Our team developed both the frontend and backend systems for CareGPT, a healthcare platform that leverages AI to assist providers. We built comprehensive solutions for automating administrative tasks, implementing decision support systems, and creating patient engagement tools that enhance the quality of care through intelligent healthcare workflow automation.
Healthcare providers face challenges in managing administrative tasks and ensuring consistent patient engagement, leading to potential inefficiencies and reduced quality of care.
Our team developed an AI-powered assistant that automates routine administrative tasks, offers decision support to clinicians, and provides personalized patient engagement tools, streamlining healthcare delivery.
Frontend development
Backend systems implementation
Administrative task automation
Decision support systems
Patient engagement tools
Healthcare workflow automation
AI integration for healthcare
Building both frontend and backend systems that could handle complex healthcare workflows while maintaining compliance with medical regulations.
Creating intelligent systems to automate routine administrative tasks without compromising accuracy or compliance requirements.
Developing reliable decision support systems that could assist healthcare providers with evidence-based recommendations while respecting clinical judgment.
Designing engaging and accessible tools that could effectively involve patients in their care journey while accommodating varying levels of health literacy.
Implemented a specialized architecture that addressed healthcare-specific requirements including HIPAA compliance, audit trails, and clinical data models.
Developed a coordinated development approach where frontend and backend teams worked in tandem with shared data models and API contracts to ensure seamless integration.
Built a sophisticated workflow engine that could model complex healthcare processes and automate administrative tasks while maintaining appropriate human oversight.
Integrated medical knowledge bases and machine learning models to provide contextually relevant clinical decision support while maintaining transparency in recommendations.
Employed a collaborative design process involving patients, clinicians, and administrators to create engagement tools that addressed real needs and usage patterns.
CareGPT is an AI assistant for healthcare providers that automates administrative work, offers clinical decision support and drives patient engagement. Python services run as serverless functions and Docker containers across Azure and AWS, with an LLM orchestration layer that grounds OpenAI model calls in curated medical knowledge and always routes outputs through human review. A serverless-first shape fit the bursty, task-shaped nature of clinic workflows while a containerized workflow engine handles long-running processes.
A React web application for clinicians and administrators, plus a patient-facing engagement surface for reminders, questionnaires and follow-ups.
An authenticated gateway enforces identity, consent and rate limits before any request reaches services that touch patient data.
Python services split into short serverless tasks and a containerized workflow engine that models multi-step clinical and administrative processes with human checkpoints.
Clinical and operational data are stored encrypted with an immutable audit trail; a vector index holds the knowledge used to ground AI answers.
An orchestration layer builds grounded prompts, calls OpenAI models, validates outputs and attaches citations before anything reaches a clinician.
Serverless functions and Docker containers deployed across Azure and AWS with infrastructure as code and environment isolation per institution.
A clinician asks the assistant to summarize a visit or draft a follow-up; the gateway verifies identity, role and patient consent.
The workflow engine opens a task, pulls the relevant records from the clinical store and writes an audit entry.
The orchestration layer de-identifies where possible, retrieves guidance from the medical knowledge index and builds a grounded prompt.
The OpenAI model returns a draft; guardrails check it for unsupported claims and attach the sources it relied on.
The clinician reviews, edits and approves the output in the React workspace; nothing is filed without human sign-off.
The engagement service sends patient reminders or questionnaires via the messaging provider and records responses into the workflow.
Decision support must respect clinical judgment and regulation, so every AI output is a draft routed to a clinician for approval rather than an automatic action. This preserved trust while still cutting administrative workload by 30%.
Grounding each call in curated medical knowledge bases and returning citations made recommendations transparent and auditable, which a general-purpose chat interface could not offer.
Clinic workloads are bursty and task-shaped, so serverless functions keep idle cost near zero, while multi-step processes with waits and approvals run in a Docker-hosted engine that holds state reliably.
Healthcare compliance demands knowing who saw and changed what, so an immutable ledger sits beside the clinical store instead of being bolted onto application logs later.
“The AI integration has revolutionized our administrative processes and patient engagement, allowing our clinicians to focus more on providing quality care.”
We ship MVPs, AI agents, and production systems in 8 weeks. Bring the rough spec — we'll scope the build inside a week with a fixed timeline and quote.
