ArticlePloS one2019
Comparing record linkage software programs and algorithms using real-world data.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Enhancing PRAMS with Administrative Data Linkages: A Generalized Framework.Maternal and child health journal · 2026Article
- Bias in medical AI: Implications for clinical decision-making.PLOS digital health · 2024Review
- Person-centered hospital discharge data: Essential existing infrastructure to enhance public health surveillance of maternal substance use disorders in the midst of a national maternal overdose crisis.Annals of epidemiology · 2024Article
- Validity of ICD-10-based algorithms to identify patients with influenza in inpatient and outpatient settings.Pharmacoepidemiology and drug safety · 2024Article
- Novel linkage approach to join community-acquired and national data.BMC medical research methodology · 2024Article
- De-identified Bayesian personal identity matching for privacy-preserving record linkage despite errors: development and validation.BMC medical informatics and decision making · 2023Article
- Assessing the impact of privacy-preserving record linkage on record overlap and patient demographic and clinical characteristics in PCORnet®, the National Patient-Centered Clinical Research Network.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Relationship between patient experience and hospital readmission: system-level survey with deterministic data linkage method.BMC medical research methodology · 2022Article
- Fast prototyping of a local fuzzy search system for decision support and retraining of hospital staff during pandemic.Health information science and systems · 2021Article
- Article
- Demographic and Clinical Outcomes of Brazilian Patients With Stage III or IV Non-Small-Cell Lung Cancer: Real-World Evidence Study on the Basis of Deterministic Linkage Approach.JCO global oncology · 2021Article
- A new hybrid record linkage process to make epidemiological databases interoperable: application to the GEMO and GENEPSO studies involving BRCA1 and BRCA2 mutation carriers.BMC medical research methodology · 2021Article
- An Introduction to Probabilistic Record Linkage with a Focus on Linkage Processing for WTC Registries.International journal of environmental research and public health · 2020Review
- Automated opportunistic osteoporotic fracture risk assessment using computed tomography scans to aid in FRAX underutilization.Nature medicine · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Linkage of medical databases, including insurer claims and electronic health records (EHRs), is increasingly common. However, few studies have investigated the behavior and output of linkage software. To determine how linkage quality is affected by different algorithms, blocking variables, methods for string matching and weight determination, and decision rules, we compared the performance of 4 nonproprietary linkage software packages linking patient identifiers from noninteroperable inpatient and outpatient EHRs. We linked datasets using first and last name, gender, and date of birth (DOB). We evaluated DOB and year of birth (YOB) as blocking variables and used exact and inexact matching methods. We compared the weights assigned to record pairs and evaluated how matching weights corresponded to a gold standard, medical record number. Deduplicated datasets contained 69,523 inpatient and 176,154 outpatient records, respectively. Linkage runs blocking on DOB produced weights ranging in number from 8 for exact matching to 64,273 for inexact matching. Linkage runs blocking on YOB produced 8 to 916,806 weights. Exact matching matched record pairs with identical test characteristics (sensitivity 90.48%, specificity 99.78%) for the highest ranked group, but algorithms differentially prioritized certain variables. Inexact matching behaved more variably, leading to dramatic differences in sensitivity (range 0.04-93.36%) and positive predictive value (PPV) (range 86.67-97.35%), even for the most highly ranked record pairs. Blocking on DOB led to higher PPV of highly ranked record pairs. An ensemble approach based on averaging scaled matching weights led to modestly improved accuracy. In summary, we found few differences in the rankings of record pairs with the highest matching weights across 4 linkage packages. Performance was more consistent for exact string matching than for inexact string matching. Most methods and software packages performed similarly when comparing matching accuracy with the gold standard. In some settings, an ensemble matching approach may outperform individual linkage algorithms.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.