Part Three: The Quest for 1%
By Karen Proffitt, MHIIM, RHIA, CHP – Vice President, Industry Relations/CPO
In Part Two, we explored the role database environments, duplicate error and creation rate calculations, workforce factors, and the types of patient matching algorithms play in doing so. In Part Three, we look at the ICMMR Cycle recommended by AHIMA to achieve a 1% duplicate rate.
To begin addressing the chronic patient identification and matching problems that plague healthcare, AHIMA has recommended that healthcare organizations implement the “ICMMR Cycle” approach to achieve its recommended duplicate error rate of not more than 1%. This approach was spelled out in In “A Realistic Approach to Achieving a 1% Duplicate Record Error Rate,” a white paper published by AHIMA in July 2020.
ICMMR consists of Identifying, Cleaning, Measuring, Mitigating and Remediating. The white paper provided sample considerations for each element to help healthcare organizations reach their duplicate record goals:
- Identify: Assess the current state of duplicates by identifying and running all available duplicate reports, then calculate the initial duplicate record error rate percentage. If there is uncertainty around the results, an analysis should be undertaken that provides a higher level of detail on the number and types of potential duplicates. It’s also important to meet with system vendors to understand embedded algorithms.
- Clean: Establish a plan for cleaning the MPI to reduce the number of duplicates, including the possibility of using technology partners and/or data integrity staff resources. While the cleanup is underway, ensure that multiple records with the same medical record number are identified and resolved at the same time.
- Measure: Re-run reports to determine how many potential duplicates remain in the MPI/EMPI database and calculate duplicate record error rate percentage. Benchmark against the duplicate record error rate quarterly, set stretch goals, and account for disruptions. Establish grace periods for times when data is ingested from external sources (e.g. mergers and acquisitions, partnerships). Finally, account for complex bidirectional interfaces, which can increase the duplicate creation rate, and monitor closely and continuously.
- Mitigate/Remediate: Focus on staff education and training to eliminate human errors and standardize processes for database functions, including naming conventions, feedback loops, and duplicate prevention. Ensure merging record workflows emphasize the focus on accuracy first and operationalize a stringent process for working the duplicate error queue. Importantly, take these steps in tandem with the MPI cleanup.
Addressing the serious problems with patient identification and matching has taken on a new urgency in light of the pandemic. Doing so requires both the tools and resources to accurately identify and match patients to their records. Leveraging the ICMMR Cycle approach will allow healthcare organizations to meet their goals while also achieving and maintaining a 1% duplicate error rate.