Intended-use controls
Define the product, users, patient population, clinical purpose and operating boundaries associated with a regulated workflow.
The Regulatory Runtime
Infrastructure for regulated digital health.
Building Software as a Medical Device requires more than an algorithm. It requires a controlled environment around the algorithm.
The Regulatory Runtime
Infrastructure for regulated digital health.
GoodCare’s Regulatory Runtime is designed to help SaMD and AI-enabled medical-device developers establish the operational foundation required to develop, deploy and manage regulated healthcare software.
It combines identity, consent, data controls, model governance, clinical oversight, evidence collection and lifecycle monitoring in one environment. So teams can focus on the product, without rebuilding the regulatory infrastructure beneath it.
Lifecycle controls
From development to real-world use.
Controlled data access
Manage which information a product may access, for what purpose and under which authorization.
Version and release governance
Maintain traceability across software versions, models, configurations and deployed environments.
Evidence generation
Capture the operational and clinical evidence needed to evaluate safety, performance and effectiveness.
Change management
Support documented review and control of software, model and workflow changes throughout the product lifecycle.
Human factors and oversight
Configure where qualified professionals review, approve, intervene or receive escalations.
Post-market monitoring
Monitor performance, incidents, drift, complaints and other defined real-world signals.
Audit-ready records
Preserve structured records of system activity, access, changes and defined regulatory events.
Built for responsible machine learning.
Its governance must be designed to change with it.
AI-enabled medical software may change over time.
GoodCare helps developers create a controlled lifecycle for machine-learning-enabled medical devices, from training and validation through deployment, monitoring and approved updates.
Defined model versions and approved configurations
Dataset and provenance records
Performance monitoring across relevant populations
Bias and subgroup evaluation
Drift and anomaly detection
Documented change assessment
Predetermined change-control workflows
Transparency information for users and clinicians
Escalation and rollback procedures
Ongoing safety and effectiveness evidence