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A Practical Guide to Phylogenetics for Nonexperts

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Comparing record linkage software programs and algorithms using real-world data.

Alan F Karr¹, Matthew T Taylor^2,3, Suzanne L West¹

¹RTI International, Research Triangle Park, NC, United States of America.

|September 25, 2019

Summary

This summary is machine-generated.

Comparing four linkage software packages, this study found exact matching more consistent than inexact matching for linking electronic health records (EHRs). Blocking on date of birth (DOB) improved positive predictive value (PPV).

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Area of Science:

Health Informatics
Data Linkage
Biostatistics

Background:

Linking medical databases like insurer claims and electronic health records (EHRs) is common but software behavior is understudied.
Understanding linkage quality factors is crucial for accurate data integration.

Purpose of the Study:

To compare the performance of four nonproprietary linkage software packages.
To evaluate the impact of different algorithms, blocking variables, string matching methods, and decision rules on linkage quality.

Main Methods:

Linked inpatient and outpatient EHR data using name, gender, and date of birth (DOB).
Evaluated DOB and year of birth (YOB) as blocking variables, using exact and inexact string matching.
Compared assigned weights against a gold standard (medical record number) and assessed sensitivity and positive predictive value (PPV).

Main Results:

Exact matching demonstrated high sensitivity (90.48%) and specificity (99.78%), with consistent performance.
Inexact matching showed variable sensitivity (0.04-93.36%) and PPV (86.67-97.35%).
Blocking on DOB yielded higher PPV for top-ranked record pairs; an ensemble approach modestly improved accuracy.

Conclusions:

Few differences in record pair rankings were observed across the four linkage packages.
Exact string matching is more reliable than inexact matching for EHR linkage.
Blocking on DOB and ensemble methods can enhance linkage accuracy in specific scenarios.