Designing navigation for complex systems.

Turning system state into confidence.

Clinical labs rely on complex diagnostic instruments with strict uptime requirements, yet users lacked clear visibility into system health, diagnostics, and consumable readiness. I led the design of an instrument-centered experience that unified diagnostics, inventory, and service context, enabling labs to anticipate issues, reduce downtime, and act before problems escalated.

Instrument downtime is operational failure.

For clinical labs, an instrument that cannot run samples stops the work entirely. Users needed to answer time-critical questions quickly, but the answers were scattered and hard to interpret.

Constraints

• Highly regulated diagnostics environment
• Limited ability to expose raw diagnostic data
• Hardware-driven system states and dependencies
• Global variability in lab workflows
• Mixed audiences, both technical and non-technical users
• Regulatory requirements around data accuracy and traceability

Owning UX for instrument-focused experiences.

• Instrument overview and navigation
• Diagnostics visibility and status modeling
• Inventory and consumables tracking
• Service agreement and maintenance context
• Cross-functional collaboration with Engineering, Service, and Product
• Iterative validation through usability testing

The through line: turning complex system signals into actionable understanding.

What lab teams actually needed.

Key insights

Clustering the real signals.

To move beyond surface-level usability issues, I synthesized qualitative interview data and contextual observations into patterns that explained why users hesitated, over-ordered, or escalated prematurely. The goal was not to catalog pain points, but to identify the signals users relied on, or lacked, when making time-sensitive decisions.

Raw user statements captured during moderated usability sessions and interviews, clustered across lab managers, technicians, distributors, and global users. Confusion stemmed less from missing data and more from unclear severity, timing, and context.

From user signals to diagnostic design decisions.

To ensure the experience addressed real operational risk, not just system completeness, I mapped recurring user signals to the downstream behaviors they triggered and the design response each demanded. This framework decided which signals required clarity, guidance, or escalation, and which could remain informational.

Decision framework: common user signals mapped to behavioral risk and design response, to reduce downtime, alert fatigue, and unnecessary support escalation across instrument and inventory workflows.

Principles

Navigation and clarity for a complex system.

Four interface decisions carried the strategy: land users in context, keep them oriented, make status legible, and turn inventory from a reactive chore into a proactive signal.

Measured in downtime avoided, not speed.

Because this work focused on preventative visibility rather than workflow efficiency, results were measured in reduced downtime and avoided escalations rather than raw speed.

• Unplanned instrument downtime decreased by approximately 12 to 18% through earlier visibility into system health and consumable risk.
• Emergency reorders and rush shipments declined by approximately 14 to 22%, as users could anticipate inventory needs before hitting critical thresholds.
• Successful early identification of potential system issues increased by approximately 25 to 30%, driven by clearer prioritization of diagnostic signals.
• User confidence in understanding instrument health improved by approximately 18 to 22% in usability testing and follow-up surveys.
• Service calls triggered by uncertainty or misinterpretation of diagnostics decreased by approximately 10 to 15% following rollout.
• Proactive alerting and inventory forecasting cut unplanned intervention costs by roughly 12 to 18% as operators spent less time in reactive troubleshooting.

Representative, anonymized outcomes based on usability testing, platform usage trends, and internal service data.

Reliability is non-negotiable.

In clinical diagnostics, an instrument that cannot be trusted stops real testing. By translating complex diagnostic and inventory data into clear, preventative signals, this work helped labs maintain testing continuity, reduce stress, and operate more predictably, without exposing sensitive system internals or compromising regulatory requirements.

• Predictive failure indicators based on diagnostics trends
• Cross-instrument health dashboards
• Automated reorder suggestions tied to test volume
• Deeper integration with service scheduling
• Validating predictive indicators against real usage data to further reduce reactive workloads