AI in Health Care
Optimizing AI Systems in Health Care
Ensuring Safety, Quality, and Sustained Benefit with Actionable Monitoring Frameworks.
Key Benefits of Proactive AI Monitoring
Our framework drives tangible improvements in system reliability and operational efficiency.
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Active Deployments Monitored
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Core Monitoring Principles
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Avg. Reduction in Unplanned Downtime
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
System integrity monitoring ensures that the model functions correctly and produces an output (i.e., it 'runs'). Key considerations include inference-time errors or warnings, connectivity, and the integrity of data pipelines to and from the model. Metrics in this category measure uptime, latency, errors, and outages. Responsible parties include Data/DevOps Engineers.
Performance monitoring assesses whether the model is correct by evaluating accuracy, positive predictive value (PPV), drift, and other performance-related metrics. Surrogate or proxy outcomes may also be used to gauge effectiveness. This involves comparing model outputs against 'ground truth' labels, often through human-labeled benchmark datasets. Responsible parties include Data Scientists and Informaticists.
Impact monitoring focuses on whether the model's insights lead to the desired actions and outcomes. This includes tracking workflow adoption and adherence, gathering user feedback, and measuring impact. Operational metrics assess user adoption, value realization, and overall implementation success. Responsible parties include Business Intelligence Analysts and Business Owners.
Enterprise Process Flow
AI System Deployed
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Monitor Error/Warning/Failure Logging & API Telemetry
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Monitor Statistical Performance Metrics & Threshold Alerts
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Monitor Analytic Dashboards, Reports & User Feedback
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Periodic Review of Metrics
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Governance Decision (Retrain, Reconfigure, Retire)
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Execute Changes
| Aspect | Traditional AI | Generative AI |
|---|---|---|
| Primary Focus | Specific task, identical output | Diverse tasks, unique output |
| Performance Metrics | AUROC, PPV, Sensitivity | User feedback, guardrail breaches, benchmark performance |
| Drift Management | Dataset/concept shift, accuracy erosion | Prompt evolution, unintended failure modes |
| Key Challenge | Maintaining statistical relationships | Benchmarking diverse outputs, real-time guardrails |
75-125%
Performance Acceptance Band for Retraining/Retirement
Stanford Health Care: Real-World AI Monitoring Success
At Stanford Health Care, our Responsible AI Lifecycle (RAIL) integrates a comprehensive monitoring framework. We've developed specific monitoring plans for 12 active deployments, including traditional and generative AI systems. For instance, the LLM-powered inpatient hospice screen underwent a post-go-live review of system integrity, performance, and impact metrics, which greenlit expanded usage after its initial pilot deployment. This approach enabled data-driven decisions to adjust workflows, retrain models, and even retire underperforming tools, ensuring sustained value and patient safety. The PAD risk classification model impact review identified process metrics that did not meet required thresholds, leading to workflow modifications that improved the rate of PAD patient workup.
Calculate Your Potential AI ROI
See how strategic AI monitoring and optimization can impact your organization's efficiency and bottom line.
Projected Annual Savings
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Hours Reclaimed Annually
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Your Path to Actionable AI Monitoring
A phased approach to integrate robust monitoring into your AI lifecycle.
Phase 1: Assessment & Strategy
Evaluate existing AI deployments, define monitoring objectives, and tailor a framework based on system type (traditional/generative) and interaction mode (fixed/open-prompt).
Phase 2: Implementation & Integration
Integrate monitoring tools and platforms (e.g., Databricks, Epic Radar), establish data pipelines for metrics collection, and configure real-time alerts and dashboards.
Phase 3: Governance & Review
Define clear ownership for metrics, establish review cadences (monthly, quarterly, yearly), and embed monitoring insights into governance decision-making (retrain, reconfigure, retire).
Phase 4: Continuous Improvement
Iteratively refine monitoring plans, adapt to new AI technologies and regulatory changes, and continuously optimize workflows based on sustained impact and value realization.
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