DUPLICATE MEDICAL RECORD ANALYSIS

Most Health Information Management systems use some form of duplicate detection algorithms to help identify possible duplicate medical records within their database. These embedded algorithms typically use some form of deterministic or rules-based logic to identify duplicate records. These algorithms tend to identify duplicate medical records that match "exactly". They miss a large percentage of duplicates where the data is just off slightly. They also identify many possibly duplicates that prove to not belong to the same person (False Alarms).

Can your MPI/EHR identify this Duplicate?

The answer is likely no!

Our research has demonstrated that over 60% of duplicate records contain two or more discrepant data elements. These types of duplicates are routinely missed by MPI/EHR platforms.

While Just Associates will perform a clean-up using any duplicate reports our clients prefer, we found that a duplicate medical record analysis conducted with a more sophisticated matching algorithm yields a more accurate analysis. As a result, our efforts to identify and resolve duplicate MPI records are more effective. 

Just Associates utilizes error-tolerant record matching technology to detect duplicate patient records within a single, and across multiple, MPI / EMPI databases. We have performed hundreds of duplicate medical record assessments and have examined over a hundred million patient records, accurately identifying duplicate medical records and shared MRNs despite working with inaccurate, incomplete, inconsistent, and unreliable patient demographic information.

Our services range from delivering simple duplicate medical record analysis reports to providing complete, turnkey, on-site reconciliations performed by experienced HIM professionals.

IDAnalyze^™ record matching algorithm

Just Associates' duplicate record search uses its own proprietary patient matching algorithms to compare database records on a sliding scale. We are not limited to a fixed set of data elements but rather, the analysis benefits from using more data elements.

Unlike “exact matching” technologies, the process mathematically models the way a human would think to accurately measure how much each record has in common with other records in the EHR or MPI database. This flexible approach enables us to easily overcome impure data, data entry errors, mis-fielded data, and common variations in patient demographic information.

Our process learns by example and develops an understanding of what qualifies as a "match" in the context of your data and your needs. Frequency scores are adjusted based upon information “learned” about your data. The program deals automatically with irregularities like missing data and blank fields that confound other matching approaches. Our solution outperforms rules-based and other deterministic matching approaches, and, unlike them, does not require recalibration any time your database changes significantly.

Overlaps / Overlays

In addition to performing a duplicate medical record analysis, your database can also be analyzed for overlaps and/or overlays. An overlap occurs when a patient has more than one medical record number assigned across more than one database. An overlay occurs when one patient record is overwritten with data from another patient's record.

Reports

After the data is analyzed, the results are compiled into various reports. Potential duplicate pairs are weighted and grouped for more efficient validation. Summary Statistical Reports and graphs display the distribution of potential duplicates across variables. Data Integrity Reports summarize the quality of your data across several metrics.

Identity Fraud

Our powerful analytics can be used to identify trends in the data that can help identify instances of identity fraud.

Our Experience

Our staff has over twenty years of experience using all of the duplicate detecting methodologies available today. In our experience, a duplicate medical record analysis performed using our advanced algorithms produces a better result. Truer duplicate records are identified and at the same time, the false Alarms are exceptionally low. A better duplicate medical record assessment leads to a more complete clean-up.

Are you prepared to meet the ONC’s guidelines for duplicate records: 0.5%?

The Office of the National Coordinator for Health Information Technology (ONC) delivered its vision for achieving interoperable health IT infrastructure. Its roadmap included lowering duplicate record rates to 0.5% by 2020 and to 0.01% by 2024

Improving Vendor Solutions with IDOptimize^™

In addition to providing analytics services to our clients, we have made our record matching expertise available to vendors. We have consulted with several dozen healthcare software and analytics vendors and helped them improve the performance of their record match algorithms. We have done this with specific provider clients in mind as well as to enhance the vendor's global product offerings.