Matching identifiers in electronic health records: implications for duplicate records and patient safety
- Allison B McCoy1,
- Adam Wright2,
- Michael G Kahn3,
- Jason S Shapiro4,
- Elmer Victor Bernstam1,5,
- Dean F Sittig1
- 1School of Biomedical Informatics, The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
- 2Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- 3Department of Pediatrics, University of Colorado Denver, Aurora, Colorado, USA
- 4Department of Emergency Medicine, Mount Sinai School of Medicine, New York, New York, USA
- 5Department of Internal Medicine, Medical School, The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
- Correspondence to Dr Allison B McCoy, School of Biomedical Informatics, The University of Texas Health Science Center at Houston (UTHealth), 6410 Fannin St., UTPB 1100, Houston, TX 77030, USA;
- Received 1 August 2012
- Revised 4 October 2012
- Accepted 20 December 2012
- Published Online First 29 January 2013
Objective To quantify the percentage of records with matching identifiers as an indicator for duplicate or potentially duplicate patient records in electronic health records in five different healthcare organisations, describe the patient safety issues that may arise, and present solutions for managing duplicate records or records with matching identifiers.
Methods For each institution, we retrieved deidentified counts of records with an exact match of patient first and last names and dates of birth and determined the number of patient records existing for the top 250 most frequently occurring first and last name pairs. We also identified methods for managing duplicate records or records with matching identifiers, reporting the adoption rate of each across institutions.
Results The occurrence of matching first and last name in two or more individuals ranged from 16.49% to 40.66% of records; inclusion of date of birth reduced the rates to range from 0.16% to 15.47%. The number of records existing for the most frequently occurring name at each site ranged from 41 to 2552. Institutions varied widely in the methods they implemented for preventing, detecting and removing duplicate records, and mitigating resulting errors.
Conclusions The percentage of records having matching patient identifiers is high in several organisations, indicating that the rate of duplicate records or records may also be high. Further efforts are necessary to improve management of duplicate records or records with matching identifiers and minimise the risk for patient harm.