By Erin Benson, LexisNexis Risk Solutions – Health Care
Patient misidentification in healthcare is costly to the system and puts patient safety at risk. Records linking and identifier solutions tackle this complicated problem with remarkable results.
Mismatches and duplicate patient records lead to incorrect diagnoses and treatments, redundant provision of services, unnecessary hospitalizations, and system-wide productivity losses. The system could do much better. In 2017, an average hospital forfeited $1.5 million in denied claims resulting from inaccurate patient identification.1 That’s an average cost of $1,950 per inpatient for repeated medical care and over $800 per emergency room patient due to identification missteps.1
The financial implications of incomplete or mismatched data is only the beginning. ECRI Institute, a patient safety organization, determined that of 7,600 wrong-patient events by 181 health organizations, 9 percent resulted in death or harm to the patient.2 ECRI concluded that “proper patient ID confirmation at every step of clinical care is vital to patient safety.”
The Joint Commission also has stepped up to focus on patient safety in this regard. In October of 2018, the organization issued an advisory on accurate patient identification called Quick Safety, Issue 45: “People, processes, health IT and accurate patient identification.”3
Also, in October, the Pew Charitable Trusts reported results of research involving 11 focus groups of patients who revealed their knowledge about adequate patient matching. The organization reported that the need to accurately link electronic health records from various doctors’ offices and hospitals remains unmet, leading to the following problems:4
Study participants overwhelmingly supported having unique patient identifiers to improve matching, decrease medical mistakes, give clinicians a more complete picture of their health, and improve security.4 They also agreed that data matching presents a critical barrier to effective interoperability and are eager to have the problem addressed so there’s access to accurate, current health information.4
The industrywide problem of patient misidentification is garnering attention and, unfortunately, adding new casualties each day. From a safety perspective, patients may receive the wrong diagnoses, treatments or prescriptions with unfortunate consequences. From a workflow perspective, health systems muddle their way through hundreds of thousands of patient records with no assurance that they are complete and correct, leading to compromised decision making and often organizational incompetence.
By employing records-cleansing and matching technology, providers can work toward safer patient care and smoother workflow even amid the industry’s common interoperability challenges. Data integrity must go hand in hand with both practice growth and confident care provision.
No case of the patient matching problem is as clear as when a health system grows through mergers and acquisitions. The lack of interoperability presents an obstacle to linking health data and correctly identifying patients.
A case in point is Pennsylvania’s Butler Health System. Butler Health, which serves seven counties, had met with a serious challenge after multiple physician practice acquisitions. The good news: the health system has enjoyed rapid expansion to the tune of 1.3 million total patient records. The bad news? Those records represented 740,000 unique patients. As six different Electronic Medical Records (EMR) source systems fell under the Butler Health umbrella, the hospital administration knew these duplicates not only endangered patient safety but hindered the ability to make solid decisions organization-wide based on reliable, actionable data. Butler Health also was planning to commence population health initiatives to succeed in value-based payment models by lowering costs and improving quality. Bad data would hinder these efforts, and the problem ran deeper than any manual solution could fix.
Butler Health decided to implement the Occam Technologies enterprise Master Patient Index platform to help identify common patients across its various EMR systems and streamline administrative burdens. The module is built on the LexID technology from the Health Care segment of LexisNexis Risk Solutions, a proprietary automated patient linking technology focused on matching, registration data stewardship and deep integration.
LexisNexis is able to assign a LexID, a unique patient identifier, to a patient record using statistically based linking and referential data for the majority of the U.S. adult population.
Butler Health was able to achieve a 97 percent success rate in matching its patient records5. By having a much more accurate understanding of the patient load, Butler Health is able to proactively assess performance and quality as value-based care trends continue to grow. The organization is also able to back up its performance data for negotiations regarding additional gain-sharing contracts for improved business clarity.
In addition, Butler Health was intent on making sure its system offered accurate and complete records access so its providers could diagnose and treat patients at the highest clinical level. If inundated with duplicate records, key pieces of patient data like test results, allergy warnings, or family disease history could be spread across numerous files. If the data in one record is incorrect or incomplete, physicians could make erroneous or risky decisions about treatment. If a wrong medical health record is matched to a mistaken patient, life-altering or even fatal consequences could result.
It’s not uncommon for clerical or data entry errors to create opportunities for mix-ups. Misspellings, wrong birth dates, or even dropping a suffix like “Jr.” at any step of the patient’s care journey—primary care, pharmacy, imaging center, or laboratory—introduces erroneous material into the system. Other times, a lack of standardization in EMRs and intake forms is responsible for the errors. And most commonly, when front-desk staff doesn't immediately locate a patient record in its system, the typical workaround is creating a whole new record, leading to multiple incomplete charts. By analyzing the data automatically with linking technology, the integrity of the data can be established for a more complete and accurate identity picture.
By leveraging unique, identifier-based patient matching technology, medical practices and large health systems can get a more accurate picture of their patient records status, with an assurance that they are treating the right patient—the complete patient—along the entire healthcare journey.
About The Author
Erin Benson is Director, Market Planning and Engagement at LexisNexis Risk Solutions – Health Care.