Chronic Condition Checkup Context Report

3.25

Derivation Chain

Step 1 Chronic condition self-management and multi-prescription conflict checking

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Step 2 Tracking Health Checkup value changes

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Step 3 Interpreting the impact of current medications on checkup values

Problem

A 56-year-old patient with hypertension and diabetes receives Health Checkup results showing fasting blood sugar rose from 110 last year to 125 this year. They cannot determine whether this indicates worsening diabetes, a side effect of the beta-blocker they switched to 3 months ago for hypertension, or simply the result of a late dinner the night before the test. Even when trying to ask their doctor, it is difficult to organize the evidence to explain that 'blood sugar went up after changing medication.'

Solution

Users upload their past 3–5 years of Health Checkup data (National Health Insurance Service PDF upload or manual entry) along with their current medication list on the web. The tool displays value changes on a timeline and automatically inserts medication-checkup correlation annotations such as 'this medication is known to potentially raise blood sugar levels.' It generates a one-page summary of 'my checkup history + medication history' to show the doctor during visits.

Target: Ages 50–65 with chronic conditions, annual Health Checkup participants, taking 3+ medications, who want to interpret their own checkup results

Revenue Model: Value timeline free. Medication correlation analysis + clinical summary PDF at $2.90 per transaction (3,900 KRW). Annual Subscription $21.75 (29,000 KRW) with automatic updates at each yearly checkup.

Ecosystem Role: Education

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

4.0/5

U Urgency

3.0/5

M Market

3.0/5

R Realizability

3.0/5

V Validation

3.0/5

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 (70%)

Tech Complexity

29.3/40

Data Availability

20.6/25

MVP Timeline

20.0/20

API Bonus

0.0/15

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 (53/100)

Competition

8.0/20

Market Demand

6.2/20

Timing

14.0/20

Revenue Signals

9.0/15

Pick-Axe Fit

10.5/15

Solo Buildability

5.0/10

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

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

Dashboard