AI Governance
When AI models make decisions about patient care, data extraction, or cohort assignment, how do you ensure those decisions are accurate, fair, and explainable? As healthcare organizations increasingly rely on artificial intelligence to power clinical workflows and research initiatives, the question isn't whether to use AI—it's how to use it responsibly. The AI Governance framework provides comprehensive oversight and control over every AI model, algorithm, and automated decision within Patient Journey Intelligence, ensuring that AI augments human expertise rather than creating new risks.
Why AI Governance Matters in Healthcare
Healthcare AI operates in an environment where mistakes have consequences. An NLP model that misses a critical diagnosis in clinical notes could delay treatment. A de-identification model that fails to mask protected health information creates HIPAA violations. A classification algorithm that performs differently across demographic groups perpetuates healthcare disparities. Traditional software governance approaches don't address these AI-specific risks.
AI Governance transforms AI from a black box into a transparent, auditable, and continuously monitored system. You gain visibility into what models are deployed, how they perform, whether they exhibit bias, and why they make specific decisions. This visibility enables compliance with emerging AI regulations, supports clinical validation requirements, and builds institutional confidence in AI-powered workflows.
The Model Registry: Complete AI Inventory
Before you can govern AI, you need to know what AI you're using. The Model Registry maintains a centralized inventory of every AI model deployed within Patient Journey Intelligence, creating a single source of truth for your AI landscape.
All models used in Patient Journey Intelligence are rigorously benchmarked and tested using the Pacific AI framework, an independent evaluation platform for healthcare AI. This standardized testing ensures that every model meets established performance thresholds across diverse clinical contexts before deployment, providing evidence-based validation that supports both clinical adoption and regulatory compliance.
Deployed Model Types
The registry tracks diverse AI models serving different functions across the platform.
- NLP models for entity extraction identify and extract clinical concepts from unstructured text—diagnoses, medications, procedures, lab results, and other structured information buried in clinical notes.
- Classification models for cohort assignment automatically categorize patients into appropriate clinical populations based on their diagnoses, treatments, and clinical characteristics.
- De-identification models detect and remove protected health information from clinical text, enabling compliant data sharing for research and secondary use.
- Clinical reasoning models support advanced use cases like risk prediction, outcome forecasting, and treatment recommendation.
Each model type serves a specific purpose, and the registry ensures you understand what AI is doing what work across your platform.
Model Metadata and Provenance
For every model in the registry, comprehensive metadata provides the context needed for governance and validation.
Training data provenance documents what data was used to train each model, when it was trained, and what populations or institutions contributed training examples. This provenance is essential for understanding model applicability and potential blind spots—a model trained primarily on data from academic medical centers might not perform well in community hospital settings.
Performance metrics capture how well each model performs its designated task. Accuracy, precision, recall, F1 scores, and other relevant metrics are tracked continuously, not just at deployment. You can see whether model performance remains stable over time or degrades as data distributions shift.
Validation results document how each model performed on held-out test datasets and real-world validation studies, providing evidence of clinical utility and safety before deployment.
Deployment history records when each model version was deployed, who authorized the deployment, and what previous versions it replaced. This history supports change management, enables rollback to previous versions when issues arise, and creates an audit trail for regulatory compliance.
Continuous Performance Monitoring
Deploying a validated model is just the beginning. Model performance can drift over time as patient populations change, clinical documentation practices evolve, or data quality shifts. Continuous performance monitoring detects these changes before they impact clinical workflows or research quality.
The monitoring system tracks accuracy metrics in production, comparing current performance against baseline validation results. When accuracy drops below acceptable thresholds, automated alerts notify AI governance teams to investigate. This proactive monitoring prevents performance degradation from going unnoticed until users report problems or audits uncover issues.
Beyond simple accuracy tracking, the system monitors for more subtle performance changes across different contexts. Does the model perform differently for patients at different facilities? Has performance changed for specific clinical conditions or documentation types? Are there temporal patterns suggesting seasonal variation or systematic drift? This contextual monitoring provides early warning of issues requiring model retraining, recalibration, or replacement.
Version Control and Rollback Capabilities
AI models aren't static software—they're continuously improved through retraining on new data, architectural changes, and algorithm updates. Managing these versions requires the same rigor as software version control, with additional considerations for ML-specific concerns like training data provenance and performance characteristics.
The version control system tracks every model iteration deployed to production. When you deploy a new version, the system maintains the previous version in a ready-to-deploy state. If the new version exhibits unexpected behavior, performance degradation, or introduces bias, you can roll back to the previous version immediately without waiting for emergency fixes or retraining cycles.
Version metadata documents not just the model code but the complete training lineage. What data was used? What hyperparameters were selected? What validation studies were conducted? This documentation ensures that every deployed version is fully characterized and its behavior is predictable. When questions arise about model decisions, you can trace them back to specific training choices and validation results.
Model Explainability and Decision Documentation
When an AI model extracts a diagnosis from a clinical note or assigns a patient to a cohort, stakeholders need to understand why. Clinicians want to verify that automated extractions are correct. Researchers need to trust that cohort assignments reflect genuine clinical criteria. Auditors require evidence that AI decisions comply with institutional policies and regulatory requirements.
The explainability framework captures the reasoning behind AI decisions in human-interpretable formats. For NLP entity extraction, the system highlights the specific text spans that triggered each extraction, showing exactly where in a clinical note the model found evidence for a diagnosis or medication. For classification decisions, the system documents which features most influenced the decision—was it primary diagnosis codes, procedure history, medication patterns, or clinical note content?
This decision documentation serves multiple purposes. Clinicians can quickly validate automated extractions by reviewing highlighted evidence rather than re-reading entire charts. Data scientists can debug model errors by examining what features the model relied upon. Compliance teams can demonstrate that AI decisions are based on clinically appropriate information rather than protected attributes like race or insurance status.
Bias Detection and Fairness Audits
Healthcare AI must serve all patients equitably, but models trained on historical data can perpetuate existing disparities or introduce new ones. A model trained predominantly on data from one demographic group might perform poorly for others. An algorithm that correlates insurance type with clinical outcomes might make different predictions for otherwise similar patients based on coverage rather than clinical need. The bias detection system continuously monitors for performance disparities across patient populations.
Demographic monitoring tracks whether models perform differently based on age, gender, race, or ethnicity. If an NLP model consistently misses diagnoses more often for female patients than male patients, or if a de-identification model is more aggressive in removing information for specific ethnic groups, these disparities trigger alerts for investigation.
Geographic analysis identifies whether models exhibit regional bias, potentially reflecting differences in clinical documentation practices, local disease prevalence, or regional terminology variations.
Socioeconomic monitoring detects whether performance varies based on factors like insurance type or ZIP code-based deprivation indices, helping identify and mitigate disparities that could exacerbate healthcare inequity.
When bias is detected, the governance framework supports systematic mitigation. You might retrain models with more balanced data, adjust decision thresholds to equalize performance across groups, or implement fairness constraints during model training. The system tracks these interventions and validates that they successfully reduce bias without sacrificing overall performance.
Responsible AI in Practice
AI Governance isn't about limiting AI use—it's about enabling responsible AI deployment at scale. With comprehensive model inventory, continuous performance monitoring, explainable decisions, and proactive bias detection, you can confidently deploy AI across clinical and research workflows knowing that appropriate oversight mechanisms are in place.
This governance framework supports emerging regulatory requirements, satisfies institutional review boards concerned about algorithmic decision-making, enables clinical validation required for AI in care delivery settings, and builds trust with clinicians, researchers, and patients who rely on AI-powered insights.
By treating AI models as governed assets requiring the same rigor as clinical protocols and research methodologies, AI Governance ensures that artificial intelligence truly augments human expertise rather than creating new sources of risk, bias, or error in healthcare delivery and research.