ONC Names Patient Matching Algorithm Challenge Winners

Nov. 8, 2017
The Office of the National Coordinator for Health Information Technology (ONC) today announced the winners of the federal agency’s Patient Matching Algorithm Challenge.

The Office of the National Coordinator for Health Information Technology (ONC) today announced the winners of the federal agency’s Patient Matching Algorithm Challenge.

Announced on May 1, the challenge aimed to bring about greater transparency and data on the performance of existing patient matching algorithms, spur the adoption of performance metrics for patient data matching algorithm vendors, and positively impact other aspects of patient matching such as deduplication and linking to clinical data, according to federal officials.

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 impeded interoperability resulting in patient safety risks and decreased provider efficiency, ONC officials noted.

As such, ONC selected the winning submissions from over 140 competing teams and almost 7,000 submissions using an ONC-provided dataset. The winners include:

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): PICSURE

Each winner employed widely different methods, explained ONC officials. 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 .01 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, per ONC’s announcement.

“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 IT. 

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.

Sponsored Recommendations

A Comprehensive Workplace Safety Checklist

This checklist is designed for healthcare facilities focused on increasing workplace safety. It’s meant to inspire ideas, strengthen safety plans, and encourage joint commission...

Healthcare Rankings Report

Adapting in Healthcare: Key Insights and Strategies from Leading Systems As healthcare marketers navigate changes in a volatile industry, they know one thing is certain: we've...

Healthcare Reputation Industry Trends

Navigating the Tipping Point: Strategies for Reputation Management in a Volatile Healthcare Environment As healthcare marketers navigate changes in a volatile industry, they can...

Clinical Evaluation: An AI Assistant for Primary Care

The AAFP's clinical evaluation offers a detailed analysis of how an innovative AI solution can help relieve physicians' administrative burden and aid them in improving health ...