Evaluation of the completeness of a medical record dataset linked to administrative claims through tokenization in patients with Hemophilia B
- 1Pfizer Inc, United States of America
- 2Pfizer Inc, Bucharest, Romania
Abstract
Real-world evidence (RWE) is increasingly used to inform healthcare delivery and expedite access to new therapies, but its value depends on the quality and completeness of information in real-world data (RWD) sources. Tokenization technology enables the integration of patient data from multiple sources, facilitating the development of a comprehensive overview of the patient journey. This study evaluated the accuracy of linked electronic health record (EHR) and insurance claims data for 93 patients with hemophilia B (HB). Tokenization enables secure linkage of patient data from multiple sources by replacing personal identifiers with encrypted tokens, allowing integration while preserving privacy. We assessed the resulting linkage accuracy by comparing hemophilia-related documentation (proportion of patients with matches for hemophilia medication and laboratory results) and patient location (ZIP code). We also evaluated the completeness of these two linked datasets of additional select variables. Tokenization achieved up to 98% linkage accuracy. Completeness varied by data source: EHRs captured 89–100% of key clinical variables, which include disease severity and laboratory values, while claims data ranged from 44–100%, which include healthcare utilization and cost. Full matches were observed in 12% of medication records and 2% of factor laboratory results, highlighting availability in cross-source completeness. Tokenization proved feasible and reliable in a rare disease setting. The small cohort size and rarity of HB enabled manual validation and reduced the risk of erroneous linkages. Findings support the use of tokenization to create composite datasets that integrate clinical and utilization information to provide richer data, thus enhancing the quality of RWE studies in rare diseases. The study’s findings are also limited by the small sample size, which may affect generalizability. However, this study provides a foundational description and provides valuable methodological insights into tokenization accuracy and data completeness across EHR and claims datasets for RWE generation.
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BibTeX
@article{kane2026evaluation,
title = {{Evaluation of the completeness of a medical record dataset linked to administrative claims through tokenization in patients with Hemophilia B}},
author = {Anna Stachel Kane and Diana Madalina Stan and Darren Jeng and Yong Chen},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0355051},
url = {https://doi.org/10.1371/journal.pone.0355051},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Evaluation of the completeness of a medical record dataset linked to administrative claims through tokenization in patients with Hemophilia B
AU - Anna Stachel Kane
AU - Diana Madalina Stan
AU - Darren Jeng
AU - Yong Chen
PY - 2026
DA - 2026/09/08
JO - PLOS One
DO - 10.1371/journal.pone.0355051
UR - https://doi.org/10.1371/journal.pone.0355051
AB - Real-world evidence (RWE) is increasingly used to inform healthcare delivery and expedite access to new therapies, but its value depends on the quality and completeness of information in real-world data (RWD) sources. Tokenization technology enables the integration of patient data from multiple sources, facilitating the development of a comprehensive overview of the patient journey. This study evaluated the accuracy of linked electronic health record (EHR) and insurance claims data for 93 patients with hemophilia B (HB). Tokenization enables secure linkage of patient data from multiple sources by replacing personal identifiers with encrypted tokens, allowing integration while preserving privacy. We assessed the resulting linkage accuracy by comparing hemophilia-related documentation (proportion of patients with matches for hemophilia medication and laboratory results) and patient location (ZIP code). We also evaluated the completeness of these two linked datasets of additional select variables. Tokenization achieved up to 98% linkage accuracy. Completeness varied by data source: EHRs captured 89–100% of key clinical variables, which include disease severity and laboratory values, while claims data ranged from 44–100%, which include healthcare utilization and cost. Full matches were observed in 12% of medication records and 2% of factor laboratory results, highlighting availability in cross-source completeness. Tokenization proved feasible and reliable in a rare disease setting. The small cohort size and rarity of HB enabled manual validation and reduced the risk of erroneous linkages. Findings support the use of tokenization to create composite datasets that integrate clinical and utilization information to provide richer data, thus enhancing the quality of RWE studies in rare diseases. The study’s findings are also limited by the small sample size, which may affect generalizability. However, this study provides a foundational description and provides valuable methodological insights into tokenization accuracy and data completeness across EHR and claims datasets for RWE generation.
SN - 1932-6203
ER -