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Dhanush008/india-medical-value-travel-mvp

India Medical Value Travel (MVT) Platform – MVP Dataset A comprehensive, structured JSON dataset for building an AI-powered Medical Value Travel platform connecting international patients with Indian hospitals. Overview India is a global leader in medical tourism due to 60–80% lower treatment costs vs US/UK, world-class hospital chains, and government support through initiatives like "Heal in India" and e-Medical Visa. This dataset provides the complete data… See the full description on the dataset page: https://huggingface.co/datasets/Dhanush008/india-medical-value-travel-mvp.

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India Medical Value Travel (MVT) Platform – MVP Dataset

A comprehensive, structured JSON dataset for building an AI-powered Medical Value Travel platform connecting international patients with Indian hospitals.

Overview

India is a global leader in medical tourism due to 60–80% lower treatment costs vs US/UK, world-class hospital chains, and government support through initiatives like "Heal in India" and e-Medical Visa. This dataset provides the complete data foundation for an MVP platform that solves:

  • —No single coordination platform for end-to-end medical travel
  • —Price opacity across hospitals and treatments
  • —Language barriers in medical communication
  • —Safety concerns for international patients
  • —No post-treatment follow-up after patients return home

Dataset Structure

SectionCountDescription
hospitals13Major Indian hospital chains (Apollo, Manipal, Fortis, Medanta, Max, Narayana, CARE, Kokilaben, Artemis)
treatments18CABG, Angioplasty, Knee/Hip Replacement, Craniotomy, Cancer Therapy, Spine Surgery, DBS, Transplants, and more
cities9Bangalore, Chennai, Delhi, Mumbai, Hyderabad, Gurugram, Manipal, Mysore, Kolkata
patient_profiles12Synthetic international patients from Iraq, Nigeria, Bangladesh, Kenya, Afghanistan, Maldives, Oman, etc.
recommendations17Patient → Hospital + Treatment mappings with cost estimates and reasoning
services10Visa support, flights, accommodation, food, transport, IPC, language, safety tracking, follow-up, insurance
patient_journey7End-to-end stages from inquiry to post-return follow-up
problems10Common challenges with severity scores and platform solutions
govt_alignment5Heal in India, e-Medical Visa, Unified MVT Portal, AYUSH, NABH alignment

Key Features

  • —Cross-referenced IDs: All hospital, treatment, patient, and service IDs are internally consistent
  • —Realistic pricing: India medical tourism market rates (2025-2026) in USD
  • —MongoDB/API ready: Structured for direct database seeding
  • —Rich metadata: Each entity includes detailed attributes beyond the minimum spec

Hospital Chains Covered

Apollo Hospitals, Manipal Hospitals, Fortis Healthcare, Medanta, Max Healthcare, Narayana Health, CARE Hospitals, Kokilaben Dhirubhai Ambani Hospital, Artemis Health Institute

Treatment Categories

CategoryTreatments
CardiacCABG ($3,500-7,500), Angioplasty ($2,000-5,000), Valve Replacement ($5,000-9,500)
OrthopedicsKnee Replacement ($3,500-6,500), Hip Replacement ($4,000-7,000), Bilateral Knee ($6,000-11,000), Fracture Fixation ($1,500-4,000)
NeurologyCraniotomy ($5,000-12,000), Spinal Fusion ($5,000-10,000), DBS ($15,000-28,000), Disc Replacement ($6,000-11,000)
OncologyChemotherapy ($2,000-8,000), Radiation ($3,000-7,000), BMT ($15,000-25,000), Immunotherapy ($8,000-25,000)
TransplantLiver ($25,000-40,000), Kidney ($10,000-18,000)
UrologyRobotic Prostatectomy ($4,500-8,000)

Usage

python
import json

with open('mvt_dataset.json', 'r') as f:
    data = json.load(f)

# Access sections
hospitals = data['hospitals']
treatments = data['treatments']
recommendations = data['recommendations']

# Find recommendations for a patient
patient_recs = [r for r in recommendations if r['patient_id'] == 'P001']

# Find hospitals in a city
mumbai_hospitals = [h for h in hospitals if h['city'] == 'Mumbai']

# Find cardiac treatments under $5000
affordable_cardiac = [t for t in treatments 
                      if t['category'] == 'cardiac' and t['avg_cost_usd'] < 5000]

Data Quality

  • —✅ All cross-references validated (hospital IDs, treatment IDs, patient IDs, service IDs)
  • —✅ All cost ranges internally consistent (min ≤ avg ≤ max)
  • —✅ All recommendation costs within patient budgets
  • —✅ No duplicate IDs
  • —✅ No placeholder data

License

MIT