ArticleJournal of the American Medical Informatics Association : JAMIA2019
Evaluating the effect of data standardization and validation on patient matching accuracy.
Article in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Performance of Machine Learning for Systemic Lupus Erythematosus: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
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- Linking patient records at scale with a hybrid approach combining contrastive learning and deterministic rules.Biology methods & protocols · 2026Article
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- Nursing Future Horizon: Simultaneous Continual Nursing Model and Homemade Dialogue Tool for Controlling Cardiac Function and Blood Pressure in Hypertensive Heart Diseases.Patient preference and adherence · 2025Article
- Linking Patient Encounters across Primary and Ancillary Electronic Health Record Systems: A Comparison of Two Approaches.ACI open · 2024Article
- Article
- Evolving availability and standardization of patient attributes for matching.Health affairs scholar · 2023Article
- Improving Cohort-Hospital Matching Accuracy through Standardization and Validation of Participant Identifiable Information.Children (Basel, Switzerland) · 2022Article
- Development of a North American coordinated registry network for surgical treatment of benign prostatic hyperplasia.World journal of urology · 2022Article
- A framework for a consistent and reproducible evaluation of manual review for patient matching algorithms.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Evaluation of real-world referential and probabilistic patient matching to advance patient identification strategy.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Patient-Centered Data Home: A Path Towards National Interoperability.Frontiers in digital health · 2022Article
- The Impact of Name Transformation on Match Rates Within a Large Consumer Database.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Record linkage under suboptimal conditions for data-intensive evaluation of primary care in Rio de Janeiro, Brazil.BMC medical informatics and decision making · 2021Article
- Agile Implementation of Innovative End to End Technical Solutions for Respiratory Testing in the COVID-19 Pandemic.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- Defragmenting Heart Failure Care: Medical Records Integration.Heart failure clinics · 2020Review
- Patient Identification Techniques - Approaches, Implications, and Findings.Yearbook of medical informatics · 2020Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study evaluated the degree to which recommendations for demographic data standardization improve patient matching accuracy using real-world datasets. MATERIALS AND
methodsWe used 4 manually reviewed datasets, containing a random selection of matches and nonmatches. Matching datasets included health information exchange (HIE) records, public health registry records, Social Security Death Master File records, and newborn screening records. Standardized fields including last name, telephone number, social security number, date of birth, and address. Matching performance was evaluated using 4 metrics: sensitivity, specificity, positive predictive value, and accuracy.
resultsStandardizing address was independently associated with improved matching sensitivities for both the public health and HIE datasets of approximately 0.6% and 4.5%. Overall accuracy was unchanged for both datasets due to reduced match specificity. We observed no similar impact for address standardization in the death master file dataset. Standardizing last name yielded improved matching sensitivity of 0.6% for the HIE dataset, while overall accuracy remained the same due to a decrease in match specificity. We noted no similar impact for other datasets. Standardizing other individual fields (telephone, date of birth, or social security number) showed no matching improvements. As standardizing address and last name improved matching sensitivity, we examined the combined effect of address and last name standardization, which showed that standardization improved sensitivity from 81.3% to 91.6% for the HIE dataset.
conclusionsData standardization can improve match rates, thus ensuring that patients and clinicians have better data on which to make decisions to enhance care quality and safety.
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