Embeds AI for Referential Matching to Provide Unprecedented Accuracy and Efficiency in Linking Archive Patient Data with Active EHR
BRECKSVILLE, Ohio–(BUSINESS WIRE)–MediQuant®, healthcare’s leading provider of enterprise active archiving and conversion services and solutions, has enhanced its current eMPI strategy, embedding machine-learning-enhanced robotic processing automation (RPA) for referential matching to its DataArk® solution.
Before either migrating multiple retired electronic medical records (EMRs) to DataArk, linking those historical patient records to the active EHR, or converting legacy data in retired EMRs to a health system’s enterprise MPI, MediQuant connects, retrieves, cleans and matches the archived legacy data. This eMPI strategy ensures DataArk users retrieve any and all legacy patient records for patients not contained in the active EHR.
By embedding new referential matching capabilities that utilize a cloud-based RPA resource to analyze demographic data and historical information spanning 30 years across the U.S. population, MediQuant improves data accuracy, with scalability across systems, securely and simply. This not only shortens data conversion and migration implementation timelines, but also strengthens DataArk’s single-patient-record view spanning both old and new EMRs.
Connecting current and legacy patient records at the point of care dramatically improves the depth of information clinicians have to care for patients.
“The benefits of linking an enterprise EHR to a single-legacy medical record archive include avoiding duplicate tests, incorrect diagnoses and treatments, patient and physician dissatisfaction, billing errors, and compliance issues,” said Jim Jacobs, MediQuant CEO. “This advancement brings the best practices for archiving legacy data to a new industry standard of accuracy by overcoming serious errors in the original EHRs that cause mismatches and inconsistencies, whether they stem from out-of-date, ambiguous, incomplete or bad default information.”
Industry studies show:
- 33 percent of patient demographic data is inaccurate (out of date, incorrect, or incomplete) and 18 percent of an organization’s medical records tend to be duplicated. Duplicate records alone cost the average hospital $1.5 million and the U.S. healthcare system over $6 billion dollars annually. (2018 Black Book™ poll of MPI users and survey of health technology users)
- Mismatch rates multiply when organizations exchange data across systems, consolidate and add more data to their networks. (CHIME)
- Up to half of the information exchanges made by healthcare organizations may fail to accurately match records for the same patient. (Office of the National Coordinator for Health Information Technology)
According to Jacobs, the benefits of enterprise MPIs begin during patient registration, with better matches to pre-existing records. They avoid gaps in medical records, improve patient safety, reduce clinician error and support practice management by curbing health information management costs and accelerating reimbursement.