How do you keep patient data governed when using AI?
Start with the actual data flow and legal roles, then limit purpose, access, retention, locations, and subprocessors. Define authorized-clinician approval for clinical decisions and verify security and audit evidence for the deployment.
Put the evidence in context
Review the intended task, source information, human checkpoints and observed results together. A benchmark or sandbox result has a defined scope; it does not establish suitability, approval or performance for every institution.
Read the full explanationCare Workspace connects healthcare work such as appointments, reviewed reporting and follow-up. Micromeet Cowork supports team work with digital employees and reusable Skills, including meeting summaries, decisions and task handoffs.
Read the full explanationPatient-data governance starts before a model is selected. Map what data enters the workflow, where it is processed and stored, who can access it, which party decides each purpose, which subprocessors are involved, and whether data or support access crosses borders. Then apply data minimization, role-based least privilege, purpose limits, authentication, retention and deletion rules, incident handling, and contractual instructions appropriate to that flow.
The AI boundary is equally important. The institution should define what the system may prepare, what an authorized clinician must approve, what non-clinical staff may action, and what must stop or escalate. Audit evidence should cover the high-risk events the institution needs to reconstruct, but logging scope, integrity, export, and retention must be tested for the actual configuration. Data does not necessarily remain in the hospital's own systems, and no single residency or controller-processor model fits every deployment. These points must be confirmed under the applicable law, architecture, and contract before production.