B
Customized Health Checkup Design After Retirement
2.95
Derivation Chain
Step 1
AI technology proliferation and health management interest
→
Step 2
Disappearance of workplace health checkups after retirement
→
Step 3
Selection of necessary checkup items based on personal health history
→
Step 4
Cost and hospital comparison by checkup item + optimal combination design
Problem
When office workers retire, the comprehensive health checkup (worth 800,000-1,500,000 KRW, approx. $600-$1,125) provided annually by the company disappears, replaced by the National Health Checkup (once every 2 years, basic items only). People in their late 50s to 60s are at high risk for major diseases such as cancer, cardiovascular disease, and diabetes, but there is no way to determine which additional checkup items are needed or to compare prices for the same items across hospitals, which can vary 2-3 times. This leads to spending on unnecessary items or missing necessary ones, losing opportunities for early detection.
Solution
A web service that recommends checkup items for the current year based on medical guidelines and compares prices at nearby hospitals after users input age, gender, family history, and past checkup abnormalities. Key features: (1) classification of essential/recommended/optional checkup items based on risk factors, (2) price comparison table for checkup centers in the region by item, (3) optimal checkup combination recommendations by budget (300,000/500,000/1,000,000 KRW, approx. $225/$375/$750). Differentiation: evidence-based item recommendations rather than hospital marketing, plus price transparency.
NUMR-V Scores
NUMR-V Scoring System
| N Novelty | 1-5 | How uncommon the service is in market context. |
| U Urgency | 1-5 | How urgently users need this problem solved now. |
| M Market | 1-5 | Market size and growth potential from proxy indicators. |
| R Realizability | 1-5 | Buildability for a small team with realistic constraints. |
| V Validation | 1-5 | Validation signal quality from competition and demand data. |
N=.15 U=.20 M=.15 R=.30 V=.20
Feasibility (65%)
Data Availability
20.8/25
Feasibility Breakdown
| Tech Complexity | / 40 | Difficulty of core implementation stack. |
| Data Availability | / 25 | Practical availability and cost of required data. |
| MVP Timeline | / 20 | Expected time to ship a usable MVP. |
| API Bonus | / 15 | Bonus for viable public API leverage. |
Market Validation (56/100)
Validation Breakdown
| Competition | / 20 | Signal quality from competitor landscape. |
| Market Demand | / 20 | Demand proxies from search and mention patterns. |
| Timing | / 20 | Fit with current shifts in tech, behavior, and regulation. |
| Revenue Signals | / 15 | Reference evidence for monetization viability. |
| Pick-Axe Fit | / 15 | How well the concept serves participants in a trend. |
| Solo Buildability | / 10 | Practicality for lean-team implementation. |
Technical Requirements
Frontend [medium]
Backend [medium]
Data Pipeline [medium]