Matching identifiers in electronic health records: implications for duplicate records and patient safety

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Article by:McCoy, A. B., Wright, A., Kahn, M. G., Shapiro, J. S., Bernstam, E. V., & Sittig, D. F. (2013)

Background and Significance

The introduction of Information Technology to the healthcare field in the form of electronic health records (EHR) and health information exchange (HIE) has increased the availability of electronic patient data that has the potential to decrease errors. It has also introduced a new challenge in the form of duplicate records, which can lead to clinicians missing important information that may exist on one record and not the one the other. [1]


The authors performed a retrospective evaluation of patient records using EHR at five different organizations. They used distinct medical record numbers (MRNs) to retrieve de-identified patient counts and matching elements (first, last names, and/or date of birth). They conducted literature reviews and current workflow at each study site to identify methods for preventing, detecting and removing duplicate records.


The research found matching rates across organization ranging from 16.49% to 40.66% for matching based on first and last names. But the inclusion of the patient date of births dropped matching rates range to between 0.16% and 15.47%. They also found varying methodology (prevention, detection, Removal and/or error mitigation) in managing duplicate or potential duplicates.


To minimize the risk of harm resulting from duplicated patient records, efforts should be made to adopt methodologies for preventing, detecting, removing duplicates and mitigating errors.

Remarks about the article

There are several limitations as pointed out in this article. The authors only reported rates of duplications and potential duplications without discussing the sources of the duplications and disaggregating potential duplicates and duplicates. Without the contextual information of the duplications, it will be difficult to apply the methodology to other settings particularly settings with resource constraints.

Related Topics

Master patient index

Performance of probabilistic method to detect duplicate individual case safety reports


  1. McCoy, 2013. Matching identifiers in electronic health records: implications for duplicate records and patient safety