Overview
Menstrual cycle data is often spread across multiple views, making it difficult to identify patterns during consultations.
This dashboard was designed to help clinicians understand cycle status, ovulation, symptoms, and trends at a glance, reducing the need to navigate fragmented information.
Clinicians need to answer several questions quickly:
What phase is the patient currently in?
Has ovulation occurred?
Are there any notable patterns or irregularities?
Most existing experiences require switching between multiple screens and data sources, increasing cognitive effort during consultations.
Goal: Create a single dashboard that transforms complex cycle data into actionable insights.
Understanding the Clinical Workflow
Menstrual cycle tracking generates a variety of data points including basal body temperature (BBT), bleeding patterns, energy levels, symptoms, and ovulation tests.
While each metric is valuable individually, meaningful insights often emerge only when these signals are interpreted together.
The dashboard was structured around the typical diagnostic process:
Identify the patient's current cycle phase
Confirm ovulation status
Recognize physiological patterns over time
Detect irregularities quickly
Make informed decisions without navigating across multiple screens
Rather than forcing users to adapt to the interface, the interface was designed to adapt to the clinician's natural thinking process.
Designing Around Scanning Behavior
Clinicians often review information under time constraints. To support fast scanning, information was organized by priority:
Key indicators at the top
Trend analysis in the center
Supporting insights alongside the data
This creates a natural overview-to-detail flow while reducing cognitive effort.
Establishing Context First
The top section was designed to answer the most important questions immediately.
The summary cards communicate:
Current Cycle Phase
Ovulation Status
Cycle Length
Key Clinical Insight
The order follows clinical priority:
Context → Validation → Supporting Information → Insight
This helps clinicians establish a quick understanding before exploring detailed data
Visualizing Data for Faster Interpretation
Different data types require different visualizations. Instead of forcing every metric into a single format, each chart was selected based on how clinicians interpret that information.
BBT & Energy Trends
BBT and Energy Levels are displayed using line graphs.
Temperature trends help reveal ovulation patterns, while energy levels provide additional physiological context. Displaying both on a shared timeline makes correlations easier to identify.
LH Test Results
LH test results are displayed as event markers directly on the temperature timeline.
This preserves a single time reference while reducing visual fragmentation and simplifying interpretation.
Pain Tracking
Pain is represented using a bar graph.
Since pain is intensity-based and varies throughout the cycle, a bar graph enables quick comparison across days and makes peaks or fluctuations easier to identify.
Blood Flow Tracking
Blood flow is represented using a strip visualization.
Since bleeding is primarily evaluated through presence and duration, a timeline-based strip communicates this information more effectively than traditional charts.
Creating a Unified Timeline
All data is organized around a shared 28-day timeline to reduce the effort required to connect multiple physiological signals.
Key checkpoints such as Day 1, Day 14, and Day 21 provide familiar clinical reference points and help clinicians correlate:
Temperature changes
Ovulation indicators
Symptom patterns
Bleeding activity
without switching between disconnected views.
Cycle phases are mapped directly onto the timeline, with additional emphasis placed on the ovulation window.
The ovulation window is visually highlighted to indicate the most critical phase.
This supports faster pattern recognition by connecting physiological changes directly to cycle phases and time.
Focused Insights
Only one key insight is highlighted at a time.
This helps:
Reduce cognitive overload
Direct attention to the most relevant observation
Support faster clinical review
The insight acts as a signal rather than additional noise.
Clinical Summary
A structured summary translates raw data into meaningful observations, including:
Cycle behavior
Ovulation confirmation
Symptom trends
Cycle comparisons
This helps clinicians identify patterns quickly without manually interpreting multiple data sources.
Outcome
The final dashboard was designed around a simple principle:
Help clinicians understand what matters most, as quickly as possible.
By aligning the interface with real clinical workflows, simplifying data interpretation, and reducing cognitive effort, the experience transforms fragmented health data into actionable insights.
The result is a dashboard that supports faster consultations, clearer pattern recognition, and more confident clinical decision-making while keeping the clinician firmly in control of the diagnostic process.
Menstrual cycle data is often spread across multiple views, making it difficult to identify patterns during consultations.
This dashboard was designed to help clinicians understand cycle status, ovulation, symptoms, and trends at a glance, reducing the need to navigate fragmented information.
Clinicians need to answer several questions quickly:
What phase is the patient currently in?
Has ovulation occurred?
Are there any notable patterns or irregularities?
Most existing experiences require switching between multiple screens and data sources, increasing cognitive effort during consultations.
Goal: Create a single dashboard that transforms complex cycle data into actionable insights.
Understanding the Clinical Workflow
Menstrual cycle tracking generates a variety of data points including basal body temperature (BBT), bleeding patterns, energy levels, symptoms, and ovulation tests.
While each metric is valuable individually, meaningful insights often emerge only when these signals are interpreted together.
The dashboard was structured around the typical diagnostic process:
Identify the patient's current cycle phase
Confirm ovulation status
Recognize physiological patterns over time
Detect irregularities quickly
Make informed decisions without navigating across multiple screens
Rather than forcing users to adapt to the interface, the interface was designed to adapt to the clinician's natural thinking process.
Designing Around Scanning Behavior
Clinicians often review information under time constraints. To support fast scanning, information was organized by priority:
Key indicators at the top
Trend analysis in the center
Supporting insights alongside the data
This creates a natural overview-to-detail flow while reducing cognitive effort.
Establishing Context First
The top section was designed to answer the most important questions immediately.
The summary cards communicate:
Current Cycle Phase
Ovulation Status
Cycle Length
Key Clinical Insight
The order follows clinical priority:
Context → Validation → Supporting Information → Insight
This helps clinicians establish a quick understanding before exploring detailed data
Visualizing Data for Faster Interpretation
Different data types require different visualizations. Instead of forcing every metric into a single format, each chart was selected based on how clinicians interpret that information.
BBT & Energy Trends
BBT and Energy Levels are displayed using line graphs.
Temperature trends help reveal ovulation patterns, while energy levels provide additional physiological context. Displaying both on a shared timeline makes correlations easier to identify.
LH Test Results
LH test results are displayed as event markers directly on the temperature timeline.
This preserves a single time reference while reducing visual fragmentation and simplifying interpretation.
Pain Tracking
Pain is represented using a bar graph.
Since pain is intensity-based and varies throughout the cycle, a bar graph enables quick comparison across days and makes peaks or fluctuations easier to identify.
Blood Flow Tracking
Blood flow is represented using a strip visualization.
Since bleeding is primarily evaluated through presence and duration, a timeline-based strip communicates this information more effectively than traditional charts.
Creating a Unified Timeline
All data is organized around a shared 28-day timeline to reduce the effort required to connect multiple physiological signals.
Key checkpoints such as Day 1, Day 7, Day 14 and Day 21 provide familiar clinical reference points and help clinicians correlate:
Temperature changes
Ovulation indicators
Symptom patterns
Bleeding activity
without switching between disconnected views.
Phase Mapping
Cycle phases are mapped directly onto the timeline, with additional emphasis placed on the ovulation window.
The ovulation window is visually highlighted to indicate the most critical phase.
This supports faster pattern recognition by connecting physiological changes directly to cycle phases and time.
Focused Insights
Only one key insight is highlighted at a time.
This helps:
Reduce cognitive overload
Direct attention to the most relevant observation
Support faster clinical review
The insight acts as a signal rather than additional noise.
Clinical Summary
A structured summary translates raw data into meaningful observations, including:
Cycle behavior
Ovulation confirmation
Symptom trends
Cycle comparisons
This helps clinicians identify patterns quickly without manually interpreting multiple data sources.
Outcome
The final dashboard was designed around a simple principle:
Help clinicians understand what matters most, as quickly as possible.
By aligning the interface with real clinical workflows, simplifying data interpretation, and reducing cognitive effort, the experience transforms fragmented health data into actionable insights.
The result is a dashboard that supports faster consultations, clearer pattern recognition, and more confident clinical decision-making while keeping the clinician firmly in control of the diagnostic process.
