Improving Patient Data Matching: The “Key” to Health Information Exchange
Patient matching is a critical patient safety issue. Meaningful Use requires health information exchange between unaffiliated providers. According to AHIMA, in spite of the widespread deployment of EMPIs, the average duplicate medical record rate in the healthcare industry is an unacceptable 8%. The dup rate is much higher for larger, more complex organizations such as integrated delivery networks and health information exchanges. The main problem is data integrity – missing, incorrect, non-standardized, and out of date data. Data elements that change most frequently over time are addresses, phone numbers, aliases, and names (due to marriage and divorce). An existing dynamic national consumer database of hundreds of millions of unique individuals containing current and historical information about addresses, phone numbers, aliases, and names can dramatically improve patient matching. Big data can be leveraged to improve the identification of unique patient records in healthcare organization patient files and/or EMPI files and the big data “key” can then be linked to those unique patient records at disparate healthcare organizations such as hospitals, labs, pharmacies and health plans, which as a result, will greatly enhance and facilitate health information exchange. This presentation will focus on how big data solutions can improve patient matching.
- Discover how to leverage big data to improve patient matching
- Identify less threatening naming conventions for a “unique patient identifier”
- Discuss how big data can prevent the creation of overlays and duplicate medical records
- Explain how more accurate identification of the number of unique patients can mitigate meaningful requirements
Start Date: 07/16/2015
Event Type: Archived , College LIVE
Michael L. Nelson, DPM, VP of Healthcare Strategy and Business Development, Identity and Fraud Solutions, Equifax Inc.