Creating an AI Chabot EHR Platform
Overview
Led end-to-end design of Caregiver 360, a revolutionary Electronic Health Record (EHR) platform focusing on both quantitative and qualitative patient care data. As lead designer, I developed an intuitive interface that helped caregivers document patient progress, set goals, and track meaningful outcomes for individuals with intellectual and developmental disabilities. Conducted extensive research on AI implementation, specifically designing a sophisticated RAG (Retrieval-Augmented Generation) LLM chatbot interface tailored for specialized IDD care contexts.
Role
Lead UX/UI Designer
Full ownership of user experience strategy and visual design system
Created comprehensive user flows and information architecture
Designed intuitive interface components optimized for caregiver workflows
Collaborated with stakeholders to prioritize features for maximum impact
Conducted comprehensive research on AI implementation for healthcare contexts
Developed detailed specifications for role-based, context-aware AI interactions
Partnered with offshore development team to ensure design fidelity
Solution
Developed an innovative EHR platform balancing clinical requirements with human-centered care:
Clean, accessible interface reducing documentation time by approximately 40%
Goal-setting and tracking tools to improve patient outcomes
Comprehensive patient summary dashboards highlighting both health metrics and behavioral insights
Thoughtful information architecture optimizing caregiver workflows
Designed AI-ready interface with embedded pathways for future RAG implementation
Created progressive disclosure models for future AI interactions
Developed specifications for role-specific AI assistance tailored to different user types
https://www.figma.com/proto/OUgDy5CeOFPPC6I1G12YTJ/Caregiver360-Application---MVP-1-(Copy)?page-id=439%3A67089&node-id=439-68176&p=f&viewport=403%2C339%2C0.07&t=Dis4WEgrdJ6sGCEe-1&scaling=min-zoom&content-scaling=fixed&starting-point-node-id=439%3A68176&show-proto-sidebar=1
https://www.figma.com/design/OUgDy5CeOFPPC6I1G12YTJ/Caregiver360-Application---MVP-1-(Copy)?node-id=439-67089&t=kNOBPCzuAr2nO5Ig-1
Impact
Successfully delivered a beta version within a compressed 5-month timeline, creating a platform that transformed how caregivers document and monitor patient progress. The design strikes the perfect balance between collecting necessary clinical data and focusing on qualitative care metrics, demonstrating my ability to solve complex problems through thoughtful, user-centered design.
AI Strategy & Implementation Research
A significant portion of my work involved comprehensive research into implementing advanced AI capabilities through a RAG LLM chatbot:
Developed detailed specifications for an IDD-specific healthcare AI assistant
Created models for role-based AI interactions (clinicians, caregivers, administrators)
Designed context-aware prompt systems with progressive information disclosure
Conducted cost-benefit analysis comparing immediate vs. phased AI deployment
Led strategic decision-making that prioritized user adoption over cutting-edge features
Engineered the system architecture to accommodate future AI integration without rework
After thorough analysis of cost volatility and onboarding complexity, I recommended a phased approach to AI integration, designing clear implementation pathways while prioritizing initial user comfort and adoption—a decision that demonstrated both technical expertise and strategic business thinking.
Overview
Led end-to-end design of Caregiver 360, a revolutionary Electronic Health Record (EHR) platform focusing on both quantitative and qualitative patient care data. As lead designer, I developed an intuitive interface that helped caregivers document patient progress, set goals, and track meaningful outcomes for individuals with intellectual and developmental disabilities. Conducted extensive research on AI implementation, specifically designing a sophisticated RAG (Retrieval-Augmented Generation) LLM chatbot interface tailored for specialized IDD care contexts.
Role
Lead UX/UI Designer
Full ownership of user experience strategy and visual design system
Created comprehensive user flows and information architecture
Designed intuitive interface components optimized for caregiver workflows
Collaborated with stakeholders to prioritize features for maximum impact
Conducted comprehensive research on AI implementation for healthcare contexts
Developed detailed specifications for role-based, context-aware AI interactions
Partnered with offshore development team to ensure design fidelity
Solution
Developed an innovative EHR platform balancing clinical requirements with human-centered care:
Clean, accessible interface reducing documentation time by approximately 40%
Goal-setting and tracking tools to improve patient outcomes
Comprehensive patient summary dashboards highlighting both health metrics and behavioral insights
Thoughtful information architecture optimizing caregiver workflows
Designed AI-ready interface with embedded pathways for future RAG implementation
Created progressive disclosure models for future AI interactions
Developed specifications for role-specific AI assistance tailored to different user types
https://www.figma.com/proto/OUgDy5CeOFPPC6I1G12YTJ/Caregiver360-Application---MVP-1-(Copy)?page-id=439%3A67089&node-id=439-68176&p=f&viewport=403%2C339%2C0.07&t=Dis4WEgrdJ6sGCEe-1&scaling=min-zoom&content-scaling=fixed&starting-point-node-id=439%3A68176&show-proto-sidebar=1
https://www.figma.com/design/OUgDy5CeOFPPC6I1G12YTJ/Caregiver360-Application---MVP-1-(Copy)?node-id=439-67089&t=kNOBPCzuAr2nO5Ig-1
Impact
Successfully delivered a beta version within a compressed 5-month timeline, creating a platform that transformed how caregivers document and monitor patient progress. The design strikes the perfect balance between collecting necessary clinical data and focusing on qualitative care metrics, demonstrating my ability to solve complex problems through thoughtful, user-centered design.
AI Strategy & Implementation Research
A significant portion of my work involved comprehensive research into implementing advanced AI capabilities through a RAG LLM chatbot:
Developed detailed specifications for an IDD-specific healthcare AI assistant
Created models for role-based AI interactions (clinicians, caregivers, administrators)
Designed context-aware prompt systems with progressive information disclosure
Conducted cost-benefit analysis comparing immediate vs. phased AI deployment
Led strategic decision-making that prioritized user adoption over cutting-edge features
Engineered the system architecture to accommodate future AI integration without rework
After thorough analysis of cost volatility and onboarding complexity, I recommended a phased approach to AI integration, designing clear implementation pathways while prioritizing initial user comfort and adoption—a decision that demonstrated both technical expertise and strategic business thinking.
Overview
Led end-to-end design of Caregiver 360, a revolutionary Electronic Health Record (EHR) platform focusing on both quantitative and qualitative patient care data. As lead designer, I developed an intuitive interface that helped caregivers document patient progress, set goals, and track meaningful outcomes for individuals with intellectual and developmental disabilities. Conducted extensive research on AI implementation, specifically designing a sophisticated RAG (Retrieval-Augmented Generation) LLM chatbot interface tailored for specialized IDD care contexts.
Role
Lead UX/UI Designer
Full ownership of user experience strategy and visual design system
Created comprehensive user flows and information architecture
Designed intuitive interface components optimized for caregiver workflows
Collaborated with stakeholders to prioritize features for maximum impact
Conducted comprehensive research on AI implementation for healthcare contexts
Developed detailed specifications for role-based, context-aware AI interactions
Partnered with offshore development team to ensure design fidelity
Solution
Developed an innovative EHR platform balancing clinical requirements with human-centered care:
Clean, accessible interface reducing documentation time by approximately 40%
Goal-setting and tracking tools to improve patient outcomes
Comprehensive patient summary dashboards highlighting both health metrics and behavioral insights
Thoughtful information architecture optimizing caregiver workflows
Designed AI-ready interface with embedded pathways for future RAG implementation
Created progressive disclosure models for future AI interactions
Developed specifications for role-specific AI assistance tailored to different user types
https://www.figma.com/proto/OUgDy5CeOFPPC6I1G12YTJ/Caregiver360-Application---MVP-1-(Copy)?page-id=439%3A67089&node-id=439-68176&p=f&viewport=403%2C339%2C0.07&t=Dis4WEgrdJ6sGCEe-1&scaling=min-zoom&content-scaling=fixed&starting-point-node-id=439%3A68176&show-proto-sidebar=1
https://www.figma.com/design/OUgDy5CeOFPPC6I1G12YTJ/Caregiver360-Application---MVP-1-(Copy)?node-id=439-67089&t=kNOBPCzuAr2nO5Ig-1
Impact
Successfully delivered a beta version within a compressed 5-month timeline, creating a platform that transformed how caregivers document and monitor patient progress. The design strikes the perfect balance between collecting necessary clinical data and focusing on qualitative care metrics, demonstrating my ability to solve complex problems through thoughtful, user-centered design.
AI Strategy & Implementation Research
A significant portion of my work involved comprehensive research into implementing advanced AI capabilities through a RAG LLM chatbot:
Developed detailed specifications for an IDD-specific healthcare AI assistant
Created models for role-based AI interactions (clinicians, caregivers, administrators)
Designed context-aware prompt systems with progressive information disclosure
Conducted cost-benefit analysis comparing immediate vs. phased AI deployment
Led strategic decision-making that prioritized user adoption over cutting-edge features
Engineered the system architecture to accommodate future AI integration without rework
After thorough analysis of cost volatility and onboarding complexity, I recommended a phased approach to AI integration, designing clear implementation pathways while prioritizing initial user comfort and adoption—a decision that demonstrated both technical expertise and strategic business thinking.
