Publications
1.
Marsolo, Keith; Kiernan, Daniel; Toh, Sengwee; Phua, Jasmin; Louzao, Darcy; Haynes, Kevin; Weiner, Mark G.; Angulo, Francisco; Bailey, Charles; Bian, Jiang; Fort, Daniel; Grannis, Shaun J.; Krishnamurthy, Ashok Kumar; Nair, Vinit; Rivera, Pedro; Silverstein, Jonathan; Zirkle, Maryan; Carton, Thomas W.
In: Journal of the American Medical Informatics Association, vol. 30, iss. 3, pp. 447-455, 2023.
Abstract | Links | BibTeX | Tags: distributed research networks, privacy-preserving record linkage, real-world data
@article{nokey,
title = {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},
author = {Keith Marsolo and Daniel Kiernan and Sengwee Toh and Jasmin Phua and Darcy Louzao and Kevin Haynes and Mark G. Weiner and Francisco Angulo and Charles Bailey and Jiang Bian and Daniel Fort and Shaun J. Grannis and Ashok Kumar Krishnamurthy and Vinit Nair and Pedro Rivera and Jonathan Silverstein and Maryan Zirkle and Thomas W. Carton},
doi = { https://doi.org/10.1093/jamia/ocac229},
year = {2023},
date = {2023-02-16},
urldate = {2023-02-16},
journal = {Journal of the American Medical Informatics Association},
volume = {30},
issue = {3},
pages = {447-455},
abstract = {Objective: This article describes the implementation of a privacy-preserving record linkage (PPRL) solution across PCORnet®, the National Patient-Centered Clinical Research Network.
Material and methods: Using a PPRL solution from Datavant, we quantified the degree of patient overlap across the network and report a de-duplicated analysis of the demographic and clinical characteristics of the PCORnet population.
Results: There were ∼170M patient records across the responding Network Partners, with ∼138M (81%) of those corresponding to a unique patient. 82.1% of patients were found in a single partner and 14.7% were in 2. The percentage overlap between Partners ranged between 0% and 80% with a median of 0%. Linking patients' electronic health records with claims increased disease prevalence in every clinical characteristic, ranging between 63% and 173%.
Discussion: The overlap between Partners was variable and depended on timeframe. However, patient data linkage changed the prevalence profile of the PCORnet patient population.
Conclusions: This project was one of the largest linkage efforts of its kind and demonstrates the potential value of record linkage. Linkage between Partners may be most useful in cases where there is geographic proximity between Partners, an expectation that potential linkage Partners will be able to fill gaps in data, or a longer study timeframe.},
keywords = {distributed research networks, privacy-preserving record linkage, real-world data},
pubstate = {published},
tppubtype = {article}
}
Objective: This article describes the implementation of a privacy-preserving record linkage (PPRL) solution across PCORnet®, the National Patient-Centered Clinical Research Network.
Material and methods: Using a PPRL solution from Datavant, we quantified the degree of patient overlap across the network and report a de-duplicated analysis of the demographic and clinical characteristics of the PCORnet population.
Results: There were ∼170M patient records across the responding Network Partners, with ∼138M (81%) of those corresponding to a unique patient. 82.1% of patients were found in a single partner and 14.7% were in 2. The percentage overlap between Partners ranged between 0% and 80% with a median of 0%. Linking patients' electronic health records with claims increased disease prevalence in every clinical characteristic, ranging between 63% and 173%.
Discussion: The overlap between Partners was variable and depended on timeframe. However, patient data linkage changed the prevalence profile of the PCORnet patient population.
Conclusions: This project was one of the largest linkage efforts of its kind and demonstrates the potential value of record linkage. Linkage between Partners may be most useful in cases where there is geographic proximity between Partners, an expectation that potential linkage Partners will be able to fill gaps in data, or a longer study timeframe.
Material and methods: Using a PPRL solution from Datavant, we quantified the degree of patient overlap across the network and report a de-duplicated analysis of the demographic and clinical characteristics of the PCORnet population.
Results: There were ∼170M patient records across the responding Network Partners, with ∼138M (81%) of those corresponding to a unique patient. 82.1% of patients were found in a single partner and 14.7% were in 2. The percentage overlap between Partners ranged between 0% and 80% with a median of 0%. Linking patients' electronic health records with claims increased disease prevalence in every clinical characteristic, ranging between 63% and 173%.
Discussion: The overlap between Partners was variable and depended on timeframe. However, patient data linkage changed the prevalence profile of the PCORnet patient population.
Conclusions: This project was one of the largest linkage efforts of its kind and demonstrates the potential value of record linkage. Linkage between Partners may be most useful in cases where there is geographic proximity between Partners, an expectation that potential linkage Partners will be able to fill gaps in data, or a longer study timeframe.