S
Post-Checkup 90-Day Follow-Up Coach
4.25
Derivation Chain
Step 1
Longitudinal interpretation of health checkup results
→
Step 2
Identifying abnormal items through trend analysis
→
Step 3
Not knowing which department to visit or when after discovering abnormal findings
Problem
45% of people in their 50s who receive abnormal findings on health checkups never follow up with detailed examinations (NHIS 2024 statistics). Even after getting a result like 'Findings: Abnormal liver function,' they don't know which department to visit (gastroenterology? hepatobiliary surgery?), how much the detailed exam costs, whether national health insurance covers it, or by when they should go. As a result, they miss early detection opportunities and discover worsened conditions at the next year's checkup.
Solution
(1) Enter abnormal checkup findings to get a breakdown of required follow-up exams by item, which department to visit, estimated cost (with national insurance coverage), and recommended deadline, (2) auto-generate a 90-day follow-up calendar (schedule exam → review results → lifestyle adjustment), and (3) send email/push reminder notifications for follow-up actions — all as a web service.
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. |
SaaS N=.15 U=.20 M=.15 R=.30 V=.20
Senior N=.25 U=.25 M=.05 R=.30 V=.15
Feasibility (82%)
Data Availability
22.5/25
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 (58/100)
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 [low]
Backend [low]