SWASTHYA PATH
AI-Powered Blood Report to Regional Meal Plans
📋 Project Overview
SwasthyaPath is a high-fidelity interactive prototype that translates blood test reports into personalized regional Indian meal plans. It addresses a critical gap: 40 million Indians get blood tests annually (₹300-800 each), but 56-71% can't interpret their results or connect medical parameters to daily dietary decisions.
SwasthyaPath prototype flow: Start → Regional Cuisine → Dietary Preferences → Blood Upload → AI Analysis → Health Priorities → Food Recommendations → Recipes → Dashboard
👨💻 My Role & Responsibilities
Role: Solo UX Designer & Frontend Developer (June - August 2025)
What I Did:
User research and synthesis
- Designed and conducted an online survey with 8 participants aged 26 to 45
- Created empathy maps identifying 12 pain points using the SAY THINK DO FEEL framework
- Developed 2 user personas from response analysis
Competitive analysis
- Evaluated 11 health platforms including HealthifyMe, Practo, Zoe, and Levels
- Identified 3 critical market gaps through feature comparison matrices
Information architecture
- Mapped an 8-screen user journey with decision points
- Built a regional cuisine taxonomy covering 6 Indian regions
- Structured 6 dietary restriction categories
Wireframing and prototyping
- Created wireframes for core features
- Designed a system with reusable components
- Iterated through peer feedback rounds
High-fidelity prototype development
- Built an interactive clickable prototype using React + Vite
- Implemented smooth transitions with Framer Motion
- Created realistic data flows and state management (without backend)
- Deployed a live prototype on Vercel with a mobile-first responsive design
Note: Usability testing was limited to peer feedback during the 8-week timeline. Future iterations would include formal usability testing with 10 to 15 target users to validate blood report comprehension and meal recommendation flows.
📊 The Problem
Market Context
The Indian health app market is valued at ₹912 Crore ($109.4M) in 2024 and projected to reach ₹2,140 Crore ($256.8M) by 2030 (15.3% CAGR). Despite this growth, existing apps fail to bridge the gap between medical test results and practical meal planning.
User Pain Point
40 million Indians receive blood tests annually showing numbers like:
- Hemoglobin: 11.2 g/dL
- Vitamin D: 18 ng/mL
- Blood Sugar: 110 mg/dL
But no one tells them what to eat.
AI-powered blood report analysis showing 62% completion status
Research-Backed Evidence
- 56-71% can't understand what their test results mean (NIH reports)
- 50% lack time to plan meals after getting health recommendations
- 75% expressed interest in dietary changes but abandoned plans
- 37.5% felt existing apps didn't match their regional or family food habits
🎯 The Goal
Design and validate a high-fidelity interactive prototype that automatically translates blood test parameters into specific regional Indian meal plans.
Success Criteria:
Create an 8-screen flow addressing:
- Information overload → Plain language instead of medical jargon
- Time constraints → Automated meal planning, no manual research
- Cultural mismatch → 6 regional Indian cuisines, not generic "Indian food"
Core Flow: Blood report upload → Regional cuisine selection → Personalized food recommendations → Progress tracking dashboard
Upload Blood Report screen with secure file upload functionality
🔍 Research & Discovery
Research Methodology
To validate whether health-to-meal translation was a real problem, I conducted mixed-method research over 2 weeks in May 2025:
User Survey (n=8, ages 26-45)
- 13 closed + 2 open questions via Google Form
- Recruitment: WhatsApp, social media, personal contacts
- Criteria: Interested in diet changes, live in India, receive regular health checkups
Empathy Mapping
- Framework: SAY / THINK / DO / FEEL
- Identified 12 recurring pain points
- Applied confidence ratings to prioritize issues
Competitive Analysis (11 apps)
- Evaluated: HealthifyMe, Practo, Zoe, Levels, Noom, MyFitnessPal, Dr Lal PathLabs, Cure.fit, and others
- Built feature comparison matrices
- Identified gaps in cultural fit, accessibility, and behavioral support
💡 Key Research Findings
Finding #1: Planning Gap - "I don't have time to plan meals"
Evidence:
- 50% (4/8) cited "Not enough time to plan or cook"
- High confidence from empathy mapping
- Users eat based on cravings/convenience, then experience guilt cycles
- They consume health content on social media but can't translate generic advice into family-appropriate meals
Design Implication: Automated meal planning with specific regional recipes (e.g., Palak Paneer with iron value 4.5mg) to remove the manual research burden.
Finding #2: Motivation Gap - "Every diet I try fails"
Evidence:
- 75% (6/8) answered "No, but I'm interested in starting one"
- Indicates interest without follow-through
- Participants abandon diet plans within days
- Meal logging feels burdensome without visible progress feedback
Design Implication: Progress tracking dashboard + flexible adjustments (e.g., "compensate for cheat meals") + behavioral nudges through smart reminders.
Smart health reminders addressing the 75% motivation gap - Take Iron Supplement 09:00, Eat Iron-Rich Food 13:00, Get Sunlight (Vitamin D) 07:30
Finding #3: Cultural Blindspot - "Apps don't fit how my family eats"
Evidence:
- 37.5% (3/8) cited "I haven't found anything that feels right for me"
- Users rely on "family recipes or traditions (e.g., Ayurveda)"
- Existing apps don't accommodate cultural dietary patterns
- Need for regional cuisines, not generic "healthy eating"
Design Implication: 6 regional cuisine filters (North, South, Western, Eastern, Punjabi, Maharashtrian) + family-portion planning + dietary restrictions (vegetarian, vegan, gluten-free, dairy-free etc).
Regional cuisine selection showing North Indian (Roti, Dal, Paneer), South Indian (Dosa, Idli, Sambar), Western (Pav Bhaji, Vada Pav), Eastern (Fish Curry, Rice, Rosogolla), and Punjabi (Makki Roti, Sarson Saag) options
Finding #4: Information Overload - "I don't know what works for my body"
Evidence:
- NIH reports 56-71% struggle to interpret blood test results
- Survey participants echoed confusion with medical jargon
- Doctors give generic tips like "eat more greens"
- Users don't understand what health parameters mean for their diet
Design Implication: Blood report upload → AI-powered plain language analysis → Health impact explanation → Actionable food recommendations with specific amounts.
Blood report analysis translating "Hemoglobin 9.2 g/dL" into plain language: "LOW - Significantly below optimal levels" with Analysis, Recommendation, and Health Impact
👥 User Personas
Based on research synthesis, I developed 2 primary personas representing target user segments:
👩 Neetu Singh — The Health-Conscious Homemaker
Age 40 | Homemaker | Urban India | Moderate tech comfort
"I don't have time to plan my meals."
Background: Neetu's day starts at 6 AM preparing breakfast. She discovered her hemoglobin and iron levels are low but struggles to translate dietary advice into her hectic routine.
Goals:
- Keep energy levels up despite anemia
- Follow a diet plan that fits family meals
- Recover iron levels through food, not just supplements
Pain Points:
- "No time to plan meals" → constant anxiety
- Conflicting online advice about iron absorption
- Doctors give generic tips like "eat more greens"
- Guilt from failed "iron diets"
Behaviors:
- Saves diet plans in WhatsApp but rarely uses them
- Scrolls Instagram for quick recipes
- Starts morning walks but struggles with consistency
👨 Raj Patel — The Tech-Savvy Health Skeptic
Age 26 | Freelance Designer | Urban India | High tech comfort
"Every diet I try fails — why bother?"
Background: Raj works long hours balancing freelance projects. Despite trying keto, intermittent fasting, and detox plans after pre-diabetic warnings, he sees no lasting results.
Goals:
- Break the cycle of failed diets
- Find a science-backed, sustainable plan
- Boost confidence with visible progress
Pain Points:
- Conflicting nutrition advice (carbs vs. fats, keto vs. fasting)
- Discouraged after multiple failed attempts
- Generic apps ignore his health markers
- Skeptical of new "miracle" solutions
Behaviors:
- Downloads tracking apps but quits after days
- Saves diet content but struggles with unclear guidance
- Joins 30-day challenges, motivation drops quickly
- Scrolls YouTube/Instagram for transformation stories
🏆 Competitive Analysis
I analyzed 11 health platforms to understand the market landscape and identify strategic gaps.
Key Competitors:
- HealthifyMe (40M users): Generic "Indian food" suggestions, no blood report analysis
- Zoe/Levels (Global): Need expensive CGM devices (₹15K+), recommend Western foods
- Practo (400M users): Doctor interpretation only, no nutritional guidance
- Dr Lal PathLabs (10M users): Deliver reports but zero dietary recommendations
Three Critical Market Gaps Identified:
Gap #1: No Blood-to-Meal Bridge
Apps give raw data OR generic tips, never "your hemoglobin is 11.2, here are 3 Punjabi recipes with exact iron content."
Gap #2: Cultural Mismatch
Western apps suggest quinoa and kale. HealthifyMe says "eat Indian" but doesn't distinguish North vs South cooking patterns.
Gap #3: Expensive or Incomplete
HealthifyMe charges ₹1,999-4,999/month. Budget apps give basic tracking but no insights. Zoe/Levels require ₹15K+ hardware.
SwasthyaPath's Positioning:
- Blood report analysis: ✅ AI-powered, no expensive devices
- Regional recipes: 6 specific cuisines, not generic "Indian"
- Price: ₹99/month (95% cheaper than HealthifyMe)
🎨 Information Architecture & User Flow
Based on research insights, I mapped an 8-screen journey addressing all three user pain points:
Complete User Journey:
Start → Regional Cuisine → Dietary Preferences → Blood Upload → AI Analysis → Health Priorities → Food Recommendations → Dashboard
Low-fidelity wireframes showing initial structure - Login, Profile setup, Regional selection, Blood upload flow with decision points and navigation patterns
High-fidelity wireframe overview displaying complete 8-screen journey with consistent UI patterns, information hierarchy, and interaction states
Design Decisions:
Regional Cuisine Selection (Step 1 of 3)
- Why first? Cultural fit identified as a critical barrier (37.5%)
- Shows 6 cuisines with dish examples (Roti, Dal vs Dosa, Idli)
- Sets context for all future recommendations
Dietary Preferences (Step 2 of 3)
- Accommodates vegetarian, vegan, gluten-free, dairy-free, etc
- Ensures recommendations match actual dietary constraints
- Reduces abandonment from unsuitable suggestions
Blood Report Upload (Step 3 of 3)
- Simple file upload or manual entry
- Reduces friction compared to device pairing (Zoe/Levels)
- Addresses "I don't have expensive devices" barrier
💡 Design Solutions
Solution #1: Plain Language Blood Report Analysis
Addresses: Information Overload (56-71% can't interpret results)
Blood Report Analysis dashboard showing Health Score 72, 2 markers needing attention, 1 normal - color-coded severity with expandable detailed analysis
Key Design Elements:
- Color coding: Red borders for LOW/DEFICIENT, Green for NORMAL
- Plain language: "LOW" instead of just "9.2 g/dL"
- Context: Normal range shown (12.0-15.5)
- Action: Specific recommendation with timeframe
- Impact: Why this matters for health
Solution #2: Personalized Regional Food Recommendations
Addresses: Planning Gap (50% lack time) + Cultural Mismatch (37.5%)
Iron-Rich Foods prioritized as HIGH with specific Indian options - Spinach Sabji (1 cup daily), Liver (2-3 times/week), Pumpkin Seeds (1 handful), Dark Chocolate (1 square)
Key Design Elements:
- Priority labels: "HIGH" badge for urgent deficiencies
- Specific amounts: "1 cup daily" not "eat more spinach"
- Regional relevance: Spinach Sabji not generic "leafy greens"
- Benefit clarity: "High in iron & folate" shows direct connection
- Multiple options: 4+ choices per deficiency for variety
Solution #3: Regional Recipe Integration
Addresses: Cultural Mismatch (37.5% didn't find right fit)
Healthy Recipes with filters (Iron Rich, Vitamin D) showing Palak Paneer (25 mins, serves 4, 4.8★), Liver Masala (30 mins, serves 3, 4.5★), Pumpkin Seed Chutney (10 mins, serves 6, 4.3★)
Key Design Elements:
- Filter tabs: Iron Rich, Vitamin D for quick navigation
- Time indication: 25 mins, 30 mins shows feasibility
- Serving size: Serves 4, 3, 6 for family planning
- Rating system: 4.8, 4.5 stars builds trust
- Benefit clarity: "High in iron and protein" connects to the health goal
- Key ingredients: Spinach, Paneer, Onions preview
Solution #4: Progress Tracking & Behavioral Nudges
Addresses: Motivation Gap (75% abandon diets)
Onboarding progress animation - "Setting Up Your Health Profile" with gradient orb and checklist (✓ Profile Created, ✓ Preferences Saved, ✓ Reminders Set) provides feedback during setup
Key Design Elements:
- Health score gamification: 85% with positive messaging
- Visible progress: 12 active days, 8/10 goals met
- Quick actions: Easy access to core features
- Color-coded stats: Green for health score, activity indicators
🔐 User Authentication
SwasthyaPath login screen with email/password fields, social login options (Google, Apple, Facebook), and secure authentication flow
Design Considerations:
- Multiple login options for user convenience
- Secure authentication with encrypted data storage
- Smooth onboarding experience with social integration
- Clear visual hierarchy and call-to-action buttons
🧪 Early Validation Feedback
After completing the prototype, I shared it with 2 users for initial reactions:
User 1 (Age 27, Tech Professional)
- Found the flow smooth and intuitive
- Appreciated the animation quality and visual polish
- Regional cuisine approach resonated immediately
User 2 (Age 22, Delhi)
- Positive reception to overall concept
- Found the app interface appealing
Key Takeaway: Both users responded favorably to the core concept and execution quality. However, this informal feedback (n=2, unstructured) served as directional validation rather than comprehensive usability testing. A structured evaluation with task-based scenarios and diverse user segments remains necessary to identify specific pain points and validate design decisions.
📈 Impact & Learnings
What Worked Well
Research-Driven Validation
Validating the health-to-meal gap with quantitative data (50% time constraints, 75% abandonment) before designing saved weeks of iteration. The mixed-method approach (surveys + empathy mapping) built a strong, evidence-based foundation.
Regional Customization Focus
Focusing on 6 regional Indian cuisines (not generic "healthy eating") directly addressed 37.5% of participants who cited not finding the right fit. This differentiation became the core competitive advantage.
Areas for Improvement
Larger Sample Size
8 participants provided strong directional insights, but testing with 15-20 users (including elderly parents managing diabetes, college students with PCOS, working professionals) would reveal more edge cases and diverse needs across age groups and health conditions.
Technical Scope Management
I underestimated the difficulty of health document OCR. Future iterations should start with manual data entry before automating document scanning. This prototype uses manual entry as a validated approach.
Next Steps
If I had 4 more weeks, I would:
Formal Usability Testing (Week 9-10)
- Recruit 10-15 target users matching persona profiles
- Test blood report comprehension and meal recommendation flows
- Validate regional cuisine taxonomy with users from different states
- Measure task completion rates and time-on-task
Nutritionist Validation (Week 10-11)
- Consult certified nutritionists to validate food-to-health-marker mappings
- Ensure recipe nutritional values are medically accurate
- Get professional review of portion size recommendations
Feature Refinement (Week 11-12)
- Add grocery list generation from meal plans
- Integrate cooking video tutorials for regional recipes
- Build social features (family meal planning, progress sharing)
- Explore partnerships with local labs for direct report integration
🎯 Conclusion
SwasthyaPath demonstrates how research-driven UX design can bridge the gap between medical test results and practical dietary action. By validating problems before solutions (50% time constraints, 75% motivation gaps, 37.5% cultural mismatch), the prototype addresses real user needs rather than assumed requirements.
The key insight: Cultural relevance matters more than feature completeness. Users don't need another meal tracking app, they need one that speaks their dietary language (Palak Paneer not quinoa bowls) and fits their family routines.
This prototype proves that personalized health management doesn't require expensive hardware or generic advice, just thoughtful design that respects user's time, culture, and motivation.