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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2017-04-28T00:00:00 |
false |
false |
closed |
0 |
894 |
false |
4 |
true |
Integrating patient information for better medical decision making |
Department of Health and Human Services |
false |
ideation |
09/12/2017 02:00 PM |
05/02/2017 12:00 AM |
FY17 |
$75,000 |
Adam Wong ([email protected]) |
America COMPETES |
102 |
No |
No |
Software and apps |
161715 |
Hosted on this platform |
Yes |
Software and apps |
25000 |
First Place Winner |
20000 |
Second Place Winner |
Third Place Winner |
15000 |
Best in category: Precision |
5000 |
5000 |
Best in Category: Best recall |
5000 |
Best in Category: First F-Score run |
false |
true |
<h1>HHS Names Patient Matching Algorithm Challenge Winners</h1> <div class="news-subheadline"><i>Thousands of submissions received from more than 140 teams</i></div> <div class="field field-name-body field-type-text-with-summary field-label-hidden"> <div class="field-items"> <div class="field-item even"> <div class="syndicate"> 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 <a id="anch_29" href="https://www.healthit.gov/buzz-blog/interoperability/demystifying-patient-matching-algorithms/" data-vars-outbound-link="https://www.healthit.gov/buzz-blog/interoperability/demystifying-patient-matching-algorithms/">Patient Matching Algorithm Challenge</a>. 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. Winners include: Best “F-score” (a measure of accuracy that factors in both precision and recall): <ul> <li>First Place ($25,000): Vynca</li> <li>Second Place ($20,000): PICSURE</li> <li>Third Place ($15,000): Information Softworks</li> </ul> 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 <a id="anch_30" class="external-link" href="https://www.patientmatchingchallenge.com/" data-vars-outbound-link="https://www.patientmatchingchallenge.com/">Patient Matching Algorithm Challenge</a> <a class="exit-disclaimer" title="Exit Disclaimer" href="https://www.hhs.gov/disclaimer.html"><img class="icon-image" src="https://www.hhs.gov/sites/all/themes/project_h/css/images/exit_disclaimer.png" alt="exit disclaimer icon" border="0" /></a> website. </div> </div> </div> </div> |
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Patient Matching Algorithm Challenge |
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0 |
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/challenge/patient-matching-algorithm-challenge/ |
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