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Purpose of Health Records I01:11

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LinkMD: Linking Medical and Dental Records with 4 Linking Algorithms.

J S Patel1, E Dinh2

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Journal of Dental Research
|November 19, 2025
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Summary
This summary is machine-generated.

Linking electronic health and dental records is now possible with new algorithms. This improves patient data completeness and supports integrated healthcare, bridging the gap between medicine and dentistry.

Keywords:
artificial intelligencebig datadata linkagehealth information exchangehealth information interoperabilityinformatics

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Area of Science:

  • Health Informatics
  • Biomedical Data Linkage
  • Interdisciplinary Healthcare

Background:

  • Electronic health records (EHRs) and electronic dental records (EDRs) are typically siloed, hindering integrated care and research.
  • Existing infrastructural and interoperability challenges prevent seamless data sharing between medical and dental fields.
  • This separation limits the potential for practice-based evidence generation and comprehensive patient management.

Purpose of the Study:

  • To develop and validate algorithmic frameworks for linking siloed EHR and EDR data across non-integrated systems.
  • To assess the performance of different linkage strategies in accurately connecting patient records.
  • To improve the completeness of patient demographic information by integrating medical and dental data.

Main Methods:

  • Developed and evaluated four algorithmic frameworks: direct Social Security number matching, unweighted similarity scoring, weighted average similarity scoring, and a probabilistic classification model.
  • Utilized a large dataset comprising over 1.7 million medical records and 222,480 dental records from Temple University over a 10-year period.
  • Validated linkage approaches using expert review of 1,000 candidate record pairs and determined optimal similarity thresholds.

Main Results:

  • The weighted average similarity algorithm achieved the highest performance, with 100% specificity and 99% sensitivity at a threshold of 0.82.
  • This method successfully linked 121,771 unique patients, demonstrating 96% sensitivity, 78% specificity, and 89% accuracy.
  • Post-linkage, demographic data completeness significantly improved, reducing missing race data from 79% to 11% and ethnicity data from 82% to 17%.

Conclusions:

  • A novel weighted average similarity algorithm effectively links disparate EHR and EDR systems, overcoming interoperability challenges.
  • The developed algorithm enhances patient data completeness, facilitating better clinical decision support and interdisciplinary care.
  • This linkage provides a foundational bridge between medicine and dentistry, enabling safer procedures, timely referrals, and integrated research.