AI systems that work well at deployment don’t always keep working well. Performance can degrade over time, sometimes gradually, sometimes suddenly. Detecting and responding to this degradation is one of the harder parts of clinical AI operations.

This isn’t hypothetical. I’ve been involved in cases where AI performance dropped significantly without anyone noticing for months. Understanding the patterns helps organisations avoid similar problems.

How AI Performance Degrades

Several mechanisms cause AI performance to deteriorate:

Data Drift

AI systems are trained on historical data. If the characteristics of current data diverge from training data, performance suffers.

In healthcare, data drift happens when:

• Patient population demographics shift
• Disease prevalence changes (seasonal, epidemic, or longer-term)
• Clinical practices evolve (new treatments, new protocols)
• Documentation patterns change (new staff, new templates, new workflows)
• Source systems change (equipment upgrades, software updates)

Data drift is usually gradual. The AI doesn’t suddenly stop working—it slowly becomes less accurate.

Concept Drift

Sometimes the underlying relationship between inputs and outputs changes. The clinical meaning of patterns in data shifts.

For example, an AI trained to predict hospital readmission might incorporate social determinants of health. If social support systems in your region improve, the historical relationship between those factors and readmission might change.

Concept drift is harder to detect than data drift because the data may look similar even though its clinical meaning has changed.

Technical Issues

More mundane technical problems can also cause performance degradation:

• Integration errors causing incomplete data transfer
• Database issues affecting query performance
• Software bugs introduced in updates
• Infrastructure problems affecting processing speed
• Cache or memory issues accumulating over time

Technical issues often cause sudden rather than gradual degradation.

Model Updates

If the AI vendor updates the model, performance might change—sometimes improving, sometimes not. Updates intended to improve performance in one context might degrade it in another.

Organisations don’t always control when vendor updates occur, and may not even know when they happen.

Why Degradation Goes Unnoticed

Given these risks, you’d expect organisations to monitor AI performance closely. Often they don’t, for several reasons:

Baseline establishment failure. If you don’t measure performance at deployment, you can’t detect degradation. Many implementations don’t establish clear performance baselines.

Monitoring infrastructure gaps. Ongoing performance measurement requires infrastructure—data pipelines, analytics capability, dashboard systems. This infrastructure often isn’t built.

Resource constraints. Even if monitoring is technically possible, someone needs to actually review performance data. In busy health informatics teams, this often doesn’t happen consistently.

Outcome measurement complexity. For some AI applications, measuring outcomes is inherently complex. A diagnostic AI requires knowing true diagnoses, which may not be available until later or may never be definitively established.

Clinician adaptation. Clinicians may unconsciously compensate for AI degradation, adding more clinical scrutiny as they lose trust, without formally reporting performance issues.

Confirmation bias. If the AI seemed to work initially, there’s psychological resistance to acknowledging it’s no longer working as well.

Signs of Degradation

Indicators that AI performance may be degrading:

Changing clinician behaviour. Clinicians ignoring AI recommendations more frequently. Increased override rates. Clinicians adding more verification steps.

Outcome pattern changes. Increased adverse events in AI-supported processes. More near-misses. Longer times to correct diagnosis.

User complaints. Clinicians expressing frustration or dissatisfaction with AI outputs. Requests to disable AI.

Technical anomalies. Changing response times. Increased error rates. Unexpected output patterns.

Metric changes. If you’re measuring AI accuracy, sensitivity, specificity, or other performance metrics, trends indicating decline.

Monitoring Approaches

Effective monitoring requires:

Automated Performance Tracking

Track key performance indicators automatically, not manually. This requires:

• Defining metrics that indicate AI performance
• Building data pipelines to calculate these metrics
• Setting thresholds that trigger alerts when metrics deteriorate
• Dashboard visibility for relevant stakeholders

For diagnostic AI, this might mean tracking agreement rates between AI and final clinical diagnosis. For risk prediction, it might mean calibration plots comparing predicted and actual outcomes. For documentation AI, it might mean clinician edit rates.

Regular Performance Reviews

Even with automated monitoring, human review matters. Schedule regular performance reviews that:

• Examine performance trends over time
• Compare performance across patient subgroups
• Review any incidents or near-misses involving AI
• Assess ongoing fit between AI and clinical needs

Monthly or quarterly reviews are typical, depending on AI criticality.

Clinician Feedback Systems

Create channels for clinicians to report AI performance concerns:

• Feedback buttons in AI interfaces
• Reporting pathways through clinical governance
• Regular clinician surveys about AI utility

Clinician feedback often catches issues before formal metrics detect them.

Vendor Communication

Maintain communication with AI vendors about:

• When model updates occur
• Known issues affecting performance
• Changes in recommended use or limitations
• Data about performance across their customer base

Vendors sometimes know about problems before their customers detect them. AI consultants Sydney have told me they increasingly include vendor communication protocols in their implementation frameworks for exactly this reason.

Response Protocols

When degradation is detected, you need response protocols:

Immediate Assessment

Determine the nature and severity of degradation:

• Is performance still adequate for clinical use?
• Is degradation sudden or gradual?
• What is the suspected cause?
• Are patients at risk?

Clinical Risk Management

If there’s patient safety concern:

• Consider restricting or stopping AI use
• Notify affected clinical areas
• Review recent AI-supported decisions for potential errors
• Activate incident reporting if harm may have occurred

Root Cause Analysis

Investigate the cause of degradation:

• Technical issues (integration, infrastructure, bugs)
• Data issues (drift, quality problems)
• Model issues (vendor updates, inherent limitations)
• Contextual changes (practice changes, population changes)

The cause determines the response.

Corrective Actions

Depending on root cause:

• Technical fixes for technical problems
• Model retraining or recalibration for drift issues
• Workflow adjustments if context has changed
• Vendor engagement for vendor-caused issues

Return to Service

Before returning degraded AI to full service:

• Validate that corrective actions resolved the issue
• Confirm performance meets acceptable standards
• Communicate with affected clinicians
• Update governance documentation

Building Degradation Resilience

Some approaches that help organisations manage degradation risk:

Invest in monitoring infrastructure. The upfront cost is worthwhile. Organisations working with AI consultants Brisbane on implementations should include monitoring capability in project scope, not treat it as an afterthought.

Define performance thresholds prospectively. Decide before deployment what performance levels are acceptable and what triggers concern. This avoids arguing about standards when problems emerge.

Maintain manual capability. Ensure clinical processes can continue without AI. If AI degrades badly enough to require shutdown, clinicians should be able to work effectively.

Build organisational capability. Having staff who understand AI performance, can interpret monitoring data, and can lead response activities matters. This is a health informatics capability, not just a technical one.

Learn from incidents. When degradation occurs, formal review captures lessons. What could have detected this earlier? How should monitoring change?

TGA Considerations

TGA-regulated AI medical devices have requirements around ongoing performance monitoring. Sponsors are expected to have post-market surveillance systems that detect performance issues.

Health services using regulated AI devices should understand:

• What monitoring the vendor/sponsor is conducting
• How performance issues would be communicated to users
• Their own obligations for reporting issues to the vendor

The regulatory framework assumes ongoing surveillance, not set-and-forget deployment.

The Continuous Improvement Mindset

Clinical AI operations should be thought of as ongoing programs, not one-time implementations. Like any clinical system, AI needs continuous attention:

• Monitoring for problems
• Maintenance and updates
• Quality improvement
• Adaptation to changing needs

Organisations that treat AI as “implemented and done” are surprised when performance degrades. Those that plan for ongoing operations are better prepared.

The payoff for good monitoring isn’t just catching problems—it’s confidence that AI is actually delivering the benefits expected. Without monitoring, you’re trusting AI without evidence. With monitoring, you know whether that trust is justified.

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Dr. Rebecca Liu is a health informatics specialist and former Chief Clinical Information Officer. She advises healthcare organisations on clinical AI strategy and implementation.