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Journal of Digital Imaging

, Volume 32, Issue 6, pp 919–924 | Cite as

Deterministic vs. Probabilistic: Best Practices for Patient Matching Based on a Comparison of Two Implementations

  • Jason NagelsEmail author
  • Sida Wu
  • Valentina Gorokhova
Patient Matching

Abstract

In order to successfully share patient data across multiple systems, a reliable method of linking patient records across disparate organizations is required. In Canada, within the province of Ontario, there are four centralized diagnostic imaging repositories (DIRs) that allow multiple hospitals and independent health facilities (IHF) to send diagnostic images and reports for the purpose of sharing patient data across the region (Nagels et al. J Digit Imaging 28: 188, 2015). In 2017, the opportunity to consolidate the two regional DIRs that share the south-central and southeast area of the province was reviewed. The two DIRs use two different methods for patient matching. One uses a deterministic match based on one specific value, while the other uses a probabilistic scorecard that weighs a variety of patient demographics to assess if the patients are a match. An analysis was conducted to measure how a patient identity domain that uses a deterministic approach would compare to the accepted “standard.” The intention is to review the analysis as a means of identifying interesting insights in both approaches. For the purpose of this paper, the two DIRs will be referred to as DIR1 and DIR2.

Keywords

PACS Health information exchange (HIE) Digital Imaging and Communications in Medicine (DICOM) Enterprise PACS Foreign exam management (FEM) EMPI Patient matching 

Notes

References

  1. 1.
    Nagels J, MacDonald D, Parker D: Foreign exam management in practice: seamless access to foreign images and results in a regional environment. J Digit Imaging 28:188–193, 2015.  https://doi.org/10.1007/s10278-014-9735-7 CrossRefPubMedGoogle Scholar
  2. 2.
    Nagels J, Macdonald D, Coz C: Measuring the benefits of a regional imaging environment. J Digit Imaging 30:609–614, 2017.  https://doi.org/10.1007/s10278-017-9963-8 CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Hallet J, Coburn NG, Alberga A, Fu L, Tharmalingam S, Beyfuss K, Milot L, Law CHL: Reducing repeat imaging in hepato-pancreatico-biliary surgical cancer care through shared diagnostic imaging repositories. HPB (Oxford) 21(1):96–106, 2019.  https://doi.org/10.1016/j.hpb.2018.06.1807 CrossRefGoogle Scholar
  4. 4.
    Torkzadeh R: Advancing a nationwide patient matching strategy. Journal of AHIMA 89(7):30–35, 2018Google Scholar
  5. 5.
    Zech J, Husk G, Moore T, Shapiro JS: Measuring the degree of unmatched patient Records in a health information exchange using exact matching. Applied clinical informatics 7(2):330–340, 2016.  https://doi.org/10.4338/ACI-2015-11-RA-0158 CrossRefGoogle Scholar
  6. 6.
    Sayers A , Ben-Shlomo Y, Blom AW, Steele F. Probabilistic record linkage. Int J Epidemiol 45:954–64, 2015CrossRefGoogle Scholar
  7. 7.
    Kesinger MR, Kumar RG, Ritter AC, Sperry JL, Wagner AK: Probabilistic matching approach to link deidentified data from a trauma registry and a traumatic brain injury model system center. Am J Phys Med Rehabil 96(1):17–24, 2017.  https://doi.org/10.1097/PHM.0000000000000513 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2019

Authors and Affiliations

  1. 1.HDIRSMarkhamCanada

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