This series outlines the Seven Informatics Challenges for Clinical Data Registries, the questions you should ask when addressing research data management, and how our RexStudy platform is engineered to ensure your research teams generate high-quality, reliable, and statistically sound data.
Informatics Challenge #5: Security and Privacy
Both the regulatory environment and practical privacy concerns create the need for privilege assignment at the entity, record, and attribute level. Privileging and security must be deeply embedded in the system architecture and cannot depend on the user interface layer or on any design pattern that is vulnerable to inadvertent exposure of more data than necessary. Privileging models must be configurable for each install, with granular user-specific permissions calculated and enforced server-side.
Questions you should ask before building your CDR
Does the system support sufficiently granular privileging to meet regulatory requirements and privacy concerns?
Is the privileging model customizable to local environments?
Can new roles and privilege levels be added without rework of existing workflows?
Can the vendor provide a reasonable set of privacy and security policies that match your institutional requirements?
How will the platform integrate with existing authentication systems and user management processes?
RexStudy provides optimal Security and Privacy supporting Regulatory Compliance
RexStudy addresses the need for granular privileging to meet regulatory requirements and privacy concerns, and makes it easy to setup and manage different privileging rules for research operations staff and downstream data consumers. Access to database fields, participant records, and even entire workflows can be controlled by Administrators. RexStudy’s user management model supports cascading privileges and robust authorization controls, and can integrate with institutional Single Sign-On (SSO) to create a seamless experience for end users.
Don’t miss the first four parts of this series:
Part 1: Metadata Variety
Part 2: Schema Volatility
Part 3: Workflow Variability
Part 4: Complex Data Provenance
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