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Interoperability in Action Day #3: Patient Matching Efforts at ONC
Date: December 11, 2017
Time: 1-4:30pm
This webinar focused on recent milestones in the area of patient matching at the Office of the National Coordinator for Health Information Technology (ONC). ONC established a variety of patient matching projects with the overarching goals of improving data quality, achieving higher interoperability, and delivering safer health care. The intent of the webinar was to provide an overview of the design and outcome of each project, including lessons learned and best practices for patient data capture and record matching.
Facilitator: Carmen Smiley, ONC
Time
Session Title
Slides and Video
1:00pm-1:10pm
Welcome and Introductions: Steve Posnack, ONC/Genevieve Morris, ONC
Introductions
ONC Patient Matching Overview
1:10pm-2:20pm
Patient Matching Algorithm Challenge (PMAC): Adam Culbertson, ONC/MITRE/Challenge Winners
The goals of the ONC Patient Matching Algorithm Challenge (PMAC) were to bring about greater transparency and data on the performance of patient matching algorithms, spur the adoption of performance metrics for patient matching algorithm developers, and positively impact other aspects of patient matching such as deduplication and linking to clinical data.
PMAC Overview
Outcomes and analyses
Determination of winners
Feedback, lessons learned, recommendations, and next steps from top-scoring teams
Q&A
2:20pm-2:30pm
Break
2:30pm-3:00pm
Gold Standard and Algorithm Testing Pilot: Carmen Smiley, ONC/OCHIN/Kaiser Permanente/MITRE
Despite the increased adoption of electronic health records (EHRs) in recent years and progress made towards interoperability, there is no widely used standard for assessing or reporting the accuracy of patient matching algorithms. The goal of the Gold Standard and Algorithm Testing (GSAT) pilot was to create a gold standard dataset containing thousands of pairs of known duplicate records against which algorithm performance may be evaluated.
GSAT Overview
Adjudication and creation of gold standard data set
Scoring of algorithm
Findings, challenges, and recommendations
Q&A
3:00pm-3:10pm
Break
3:10pm-3:40pm
Patient Demographic Data Quality: Lee Stevens, ONC
Patient demographic data is the primary key used for matching patient records. Unfortunately, patient demographic data has historically been of poor data quality, resulting in both inaccurate matching of patient records and low match rates, particularly when data is exchanged across organizations. The Patient Demographic Data Quality (PDDQ) initiative worked to establish a standardized framework and guidance aimed at improving the quality of patient demographic data.
High quality demographic data are particularly critical for preventing or minimizing the creation of duplicate records. The Data Quality Framework (DQF) aimed to streamline and standardize the patient registration process in order to improve the overall quality of patient demographic data and reduce the number of duplicate patient records.
DQF Overview
Approach
Literature Review
Data Collection
Data Quality Improvement Training
Findings, challenges, limitations, and recommendations