B

My Medication Interaction Checker

3.35

Derivation Chain

Step 1 AI paradox/economic changes in the AI era
→
Step 2 Increase in polypharmacy among 50s
→
Step 3 Blind spots in drug interaction side effects

Problem

68% of chronic disease patients aged 50 and older take three or more medications simultaneously, but information about interactions between drugs prescribed at different hospitals and pharmacies is scattered. A 55-year-old office worker visiting 2-3 departments (internal medicine, orthopedics, urology, etc.) must ask the pharmacist each time or understand DUR system results to check interactions with existing drugs, which is difficult for the general public. This leads to unnecessary side effects or taking medications with canceled-out effects for months without knowing.

Solution

Enter the names of current medications on the web, and it (1) visualizes drug interaction risk using a traffic light system, (2) automatically detects duplicate and conflicting ingredients, and (3) generates a 'My Medication Summary Card' to show the doctor at the next hospital visit. Based on Korea's MFDS DUR data and the Drug Safety Korea API, it translates into language that the general public can understand.

Target: Health managers in the household aged 50-60, using multiple hospitals for two or more chronic conditions, and also managing medications for spouses or elderly parents.
Revenue Model: Basic drug interaction search is free. Monthly medication report PDF and family member medication integration management feature: 3,900 KRW (~$2.93) per month Premium.
Ecosystem Role: Infrastructure
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
2.0/5
U Urgency
4.0/5
M Market
4.0/5
R Realizability
4.0/5
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 (68%)

Tech Complexity
29.3/40
Data Availability
19.2/25
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 (56/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
14.0/20
Revenue Signals
10.5/15
Pick-Axe Fit
10.5/15
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