Privacy-preserving record linkage across disparate institutions and datasets to enable a learning health system: The national COVID cohort collaborative (N3C) experience.

Introduction: Research driven by real-world clinical data is increasingly vital to enabling learning health systems, but integrating such data from across disparate health systems is challenging. As part of the NCATS National COVID Cohort Collaborative (N3C), the N3C Data Enclave was established as a centralized repository of deidentified and harmonized COVID-19 patient data from institutions across the US. However, making this data most useful for research requires linking it with information such as mortality data, images, and viral variants.

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.

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.

Establishing a framework for privacy-preserving record linkage among electronic health record and administrative claims databases within PCORnet, the National Patient-Centered Clinical Research Network.

Objective: The aim of this study was to determine whether a secure, privacy-preserving record linkage (PPRL) methodology can be implemented in a scalable manner for use in a large national clinical research network.

Results: We established the governance and technical capacity to support the use of PPRL across the National Patient-Centered Clinical Research Network (PCORnet). As a pilot, four sites used the Datavant software to transform patient personally identifiable information (PII) into de-identified tokens.

A Novel Patient Recruitment Strategy: Patient Selection Directly from the Community through Linkage to Clinical Data.

Objective: This article presents and describes our methods in developing a novel strategy for recruitment of underrepresented, community-based participants, for pragmatic research studies leveraging routinely collected electronic health record (EHR) data.

Methods: We designed a new approach for recruiting eligible patients from the community, while also leveraging affiliated health systems to extract clinical data for community participants. The strategy involves methods for data collection, linkage, and tracking.

Patient-Centered Outcomes Research in Practice: The CAPriCORN Infrastructure.

CAPriCORN, the Chicago Area Patient Centered Outcomes Research Network, is one of the eleven PCORI-funded Clinical Data Research Networks. A collaboration of six academic medical centers, a Chicago public hospital, two VA hospitals and a network of federally qualified health centers, CAPriCORN addresses the needs of a diverse community and overlapping populations. To capture complete medical records without compromising patient privacy and confidentiality, the network created policies and mechanisms for patient consultation, central IRB approval, de-identification, de-duplication, and integration of patient data by study cohort, randomization and sampling, re-identification for consent by providers and patients, and communication with patients to elicit patient-reported outcomes through validated instruments.

Genome-wide identification and molecular characterization of Ole_e_I, Allerg_1 and Allerg_2 domain-containing pollen-allergen-like genes in Oryza sativa.

Pollen allergens play important roles in plant development in addition to their allergenic nature for human. More than 10 groups of pollen allergens have been reported. Among them, Pollen_Ole_e_I (Ole), Pollen_allerg_1 (Allerg1) and Pollen_allerg_2 (Allerg2) domain-containing proteins are the majority of allergens.