Design and development of a secure and patient-controlled system to share healthcare data for research
Notice bibliographique
Résumé
Nowadays, almost all hospitals and clinics in developed countries store patients' personal medical data in digital format taking into account appropriate security measures and in compliance with applicable legal requirements. Due to the rapid development of so-called Big Data research tools, such as artificial intelligence and machine learning, these data, if accessible, have the potential to benefit medical research in the search for new medications and improved treatment approaches. However, in Quebec as is the case in many jurisdictions, access to patient data is difficult for two main reasons. First, patient consent is required, and second, patients typically have their medical data spread across multiple source systems in multiple institutions, which makes it difficult to piece together their complete medical history. Moreover, in most contexts patients do not know who has access to their data and they cannot control access rights.Opal (opalmedapps.com) is a patient portal developed at the Research Institute of the McGill University Health Centre (RI-MUHC) that provides patients with access to some of their medical data at the MUHC. Opal's long-term roadmap calls for carefully developed infrastructure to link multiple hospitals simultaneously so that patients will be able to access their medical records that are stored in different institutions. However, the originally-designed infrastructure does not allow patients to control access to their data and contribute them for research. Therefore, in this thesis project, we explored the design and development of a new infrastructure for secure and user-controlled personal medical data sharing. Initially, we studied the architecture and workflow of the existing Opal platform. Then, we analyzed various modern decentralized tools and technologies for storing and controlling personal data. Based on the analysis and knowledge acquired, we designed and implemented a novel prototype system for controlling and sharing personal medical data with researchers using a blockchain-based infrastructure. The blockchain is a tamper-proof mechanism for storing data in an immutable way by using cryptographic and network technologies. As described in this thesis, our novel data-sharing infrastructure is designed to record (1) the permissions that each patient gives to research study personnel to access their data, (2) the data-access privileges that a "public trust" committee provides to researchers to access shared data, and (3) the data access logs of researchers who access the shared data.Thus, patients can contribute their data to a specific research study by providing electronic consent in a patient portal such as Opal and by specifying which of their data records they wish to share. The system is designed to allow patients to withdraw their consents at any time and stop further sharing of their data if they change their minds. Also, as all data access requests are automatically recorded on the blockchain, each patient has the ability to know who accessed their data and when
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».