Parent Hospitalization True-Cost Predictor

3.55

Derivation Chain

Step 1 Parent care administration outsourcing

→

Step 2 Unable to estimate costs for parent hospitalization

→

Step 3 Non-covered item price differences between hospitals create variations of thousands of dollars for the same condition/procedure

Problem

Adults aged 40-55 cannot predict the total cost when a parent in their 70s-80s faces hospitalization or surgery. While the insured portion is available through HIRA (Health Insurance Review & Assessment Service), non-covered items — which account for over 50% of actual costs (premium rooms, special materials, MRI, etc.) — vary 2-5x between hospitals. Families only learn the true cost after receiving the bill, and cannot determine in advance whether costs will exceed their parent's private insurance limits.

Solution

On the web, users select a condition (or procedure) and 2-3 preferred hospitals, and the service: (1) shows estimated insured co-payment from HIRA published data, (2) adds each hospital's non-covered item prices (premium rooms, MRI, special materials, etc.) to calculate 'total estimated hospitalization cost' for comparison, and (3) calculates expected out-of-pocket costs when private insurance limits are entered.

Target: Ages 42-55, children of parents aged 75+ facing hospitalization or surgery within 1-2 weeks, parents with private health insurance

Revenue Model: Basic comparison free; 3+ hospital comparison + insurance limit simulation at $2.25 per transaction

Ecosystem Role: Consumer

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

4.0/5

U Urgency

4.0/5

M Market

4.0/5

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 (63%)

Tech Complexity

24.0/40

Data Availability

19.2/25

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

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

Dashboard