B

Checkup History TrendBook

3.65

Derivation Chain

Step 1 Post-health checkup follow-up management
→
Step 2 Year-over-year checkup metric trend visualization
→
Step 3 Trend-based guidance on follow-up test necessity

Competitor references

No usable source links are available for this idea. Its signal notes and scores need independent verification.

Problem

Office workers in their 50s get annual national health checkups, but results only show that year's numbers without visualizing 3–5 year trends. Even when fasting blood sugar rises steadily from 95→100→108, each result is marked 'normal,' so they fail to recognize they're entering pre-diabetes. The National Health Insurance Service app lets users view past results, but it's just a list of PDFs with no trend graphs or risk-change alerts.

Solution

Users upload Health Checkup result PDFs or manually enter values on a web interface, and the system visualizes year-over-year trends for 12 key metrics including blood sugar, blood pressure, cholesterol, and liver function. Items that are 'within normal range but trending worse' are flagged with orange warnings, along with evidence-based guidance on which follow-up tests to consider.

Target: Ages 45–60, employees or regional health insurance subscribers who get annual checkups but can't properly interpret their results, at-risk for pre-chronic conditions
Revenue Model: 3-year trend analysis: Free. 5+ year long-term trends + risk score + follow-up test recommendation Report: 3,000 won (~$2.25) Per Transaction. Family member integrated trend management: 4,900 won (~$3.70)/month.
Ecosystem Role: Consumer
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
3.0/5
U Urgency
3.0/5
M Market
4.0/5
R Realizability
5.0/5
V Validation
3.0/5
NUMR-V Scoring System
N Novelty1-5How uncommon the service is in market context.
U Urgency1-5How urgently users need this problem solved now.
M Market1-5Market size and growth potential from proxy indicators.
R Realizability1-5Buildability for a small team with realistic constraints.
V Validation1-5Validation signal quality from competition and demand data.
Each dimension is scored 1-5 and weighted into grade S/A/B/C/D.
N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (71%)

Tech Complexity
29.3/40
Data Availability
21.7/25
MVP Timeline
20.0/20
API Bonus
0.0/15
Feasibility Breakdown
Tech Complexity/ 40Difficulty of core implementation stack.
Data Availability/ 25Practical availability and cost of required data.
MVP Timeline/ 20Expected time to ship a usable MVP.
API Bonus/ 15Bonus for viable public API leverage.
Total 100 points; below 50% is filtered out.

Market Validation (50/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
14.0/20
Revenue Signals
7.5/15
Pick-Axe Fit
7.5/15
Solo Buildability
7.0/10
Validation Breakdown
Competition/ 20Signal quality from competitor landscape.
Market Demand/ 20Demand proxies from search and mention patterns.
Timing/ 20Fit with current shifts in tech, behavior, and regulation.
Revenue Signals/ 15Reference evidence for monetization viability.
Pick-Axe Fit/ 15How well the concept serves participants in a trend.
Solo Buildability/ 10Practicality for lean-team implementation.
Total 100 points; below 50 is filtered out.

Technical Requirements

Frontend [medium] Backend [medium] Data Pipeline [low]
Dashboard