Optimization
Learn, improve, and scale continuously
Healthcare doesn’t stand still, and neither should your solutions.
Care delivery is dynamic. Clinical evidence evolves, workflows change, and patient populations shift. Mayo Clinic Platform is designed to support continuous learning, so solutions can be refined, validated, and scaled over time using real-world performance data from live clinical use.
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Capture insight from real-world clinical use
Once solutions are deployed, the real learning begins. Mayo Clinic Platform enables the capture of performance data from live clinical environments to inform ongoing improvement.
- Feedback loops from real-world clinical use
- Insights derived from how solutions perform in practice
- Data to support refinement, optimization, and evolution
- Learning grounded in everyday care delivery
Monitor performance and refine over time
Continuous improvement requires visibility. Mayo Clinic Platform supports performance monitoring and refinement across models, tools, and workflows.
- Ongoing performance monitoring and evaluation
- Model refinement based on real-world data
- Identification of drift, bias, or performance changes
- Support for safe, responsible iteration
Continuously validate with new data
Validation is not a one-time event. Mayo Clinic Platform enables continuous validation as new data becomes available, strengthening confidence in ongoing use and expansion.
- Revalidate solutions as patient populations evolve
- Maintain clinical credibility and trust
- Support regulatory, quality, and governance needs
- Ensure solutions remain aligned with real-world care
Scale across sites, populations, and use cases
As solutions mature, Mayo Clinic Platform supports responsible scaling, extending impact while maintaining quality and consistency.
- Expansion across clinical sites and care settings
- Adaptation to new patient populations
- Support for additional use cases and workflows
- Foundations for system-wide and multi-site impact
Built for learning health systems
This capability reflects a learning health system approach, where every interaction contributes to improvement.
- Closed-loop learning across discovery, build, and deployment
- Continuous feedback between data, insight, and care delivery
- Improvement driven by real-world evidence
- Innovation that evolves alongside care
Create learning systems that get better with every patient and every deployment
- Improve performance over time
- Maintain trust and relevance in changing environments
- Scale solutions responsibly and confidently
- Deliver lasting impact across care delivery