CASE STUDY

MediFlow AI: Healthcare Referral Management

A full-stack platform designed to streamline referral intake, automate OCR and transcription, and provide real-time clinical workflow visibility for healthcare teams.

Challenge

Healthcare teams struggled with fragmented referral workflows. Documents arrived via fax, email, and portal—each requiring manual transcription, document review, and status tracking. Clinical staff spent nearly 30% of their time on paperwork instead of patient care.

Before

Manual document review (27 mins avg per referral)
Fax delays and lost pages
Handwritten notes, inconsistent data
No real-time visibility
High error rate in data entry

After

Automated OCR + transcription (30 seconds processing)
Unified document intake
Structured data, DICOM imaging ready
Live dashboard for all stakeholders
92% recognition accuracy

Research & Insights

Interviews with 12 clinical teams revealed:

30% Average time on referral admin vs. patient care

27 min Avg delay from receipt to clinical review

3–5 steps Manual handoffs per referral

12% Referrals with missing or unclear data

Key finding: Teams wanted a unified inbox and faster document processing. They needed better communication across departments.

Solution Architecture

MediFlow AI processes referrals through a structured pipeline that converts unstructured clinical inputs into structured, reviewable data.

Input Layer

Upload of referrals via web interface (documents, notes, and voice inputs)

Processing Layer

OCR (Tesseract), Speech-to-Text (Whisper), and LLM-based summarization (Groq/OpenAI)

Backend Layer

FastAPI service handling authentication, processing orchestration, and data validation

Storage Layer

PostgreSQL for structured clinical data and S3-compatible storage for documents

Results & Impact

              92%
              Reduction in referral handling time
            

              92%
              OCR recognition accuracy in pilot
            

              2x
              Faster imaging access for specialists
            

              89%
              User satisfaction in usability testing
            

Projected annual impact (500+ referrals/month): ~360 hours saved per clinic, reduced delays, improved patient outcomes through faster specialist coordination.

Technologies Used

FastAPI PostgreSQL AWS (S3, Lambda, EC2) OpenAI API Groq Llama 3.1 Whisper (Speech-to-Text) Medical Imaging (DICOM-ready storage) React WebSockets Scikit-learn