Clinical variation is one of healthcare’s most persistent challenges. Patients with similar conditions receive different treatments depending on which clinician they see, which hospital they attend, or which day they present.

Some variation is appropriate—patients are different, and care should be individualised. But much variation is unwarranted—it doesn’t improve outcomes and often reflects habit, preference, or lack of awareness rather than clinical reasoning.

AI offers new tools to identify and address unwarranted variation. Here’s how I think about this.

Understanding the Problem

The data on clinical variation in Australia is striking:

• Rates of common procedures vary dramatically across geographic areas, beyond what patient population differences would explain
• Prescribing patterns for similar conditions vary significantly between practitioners
• Length of stay for similar diagnoses varies between hospitals even after case-mix adjustment
• Adherence to clinical guidelines varies widely

This isn’t about clinical quality—high-quality clinicians can vary from each other. It’s about systematic differences that suggest some patients aren’t receiving optimal care.

The Australian Institute of Health and Welfare and the Australian Commission on Safety and Quality in Health Care publish variation data. The numbers are confronting.

How AI Can Help

AI approaches variation in ways that traditional quality improvement can’t.

Pattern recognition at scale. Humans struggle to identify patterns across thousands of patient encounters. AI can analyse entire populations to identify systematic variation that wouldn’t be visible case-by-case.

Adjustment for complexity. Raw variation data is misleading because patient populations differ. AI can adjust for case-mix, comorbidities, and presentation complexity to identify variation that remains unexplained.

Real-time identification. Traditional variation analysis happens retrospectively. AI can identify variation as it occurs, enabling intervention before patterns become embedded.

Natural language analysis. Clinical documentation contains explanations for clinical decisions. AI can analyse free-text documentation to understand why variation occurs, not just that it occurs.

Practical Applications

Prescribing variation. AI systems can flag when prescribing patterns deviate significantly from peer practice or guidelines. Not as alerts that interrupt workflow, but as information available for quality review.

One medication safety AI I’ve seen presents prescribers with their patterns compared to peers: “Your opioid prescribing rate for post-operative patients is in the 85th percentile compared to similar practitioners.” This enables reflection without mandate.

Procedural variation. AI analysis of procedural data can identify practitioners or sites with unusual patterns. High intervention rates might reflect patient population—or might reflect practice patterns worth examining.

The goal isn’t standardisation for its own sake. It’s surfacing patterns that clinicians might not be aware of, enabling reflection and (where warranted) change.

Pathway adherence. Clinical pathways define evidence-based care sequences for common conditions. AI can monitor pathway adherence, identifying where patients deviate and why.

Some deviation is appropriate—pathways are guides, not mandates. AI helps distinguish systematic deviation (suggesting the pathway might need updating) from individual exceptions (which may or may not be appropriate).

Length of stay variation. Patients with similar conditions staying different lengths of time may indicate variation in clinical practice or discharge planning. AI can identify cases that deviate from expected length of stay, flagging them for review.

Implementation Considerations

If you’re considering AI for variation reduction:

Start with data quality. Variation analysis requires good data. If your clinical coding is inconsistent, your documentation is incomplete, or your data integration is poor, fix those first. AI amplifies data quality issues—it doesn’t solve them.

Engage clinicians from the start. Variation work is sensitive. Clinicians can feel targeted or criticised. Position AI as a tool for reflection, not a surveillance system. Clinical leadership involvement is essential.

Focus on unwarranted variation. Not all variation is bad. Clear criteria for what constitutes “unwarranted” variation—deviation from guidelines, unexplained by patient factors, associated with worse outcomes—help maintain clinical support.

Create feedback loops, not mandates. The goal is informing clinical practice, not automating it. AI that provides information for clinician decision-making maintains professional autonomy. AI that mandates specific actions creates resistance and may harm patient care.

Measure outcomes, not just process. Variation reduction is valuable only if it improves outcomes. Track patient outcomes alongside variation metrics to ensure you’re improving care, not just standardising it.

Case Study: Laboratory Test Ordering

One example I’ve seen work well: AI analysis of laboratory test ordering patterns.

Laboratory tests are often ordered routinely rather than thoughtfully. “Admission panel” ordering means every patient gets the same tests regardless of clinical indication. Duplicate tests are common when results from previous encounters aren’t easily visible.

An AI system analysed ordering patterns across a hospital network:

• Identified practitioners with ordering rates significantly above peers for specific test types
• Flagged test orders where results from recent encounters already existed
• Tracked patterns over time to distinguish systematic variation from individual cases

The AI generated regular reports for department heads, showing variation data. No individual clinician was named; patterns were presented at specialty or unit level.

Over 12 months, unnecessary test ordering declined by 15%. Some variation remained appropriate; much was simply habit that clinicians weren’t aware of until they saw the data.

Cautions

AI for variation reduction has risks:

Gaming metrics. When clinicians know they’re being measured, behaviour changes—not always in useful ways. Documentation changes to appear compliant; underlying practice may not change.

Discouraging appropriate variation. Personalised medicine means appropriate variation. AI systems that push toward homogeneity may discourage care individualisation that benefits patients.

Privacy concerns. Detailed analysis of individual clinical practice raises privacy questions for clinicians. Appropriate use, data governance, and clear policies matter.

Oversimplification. Clinical decisions are complex. AI that reduces this complexity to metrics may miss important nuance.

These cautions don’t mean AI shouldn’t be used for variation work. They mean implementation requires thoughtful governance and clinical engagement.

The Bigger Picture

Unwarranted clinical variation represents a significant opportunity to improve healthcare quality and efficiency. Billions of dollars are spent on care that doesn’t improve outcomes. Patients receive treatment based on who they see rather than what they need.

AI provides tools to identify and address this variation that weren’t previously available. The technology is ready. The implementation challenge is social and organisational—building the governance, engagement, and change capability to use these tools effectively.

Done well, AI-enabled variation reduction improves patient care, reduces waste, and supports clinical excellence. Done poorly, it creates conflict, erodes trust, and doesn’t achieve its goals.

The technology isn’t the hard part.

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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.