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Reducing duplicate patient creation using a probabilistic matching algorithm in an open-access community data sharing

Sidney N Thornton1, Shannon K Hood

  • 1Department of Medical Informatics, Intermountain Health Care, Salt Lake City, Utah, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|June 17, 2006
PubMed
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Intermountain Health Care reduced duplicate patient record creation by 30% using a dynamic probabilistic matching algorithm. This approach effectively manages data sharing in open-access environments, maintaining low duplicate rates with partners.

Area of Science:

  • Health Informatics
  • Data Management
  • Algorithm Development

Background:

  • Managing patient data in open-access environments presents challenges with duplicate record creation.
  • Intermountain Health Care (IHC) sought to optimize its data sharing processes.

Purpose of the Study:

  • To implement and evaluate a probabilistic matching algorithm for managing duplicate patient records.
  • To dynamically assign threshold limits for data sharing partners.

Main Methods:

  • Utilized a probabilistic matching algorithm with dynamically assignable threshold limits.
  • Applied the algorithm within internal hospital systems and for a community data sharing partner.

Main Results:

  • Achieved a 30% reduction in duplicate patient record creation within internal systems in six months.

Related Experiment Videos

  • Maintained duplicate creation rates below the acceptable internal Health Plans rate for the first community data sharing partner.

    • Conclusions:

    • The dynamic probabilistic matching algorithm is effective in reducing duplicate patient records in open-access data sharing.
    • This method supports efficient and accurate data management for healthcare organizations and their partners.