Project at a Glance
CareEscapes AI is building a platform that helps patients navigate global healthcare decisions: affordable treatment, minimal wait times, transparent costs. I joined as a graduate analyst to conduct the data analysis underpinning their AI recommendation engine, working directly with the founding team.
Business Problem
Patients face fragmented pricing, hidden travel costs, and zero transparency on wait times when exploring medical tourism. CareEscapes needed data to power a trustworthy recommendation engine.
My Role
End-to-end EDA on the financial dataset, Power BI dashboard development across 11 analytical views, cost structure decomposition, and a strategic recommendations framework delivered to the founding team.
Dataset Scope
Multi-tab financial plan covering 4 dental procedures × 4 countries, 5-year operational projections ($1M to $57M), per-patient cost breakdowns, and wait-time data across domestic and international destinations.
Business Impact
Findings shaped the platform's pricing strategy, redirected partnership focus toward airlines (the dominant cost driver), and validated the LTV:CAC unit economics at 45:1 for investor discussions.
Analytical Approach
Starting with the data: what do we actually have?
The first thing I did was sit with the financial plan and ask: what's real here, and what's a projection? I mapped out treatment costs across four countries, separated the fixed expenses from the variable ones, and got a clear picture of how patient volume was expected to scale from 100 patients in Year 1 to 15,000 by Year 5. Before I built anything, I needed to trust the numbers.
EDA · Data CleaningThe airfare finding that changed everything
I went in expecting treatment cost to be the big story. It wasn't. When I broke down what a patient actually pays: treatment, flight, hotel. airfare came out at 58% of the total bill. Every year, every volume scenario, same number. That's when I told the team: your biggest cost-reduction lever isn't negotiating with clinics, it's getting an airline deal. That single insight reframed a major part of the product strategy.
Cost Modeling · Feature EngineeringBenchmarking destinations: where does the real value live?
I compared the full cost of care, not just the procedure price, but flights and accommodation included, across the US, Canada, Mexico, and Costa Rica. The numbers were striking. For a Full Arch Restoration, a patient spends $32,500 in the US. In Mexico, even after a flight and a week in a hotel, they're paying under $12,000. The more complex the procedure, the stronger the case for traveling. That's where CareEscapes wins.
Comparative Analysis · BenchmarkingWait times: the argument beyond price
Some patients aren't primarily motivated by cost. They're in pain, or they've been waiting months with no end in sight. So I pulled the wait-time data and the contrast was stark. Full Mouth Restoration: up to 270 days in the US or Canada. Abroad: roughly 5 days. I flagged this to the team as a separate conversion story entirely, one that speaks to urgency, not just savings. The recommendation engine needed to surface this for the right patients.
Time Analysis · Decision ModelingTurning it into something the team could actually use
The analysis was only useful if the founding team could explore it themselves. I built an 11-page Power BI dashboard covering everything from a treemap of expense categories to a scatter plot of profit vs. patient volume growth. The goal was to give Shiv and the team a tool they could open before any investor meeting and answer questions on the spot, without needing to run a new analysis each time.
Power BI · Data VisualizationWhat the Data Revealed
Treatment savings abroad
Full Arch Restoration: $32.5K in the US, $10.8K in Mexico. Even after adding flights and hotels the total cost abroad is dramatically lower.
Airfare dominates cost
Airfare is the single largest component of patient spend, consistent across all years and volume levels. Airline partnerships are the highest-leverage opportunity.
Days wait time eliminated
Complex procedures take 6–12 months domestically. Abroad: ~5 days. This time advantage is the strongest non-price conversion driver for urgent patients.
LTV-to-CAC ratio
With $21K–$36K patient LTV against a $460 CAC, unit economics validate aggressive acquisition. Year 5 projects $105M–$180M revenue at 15,000 patients.
Interactive Visualizations
Key insight: Full Arch Restoration costs $32.5K in the US vs. $10.8K in Mexico, a 67% saving before even accounting for travel. Mexico and Costa Rica are near-equivalent in value, giving patients a choice of destination.
Key insight: The wait-time advantage is most dramatic for complex procedures: Full Mouth Restoration patients wait up to 270 days in the US or Canada vs. just 5 days abroad. For urgent cases, this is the primary conversion driver.
Key insight: Cost structure is perfectly stable across all 5 years regardless of volume: 58.14% airfare, 23.26% treatment, 18.60% hotel. This means airline partnerships have the same outsized impact at 100 patients or 15,000 patients.
Key insight: Total patient costs scale dramatically faster than all operational categories: by Year 5 they dwarf staffing, marketing, and technology costs combined. This validates a patient-acquisition-first growth strategy.
Patient Savings Calculator
One of my core recommendations was building a transparent cost comparison tool into the CareEscapes platform. Here's a working prototype based on the financial data I analyzed.
Strategic Actions I Proposed
Airline partnerships: the highest-leverage move
Airfare is 58% of total patient cost, consistent across all years and volumes. Negotiated bulk fares or co-branded travel bundles would deliver the single largest reduction in patient spend, directly improving conversion.
Bundled all-inclusive pricing model
Patients currently face fragmented quotes from clinics, airlines, and hotels separately. A single bundled package removes decision friction and increases the platform's perceived value vs. DIY medical tourism.
Transparent total-cost comparison dashboard
Surface full cost comparisons (treatment + travel + hotel + wait time): not just treatment price. This multi-dimensional view builds the trust that drives conversion for high-stakes healthcare decisions.
AI recommendation engine: data input framework
This analysis defines the three core inputs for a recommendation model: budget threshold, urgency tier (wait-time sensitivity), and procedure complexity. These three variables fully stratify the patient decision space.
What This Work Enabled
The airfare finding specifically redirected partnership strategy, shifting from provider-only relationships to travel-integrated bundling. The cost structure stability finding (58% airfare across all years) gave the team confidence that airline partnerships remain the #1 lever even at scale.