a general breakdown of how these solutions are implemented:
- A percentage of records will be automatically linked by the software.
- A “statistically significant” sample of these auto-links should always be manually reviewed.
- Even higher rates of record linkage can be achieved if manual reviews also look into record matching that nearly but did not meet auto-link criteria.
Because algorithms “usually only identify 10 to 40 percent of the existing duplicate records,” a nuanced manual review system serves as an essential supplement.
Beyond that, auto-links may also result in:
- False positives (incorrectly linking similar records belonging to two different people),
- False alarms (detecting records that do not belong to the same person),
- False negatives (not detecting multiple records belonging to the same person).
These errors often occur because of
- Two closely related people with very similar names and dates of birth who live near each other, such as cousins who are named after the same individual;
- Two individuals living in a dense urban area with the same common name, date of birth, and address;
- Twins with the same first name.
Though algorithm technology is certainly a step in the right direction, it’s painfully apparent that more of a cornucopia of solutions must be implemented.
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Contact tekMountain today to learn more about what identity management can do for your healthcare organization.