TRACE Toolkit
Track what happens
after you ship.
after you ship.
A monitoring framework for AI-enabled clinical decision support tools, organized across five domains. Click any domain below to explore metrics and implementation guidance.
Monitoring Domains
01
Technical Integrity
Is the tool functioning reliably and consistently across time and settings?
5 metrics
→
02
Real-World Use
How are clinicians actually using the tool, and is that use meeting expectations?
5 metrics
→
03
Alignment & Accuracy
Are the tool's outputs clinically sound and consistent with current guidelines?
5 metrics
→
04
Clinical Fairness
Does the tool work equally well across patient subgroups, or are disparities emerging?
5 metrics
→
05
Escalation & Safety
When problems arise, is there a process in place to detect, investigate, and respond?
5 metrics
→
T
Domain 01 of 05
Technical Integrity
Technical integrity refers to the consistency, transparency, and auditability of the system in which the AI model and tool operate. Monitoring in this domain ensures that model outputs are stable, logs are preserved, and the system infrastructure supports traceability and reproducibility. Strong technical integrity allows organizations to verify the source of outputs, evaluate performance trends over time, and conduct meaningful investigations when reviewing safety events or quality concerns.
Sample Metrics
Identical-Input Stability Variation
+
Percentage of identical-input stability tests that yield output variation beyond an acceptable threshold
system logsquery
Model or Configuration Changes Logged
+
Number of model or configuration changes fully documented in version control
version controlautomated
Model Response Latency
+
Average and maximum latency for model responses during clinical use
system telemetryautomated
Log Completeness
+
Percentage of complete and retrievable log records for all tool interactions
audit logsautomated
System Uptime
+
System uptime percentage during clinical operating hours
system telemetryautomated
Best Practices
01Maintain comprehensive version control for all deployed models, including documentation of model updates, retraining events, and configuration changes.
02Log all model inputs and outputs with associated metadata (e.g., time stamps, clinician identifiers, patient encounter context).
03Conduct stability testing for identical inputs over time, particularly for systems based on large language models (LLMs), which may produce different outputs even when given the same input.
04Track system uptime, latency, and availability within clinical workflows.
05Implement automated alerts for configuration or infrastructure anomalies.
R
Domain 02 of 05
Real-World Use
Understanding how clinicians engage with AI-CDS tools in practice provides essential insight into both safety and utility. Real-world use monitoring reveals whether the tool is being used as intended, how it fits within existing workflows, and the extent to which it influences clinical decisions. This information helps identify opportunities to improve usability, build clinician trust, and ensure that the tool is delivering value in everyday clinical contexts.
Sample Metrics
Tool Utilization Rate
+
Percentage of eligible patient encounters in which the AI-CDS tool is accessed
EHR logsquery
Override or Dismissal Rate
+
Percentage of tool recommendations dismissed or overridden, segmented by clinician role, specialty, and site
EHR logsquery
Engagement Duration
+
Median time clinicians spend interacting with the tool during each encounter
EHR logsquery
Workflow Disruption Rate
+
Rate of workflow disruptions attributed to tool use (e.g., task detours, abandonment, clinician-reported disruptions)
observation / EHR logsmanual
Change in Utilization After Interventions
+
Change in utilization rate following workflow adjustments, training interventions, or system updates
EHR logsquery
Best Practices
01Track utilization patterns across departments, clinician types, and time periods to assess reach and consistency of use.
02Monitor override and dismissal rates, along with contextual factors that may explain these actions (e.g., time of day, patient complexity).
03Identify patterns of selective, inconsistent, or excessive use that may signal workflow misalignment, insufficient training, or over-reliance by clinicians who may be overworked or overly confident in the tool's capabilities.
04Where feasible, capture measures such as decision time, engagement duration, or workflow interruptions to assess impact on efficiency.
05Segment usage and override data by role and location to identify where adoption is strongest and where additional support may be required.
06Incorporate findings into regular feedback loops with clinical teams to refine deployment and training strategies.
A
Domain 03 of 05
Alignment & Accuracy
Alignment and accuracy monitoring evaluates whether the AI-CDS tool continues to produce clinically valid, relevant, and guideline-aligned outputs after deployment. Tracking this domain ensures that recommendations remain appropriate as clinical guidelines evolve, patient populations change, or the tool's underlying model is updated. Consistent alignment with evidence-based practice supports clinical trust, safeguards patient outcomes, and ensures that the tool continues to deliver meaningful value in its intended use case.
Sample Metrics
Guideline Concordance Rate
+
Percentage of outputs concordant with current clinical guidelines or expert consensus
structured auditmanual
Clinician Disagreement Rate
+
Frequency of clinician-reported disagreements with tool recommendations
surveys / EHRmanual
Outcome Impact
+
Change in diagnostic accuracy or treatment appropriateness when tool recommendations are followed
labeled outcomesquery
Adverse Event Association
+
Frequency of adverse events or inappropriate care associated with tool-guided decisions
clinical recordsmanual
Guideline Update Response Time
+
Time between guideline updates and documented tool reassessment or update
governance recordsmanual
Best Practices
01Establish processes to routinely review changes to relevant clinical guidelines and reevaluate the tool's outputs for alignment, including documenting when guidelines change and assessing whether retraining, rule adjustments, or workflow updates are needed.
02Evaluate the correctness and clinical appropriateness of the tool's outputs over time.
03Use regular clinician feedback, audit logs, or incident reviews to determine whether the tool is contributing to adverse events, unnecessary testing, or inappropriate care pathways.
04Where feasible, link model-guided decisions to clinical outcomes (e.g., diagnostic accuracy, treatment effectiveness, patient trajectory) to assess impact.
C
Domain 04 of 05
Clinical Fairness
Clinical fairness monitoring evaluates whether the AI-CDS tool performs consistently across different patient populations and does not introduce or reinforce bias. Stratifying performance and usage data by demographic, socioeconomic, and geographic factors helps identify disparities in accuracy, access, and outcomes. Proactive clinical fairness monitoring supports inclusive care, reduces the risk of exacerbating existing health inequities, and strengthens institutional accountability to patients and communities.
Sample Metrics
Accuracy Differences Across Subgroups
+
Difference in accuracy and error rates between demographic subgroups
EHR + model logsquery
Utilization Differences Across Patient Groups
+
Variation in tool usage rates across patient demographic groups
EHR logsquery
Subgroup-Specific Override Rates
+
Override rates for specific patient subgroups compared to overall averages
EHR logsquery
Demographic Data Completeness
+
Percentage of encounters with complete demographic information
EHRautomated
Corrective Actions for Fairness Issues
+
Number of fairness-related corrective actions implemented (model updates, workflow changes, retraining)
governance recordsmanual
Best Practices
01Stratify key performance metrics (e.g., accuracy, false positive/negative rates) by demographic variables such as race, ethnicity, gender, age, insurance status, language preference, and geography.
02Analyze usage patterns and override rates across subgroups to identify potential access or trust gaps.
03Investigate differences in outcomes, including missed detections or false reassurance, for specific patient subgroups.
04Ensure demographic data is captured in a standardized and complete manner to enable meaningful analysis.
05Incorporate patient and community representatives into governance structures to inform monitoring priorities and interpretation of findings.
06Develop and document action plans for addressing identified disparities, including targeted retraining or workflow changes.
E
Domain 05 of 05
Escalation & Safety
Escalation and safety response monitoring ensures that findings from all other TRACE domains lead to timely and appropriate action. Clear protocols for investigating deviations, addressing safety concerns, and implementing corrective measures are essential for maintaining trust, minimizing risk, and supporting continuous improvement. This domain links monitoring activities to governance processes, ensuring that observed issues are not only identified but also resolved in a systematic and accountable manner.
Sample Metrics
Escalation Event Rate
+
Number of escalation triggers (performance drops, safety reports) within a defined period
incident reportsmanual
Time to Investigation
+
Average time between issue detection and the start of formal investigation
incident logsquery
Resolution Timeliness
+
Percentage of escalation events resolved within defined timeframes
incident logsquery
Model Intervention Frequency
+
Number of model updates, suspensions, or removals triggered by monitoring findings
governance recordsmanual
Governance or Policy Changes
+
Number of policy, workflow, or governance changes implemented following escalation reviews
governance recordsmanual
Best Practices
01Define thresholds or trigger conditions for escalation (e.g., sudden performance decline, clinical fairness flags, safety event reports).
02Establish a multidisciplinary review body -- such as a clinical AI oversight committee or patient safety board -- to evaluate escalated cases.
03Integrate monitoring data into existing institutional quality improvement and incident reporting systems.
04Develop predefined workflows for model retraining, suspension, or removal from clinical use when necessary.
05Document all escalation events, investigations, and resolutions to create an institutional knowledge base for future risk mitigation.
06Communicate outcomes of escalation processes to all relevant stakeholders, including clinical teams, governance bodies, and those responsible for maintaining or improving the tool.
TRACE Toolkit
Post-deployment monitoring
for AI in clinical settings.
for AI in clinical settings.
The problem
Most monitoring frameworks for AI in healthcare focus on pre-deployment validation. TRACE fills the gap that comes after go-live. AI-CDS tools are often evaluated carefully before deployment — then monitored loosely, or not at all, once they're in production. Real-world clinical environments are dynamic: patient populations shift, EHR configurations change, documentation practices evolve, and workflow pressures vary in ways that validation datasets don't capture. Models that performed well in validation can silently degrade in production, introducing safety risks that pre-deployment testing never anticipated.
What TRACE is
TRACE is a structured post-deployment monitoring framework for AI-enabled clinical decision support (AI-CDS) tools. It organizes monitoring across five domains, each covering a distinct dimension of how a deployed tool behaves in the real world — from infrastructure and adoption through to equity and safety governance.
T — Technical Integrity. Is the system running? Is it logging? Is the model behaving consistently across inputs and over time?
R — Real-World Use. Are clinicians actually using this? What do override patterns reveal? Is the tool creating friction or reducing it?
A — Alignment & Accuracy. Is performance drifting? Is the model still doing what it was validated to do, with real patients and real data?
C — Clinical Fairness. Does the tool perform equitably across patient demographics? Where are the representation and access gaps?
E — Escalation & Safety. When something goes wrong, how fast is it flagged — and how fast is the response? What does your safety governance loop look like in practice?
How to use this toolkit
The 25 metrics in this toolkit are sample metrics — illustrative examples organized by domain, not a mandatory checklist. Select and adapt based on your tool's risk level, clinical context, and data infrastructure. Higher-risk tools warrant more comprehensive monitoring; lower-risk decision support applications may need a targeted subset.
Each metric includes a difficulty tag indicating the typical collection overhead:
Each metric includes a difficulty tag indicating the typical collection overhead:
automated
— capturable from existing logs and telemetry with minimal added infrastructure
query
— requires a structured EHR or database query, typically run on a scheduled basis
manual
— requires surveys, structured audits, or clinical record review
Start with automated metrics. Build toward query-based and manual metrics as your monitoring program matures and your infrastructure develops.
About the author
TRACE was developed by Arfa Rehman, a healthcare founder and a Science and Tech Policy Fellow at the Aspen Institute.
TRACE Toolkit
Frequently asked
questions
questions
TRACE is a post-deployment monitoring framework for AI-enabled clinical decision support (AI-CDS) tools. It gives development and implementation teams a structured approach to monitoring after go-live — covering five domains: Technical Integrity, Real-World Use, Alignment & Accuracy, Clinical Fairness, and Escalation & Safety.
Primarily for developers and implementation teams building or deploying AI-CDS tools in clinical settings. Also relevant for health system leaders, clinical informatics teams, and policy organizations like the Coalition for Health AI (CHAI) establishing monitoring standards.
Pre-deployment evaluation validates performance on held-out data before a model goes live. TRACE is specifically about what happens after deployment — whether the model continues to perform as expected with real patients, under real workflow conditions. Most current frameworks stop at pre-deployment; TRACE fills the post-market surveillance gap.
Not currently. TRACE is a voluntary framework designed to align with emerging regulatory expectations around AI-CDS post-market surveillance, including FDA guidance and CHAI standards. Adopting TRACE now puts organizations ahead of requirements that are likely coming.
No. The 25 metrics are sample metrics — illustrative examples, not a mandatory checklist. Select based on your tool's risk level, clinical context, and available data infrastructure. Higher-risk tools warrant more comprehensive monitoring; lower-risk tools may need a targeted subset.
Start with what you have. The difficulty tags (automated, query, manual) are designed to help you prioritize. Automated metrics can be captured with minimal overhead. Begin there, and build toward query-based and manual metrics as your infrastructure develops.
Automated metrics: continuously, with anomaly alerts. Query-based metrics: at least monthly. Manual metrics (surveys, audits, incident reviews): quarterly cadence. After any model update or significant population change, run a full review across all five domains.
TRACE was developed by Arfa Rehman, a healthcare founder and a Science and Tech Policy Fellow at the Aspen Institute.