Smart Triage
Designing AI-Powered Triage for Faster Diagnosis and Treatment
Quin MD | Amsterdam Netherlands 2023 - 2024
About Quin
Quin is passionate about making healthcare more accessible by bridging the gap between general practitioners, specialists, and patients. By simplifying care pathways and streamlining workflows, we help overburdened healthcare providers manage growing patient demands with smart, technology-driven solutions.
Business challenge
Irritable Bowel Syndrome (IBS) affects 5.8% of Dutch adults, leading to long waiting times, increased healthcare visits, and a heavy burden on specialists. Many patients cycle through GPs, physical therapists, and alternative care before finally receiving specialized treatment. This inefficiency drives up costs—29% in primary care and 116% in secondary care after diagnosis.
We developed a digital tool to speed up IBS diagnosis with AI-powered triage, provide patients with digital treatment advice to reduce in-person visits, and ensure compliance while maintaining trust in AI recommendations.
Our approach
How we built it:
Researched workflow challenges with MDL specialists.
Mapped user flows to identify inefficiencies in triage and referrals.
Analyzed existing solutions to find gaps and opportunities.
Developed wireframes and explainable AI models to build trust.
Designed an AI triage tool for faster diagnosis.
Created high-fidelity prototypes, ready for clinical studies and hospital collaboration.
Continuously refined UX for clarity, compliance, and usability.
Worked with developers to bring UI designs to production.
My role
This project was a deeply collaborative effort, shaped by continuous research, user feedback, and cross-functional teamwork. While the UX designer led flow mapping, I contributed ideas and co-developed wireframes with the UX and service designer. My primary focus was designing interactive UI prototypes (emphasized in this showcase) that informed implementation and secured hospital buy-in. throughout the project, I worked closely with developers to ensure the designs were practical, aligned with the design system, and ready for real-world clinical use.
The solution: Quin’s smart triage tool
Quin’s solution optimizes MDL workflows and accelerates IBS patient care with the following features:
Smart patient questionnaire: Captures crucial health data for an accurate diagnosis.
Triage list: Prioritizes referrals to ensure urgent cases are addressed first.
AI-powered triage analysis – Automates diagnosis through an explainable AI approach, evaluating referrals and questionnaire responses to prioritize patients efficiently.
Treatment form: Automates treatment selection based on diagnostic data generated by AI.
AI-Generated Treatment Advice: Provides personalized advice to patients based on the selected treatment, ensuring timely and accurate care and in a language that the patient can understand.
Result (expected outcomes)
The AI-powered triage solution is anticipated to:
Accelerate patient diagnosis and treatment, significantly reducing wait times and improving access to care.
Reduce the workload of MDL specialists, enabling them to focus on complex cases while routine assessments are automated.
Enhance diagnostic consistency and accuracy, as AI-powered analysis provides standardized assessments based on comprehensive patient data.
Optimize workflows, lowering administrative burdens, reducing manual tasks, and improving specialist efficiency.
Improve patient experience, offering personalized digital treatment plans that minimize unnecessary hospital visits and enhance overall satisfaction with care.
As the solution is classified as a medical device, it must meet strict compliance regulations before deployment in clinical settings. The product is now prepared to undergo a clinical study to rigorously validate its effectiveness, ensuring it meets both safety and regulatory standards before broader adoption.
Reflection
One of the biggest challenges in AI-driven healthcare solutions is ensuring trust and transparency in AI-generated outcomes. Specialists need clear, explainable insights rather than black-box decisions to feel confident in adopting AI recommendations. Through iterative UX design and direct collaboration with MDL specialists, we learned that designing AI tools with transparent logic, adjustable recommendations, and clinician oversight is critical to gaining user trust. By prioritizing explainability, the AI-powered triage solution not only enhances efficiency but also fosters greater acceptance among medical professionals, paving the way for scalable AI adoption in healthcare.
