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- GazeHealth
Mental Healthcare Analytics Platform Development
A long-term engineering partnership supporting a US-based healthcare technology company in building a modern EMR and analytics platform for data-driven mental healthcare.
client
GazeHealth
cooperation
Since 2019
country
USA
industry
Healthcare / Mental Health
product
EMR system & analytics platform for mental healthcare providers
About the platform
GazeHealth is a modern platform designed for mental health providers. It consists of two independent but interoperable components:
- An EMR system for collecting, managing, and visualizing patient data.
- An analytics platform that helps clinicians analyze patient groups, treatment paths, and outcomes.
The platform enables providers to make data-driven clinical decisions and improve treatment effectiveness across mental healthcare programs.
Team
6
Full-stack Developers
2
AI / Data Scientists
Tech stack
Front-end
React
TypeScript
Redux
Redux-Saga
Material UI
Styled-components
React Router
Formik
Yup
Storybook
D3
DevExpress
React-vis
Back-end
NestJS
TypeScript
TypeORM
PostgreSQL
RxJS
Faker
Cloud & Security
AWS
AWS Cognito
Objectives
The client engaged us to:
- Build a scalable EMR system for mental healthcare workflows.
- Develop an advanced analytics platform for patient cohort analysis.
- Enable AI-driven treatment recommendations.
- Visualize complex mental health data in a clinically meaningful way.
- Support role-based access for solid data security.
The goal was to create a cutting-edge, secure platform that accelerates clinical decision-making and improves patient outcomes.
Challenges
- Visualizing complex patient case records based on multiple dimensions.
- Identifying optimal treatment paths using large, heterogeneous datasets.
- Tracking treatment progress, medication intake, and crisis events over time.
- Supporting multiple organizations with role-based access control.
- Ensuring the platform remained intuitive despite analytical complexity.
Engineering strategy
Our development was guided by four principles:
- Build clinical value first, visuals second.
- Design analytics around real decision workflows.
- Use AI to support clinicians, not replace judgment.
- Keep the platform modular and maintainable.
Our approach
Step 1
EMR system development
The EMR was designed to work independently or as a data source for advanced analytics. We built a full-featured EMR system covering core mental healthcare workflows:
- Patient profiles with medical history, notes, test results, prescriptions, and attachments.
- Online doctor appointment booking.
- Recommendations for patients and treatment history.
- Unified data model shared with the analytics platform.
Step 2
Advanced analytics & data visualization
To analyze treatment paths and patient outcomes, we implemented a custom analytics engine.
- Designed a Sankey-based visualization model to represent treatment flows.
- Built the Sankey Chart from scratch using D3 due to the lack of suitable off-the-shelf solutions.
- Enabled interactive flow selection and drill-down analysis.
- Implemented ~15 supporting charts to visualize symptoms, screenings, diagnoses, treatments, outcomes, and other criteria.
- Enabled the dynamic presentation of secondary charts based on the selected analytical flow.
Step 3
AI-driven treatment recommendations
The platform’s core differentiator was its ability to suggest optimal treatment paths.
- AI models analyze historical case records and outcomes.
- The system proposes alternative treatment options.
- Visual and tabular explanations show why certain options perform better.
- Clinicians can compare outcomes using percentages and statistical indicators.
Step 4
Treatment process & crisis tracking
To review treatment over time, we designed a custom hybrid visualization:
- Timeline-based medication intake tracking.
- Line charts for test result progression.
- Explicit visualization of crisis events during treatment.
Step 5
Role-based routing & access control
Multi-tenant access was implemented using AWS Cognito.
- Defined user groups by role and organization.
- Integrated user access with company-level data permissions.
- Used React Router v6 to implement routing logic where inaccessible pages are excluded at runtime.
- Created maintainable and secure navigation.
Engineering outcomes
- Fully functional EMR system tailored to mental healthcare.
- Advanced analytics platform with interactive, custom-built visualizations.
- AI-powered treatment recommendation engine.
- Secure, role-based access for multiple healthcare organizations.
- Maintainable architecture supporting long-term platform evolution.
Business outcomes
Improved clinical decision-making
- Data-driven treatment comparisons.
- Clear visualization of patient group trends.
Better treatment tracking
- Longitudinal view of medication, tests, and crises.
- Reduced blind spots in patient care.
Scalable healthcare platform
- Multi-tenant support for healthcare organizations.
- Modular architecture for future expansion.
Operational efficiency
- Unified EMR workflows.
- Reduced manual data analysis effort for clinicians.
Client
success
GazeHealth offers an advanced healthcare platform for healthcare facilities. With its help, mental health providers can improve treatment quality, consistency, and outcomes, while patients can benefit from more personalized and effective care.
The delivered solution is secure, scalable, and easy to maintain, supporting clinicians and healthcare organizations in their day-to-day operations.
This case reflects Flyant’s ability to build data-intensive healthcare platforms where analytics, AI, and clinical workflows must work together without compromising usability or trust.
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