Clinical handover is a known patient safety risk point. Information loss during shift changes, ward transfers, and care transitions contributes to adverse events. It’s also a process with significant time burden on clinical staff.

AI applications targeting handover are emerging. Some are promising. Some raise concerns I think organisations should carefully consider.

The Handover Problem

Studies consistently show that information is lost during clinical handovers. Estimates vary, but something like 15-25% of critical information fails to transfer effectively between clinicians or care settings.

The consequences include:

• Delayed recognition of deteriorating patients
• Repeated or missed investigations
• Medication errors
• Extended hospital stays
• Adverse events and near-misses

Traditional solutions—standardised handover formats (ISBAR), structured handover tools, protected handover time—help but don’t eliminate the problem.

AI proponents see opportunity here. If AI can synthesise patient information, identify critical issues, and present them in structured formats, maybe handover becomes more complete and efficient.

Current AI Handover Applications

Several categories of AI are being applied to handover:

Summarisation AI

These systems generate natural language summaries from patient records. Instead of clinicians manually synthesising information from multiple sources, AI provides a draft summary.

Applications include:

• Shift handover summaries
• Ward-to-ward transfer summaries
• Discharge summaries
• Referral letters

The technology is increasingly capable. Large language models can produce fluent, readable summaries that capture key clinical information.

Prioritisation and Alerting

AI that identifies which patients need attention during handover. Rather than reviewing all patients equally, AI highlights those with:

• Concerning vital sign trends
• Outstanding critical results
• Recent significant changes
• High deterioration risk scores

This isn’t summarisation—it’s triage of the handover list itself.

Structured Extraction

AI that extracts specific handover-relevant information and populates structured templates. Takes unstructured clinical notes and pulls out medication changes, pending investigations, care plan elements, and so on.

Predictive Elements

Some systems incorporate predictions—patients at risk of deterioration, likely discharge dates, potential complications—into handover materials.

What I’ve Seen Working

A few implementations I’ve observed that seemed genuinely useful:

Draft discharge summaries. AI-generated drafts that clinicians reviewed and edited before finalising. Reduced documentation time while maintaining clinical oversight. Key was treating AI output as a draft, not a final product.

Overnight summary for morning handover. AI that consolidated overnight events across multiple documentation sources into a single summary for morning teams. Clinicians reported feeling better informed.

Transfer checklist population. AI that pre-populated transfer checklists from patient records, flagging gaps where information was missing. Reduced manual checking while improving completeness.

In each case, success factors included:

• AI output being reviewed and verified by clinicians
• Clear delineation of AI role (assist, not replace)
• Integration with existing workflows rather than creating new ones
• Clinician input in design and iteration

I’ve seen similar patterns in discussions with AI consultants Melbourne who work across multiple health services—the implementations that succeed treat AI as a tool, not a replacement.

Safety Concerns

I have several concerns about AI in handover that I think organisations should carefully consider:

The Automation Complacency Problem

When AI generates handover content, there’s risk that clinicians stop critically evaluating it. If the AI usually gets it right, the natural tendency is to trust without verifying.

This is particularly dangerous because AI errors in summarisation aren’t random—they’re systematic. If the AI consistently misses a certain type of information or misinterprets a particular documentation pattern, that error propagates through every handover.

Hallucination in Clinical Context

Large language model summarisation can produce “hallucinated” content—text that sounds correct but isn’t actually supported by the source documents. In clinical contexts, this could mean:

• Medications the patient isn’t actually taking
• Investigations that weren’t actually performed
• Clinical findings that weren’t actually documented

The fluent, confident style of AI-generated text makes hallucinations harder to spot than obvious errors would be.

Information Selection Bias

AI summarisation makes choices about what to include and exclude. Those choices reflect the training data and algorithmic design, which may not match what a skilled clinician would prioritise.

Subtle but important clinical nuances—the patient’s social situation affecting discharge planning, a clinician’s clinical intuition that something is “off,” a family concern that doesn’t fit neat categories—may be systematically under-represented in AI summaries.

Accountability Gaps

If an AI-generated handover summary omits critical information and patient harm results, who is responsible? The clinician who relied on it? The organisation that deployed it? The vendor who built it?

These questions aren’t yet clearly answered, and that uncertainty is itself a risk.

Recommendations for Safe Implementation

If you’re implementing AI in clinical handover:

Treat AI Outputs as Drafts

Train clinicians to treat AI-generated handover content as a starting point, not a final product. Verification is mandatory, not optional. Build workflows that require active engagement with content, not passive acceptance.

Validate Against Source Documents

For high-stakes handovers (ICU, high-risk patients, complex cases), clinicians should verify AI summaries against primary documentation. Random audits should check AI accuracy on an ongoing basis.

Maintain Manual Capability

Don’t let AI dependency eliminate the skills needed to perform handover without AI. If the AI system fails, clinicians should still be able to do effective handover.

Monitor for Systematic Errors

Review AI outputs for patterns of error. Are certain types of information consistently missed? Are certain documentation patterns misinterpreted? Systematic errors require systematic solutions.

Include Safety Incident Reporting

Ensure AI-related handover issues are captured in safety reporting systems. Near-misses where AI omission or error was caught before harm matters. Track these trends.

Involve Clinical Governance

AI in handover is a patient safety issue, not just an IT project. Clinical governance should oversee implementation, monitor outcomes, and have authority to modify or stop AI use.

For organisations developing these implementations, working with specialists helps. AI consultants Brisbane and similar firms often have experience across multiple healthcare settings that reveals common pitfalls.

What I’d Like to See

Looking forward, I’d like to see:

Evidence development. Rigorous studies of AI handover tools measuring patient safety outcomes, not just clinician satisfaction or time savings. Are adverse events actually reduced?

Standardised evaluation frameworks. Common approaches to assessing AI handover tools before and during deployment.

Transparency about limitations. Vendors being explicit about where their tools perform poorly and what types of clinical information are at risk of being missed.

Clinician-led implementation. Implementation designs that keep clinicians in control rather than automating them out of the loop.

The Bottom Line

AI has genuine potential to improve clinical handover. The volume of clinical information makes manual synthesis difficult, and AI capabilities in summarisation and extraction are increasingly strong.

But handover is a safety-critical process. AI failures here can directly harm patients. The potential benefits don’t justify uncritical adoption.

Thoughtful implementation—treating AI as an assistant, maintaining verification, monitoring for errors, keeping clinicians engaged—can capture benefits while managing risks. Rushing deployment without these safeguards is a mistake I hope the sector avoids.

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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.