MediStats
Healthcare prototype that helps caregivers explore behavioral patterns in children using ML-assisted analysis.
MediStats supports exploration only - not diagnostic software. Seek qualified clinicians for medical decisions.
Launch metrics
Shipped with intent
Numbers pulled from this project record - add real usage data when you have it.
Healthcare prototype · 2024
Ship period
4
Capabilities
4
Stack depth
Live
Production
3
Objectives
3
Lessons
Research & discovery
How we framed the problem
We wanted hands-on experience bridging healthcare UX, data handling, and machine learning in one deployed prototype.
Discovery questions
- Prototype behavioral assessment flows with ML support
- Practice secure, respectful handling of sensitive inputs
- Deliver a live demo for iteration and feedback
Constraints we hit early
- Communicating ML output responsibly to non-expert users
- Managing sensitive health data expectations in a student-scale prototype
- Coordinating frontend and model pipelines across deployment targets
Product timeline
Idea to production
The arc of how MediStats moved from concept to a live deployment.
Idea
The gap we noticed
Parents and clinicians often lack structured, early visibility into behavioral patterns that may need professional attention.
Research
What we validated
We wanted hands-on experience bridging healthcare UX, data handling, and machine learning in one deployed prototype.
Design
Product direction
MediStats collects assessment inputs, applies ML-backed analysis, and presents understandable insights while emphasizing privacy and the need for professional follow-up.
Development
What we engineered
Kaizen built the React application, integrated ML components, and deployed to Render. We prioritized clear UX for non-technical caregivers and documented data-handling intentions.
Testing
Iteration pressure
- Communicating ML output responsibly to non-expert users
- Managing sensitive health data expectations in a student-scale prototype
- Coordinating frontend and model pipelines across deployment targets
Deployment
In production
Shipped on Render. Live at medistats-1yhm.onrender.com/.
The shift
Before and after
What changed once the product shipped - framed from our documented problem and solution.
Before
Parents and clinicians often lack structured, early visibility into behavioral patterns that may need professional attention.
- Communicating ML output responsibly to non-expert users
- Managing sensitive health data expectations in a student-scale prototype
- Coordinating frontend and model pipelines across deployment targets
After
MediStats collects assessment inputs, applies ML-backed analysis, and presents understandable insights while emphasizing privacy and the need for professional follow-up.
- Behavioral assessment workflows
- ML-assisted insight summaries
- Caregiver-focused interface
- Deployed full-stack prototype on Render
User journey
How people move through it
A linear flow derived from the product narrative and shipped capabilities.
Discover
Healthcare prototype that helps caregivers explore behavioral patterns in children using ML-assisted analysis.
Understand
Parents and clinicians often lack structured, early visibility into behavioral patterns that may need professional attention.
Use
Behavioral assessment workflows
Use
ML-assisted insight summaries
Use
Caregiver-focused interface
Outcome
MediStats collects assessment inputs, applies ML-backed analysis, and presents understandable insights while emphasizing privacy and the need for professional follow-up.
Product
Interface in context
Screens wrapped in a browser frame. Drop PNGs into the project folder to grow this gallery.
Build
What shipped
Behavioral assessment workflows
ML-assisted insight summaries
Caregiver-focused interface
Deployed full-stack prototype on Render
Architecture
System layers
Grouped from the documented stack - a structural view, not speculative infra.
Interface
Intelligence
Infrastructure
Deployment pipeline
Path to production
From codebase to the live URL we ship and maintain.
Source
Application codebase
Build
React
Deploy
Render
Live
medistats-1yhm.onrender.com/
Outcomes
What we aimed to prove
Launch objectives from our project record. Replace with measured results when available.
Prototype behavioral assessment flows with ML support
Practice secure, respectful handling of sensitive inputs
Deliver a live demo for iteration and feedback
MediStats assists parents and healthcare professionals in exploring early behavioral patterns in children. The platform combines structured assessments with machine learning to surface insights that can inform - not replace - professional judgment.
We built MediStats to learn how health AI products are designed, deployed, and discussed honestly at small-team scale.
Lessons
What we learned shipping
Healthcare prototypes must under-promise and over-explain limitations
Interpretability matters more than raw model complexity at this stage
Split deployments (UI + API) teach real integration lessons early
Roadmap
Where it goes next
Direction from stated objectives until a dedicated roadmap is added.
Prototype behavioral assessment flows with ML support
Practice secure, respectful handling of sensitive inputs
Deliver a live demo for iteration and feedback
Engage
Build something similar?
Similar builds start at our MVP tier. Submit your details through the contact form for a free custom quote - we respond with an honest estimate.