Chart Editor Help Guide – Resilience Software

The Chart Editor will be continuously improved based on user input and feedback. If you have a request, please let us know at support@t-res.net.

Welcome to the Chart Editor

Transform your training data into meaningful visualizations. Track competency development over time, analyze procedure patterns, or compare performance across different areas. With AI assistance and manual controls, create exactly the insights you need.

Using advanced machine learning, T-Res can generate complete chart configurations simply by describing what you want to see in plain language. You might ask, "Show me my technical skills progress over the last year" or "Compare procedure counts by resident" - and we'll automatically configure chart type, data sources, filters, and styling to create the visualization you're looking for.

What This Means for You

If you're a learner or resident, you can create charts to:

Track your progress in specific competency areas like technical performance, communication, or clinical reasoning
Visualize how often you've performed different procedures or clinical activities
Compare your evaluation trends against your training stage expectations
Analyze patterns in your self-assessments versus supervisor evaluations
Monitor your activity distribution across different clinical settings or specialties

If you're an instructor or attending physician, you can build charts to:

Monitor individual student progress and identify those who need additional support
Compare performance across your residents or students
Track evaluation patterns for different procedures or clinical activities
Analyze your teaching effectiveness and student outcome correlations
Create educational dashboards for learner feedback sessions

If you're a program director or administrator, you can develop charts to:

Establish program-wide performance benchmarks and track trends
Monitor resident progression patterns across training stages
Ensure evaluation frequency and quality assurance standards
Analyze competency achievement rates across different areas
Conduct data quality analysis to ensure comprehensive assessment coverage

What You Can Do

AI-Powered Chart Generation

Describe Your Vision: Simply tell the system what kind of chart you want in plain language
Voice Input Support: Use the microphone button to dictate your chart requirements instead of typing
Automatic Configuration: Let AI determine the best chart type, data sources, and settings for your request
Smart Field Selection: The system automatically selects relevant fields from your chosen form data

Manual Chart Configuration

Precise Control: Fine-tune every aspect of your chart configuration
Multiple Chart Types: Choose from bar, line, pie, scatter, doughnut, polar area, and radar charts
Advanced Filtering: Apply complex filters to focus on specific data subsets
Flexible Aggregation: Count activities, calculate averages, or display raw values
Custom Styling: Control labels, limits, and sorting options

Preview and Iterate

Real-Time Preview: See your chart update instantly as you make configuration changes
Interactive Filters: Test how filters will work with your chart
Validation Feedback: Get helpful error messages if your configuration needs adjustment

Drill Down and Explore

Click Any Data Point: Click on bars, points, or chart segments to see the individual training records behind that data
Detailed Activity View: Open a new window showing the specific evaluations, procedures, or assessments that created each chart element
From Summary to Detail: Move seamlessly from high-level insights to the specific training activities that tell the complete story

Save and Share

Dashboard Integration: Add your charts directly to dashboards upon saving
Chart Templates: Create chart configurations that can be made available to other users in your program
Chart Library: Access your saved charts from any dashboard

Important: Data Privacy When you create a chart, you're creating a template or configuration, not sharing your actual data. Each user who views your chart will see only their own authorized data:

Learners see only their own training activities
Instructors see their own data plus supervised learners
Program Directors see program-wide data

Getting Started

Creating Your First Chart

Option 1: AI-Powered Generation

Select Your Data Source
Choose the form that contains the data you want to analyze
The form determines what fields are available for your chart
Note: Changing the form after a chart has been generated will create a new chart from scratch
Describe Your Chart
Under "Your Prompt" describe what you want to see: e.g. "Show me average technical skills by resident stage"
Be as specific as possible about:
- What data you want to analyze
- How you want it displayed
- Any time periods or filters you need
- Who or what you want to compare
Generate and Review
Click "Generate Chart" to let AI create your configuration
Review the generated preview to see if it matches your needs
The system will automatically select appropriate chart types, fields, and settings
Refine if Needed
Use the manual controls to adjust any settings
Change chart type, axes, or filters as desired
Click "Update Preview" to see your changes

Option 2: Manual Configuration

Choose Your Data
Select the form containing your data
The Chart Settings panel will populate with available options
Configure Step by Step
Start with chart type (bar, line, pie, etc.)
Set up your data source and grouping
Configure X and Y axes
Add filters if needed
Set display options like limits and sorting
Preview Your Work
Click "Update Preview" to see your chart
Iterate on your configuration until you're satisfied

Saving Your Chart

Save with Dashboard Assignment
Click the "Save Chart" button dropdown
Select which dashboards should include this chart
Your chart will be added to the selected dashboards
Any quick filters on the chart will also be added to the dashboard
Chart Template Access
Program directors and administrators can create chart templates for different user roles
When you use these templates, you see only your own authorized data
Templates help ensure consistent analysis across your training program

AI-Powered Chart Generation

Using Natural Language Descriptions

The AI system understands medical education terminology and can interpret requests like:

Time-Based Analysis:

"Show my technical skills improvement over the past year"
"Display procedure counts by month for this quarter"
"Track evaluation scores across my residency stages"

Comparative Analysis:

"Compare average competency scores between residents"
"Show which procedures I perform most vs least frequently"

Specific Focus Areas:

"Analyze my communication evaluations based on medical site"
"Display self-assessments vs supervisor ratings for technical skills"
"Show EPA completion rates by resident stage"

Tips for Effective AI Prompts

Be Specific About Data:

Mention specific competency areas, procedure types, or evaluation metrics
Include time frames: "From Feb to June 2025," "since starting residency"
Specify if you want counts, averages, trends, or comparisons
Include any flters you might want: "selectable by resident and stage," "filtered by clinical site"

Describe the Visualization:

"Bar chart showing…" for comparisons
"Line graph of…" for trends over time
"Pie chart displaying…" for proportions
"Scatter plot comparing…" for relationships

Include Context:

Mention if you want personal data vs. program-wide analysis
Specify particular rotations, sites, or supervisors if relevant
Note if you want current stage vs. progression over time

Manual Chart Configuration Guide

Chart Settings

Chart Type

What it does: Determines the visual style of your chart

Bar: Shows data as vertical bars, great for comparing values across categories
Line: Displays data as connected points, ideal for showing trends over time
Pie: Shows data as slices of a circle, perfect for showing proportions
Scatter: Plots individual data points, useful for finding correlations, can include trend lines
Doughnut: Like pie charts but with a hollow center
Polar Area: Circular chart where each slice's radius represents the value
Radar: Spider web-like chart for comparing multiple metrics

Chart Title

What it does: The main heading displayed at the top of your chart

Keep it descriptive but concise
Example: "Average Technical Skills by Resident Stage"

Stacked (Bar Charts Only)

What it does: When enabled, multiple data series are stacked on top of each other instead of side-by-side

Enabled: Shows cumulative totals, useful for seeing part-to-whole relationships
Disabled: Shows data series side-by-side for comparison between data series

Data Settings

Create Datasets For

What it does: Determines how to split your data into different colored series

Fields: Creates one dataset per field (e.g., separate lines for "Technical Skills" and "Communication Skills")
Values: Creates one dataset per unique value of a chosen field (e.g., separate lines for each resident)

Split By (When "Values" is selected)

What it does: Chooses which field's values become separate datasets

Example: Splitting by "Resident Name" creates a different colored line for each resident

Axes Settings

X-Axis

Field:

What it does: Determines what appears along the horizontal axis
For bar charts: Categories to compare
For scatter plots: Independent variable
Example: "Date", "Resident Stage", "Procedure Type"

Aggregation:

What it does: How to handle multiple data points with the same X value
None: Show individual data points (scatter plots)
Group: Combine data points with the same X value (bar/line charts)

Unit (Date Fields Only):

What it does: How to group date values
Year: Group all activities by year
Month: Group all activities by month
Note that you can also include PGY stage, where available

Y-Axis

Field:

What it does: Determines what values appear on the vertical axis
For counting: Leave empty to count activities
For averaging: Select fields to calculate averages
Multiple fields create multiple datasets

Aggregation:

What it does: How to calculate Y-axis values
Count: Count number of activities in each group
Average: Calculate average of selected field values
None: Use raw values (scatter plots)

Display Settings

Limit

What it does: Maximum number of data points or groups to show

Useful for "Top 10" or "Bottom 20" type charts
Leave empty to show all data

Sort By

What it does: Determines the ordering of your data

Value: Sort by the calculated values (count/average)
Group: Sort by the X-axis categories (alphabetical/chronological)

Sort Order

What it does: Direction of sorting

Ascending: Lowest to highest (A-Z, oldest to newest)
Descending: Highest to lowest (Z-A, newest to oldest)

Trend Lines (Scatter Plots Only)

What it does: Adds a line showing the overall trend in your data

None: No trend line
Linear: Straight line showing general direction
Polynomial: Curved line following data more closely

Filters Section

Filters let you narrow down which activities are included in your chart. Each filter has:

Field

What it does: Selects which data field to filter by

Example: "Date", "Procedure Type", "Resident Name"

Operator

What it does: Defines how to apply the filter

Between: For date ranges or numeric ranges
In: Include only specific values from a list
=, !=: Equal to or not equal to a specific value
, <=, >=: Less than, greater than comparisons

Values

What it does: The actual filter criteria

For "between": Two values defining the range
For "in": List of acceptable values
For comparisons: Single value to compare against

Understanding Quick Filters

What Quick Filters Do

Quick filters are interactive controls that make your charts dynamic and user-friendly. They allow viewers to instantly filter and explore chart data without needing to edit the chart configuration.

Key Benefits:

Instant Exploration: Change what data you're viewing with simple dropdown selections
Multi-Purpose Charts: One chart can answer many different questions through filtering
Dashboard Integration: Filters can control multiple charts at once when placed on dashboards

How Quick Filters Are Created

1. During Chart Creation: When you use AI to generate charts, you can ask for filterable options:

"Show procedures by resident" automatically creates a resident filter
"Selectable by year" creates a date filter for yearly selection
"Filterable by competency area" creates a competency filter

2. Manual Creation:

Use the "Manage Quick Filters" button in the Chart Editor
Create custom filters for any field in your forms
Set value ranges or specific options for each filter

3. Program-Wide Setup: Your program administrator can create filters that work across all charts in your program.

Where Quick Filters Appear

Individual Chart View: When viewing charts in the "All Charts" tab, quick filters appear directly above each chart. Only the filters that are relevant to that specific chart are shown.

Dashboard View: When charts are added to dashboards, their quick filters can be "promoted" to a shared filter bar above the entire dashboard. These dashboard-level filters then control all charts on that dashboard simultaneously.

Filter Coordination:

Dashboard filters update all compatible charts at once
Charts that don't use a particular filter simply ignore it
This creates a coordinated view where changing one filter updates your entire analysis

Creating Interactive Charts

When to Request Quick Filters:

Ask for "selectable" or "filterable" options in your AI prompts
Use phrases like "by resident" or "by time period" to trigger filter creation
Request filters when you want charts that can answer different questions

Examples of Filter-Enabled Charts:

"Competency progress selectable by resident" creates both a progress chart and a resident filter
"Procedure counts filterable by year and site" creates filters for both time and location
"Evaluation trends by supervisor" creates charts with supervisor selection options

Managing Your Quick Filters

From the Chart Editor:

Preview how filters will work with your charts before saving
Test different filter combinations to ensure they work as expected
Use "Manage Quick Filters" to create new filters or modify existing ones

Filter Organization:

Filters are shared across your training program
Create filters that will be useful for multiple charts and users
Choose descriptive names that make filters easy to understand

Advanced Features

Working with Computed Fields

The system provides several computed fields that add powerful analysis capabilities:

Resident Information

Resident Name: Actual resident names (privacy-controlled)
Obfuscated Resident Name: Creates anonymized names to protect privacy
Resident Stage: Training level (PGY1, PGY2, etc.) at time of activity

Time-Based Progression

Resident Stage Float: Precise position within residency (e.g., 1.5 = halfway through first year)
Days into Stage: Progress within current training level
Days into Residency: Overall residency progress (helpful when comparing different cohorts)
Before/After Residency: Boolean flags for activities outside formal training

Training Context

Instructor Name: Supervisor or attending information
Additional metadata about training context and educational relationships

Multi-Field Analysis

Creating Complex Comparisons:

Use multiple Y-axis fields to compare different metrics simultaneously
Split data by one field while aggregating others
Combine direct evaluation data with computed progression fields

Example Configurations:

Compare technical skills, communication, and professionalism scores across resident stages
Analyze procedure counts while tracking resident progression over time
Evaluate supervisor feedback patterns across different clinical rotations

Data Quality and Performance

Handling Large Datasets:

Use limits to focus on most relevant data points
Apply filters to reduce processing time for complex charts
Choose appropriate aggregation levels for your analysis needs

Ensuring Accurate Analysis:

Validate that your filters include the intended data range
Check that aggregation methods match your analysis goals
Use preview mode to verify chart behavior before saving

Workflow Tips and Best Practices

Planning Your Chart

Before You Start:

Define Your Question: What specific insight are you trying to gain?
Choose Your Scope: Personal progress, program comparison, or longitudinal analysis?
Consider Your Audience: Who will use this chart and what do they need to know?

Selecting the Right Approach:

Use AI Generation when you have a clear question but aren't sure about technical configuration
Use Manual Configuration when you need precise control over data handling and display
Combine Both by starting with AI generation and refining manually

Iterative Development

Start Simple:

Begin with basic chart configuration
Use the preview to validate your approach
Add complexity gradually (additional fields, filters, styling)
Test with different quick filter combinations

Refine and Polish:

Adjust chart titles to be specific and meaningful
Choose appropriate limits for your data size
Set logical sort orders for your analysis goals
Add filters to exclude irrelevant or outlier data

Testing and Validation

Preview Thoroughly:

Test your chart with different quick filter settings
Verify that data aggregation produces expected results
Check that chart type effectively communicates your insights

Validate Data Quality:

Ensure your filters include the intended data range
Check for missing or unexpected data patterns
Verify that computed fields are working as expected

Sharing and Documentation

Clear Communication:

Use descriptive chart titles that explain what the chart shows
Choose chart types that match your data story
Consider adding context through dashboard descriptions

Template Creation:

Create chart templates that can be made available to appropriate user roles
Add charts to relevant dashboards where they'll be most useful
Consider both individual learning and program-wide analysis needs

Chart Types and When to Use Them

Bar Charts

Best for: Comparing quantities across categories

Procedure counts by type
Average scores by resident stage
Top performers in specific competencies

Configuration Tips:

Use stacking for part-to-whole relationships
Sort by value for ranking analysis
Limit results for "top N" or "bottom N" views

Line Charts

Best for: Showing trends over time

Competency development progression
Evaluation score trends across residency
Monthly or yearly activity patterns

Configuration Tips:

Use date fields for X-axis
Group by appropriate time units (month, year)
Multiple lines for comparison across residents or metrics

Pie and Doughnut Charts

Best for: Showing proportions of a whole

Distribution of procedure types
Breakdown of competency domains
Time allocation across clinical areas

Configuration Tips:

Limit to 5-7 categories for readability
Use "top 10" or "bottom 5" limit to group smaller categories
Consider doughnut charts for modern appearance

Scatter Plots

Best for: Finding relationships between variables

Correlation between different evaluation metrics
Progress patterns over time
Identifying outliers or trends

Configuration Tips:

Use for continuous variables on both axes
Add trend lines to highlight relationships
Consider using resident stage float for time-based analysis

Radar Charts

Best for: Comparing multiple metrics simultaneously

Competency profiles across multiple domains
Multi-dimensional performance comparison
Skills assessment across different areas

Configuration Tips:

Best with 3-8 metrics for clarity
Use for standardized or normalized scales
Effective for individual vs. benchmark comparisons

Drilling Down into Your Data

Understanding Chart Data Points

Every data point in your charts represents a collection of individual training records that have been grouped and calculated according to your chart configuration. When you see a bar showing "15 procedures" or a line point showing "average score of 4.2," that represents real training activities that occurred in your program.

How Data Points Are Created:

Filtering First: The system starts with all training activities in your program and applies any filters you've specified (date ranges, procedure types, residents, etc.)
Grouping: Filtered activities are then grouped according to your chart settings (by date, by resident, by procedure type, etc.)
Aggregation: Within each group, the system calculates your results (counts, averages, or individual values)
Data Linking: Each resulting data point maintains connections to the specific training records that contributed to that calculation

Click-Through to Activity Data

One of the most powerful features of your charts is the ability to drill down from summary data to the individual training activities contributing to that data point.

How to Drill Down:

Click Any Data Point: Click on any bar, line point, pie slice, or scatter point in your chart
Automatic Navigation: A new window opens showing the detailed training records
Review Individual Records: See the specific evaluation forms, procedures, or assessments that contributed to that data point
Contextual Understanding: Understand exactly what training activities created the patterns you're seeing in your charts

What You'll See in Activity Data:

Individual Evaluation Forms: The actual assessment records with all field values
Activity Details: All the information captured during those training activities
Related Activities: Other activities from the same time period or involving the same people

Examples of Drill-Down Analysis

Scenario 1: Performance Investigation

You see a bar chart showing one resident with unusually low average technical skills
Click on that resident's bar to drill down
Review the individual evaluations contributing to that average score
Identify specific procedures, dates, or supervisors involved
Understand whether it's a consistent pattern or isolated incidents

Scenario 2: Trend Analysis

Your line chart shows a significant improvement in competency scores over time
Click on the data point representing that improvement
See exactly which activities occurred during that time period
Identify what procedures or training experiences contributed to the progress
Understand the educational interventions that were effective

Scenario 3: Quality Assurance

A pie chart shows an unexpected distribution of procedure types
Click on the largest slice to drill down
Verify that the activities are correctly categorized
Check for data quality issues or unexpected patterns
Ensure your program tracking is capturing the right information

Using Drill-Down for Deeper Insights

Validation and Verification:

Confirm that your chart summaries are based on the expected training activities
Verify data quality and completeness
Check for outliers or unusual patterns in the underlying data

Educational Planning:

Identify specific training experiences that led to positive outcomes
Find gaps where additional practice or instruction might be needed
Understand the context behind performance patterns

Program Improvement:

Discover which educational activities are most effective
Identify systemic issues that need attention
Track the impact of program changes over time

Research and Analysis:

Access detailed data for educational research
Understand the relationship between different training variables
Analyze specific subsets of your training data

Best Practices for Drill-Down Analysis

Start with Summary, Drill to Detail:

Use charts to identify interesting patterns or outliers
Drill down into specific data points that need investigation
Use the detailed activity view to understand the context
Return to charts with new insights to guide further analysis

Combine Multiple Perspectives:

Use filters to focus your drill-down analysis
Compare drill-down results across different time periods or groups
Look for patterns that repeat across different data points

Document Your Findings:

Take notes on interesting patterns you discover through drill-down
Share insights with colleagues or supervisors
Use findings to improve training programs or individual development plans

Troubleshooting Common Issues

Chart Won't Generate or Preview

Check Your Configuration:

Ensure you've selected a form as your data source
Verify that required fields (X-axis, Y-axis) are configured
Make sure your filters don't exclude all data

For AI Generation:

Check that your prompt is specific enough
Ensure the selected form contains relevant fields for your request
Try rephrasing your request with different terminology

No Data Appearing in Chart

Filter Issues:

Check that your date filters include periods with actual data
Verify that field filters aren't excluding all activities
Reset filters to "All" to see if data appears

Configuration Problems:

Ensure your aggregation settings match your data type
Check that field selections are valid for your chosen form
Verify that computed fields are available for your program

Chart Loads Slowly or Times Out

Optimize Your Configuration:

Add limits to reduce the number of data points processed
Use more restrictive filters to focus on relevant data
Choose simpler aggregation methods for large datasets

Simplify Your Analysis:

Break complex multi-field charts into simpler visualizations
Consider using date grouping (monthly vs. daily) for time-based analysis
Split complex charts into multiple focused charts

Quick Filters Not Working

Chart Compatibility:

Verify that your chart uses fields that match available quick filters
Check that quick filters are properly configured for your program
Ensure that chart fields are compatible with filter data types

System Configuration:

Use "Manage Quick Filters" to verify filter setup
Check that computed fields are properly configured if using advanced filters
Ensure that your role has access to the relevant filter options

Getting More Help

Learning Resources

Chart Examples:

Browse the "All Charts" view to see examples of effective visualizations
Look at pre-built charts in your program for configuration ideas
Study chart configurations that effectively answer similar questions

Training and Documentation:

Review dashboard help for understanding how charts work within dashboards
Ask your program administrator about training sessions
Explore different chart types to understand their strengths

Getting Support

Technical Issues:

Contact support@t-res.net for system problems
Report bugs or unexpected behavior
Ask about new features or enhancements

Program Configuration:

Work with your program administrator to set up new quick filters
Request access to additional forms or fields if needed
Discuss chart template creation and role-based availability

The Chart Editor will be continuously improved based on user input and feedback. If you have a request, please let us know at support@t-res.net

The Chart Editor is a powerful tool for transforming your training data into actionable insights. Whether you're tracking individual progress, comparing program performance, or conducting research analysis, take time to experiment with different approaches and configurations to create visualizations that truly support your educational goals.