Suchergebnisse

ABSTRACT
ObjectiveThe Ontario Brain Institute (OBI) has begun to catalyze scientific discovery in the field of neuroscience through its' large-scale informatics platform, known as Brain-CODE (Centre for Ontario Data Exploration). Brain-CODE manages the acquisition, storage, processing, and analytics of multidimensional data collected from patients with a variety of brain disorders. Our vision is for the platform to act as an informatics catalyst; encouraging multidisciplinary research collaboration, data integration, and innovation in neuroscience research. Brain-CODE's infrastructure was designed with best-practice privacy strategies built at the forefront to enable secure data capture of sensitive patient information in a manner that abides by government legislation while fostering data sharing and linking opportunities.
ApproachPrivacy and security features have been incorporated into the very foundation of Brain-CODE's comprehensive guidelines, which are reinforced by our state-of-the-art approaches to keep patient data safe. To ensure clarity for study participants, we have developed standard consent language outlining how sensitive patient data will be collected, entered, de-identified, and shared using Brain-CODE. Moreover, our tiered approach to data accessibility enables the storage of encrypted Ontario Health Card Numbers as well as other patient information, secure long-term storage of de-identified data, and data sharing opportunities by request from third parties following risk-based analysis re-identification techniques. OBI has also established a comprehensive Information Security Policy and Informatics Governance Policies, as well as a carried out a Privacy Impact Assessment and Threat Risk Assessment for Brain-CODE.
ResultsBrain-CODE is proudly named a "Privacy by Design" Ambassador by the Office of the Information and Privacy Commissioner of Ontario, Canada. Moreover, approximately 200 neuroscience researchers and 35 institutions from across Canada have adopted our standard consent language to enable secure data sharing within and across neurological disorders as well as linkage opportunities with national and international databases in a secure environment.
ConclusionOBI's rigorous approach to data sharing in the field of neuroscience maintains the accessibility of research data for big discoveries without compromising patient privacy and security. We believe that Brain-CODE is a powerful and advantageous tool; moving neuroscience research from independent silos to an integrative system approach for improving patient health. OBI's vision for improved brain health for patients living with neurological disorders paired with Brain-CODE's best-practice strategies in privacy protection of patient data offer a novel and innovative approach to "big data" initiatives aimed towards improving public health and society world-wide.

IntroductionThe Ontario Brain Institute has developed Brain-CODE, an informatics platform, to support the acquisition, storage, management and analysis of multi-modal data. The standardized research data within Brain-CODE spans several brain disorders, allowing for integrative analyses, while also providing the opportunity to leverage existing clinical administrative data holdings through external linkages.
Objectives and ApproachWithin Ontario, the majority of individuals who access the healthcare system have a unique identifier, the Ontario Health Insurance Plan (OHIP) number. The OHIP number can facilitate linkages with administrative data holdings, such as those at the Institute for Clinical Evaluative Sciences (ICES). Given that OBI is not permitted under Ontario's privacy legislation to hold OHIP numbers, identifiers for consented participants are encrypted using a public key mechanism upon entry into Brain-CODE, where the private key is inaccessible. To facilitate linkages involving OHIP numbers between Brain-CODE and ICES, Brain-CODE Link software was co-developed by members of the Indoc Consortium.
ResultsBrain-CODE Link allows a deterministic linkage between encrypted identifiers (OHIP numbers), without revealing participant identity. The same homomorphic encryption algorithm applied to identifiers upon entry to Brain-CODE, is applied to relevant identifiers within ICES data holdings. Encrypted identifiers from Brain-CODE are securely transferred to ICES, where a comparison computation calculates differences between the encrypted sets. These differences are sent to a semi-trusted third party, who has no access to the original data, to decrypt the differences using the private key. A zero difference indicates a set of matching identifiers. One of the main challenges during testing and development of Brain-CODE Link was ensuring the software was capable of scaling to a population level, performing a large number of comparisons, in a computationally efficient manner.
Conclusion/ImplicationsOngoing pilot projects within the areas of epilepsy, neurodevelopment disorders, and neurodegeneration will be the first examples of linkages between Brain-CODE and ICES. Brain-CODE Link has successfully performed several billion test comparisons, indicating its suitability to function as a scalable privacy preserving record linkage to support comprehensive analyses.

IntroductionResearch data combined with administrative data provides a robust resource capable of answering unique research questions. However, in cases where personal health data are encrypted, due to ethics requirements or institutional restrictions, traditional methods of deterministic and probabilistic record linkages are not feasible. Instead, privacy-preserving record linkages must be used to protect patients' personal data during data linkage.
ObjectivesTo determine the feasibility and validity of a deterministic privacy preserving data linkage protocol using homomorphically encrypted data.
MethodsFeasibility was measured by the number of records that successfully matched via direct identifiers. Validity was measured by the number of records that matched with multiple indirect identifiers. The threshold for feasibility and validity were both set at 95%. The datasets shared a single, direct identifier (health card number) and multiple indirect identifiers (sex and date of birth). Direct identifiers were encrypted in both datasets and then transferred to a third-party server capable of linking the encrypted identifiers without decrypting individual records. Once linked, the study team used indirect identifiers to verify the accuracy of the linkage in the final dataset.
ResultsWith a combination of manual and automated data transfer in a sample of 8,128 individuals, the privacy-preserving data linkage took 36 days to match to a population sample of over 3.2 million records. 99.9% of the records were successfully matched with direct identifiers, and 99.8% successfully matched with multiple indirect identifiers. We deemed the linkage both feasible and valid.
ConclusionsAs combining administrative and research data becomes increasingly common, it is imperative to understand options for linking data when direct linkage is not feasible. The current linkage process ensured the privacy and security of patient data and improved data quality. While the initial implementations required significant computational and human resources, increased automation keeps the requirements within feasible bounds.