Quadrivia AI — Clinical Workflow Redesign

Overview — Why this redesign matters

Clinicians today work under extraordinary pressure.
Documentation demands are rising, digital tools remain fragmented, and patient cases are increasingly complex. While healthcare systems generate more data than ever, clinicians have less time to interpret it meaningfully.

Quadrivia AI’s assistant, Qu, was created to help reduce this burden. Early versions showed real promise in interpreting patient histories and surfacing risks. However, a critical insight emerged:

AI can only reduce cognitive load if it understands how clinicians actually think and work.

In practice, Qu’s summaries lacked clinical hierarchy, its reasoning was difficult to trace, and it offered limited support for team-based workflows across nurses, physicians, and care coordinators. Rather than reducing friction, the system sometimes introduced new cognitive overhead.

This redesign explores how Qu could evolve into a clinical-grade workflow partner—one that clarifies rather than overwhelms, accelerates rather than interrupts, and strengthens collaboration rather than fragmenting it.

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My role

I led this project from a product and experience design perspective, shaping both the strategic direction and the interaction model of Qu’s next evolution.

My responsibilities included:

• analyzing clinical workflows and decision-making patterns
• identifying systemic blockers across intake, assessment, and follow-up
• designing interaction models aligned with clinical reasoning
• crafting high-fidelity interfaces focused on clarity, safety, and efficiency

This work sits at the intersection of AI behavior design, clinical workflow strategy, and human-centered UX, with a focus on building tools clinicians can trust in high-stakes environments.

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From AI tool to workflow partner

Quadrivia operates around a simple but rare conviction:
AI should serve clinicians—not the other way around.

While Qu demonstrated strong technical capability in processing patient histories, it reflected challenges common across clinical AI products:

• limited clinical context awareness
• opaque or non-explainable reasoning
• fragmented workflow integration
• weak support for team handoffs

Rather than treating these as feature gaps, this redesign reframes Qu’s role—from a tool that generates summaries into a workflow-aware assistant that participates in clinical reasoning and care coordination.

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Key problems identified

Through workflow analysis and documentation review, several systemic issues became clear:

• Fragmented documentation
  Information entered by nurses, physicians, and coordinators was not connected into a coherent clinical narrative.
• Low traceability of AI outputs
  Clinicians struggled to trust summaries without visibility into how conclusions were reached.
• Missing clinical hierarchy
  Red flags, contradictions, and chronic patterns were often buried in dense text.
• Team workflow misalignment
  Intake data didn’t consistently translate into actionable next steps, and follow-up tasks were scattered across tools.
• Multilingual care barriers
  Clinicians frequently encountered language gaps that required immediate, nuanced support.

The conclusion was clear: Qu didn’t need more features—it needed a deeper understanding of clinical realities.

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Design direction

The redesign was guided by one central question:

How might Qu become a workflow-aware, clinically aligned partner that enhances clarity, trust, and team collaboration?

This resulted in five guiding goals:

• reduce documentation time
• improve summary clarity and reasoning transparency
• create structured, clinically meaningful narratives
• support multilingual interactions for clinicians
• enable continuity across nurse → physician → follow-up

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Solution overview

AI-assisted history processing

Qu was redesigned to process patient histories more intelligently—extracting timelines, identifying missing or contradictory data, and suggesting clarifying questions. The AI shifts from passive recorder to active reasoning support.

Explainable, layered summaries

Instead of dense paragraphs, clinicians receive a clear top-line assessment supported by evidence, prioritized risk flags, medication safety checks, and timeline context—mirroring real clinical reasoning.

Unified team workflow

A shared dashboard connects nurse intake, physician assessment, and follow-up coordination, ensuring continuity across roles and reducing information loss.

Clinician-side multilingual support

Qu provides real-time transcription and contextual translation to support clinicians during time-critical interactions. This experience is intentionally not patient-facing.

Interface system

The redesigned interface is organized around four clinical modes:

• Exploration — during patient conversations, optimized for readability
• Analysis — reviewing evidence, risks, and contradictions
• Decision — placing orders, documenting, and creating tasks
• Collaboration — maintaining continuity across the care team

Each mode is designed to reduce cognitive load, reinforce clinical hierarchy, and support efficient action under pressure.

Measuring impact

Although conceptual, the redesign is oriented around measurable outcomes.

For clinicians:

• documentation time ↓ 40%
• summary clarity ↑ 50%
• task switching ↓ 25%
• clinician trust confidence ↑

For care teams:

• intake-to-physician alignment ↑
• follow-up resolution rate ↑

At a system level:

• reduced information loss
• fewer duplicate questions during patient interviews

Conclusion

This redesign repositions Qu not as an isolated AI assistant, but as a clinical ally—one that understands the flow of care, supports decision-making, and strengthens collaboration across teams.

By grounding AI in the real-world rhythms of frontline medicine, Qu becomes more than a tool. It becomes part of the care team.

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Disclaimer

This project is an independent conceptual redesign created for educational and portfolio purposes only. It is not affiliated with Quadrivia AI and does not reflect proprietary data, internal models, or clinical decision logic. All workflows and interfaces shown are fictional examples intended to explore explainable, responsible AI within clinical environments.