Part One: The Quest for 1%
By Karen Proffitt, MHIIM, RHIA, CHP – Vice President, Industry Relations/CPO
Effective patient identification and matching has been one of healthcare’s most intractable problems—and worst-kept secrets. But recent events including the COVID-19 pandemic and legislative actions designed to clear a path to a national patient identifier have put the spotlight back onto the issue, amplifying calls to find solutions that can make a difference even before mandated standards for interoperability kick in.
The problem is significant. One recent study found that 18% of patient records are duplicates, up from a decade ago when AHIMA put the rate at 8%-12%, which can lead to delayed, excessive, or unnecessary care. Further, approximately 1 in 5 records are incomplete, including an estimated 40% of demographic data missing from commercial laboratory test feeds for COVID-19. This can hamper efforts to gain control over the pandemic, as contact tracers rely on accurate and comprehensive information to locate patients, and public health agencies rely on consistent, reliable, and reproducible data for reporting. Further, a widespread and safe vaccination program requires a consistent and accurate means of identifying individuals.
In “A Realistic Approach to Achieving a 1% Duplicate Record Error Rate,” a white paper published in July 2020, AHIMA notes that “misidentification errors have been a recurring challenge in healthcare, resulting in administrative inefficiencies, serious injuries, and even death.”
The paper highlights a 2016 National Patient Misidentification Report in which 86% of respondents said they have witnessed or know of a medical error that was the result of patient misidentification. The paper further notes that misidentification costs the average healthcare facility $17.4 million per year in denied claims and lost revenue.
Consequently, AHIMA has called for healthcare organizations to achieve a 1% maximum duplicate record error rate as quickly as possible—which can be accomplished through a structured cycle wherein dedicated resources, time and effort are prioritized and supported. In fact, AHIMA’s 2020 Patient Identification Survey found that 22% of respondents had achieved a duplicate error rate of 1% or less, demonstrating “a commitment to patient safety while supporting enhanced and seamless access to health information.”
The first step on the quest for 1% should be conducting an MPI clean-up to deal with any duplicates, overlays and shell records that are already lurking in the system. This creates a clean foundation upon which to build a long-term strategy for maintaining the integrity of patient data.
Part Two of “The Quest for 1%” will take a look at the role database environments, duplicate error and creation rate calculations, workforce factors, and the types of patient matching algorithms play in achieving and maintaining a low duplicate record error rate.
