Perceived Risk of Re-Identification in OMOP-CDM Database: A Cross-Sectional Survey

Abstract

Background The advancement of information technology has immensely increased the quality and volume of health data. This has led to an increase in observational study, as well as to the threat of privacy invasion. Recently, a distributed research network based on the common data model (CDM) has emerged, enabling collaborative international medical research without sharing patient-level data. Although the CDM database for each institution is built inside a firewall, the risk of re-identification requires management. Hence, this study aims to elucidate the perceptions CDM users have towards CDM and risk management for re-identification. Methods The survey, targeted to answer specific in-depth questions on CDM, was conducted from October to November 2020. We targeted well-experienced researchers who actively use CDM. Basic statistics (total number and percent) were computed for all covariates. Results There were 33 valid respondents. Of these, 43.8% suggested additional anonymization was unnecessary beyond, “minimum cell count” policy, which obscures a cell with a value lower than certain number (usually 5) in shared results to minimize the liability of re-identification due to rare conditions. During extract-transform-load processes, 81.8% of respondents assumed structured data is under control from the risk of re-identification. However, respondents noted that date of birth and death were highly re-identifiable information. The majority of respondents (n = 22, 66.7%) conceded the possibility of identifier-contained unstructured data in the NOTE table. Conclusion Overall, CDM users generally attributed high reliability for privacy protection to the intrinsic nature of CDM. There was little demand for additional de-identification methods. However, unstructured data in the CDM were suspected to have risks. The necessity for a coordinating consortium to define and manage the re-identification risk of CDM was urged.

Exploratory pharmacovigilance with machine learning in big patient data: A focused scoping review

“…Collating data from multiple sources is non-trivial and comes with many issues and challenges, including legal and ethical ones, when patient data flow between countries, inevitably increasing the risk of leaks or even misuse. 38,40 Federated analysis with common data models is one remedy that enjoys at least two appealing characteristics. First, it delinks idea generation from data access, which can bring more minds and ideas to the table, ultimately improving the questions asked and research conducted to answer them.…”

“…Interestingly, only a single study used a standardized format for the data, and none of the studies used data from multiple healthcare systems (e.g., different countries). Collating data from multiple sources is non‐trivial and comes with many issues and challenges, including legal and ethical ones, when patient data flow between countries, inevitably increasing the risk of leaks or even misuse 38,40 …”