Posted By: Department of Health and Human Services
Category: Software/Apps Skill: Algorithms Interest: Health Submission Dates: 12 a.m. ET, May 02, 2017 - 2 p.m. ET, Sep 12, 2017
HHS Names Patient Matching Algorithm Challenge Winners
The U.S. Department of Health and Human Services’ Office of the National Coordinator for Health Information Technology (ONC) today announced the winners of the Patient Matching Algorithm Challenge.
ONC selected the winning submissions from over 140 competing teams and almost 7,000 submissions using an ONC-provided dataset. “Patient matching” in health IT describes the techniques used to identify and match the data about patients held by one healthcare provider with the data about the same patients held either within the same system or by another system (or many other systems). The inability to successfully match patients to any and all of their data records can impede interoperability, resulting in patient safety risks and decreased provider efficiency.
“Many experts across the healthcare system have long identified the ability to match patients efficiently, accurately, and to scale as a critical interoperability need for the nation’s growing health IT infrastructure. This challenge was an important step towards better understanding the current landscape,” said Don Rucker, M.D., national coordinator for health information technology.
Best “F-score” (a measure of accuracy that factors in both precision and recall):
- First Place ($25,000): Vynca
- Second Place ($20,000): PICSURE
- Third Place ($15,000): Information Softworks
Best First Run ($5,000): Information Softworks
Best Recall ($5,000): PICSURE
Best Precision ($5,000): Ocuvera
Each winner employed widely different methods. PICSURE used an algorithm based on the Fellegi-Sunter (1969) method for probabilistic record matching and performed a significant amount of manual review. Vynca used a stacked model that combined the predictions of eight different models. They reported that they manually reviewed less than .001 percent of the records. Although Information Softworks also used a Fellegi-Sunter-based enterprise master patient index (EMPI) system with some additional tuning, they also reported extremely limited manual review.
The dataset and scoring platform used in the challenge will remain available for students, researchers, or anyone else interested in additional analysis and algorithm development, and can be accessed via the Patient Matching Algorithm Challenge website.