The prescription drug monitoring program (PDMP) platform provider Appriss Health has introduced a Machine Learning- and consolidation algorithms-based patient record-matching solution, ApprissID. Driven by ML and consolidation algorithms within PMP AWARxE, the company’s PDMP solution serves into 44 PDMPs across the U.S. According to a report, patient matching is indispensable to put off or quickly spot opioid-addicted patients. ApprissID leverages combinations and segments of a number of identifiers to connect multiple records together to recognize a unique patient as well as offers a significant benefit to its users over any other PDMP.
Typically, mismatches in patient records appear due to human error when linking information from multiple facilities or systems and manual consolidation techniques. It could lead harm to real patient diagnosis. To overcome this issue, Appriss Health’s ApprissID matching and consolidation process are dynamic, enabling new IDs to be connected, deleted, and evolve in real-time through ML. It leverages a combination of probabilistic matching, referential matching, deterministic matching, and manual matching to defeat issues with minor data entry errors, variations in the spelling, tiny names, and maiden name changes and provide the most precise outcome. The PDMP solution connects records and adapts to new information in near real-time to ensure that patient history is up to date.
On this move, what’s the company’s President Rob Cohen says, “Appriss Health’s ApprissID is an unmatched patient record matching technology and represents a significant advance over prior patient history record linking in the PDMP market.” According to him, “ApprissID is a proven record matching solution given its extensive testing and use in billions of dispensations. Its flexibility to evolve, anticipate, and correct various data errors – quickly – makes it an exceptionally smart and especially accurate patient matching solution for the more than 1.35 million registered users of PMP AWARxE across the nation today.”