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

McCoy, Allison B.; Wright, Adam; Kahn, Michael G.; Shapiro, Jason S.; Bernstam, Elmer V.; Sittig, Dean F.

doi:10.1136/bmjqs-2012-001419

Cited by 58 publications

(43 citation statements)

References 19 publications

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“…We also excluded safety concerns related to erroneous editing or merging of patient records resulting in comingled or overlaid records. Although these are known safety concerns,43 they were excluded because they are not routinely analyzed by IPS and are handled primarily by a separate office in the VA. We extracted 100 consecutive records that met our search criteria. An example is provided online as supplemental Appendix C. Previous exploratory studies in patient safety have been able to shed powerful light on contributory factors with a similar sample size 44.…”

Section: Methodsmentioning

confidence: 99%

An analysis of electronic health record-related patient safety concerns

Meeks

Smith

Taylor

et al. 2014

J Am Med Inform Assoc

Self Cite

173

115

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ObjectiveA recent Institute of Medicine report called for attention to safety issues related to electronic health records (EHRs). We analyzed EHR-related safety concerns reported within a large, integrated healthcare system.MethodsThe Informatics Patient Safety Office of the Veterans Health Administration (VA) maintains a non-punitive, voluntary reporting system to collect and investigate EHR-related safety concerns (ie, adverse events, potential events, and near misses). We analyzed completed investigations using an eight-dimension sociotechnical conceptual model that accounted for both technical and non-technical dimensions of safety. Using the framework analysis approach to qualitative data, we identified emergent and recurring safety concerns common to multiple reports.ResultsWe extracted 100 consecutive, unique, closed investigations between August 2009 and May 2013 from 344 reported incidents. Seventy-four involved unsafe technology and 25 involved unsafe use of technology. A majority (70%) involved two or more model dimensions. Most often, non-technical dimensions such as workflow, policies, and personnel interacted in a complex fashion with technical dimensions such as software/hardware, content, and user interface to produce safety concerns. Most (94%) safety concerns related to either unmet data-display needs in the EHR (ie, displayed information available to the end user failed to reduce uncertainty or led to increased potential for patient harm), software upgrades or modifications, data transmission between components of the EHR, or ‘hidden dependencies’ within the EHR.DiscussionEHR-related safety concerns involving both unsafe technology and unsafe use of technology persist long after ‘go-live’ and despite the sophisticated EHR infrastructure represented in our data source. Currently, few healthcare institutions have reporting and analysis capabilities similar to the VA.ConclusionsBecause EHR-related safety concerns have complex sociotechnical origins, institutions with long-standing as well as recent EHR implementations should build a robust infrastructure to monitor and learn from them.

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Section: Methodsmentioning

confidence: 99%

An analysis of electronic health record-related patient safety concerns

Meeks

Smith

Taylor

et al. 2014

J Am Med Inform Assoc

Self Cite

173

115

View full text Add to dashboard Cite

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“…The author identity problem bears some similarity to establishing unique identifiers in patient care,[23] health information exchange,[24] and biomedical research. [25] These approaches share a common mathematical approach involving the calculation of similarity based on a set of attributes (name, date of birth, gender, etc.).…”

Section: Discussionmentioning

confidence: 99%

Automatic generation of investigator bibliographies for institutional research networking systems

Johnson

Bales

Dine

et al. 2014

Journal of Biomedical Informatics

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Objective Publications are a key data source for investigator profiles and research networking systems. We developed ReCiter, an algorithm that automatically extracts bibliographies from PubMed using institutional information about the target investigators. Methods ReCiter executes a broad query against PubMed, groups the results into clusters that appear to constitute distinct author identities and selects the cluster that best matches the target investigator. Using information about investigators from one of our institutions, we compared ReCiter results to queries based on author name and institution and to citations extracted manually from the Scopus database. Five judges created a gold standard using citations of a random sample of 200 investigators. Results About half of the 10,471 potential investigators had no matching citations in PubMed, and about 45% had fewer than 70 citations. Interrater agreement (Fleiss’ kappa) for the gold standard was 0.81. Scopus achieved the best recall (sensitivity) of 0.81, while name-based queries had 0.78 and ReCiter had 0.69. ReCiter attained the best precision (positive predictive value) of 0.93 while Scopus had 0.85 and name-based queries had 0.31. Discussion ReCiter accesses the most current citation data, uses limited computational resources and minimizes manual entry by investigators. Generation of bibliographies using named-based queries will not yield high accuracy. Proprietary databases can perform well but requite manual effort. Automated generation with higher recall is possible but requires additional knowledge about investigators.

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“…The Health Department of the United Kingdom recommends that this record is unique and used by all health services, but privacy and safety issues, such as theft of personal information, and flaws in the combination of patients in these electronic systems indicate the need to resort to other identifiers. Clinical data, name, surname, date of birth, social security number, address, phone number, postal code and gender can be combined to allow the correct patient identification and prevent duplication of medical records or multiple recording of the same user, as has been demonstrated by several authors (18)(19)(20)(21)(22)(23) . Similar or identical names can originate mistakes when computed systems require identifiers -first and last names and numerical registries -to find patient information in health services (2,24) .…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the risk of misidentification of women in a public maternity hospital

2018

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Objective: To determine the frequency of similar names and hospital records of women in a public teaching maternity hospital and the risk of misidentification resulting from the similarity in spelling and pronunciation of the names and in records. Method: Quantitative, documental and case study of 5,975 admissions that occurred between 2011 and 2014. The data name, admission and discharge date, date of birth, hospital record and bed number were collected from an electronic information system. Analysis encompassed descriptive statistics and design of an algorithm for comparison of text and sound. Results: Examination of the names revealed that 86% of the misidentification cases resulted from identical surnames and 96.5% from a sound similarity in the first names. There were patients with identical first and last names at least one day a week. Conclusion: The risk of misidentification of patients is a reality, which stresses the importance of checking and pronouncing the complete names correctly.

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

Cited by 58 publications

References 19 publications

An analysis of electronic health record-related patient safety concerns

An analysis of electronic health record-related patient safety concerns

Automatic generation of investigator bibliographies for institutional research networking systems

Evaluation of the risk of misidentification of women in a public maternity hospital

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