IP Requirement: Emory IP

Experience Requirement:

– Experience with AI

– Strong coding background

– UI/UX design

Introduction 

This proposal outlines an Epic feature: a Wound and Mole Progression Visualization Model integrated into the EHR platform to track and compare wound healing and suspicious mole changes over time using serial photography. Tailored for dermatology and oncology, including Cutaneous T-Cell Lymphoma (CTCL) and suspicious mole monitoring, it addresses the limitations of Epic’s Haiku app, enhancing clinical decision-making and patient care. 

Background and Need 

Dermatologists require precise tools to monitor chronic wounds (e.g., CTCL lesions) and suspicious moles for signs of malignancy, such as melanoma. Current methods, including Haiku, are inadequate for longitudinal tracking and analysis: 

• Manual Assessments: Ruler-based measurements and visual inspections are subjective, with up to 50% error in wound size estimation and inconsistent mole evaluations. 
• Static Imaging: Haiku allows image uploads but lacks tools for comparing changes over time or quantifying features like asymmetry or border irregularity. 
• No Specialized Metrics: Haiku cannot measure wound-specific (e.g., area, tissue type) or mole-specific (e.g., ABCDE criteria) characteristics. 
• Workflow Gaps: Haiku’s limited analysis tools require manual documentation, increasing clinician workload and error risk. 

Proposed Solution 

The Wound and Mole Progression Visualization Model is an Epic-integrated module for tracking wounds and suspicious moles with enhanced functionality over Haiku. 

Key Features 

1. Serial Image Comparison: 

○ Uploads photos via Epic (enhancing Haiku) or clinical devices, organizing them into a patient-specific timeline. 

○ Aligns images to correct for angle/lighting variations, enabling accurate comparisons. 

2. Quantitative Metrics: 

○ Measures wound area, perimeter, and tissue types (e.g., granulation, necrosis) for CTCL lesions. 

○ Analyzes moles using ABCDE criteria (Asymmetry, Border, Color, Diameter, Evolving), quantifying changes in size, shape, and color.

○ Automates Photographic Wound Assessment Tool (PWAT) scores and mole risk scores. 

3. Visualization Interface: 

○ Displays a timeline of images with metrics (e.g., wound area reduction, mole diameter changes) and centile charts. 

○ Offers 3D wound renderings and mole close-ups for detailed inspection. 4. Dermatology Applications: 

○ CTCL Wounds: Tracks lesion progression (erythema, ulceration) to assess treatment response and detect complications. 

○ Suspicious Mole Monitoring: Quantifies ABCDE changes to identify potential melanomas, flagging high-risk moles (e.g., diameter >6mm, irregular borders) for biopsy. 

○ Supports tele-dermatology for remote mole and wound monitoring. 

5. EHR Integration: 

○ Embeds in Epic, storing HIPAA-compliant images and metrics in patient records. ○ Automates documentation, reducing Haiku’s manual entry burden. 

Benefits for Clinicians 

• Mole Monitoring: Tracks subtle changes in size, color, or borders, improving early detection of melanoma compared to Haiku’s static image viewing. 
• CTCL Management: Quantifies lesion changes, aiding therapy adjustments and trial endpoints. 
• Efficiency: Saves time with automated metrics and reports, streamlining workflows. ● Patient Engagement: Visual timelines enhance patient understanding and adherence. 

Conclusion 

The Wound and Mole Progression Visualization Model overcomes Haiku’s limitations, providing dermatologists with precise, longitudinal tracking of CTCL wounds and suspicious moles. By integrating with Epic, it enhances melanoma detection, treatment monitoring, and clinical efficiency.