Insights from Adopting a Data Commons Approach for Large-scale Observational Cohort Studies: The California Teachers Study.

BACKGROUND: Large-scale cancer epidemiology cohorts (CEC) have successfully collected, analyzed, and shared patient-reported data for years. CECs increasingly need to make their data more findable, accessible, interoperable, and reusable, or FAIR. How CECs should approach this transformation is unclear. METHODS: The California Teachers Study (CTS) is an observational CEC of 133,477 participants followed since 1995-1996. In 2014, we began updating our data storage, management, analysis, and sharing strategy. With the San Diego Supercomputer Center, we deployed a new infrastructure based on a data warehouse to integrate and manage data and a secure and shared workspace with documentation, software, and analytic tools that facilitate collaboration and accelerate analyses. RESULTS: Our new CTS infrastructure includes a data warehouse and data marts, which are focused subsets from the data warehouse designed for efficiency. The secure CTS workspace utilizes a remote desktop service that operates within a Health Insurance Portability and Accountability Act (HIPAA)- and Federal Information Security Management Act (FISMA)-compliant platform. Our infrastructure offers broad access to CTS data, includes statistical analysis and data visualization software and tools, flexibly manages other key data activities (e.g., cleaning, updates, and data sharing), and will continue to evolve to advance FAIR principles. CONCLUSIONS: Our scalable infrastructure provides the security, authorization, data model, metadata, and analytic tools needed to manage, share, and analyze CTS data in ways that are consistent with the NCI's Cancer Research Data Commons Framework. IMPACT: The CTS's implementation of new infrastructure in an ongoing CEC demonstrates how population sciences can explore and embrace new cloud-based and analytics infrastructure to accelerate cancer research and translation.See all articles in this CEBP Focus section, "Modernizing Population Science."

Participative consent: beyond broad and dynamic consent for health big data resources / Consentimiento participativo: más allá de los consentimientos genérico y dinámico para las iniciativas de salud de datos masivos

Nowadays, we participate in a knowledge-based society where a large part of our lives has been digitalised. Products, services, and professional, academic and personal activities have turned digital and with that change, it became possible to record and collect data about (almost) everything. Through research and innovation, this information has offered great opportunities to improve health and the health systems it depends on. The present study is interested in initiatives that use this kind of personal information, specifically, big data initiatives that serve as resources to support research and innovation, such as 'biobanks' or 'genome projects'. They will be further referred in this paper as 'health big data resources' or 'initiatives'. The main goal of this work is to identify a model of consent that would allow these initiatives to align with EU values, respect fundamental rights and meet the expectations of participants, data subjects and society. In the EU, consent responds to the different socioeconomic contexts, the national and EU policy (including the General Data Protection Regulation), and to the abandonment of the idea of absolute anonymisation. From the point of view of citizens, consent should respond to their expectations regarding health, well-being, health systems, science and technology. Because it is shaped by multiple factors, there are different models of consent used in health big data resources and there is a constant interest in improving them. The current tendency is that consent has shifted away from the model used in traditional clinical or biomedical research. Instead, big data resources are adopting models of broad or assumed consent. This analysis supports the current general idea that the traditional concept of informed consent is insufficient for supporting health big data resources. Moreover, it stresses that it is insufficient to simply move towards models of broad or assumed consent. The main purpose of this analysis is to show that health big data initiatives should aim towards a model of consent based on a representative governance system. Through this system, participants, data subjects and society will be able to influence the initiative's operations, communicate their will and retain a certain level of control over their personal data and the activities of the resource. In other words, it is necessary to strive for mechanisms to extend the traditional concept of consent through governance which would enable the exercise of autonomy of those implicated. Therefore, consent must become not only modifiable (in the sense that it should be allowed to change through time), but participative. For these models of consent to be successful, they must be grounded in a solid relationship of trust with participants, data subjects and society in general. Consequently, it is necessary to establish a permanent process of dialogue and public engagement with the goal of informing, shaping and directing the governance systems of big data resources for health

Hoy en día participamos de una sociedad del conocimiento donde gran parte de nuestras vidas ha sido digitalizada. Esta transformación ha afectado productos y servicios y ha cambiado nuestras actividades profesionales, académicas y personales. La magnitud de la digitalización de nuestra sociedad ha permitido generar y almacenar datos de (casi) todo. A través de la investigación e innovación, esta información nos brinda grandes oportunidades para mejorar nuestra salud y los sistemas de salud de los que depende. El presente estudio está interesado en los proyectos de salud de datos masivos que utilizan este tipo de información personal y que son establecidos como facilitadores al servicio de la investigación e innovación. Puesto que llevan diferentes nombres, como 'biobancos' o 'proyectos genoma', se les referirá en este artículo como 'proyectos de salud de datos masivos' o 'iniciativas'. El objetivo principal de este estudio es identificar un modelo de consentimiento que le permita a estos proyectos funcionar de acuerdo con los valores de la UE, respetar los derechos humanos fundamentales y satisfacer las expectativas de los participantes, de los titulares de los datos y de la sociedad en general. Actualmente en la UE, los modelos de consentimiento utilizados para estas iniciativas responden a la transformación de los modelos socioeconómicos, a los cambios en las políticas nacionales y de la UE (incluyendo el nuevo Reglamento General de Protección de Datos), a los avances científicos y tecnológicos y al abandono de la idea de anonimización absoluta. Desde el punto de vista de los ciudadanos, el consentimiento debe responder a sus expectativas con respecto a su salud, bienestar, servicios sanitarios, ciencia y tecnología. Puesto que el consentimiento se ve influenciado por múltiples factores, diferentes modelos se proponen para proyectos de datos masivos en salud y hay un interés constante en mejorarlos. Hoy en día, los modelos de consentimiento utilizados en proyectos de salud de datos masivos se han alejado del modelo tradicionalmente utilizado en la investigación clínica o biomédica. En su lugar se adoptan modelos de consentimiento genérico o presunto

Una aproximación al big data y al blockchain sanitario y su implicación en la protección de datos personales / An approach to big data and health blockchain and their involvement in the protection of personal data

A mayor flujo de información, mayor será el impacto en el derecho fundamental de protección de datos personales máxime en el sector de la salud digital. El binomio de big data y blockchain propicia un "ecosistema" donde actores y participantes (por ejemplo, hospitales, médicos, investigadores, universidades, aseguradoras, farmaceúticas, etc..) interactúan e intercambian información con seguridad y legalidad

The greater the flow of information, the greater the impact on the fundamental right to protection of personal data, especially in the digital health sector. The binomial of big data and blockchain propitiates an "ecosystem" where actors and participants (for example, hospitals, doctors, researchers, universities, insurance companies, pharmaceuticals, etc.) interact and exchange information with security and legality