Round 1 Winners!
Proof of Usefulness Report

DigiCARE Realized

Analysis completed on 3/17/2026

+85.5
Proof of Usefulness Score
You're In Business

DigiCARE Realized presents a highly relevant and actionable use case for AI in Alzheimer’s disease and related dementia (ADRD) early detection. However, the submission includes major red flags regarding traction ('most people have used my product') and audience reach ('everyone'), which are unverified and contradictory for an emerging healthcare B2B platform. Due to the vague and unsupported claims in critical categories, the project falls into the minimal traction calibration tier.

Ready to Compete for $150k+ in Prizes?

Move this data into a HackerNoon blog draft to become eligible for your share of $150k+ in cash and software prizes

View All Reports

Score Breakdown

Real World Utility+40
Audience Reach Impact+5
Technical Innovation+20
Evidence Of Traction+5
Market Timing Relevance+15
Functional Completeness+5
Subtotal+90
Usefulness Multiplierx0.95
Final Score+86

Project Details

Description
DigiCARE Realized is an emerging AI-technology firm commercializing evidence-based solutions to modernize care for complex brain disease through decision intelligence in early detection and care management. Our initial focus is in Alzheimer’s disease and related dementia (ADRD) where we seek to realize equitable, accessible, and patient-centered care. We collaborate with health systems to deliver routine brain care for our growing, aging population. DigiCARE Realized offers an AI-enabled platform for delivering decision intelligence to support proactive care in ADRD. Machine learning advancements, like decision intelligence, automate processing of vast data sources to deliver faster, actionable insights for providers. Our flagship product is based on a core principle that if you can measure it, you can manage it. Early detection offers the opportunity for timely diagnosis leading to more proactive care. We aim to develop and curate care management solutions. Powered by machine learning, our software provides passive ADRD detection with ~80% performance accuracy for 1- and 3-year prediction horizons. It combs through routinely-collected data found in patient health records to sort through structured and unstructured data. Our flagship product can help detect early-stage ADRD in all patients, regardless of what language they speak, how little time their provider has, or what kind of screening approach is selected that day. When health systems can detect early-stage ADRD in patients, they can give them better care. Providers can offer interventions sooner, they can prevent hospital readmissions, and they can accelerate participation in clinical trials.

Algorithm Insights

Market Position
Growing utility with room for optimization
User Engagement
Documented reach suggests active user community
Technical Stack
Modern tech stack aligned with sponsor technologies

Recommendations to Increase Usefulness Score

Document User Growth

Provide specific metrics on user acquisition and retention rates

Showcase Revenue Model

Detail sustainable monetization strategy and current revenue streams

Expand Evidence Base

Include testimonials, case studies, and third-party validation

Technical Roadmap

Share development milestones and feature completion timeline