Triagen

AI-assisted haematology triage for faster, safer clinical decision-making

The Triage Workflow That Made Clinical Decisions Harder

Haematology triage is the process of reviewing incoming referrals and deciding how urgently each patient needs specialist care. In the current workflow, nurses and clinicians manually review referral letters, lab values, scanned reports, and clinical notes before assigning a triage pathway.

The problem was not a lack of clinical data.

• The problem was that the data was scattered, unstructured, and cognitively heavy to process under time pressure.

• Nurses had to read long referral letters, cross-check abnormal lab results, judge urgency, and document decisions without a consistent interface designed for haematology triage. This created risk around missed patterns, inconsistent decisions, and delayed prioritisation.

• Triagen was designed as an AI-assisted clinical decision-support interface that helps clinicians quickly prioritise referrals, understand AI reasoning, and retain full control over the final decision.

• The system does not automate triage.

• It structures clinical information, suggests a triage pathway, explains the reasoning, and lets the clinician accept or modify the decision with documentation.

This was a high-stakes workflow design for clinical decision-making. When haematology referrals are difficult to scan, clinicians lose time, cognitive load increases, and urgent cases can be harder to identify. For conditions such as suspected leukaemia or malignant haematology cases, triage delays can directly affect patient care.

A Structured Process To Understand Clinical Workflow Before Designing AI Support

The project followed a structured design process centered on clinical workflow, cognitive load, and clinician trust. It did not aim to prove that AI could replace triage decisions. Instead, the focus was on identifying where AI could provide meaningful support. The solution was designed to strengthen, not disrupt, clinicians’ existing decision-making process.

I reviewed existing research to understand how AI is currently used in clinical triage and where gaps remain in haematology referral workflows.

Research Goals :

I focused on four questions:

• How are referrals currently reviewed and prioritised?

• What causes delays and inconsistent triage decisions?

• How can AI process unstructured clinical information?

• How can AI support clinicians without replacing their judgement?

What I Explored :

I reviewed few peer-reviewed research and haematology triage studies focusing on :

The biggest problems clustered around four areas.

Clinicians Did Not Need More Information. They Needed Clearer Decision Support.

The research showed that haematology triage was not failing because clinicians lacked data. It was failing because the available information was difficult to interpret quickly. Referral details were often long, inconsistent, and unstructured. Lab values and scanned reports were available, but clinicians still had to manually connect fragmented evidence to the final triage decision.

I adapted my survey questions into a structured interview checklist and used it during sessions with a clinical mentor. This approach helped me gather qualitative, expert-informed insights without accessing confidential patient information or involving practising nurses directly. Although this phase did not include interviews with real triage nurses, the clinical mentor helped evaluate realistic workflow scenarios, likely decision-making behaviours, and potential points of cognitive and emotional strain. These findings were treated as directional rather than representative and would require validation with practising clinicians before implementation.

The strongest finding was simple:
AI support would only be useful if clinicians could understand and verify how it reached its recommendation.

Explored AI-assisted triage concepts to simplify referrals, reduce cognitive load, and keep clinicians in control.

Fixing The Workflow Before Refining The UI

The early wireframes focused on workflow structure, information hierarchy, and decision logic. The goal was to validate whether clinicians could move through the triage process without unnecessary context switching.

Clinical Feedback Made The Design Sharper

Think-aloud testing with three practising doctors helped evaluate whether the interface matched real clinical reasoning. The testing was especially useful because doctors could identify where the prototype looked clear from a UX perspective but still failed clinical expectations.

What experts told us :

Testing Signals That Changed The Design

Explainable AI. Clinician-Led Decisions.

Based on the research and testing, the design strategy focused on reducing cognitive load while preserving clinician authority. The interface needed to help clinicians understand urgency quickly without making the system feel like a black-box decision-maker.

Making AI-Assisted Triage Easier To Review, Trust, And Control.

The final desktop interface focused on helping triage nurses review referrals faster, understand AI recommendations, and make accountable clinical decisions. The design was not created to let AI decide independently. It was designed to make urgency, evidence, documentation, and final nurse-led decisions easier to manage in one connected workflow.

Case Dashboard: Clearer Referral Prioritisation

The dashboard gives triage nurses a structured view of all incoming cases. Cases are organised by triage decision, status, referral reason, assigned clinician, and completion level. This helps nurses identify urgent referrals faster instead of manually scanning disconnected case information.

Workspace Controls: Better Shift And Case Management

The workspace panel supports real clinical triage work beyond the case list. Nurses can control visible columns, manage alerts, pin doctors, set refresh preferences, and write shift handover notes. This makes the dashboard more useful during active clinical shifts.

Case Review: Evidence And AI Reasoning In One View

The case review screen connects patient details, referral notes, lab values, AI assessment, and triage reasoning in one place. The “Why This Decision?” section explains the clinical factors behind the recommendation instead of only showing a confidence score.

Accepted Decision State: Clear Nurse Accountability

After the nurse accepts the AI triage decision, the interface shows the decision status and who accepted it. This makes the final clinical action visible, documented, and accountable.

Modify Decision Flow: Controlled Override With Reason

When the nurse disagrees with the AI recommendation, the modify flow requires an alternative triage decision and a reason. This keeps clinician authority clear while making changes traceable.

Modified Decision State: Updated Triage Outcome

After modification, the case updates to show the final nurse-selected decision and documentation. The interface keeps the original AI reasoning visible while clearly showing the nurse's final decision.

This project taught me that AI in healthcare should not be designed to replace clinical judgment. It should reduce the effort required to review complex information while keeping the clinician fully in control of the final decision.The most important design challenge was creating trust through visibility. A confidence score alone was not enough. Clinicians needed to see the evidence, understand the reasoning, review the source information, and know exactly how to act on the recommendation. I also learned that safety-critical interfaces need restraint. More features do not automatically make the product better. The value came from simplifying the workflow, making urgent information easier to scan, and turning AI output into something clinically reviewable, explainable, and accountable.