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


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.

This is a preview of subscription content, access via your institution.

Fig. 1


  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.

    Article  PubMed  Google Scholar 

  2. 2.

    Nagels J, Macdonald D, Coz C: Measuring the benefits of a regional imaging environment. J Digit Imaging 30:609–614, 2017.

    Article  PubMed  PubMed Central  Google 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.

    Article  Google Scholar 

  4. 4.

    Torkzadeh R: Advancing a nationwide patient matching strategy. Journal of AHIMA 89(7):30–35, 2018

  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.

    Article  Google Scholar 

  6. 6.

    Sayers A , Ben-Shlomo Y, Blom AW, Steele F. Probabilistic record linkage. Int J Epidemiol 45:954–64, 2015

    Article  Google 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.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Jason Nagels.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Nagels, J., Wu, S. & Gorokhova, V. Deterministic vs. Probabilistic: Best Practices for Patient Matching Based on a Comparison of Two Implementations. J Digit Imaging 32, 919–924 (2019).

Download citation


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