A

Long-Term Care Home Benefit Planner

4.20

Derivation Chain

Step 1 Parent caregiving administration
→
Step 2 Benefit combination planning after receiving a care grade
→
Step 3 Unknown optimal combination within the monthly home care benefit cap

Problem

When parents receive long-term care grade 3-4, adult children in their 50s cannot determine the optimal combination of home-visit care, home-visit bathing, home-visit nursing, day/night care, and assistive devices within the monthly benefit cap (approximately 1,280,000 KRW / ~$960 for grade 3). Care agency recommendations are biased toward their own services, and the National Health Insurance Service simply advises 'the family decides.' As a result, families either use only 60-70% of their benefit cap or exhaust it on services that don't match actual needs.

Solution

On a web interface, users enter their parent's care grade, primary needs (mobility assistance, cognitive training, bathing, meals, etc.), and family caregiving availability by time slot. The service then: (1) simulates 3-5 possible benefit combinations within the monthly cap, (2) compares actual out-of-pocket costs (15% co-pay) and service hours for each combination, (3) matches nearby home care agencies along with their quality ratings.

Target: Ages 48-58, adult children whose parents have received or are about to apply for long-term care grades 2-4, families seeking to maintain home-based care
Revenue Model: Basic combination simulation Free, nearby agency matching + monthly benefit usage tracking Monthly Subscription $4.40 (5,900 KRW), agency referral commission (B2B)
Ecosystem Role: Education
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
4.0/5
U Urgency
5.0/5
M Market
3.0/5
R Realizability
4.0/5
V Validation
4.0/5
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.
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
20.8/25
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 (60/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
20.0/20
Revenue Signals
10.5/15
Pick-Axe Fit
10.5/15
Solo Buildability
5.0/10
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