CONFIGURABLE DASHBOARD

Through user feedback and extensive product market research, we discovered the challenge of designing a one-size-fits-all dashboard that caters to each unique use case. Our research revealed that no two surgeons, clinicians, or professionals within the same discipline (e.g., Cardiovascular Clinic) desired the same experience, product, or configuration.

The goal was to design a scalable dashboard system that could support diverse workflows without becoming overly complex, cluttered, or rigid.

The Configurable Dashboard serves as an all-in-one portal for clinicians across multiple verticals — including clinical care, performance optimization, and research.

PROJECT OVERVIEW:

Product: Remote Monitoring & Analytics Platform

Domain: Digital Health / Clinical Analytics

Role: UX Lead & Designer

Users: Clinicians, Surgeons, Researchers, Performance Specialists

Platforms: Web-based dashboard

Focus: Flexible, data-rich, configurable clinician experience

Tools: FIGMA, Confluence, JIRA, Maze

My Role:

  • Led cross-vertical discovery workshops

  • Conducted clinician interviews and workflow mapping

  • Defined dashboard architecture principles

  • Designed modular component system in Figma

  • Collaborated with engineering on data visualization constraints

  • Balanced flexibility with usability and performance

Through user interviews, shadowing sessions, and product-market research, we uncovered a critical insight:

No two clinicians — even within the same specialty — wanted the same dashboard.

The challenge:

How do you design a system that is configurable enough to serve distinct workflows — without overwhelming users or fragmenting the experience?

THE PROBLEM:

THE DESIGN GOALS:

Increase cross-vertical adoption

Enable scalable expansion into new verticals

Support diverse clinician workflows

Strengthen trust in remote monitoring data

Reduce product fragmentation

A “one-size-fits-all” dashboard would fail.
But infinite customization would create chaos.

The Design Process

    • Interviewed clinicians across multiple verticals

    • Shadowed real workflows and decision-making patterns

    • Identified variation in metric priorities — even within the same specialty

    • Mapped ecosystem touchpoints (wearable → Clinical app → dashboard)

    • Audited existing dashboards and competitor tools

    • Framed the problem: flexibility without complexity

    • Defined modular dashboard principles

    • Prioritized key workflows (triage, deep dive, longitudinal review)

    • Aligned dashboard and care app through shared design system

    • Clarified technical constraints and data requirements

    • Built modular, configurable dashboard architecture

    • Designed consistent time-range and filtering controls

    • Created clear visual hierarchy for risk and anomalies

    • Integrated annotations, alerts, and communication tools

    • Ensured cross-platform visual and functional consistency

    • Partnered with engineering on scalable component system

    • Optimized for high-frequency and large datasets

    • Iterated through usability validation

    • Rolled out across multiple clinical verticals

From Whiteboards to Products

  • Workshops

    INITIAL WIREFRAMES & STRUCTURE

  • Wireframes

    LOW FIDELITY WIREFRAMES

  • Design Concept

    UX/UI CONSIDERATIONS & STRATEGY

  • The Product

    UI ROLLOUT

Core UX Strategy

  • Instead of designing static dashboards, we built:

    • Configurable data modules

    • Adjustable time ranges

    • Re-orderable components

    • Role-sensitive layouts

    Each component functioned independently within a scalable system.

  • Clinical dashboards must support high-stakes decision-making. We focused on enabling clinicians to:

    • Interact with large datasets

    • Filter data subsets

    • Contextualize trends

    • Move between macro and micro analysis

  • To reduce cognitive load:

    • Persistent metrics bar for quick scanning

    • Clear subject list → patient deep dive hierarchy

    • Logical grouping of related metrics

    • Breadcrumb navigation

    • Consistent interaction patterns

    The experience supports fast triage and deep analysis without disorientation.

  • The dashboard extended beyond data viewing.

    Monitoring & Risk

    • Alerts and anomaly detection

    • Risk-level indicators

    • Machine learning disease detection models

    Communication & Continuity

    • Secure chat

    • Activity feeds

    • Event annotations

    Care Management

    • Prescription visibility

    • Treatment plan overview

    • Adherence tracking

  • Raw numbers are not insight. We prioritized:

    • Time-series trend visualization

    • Multi-metric overlays

    • Adjustable sample resolutions

    • Reference ranges and baseline comparisons

    • Clear anomaly indicators

    This transformed complex biometric streams into clinically meaningful patterns.

User Journey: The Configuration

The Final Product

On the surface:

A clean, controlled clinical intelligence system that:

  • Increased cross-vertical adoption

  • Reduced product fragmentation

  • Supported diverse clinician workflows

  • Strengthened trust in remote monitoring data

  • Enabled scalable expansion into new medical verticals

Technical Complexity, Simplified

Behind the interface:

  • High-frequency biometric datasets

  • Real-time + historical data blending

  • ML-driven disease detection models

  • Role-based access control

  • Performance optimization for large cohorts

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