AI-powered healthcare data analytics platform for Omcare

Omcare is a telemedicine company dedicated to helping elderly patients age safely in place through dependable, technology-led medication management. Its in-home health hub dispenses medications at scheduled times, records adherence events, and enables caregivers and care teams to intervene when necessary.

Problem: scaling insights across a growing healthcare data ecosystem

Before implementing a dedicated healthcare data analytics platform, Omcare relied on manual SQL queries to analyze data stored in PostgreSQL. While effective for basic reporting, this approach limited access to insights to a small group of technical users.

Customer support teams could not easily retrieve patient-level adherence information. Facility administrators lacked real-time visibility into trends across locations. Engineering teams depended on ad hoc reports to monitor system performance.

As data volume and operational complexity increased, manual analytics workflows became slow, error-prone, and difficult to govern. Omcare needed a secure and scalable healthcare data analytics platform that could serve different user roles without exposing sensitive information or requiring technical expertise.

Solution: an AI-driven healthcare data analytics platform

GoML designed and delivered an MVP healthcare data analytics platform built on GoML’s AI Data Analytics boilerplate. The platform enables users to ask questions in natural language and receive accurate insights generated directly from Omcare’s healthcare data.

The system converts plain English questions into structured data requests, validates them against role-based access rules, and executes approved queries through secure APIs. Results are returned as easy-to-understand summaries and visual outputs, allowing teams to make decisions quickly and confidently.  

By centralizing analytics within a single healthcare data analytics platform, Omcare eliminated the need for manual SQL while maintaining strong security and compliance controls.

Natural language analytics layer

Users interact with the healthcare data analytics platform by asking questions in plain English. The system identifies intent, extracts relevant parameters, and determines the appropriate data sources needed to answer each query.

Secure data access and execution

Rather than generating raw database queries, the healthcare data analytics platform retrieves data through validated API calls. Each request is checked against access policies to ensure users only see authorized information.

Real-time insights and visualizations

The platform produces real-time summaries and visual outputs such as charts and trend graphs. These visualizations help teams quickly understand medication adherence patterns, facility performance, and operational metrics

Role-based access control

The healthcare data analytics platform supports multiple user roles with clearly defined access boundaries.

• Customer support teams access patient-level adherence data.

• Facility administrators view aggregated location-level insights.

• Engineering teams monitor system health and performance metrics.

User interface and system integration

A lightweight React-based interface allows users to authenticate, submit questions, and view results. The healthcare data analytics platform integrates seamlessly with Omcare’s existing AWS infrastructure and exposes APIs for internal system integration.

Impact

• 70–80% reduction in reliance on manual SQL queries for routine reporting and analysis

• 60% faster access to patient- and facility-level insights for support and operations teams

• 2× improvement in visibility into medication adherence trends through real-time analytics and visualizations

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About

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Before gen AI and after gen AI

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“With Omcare’s AI-powered healthcare data analytics platform, we transformed manual, siloed reporting into a secure, self-service system that delivers real-time insights across teams.” - Prashanna Rao, Head of Engineering, GoML

Key takeaways for healthcare organizations

Common challenges

• Manual analytics workflows do not scale with data growth

• Limited insight access slows care coordination

• Uncontrolled reporting increases compliance risk

Practical guidance

• Adopt a centralized healthcare data analytics platform

• Design analytics around real user roles

• Enforce access control at the execution layer

Ready to modernize your healthcare analytics?

Partner with GoML to build a healthcare data analytics platform designed for real-world scale and compliance.

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