Implementing a hash-based privacy-preserving record linkage tool in the OneFlorida clinical research network
© The Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association..
OBJECTIVE: To implement an open-source tool that performs deterministic privacy-preserving record linkage (RL) in a real-world setting within a large research network.
MATERIALS AND METHODS: We learned 2 efficient deterministic linkage rules using publicly available voter registration data. We then validated the 2 rules' performance with 2 manually curated gold-standard datasets linking electronic health records and claims data from 2 sources. We developed an open-source Python-based tool-OneFL Deduper-that (1) creates seeded hash codes of combinations of patients' quasi-identifiers using a cryptographic one-way hash function to achieve privacy protection and (2) links and deduplicates patient records using a central broker through matching of hash codes with a high precision and reasonable recall.
RESULTS: We deployed the OneFl Deduper (https://github.com/ufbmi/onefl-deduper) in the OneFlorida, a state-based clinical research network as part of the national Patient-Centered Clinical Research Network (PCORnet). Using the gold-standard datasets, we achieved a precision of 97.25∼99.7% and a recall of 75.5%. With the tool, we deduplicated ∼3.5 million (out of ∼15 million) records down to 1.7 million unique patients across 6 health care partners and the Florida Medicaid program. We demonstrated the benefits of RL through examining different disease profiles of the linked cohorts.
CONCLUSIONS: Many factors including privacy risk considerations, policies and regulations, data availability and quality, and computing resources, can impact how a RL solution is constructed in a real-world setting. Nevertheless, RL is a significant task in improving the data quality in a network so that we can draw reliable scientific discoveries from these massive data resources.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2019 |
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| Erschienen: |
2019 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:2 |
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| Enthalten in: |
JAMIA open - 2(2019), 4 vom: 31. Dez., Seite 562-569 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Bian, Jiang [VerfasserIn] |
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| Links: |
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| Themen: |
Clinical research network |
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| Anmerkungen: |
Date Revised 12.04.2022 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1093/jamiaopen/ooz050 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM306203782 |
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| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © The Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association. | ||
| 520 | |a OBJECTIVE: To implement an open-source tool that performs deterministic privacy-preserving record linkage (RL) in a real-world setting within a large research network | ||
| 520 | |a MATERIALS AND METHODS: We learned 2 efficient deterministic linkage rules using publicly available voter registration data. We then validated the 2 rules' performance with 2 manually curated gold-standard datasets linking electronic health records and claims data from 2 sources. We developed an open-source Python-based tool-OneFL Deduper-that (1) creates seeded hash codes of combinations of patients' quasi-identifiers using a cryptographic one-way hash function to achieve privacy protection and (2) links and deduplicates patient records using a central broker through matching of hash codes with a high precision and reasonable recall | ||
| 520 | |a RESULTS: We deployed the OneFl Deduper (https://github.com/ufbmi/onefl-deduper) in the OneFlorida, a state-based clinical research network as part of the national Patient-Centered Clinical Research Network (PCORnet). Using the gold-standard datasets, we achieved a precision of 97.25∼99.7% and a recall of 75.5%. With the tool, we deduplicated ∼3.5 million (out of ∼15 million) records down to 1.7 million unique patients across 6 health care partners and the Florida Medicaid program. We demonstrated the benefits of RL through examining different disease profiles of the linked cohorts | ||
| 520 | |a CONCLUSIONS: Many factors including privacy risk considerations, policies and regulations, data availability and quality, and computing resources, can impact how a RL solution is constructed in a real-world setting. Nevertheless, RL is a significant task in improving the data quality in a network so that we can draw reliable scientific discoveries from these massive data resources | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a PCORnet | |
| 650 | 4 | |a clinical research network | |
| 650 | 4 | |a privacy-preserving record linkage | |
| 700 | 1 | |a Loiacono, Alexander |e verfasserin |4 aut | |
| 700 | 1 | |a Sura, Andrei |e verfasserin |4 aut | |
| 700 | 1 | |a Mendoza Viramontes, Tonatiuh |e verfasserin |4 aut | |
| 700 | 1 | |a Lipori, Gloria |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Yi |e verfasserin |4 aut | |
| 700 | 1 | |a Shenkman, Elizabeth |e verfasserin |4 aut | |
| 700 | 1 | |a Hogan, William |e verfasserin |4 aut | |
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