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Public hospitals (approximately 40 institutions including Seoul Medical Center) that adopt AI Solutions must quantitatively report adoption outcomes to higher authorities (local governments, Ministry of Health and Welfare). However, manually compiling KPIs such as pre/post-AI consultation time, misdiagnosis rate, and complaint processing time takes 40-60 hours per month, and inconsistent data formats across departments make consolidated Report generation difficult.
(1) Auto-extract pre/post-AI adoption KPIs (consultation time, length of stay, complaint response time, etc.) from hospital EMR/OCS systems, (2) visualize Before/After comparisons on a dashboard, (3) auto-generate PDF Reports formatted for regulatory reporting requirements. Core: standardized KPI framework + automated data integration.
| 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. |
| 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. |
| 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. |