Reminder: Do not include any PHI or PII in Confluence. If you require 508 accessibility assistance or any other support for this system, then please send an email to onc-jira-questions@healthit.gov
...
Time | Session Title | Slides and Video | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
1:00pm-1:10pm | Welcome and Introductions: Steve Posnack, ONC/Genevieve Morris, ONC
|
| |||||||||
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.
|
Office Powerpoint | | name | Patient Matching.121017.cs.dc.wide2.pptx
Office Powerpoint | ||
---|---|---|
|
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
HTML |
---|
<iframe width="480" height="270" src="https://www.youtube.com/embed/oIyB_NMNMVs?ecver=1" frameborder="0" gesture="media" allow="encrypted-media" allowfullscreen></iframe> |
Office Powerpoint | ||
---|---|---|
|
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.
- PDDQ Overview
- Need for demographic data management
- PDDQ products developed by ONC
- PDDQ Framework
- PDDQ Ambulatory Guide
- Maturity level assessment and scoring
- Q&A
HTML |
---|
<iframe width="480" height="270" src="https://www.youtube.com/embed/_MhyszLerC8?ecver=1" frameborder="0" gesture="media" allow="encrypted-media" allowfullscreen></iframe> |
Office Powerpoint | ||
---|---|---|
|
3:40 pm-4:20 pm
Data Quality Framework (DQF) Pilot: Justin Cross, ONC/Carmen Smiley, ONC/OCHIN/Kaiser Permanente
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
- Q&A
HTML |
---|
<iframe width="480" height="270" src="https://www.youtube.com/embed/RktquCM7HVs?ecver=1" frameborder="0" gesture="media" allow="encrypted-media" allowfullscreen></iframe> |
Office Powerpoint | ||
---|---|---|
|
4:20 pm-4:30 pm
Closing Remarks
...