AI-Powered Diagnostic & Patient Management Platform
AI-assisted diagnostic platform integrating medical imaging analysis, patient records, and clinical decision support for multi-specialty hospital chains
Overview
MediCore Health operates a network of 25+ diagnostic centers and specialty hospitals across South India. We built an integrated AI platform that assists radiologists with image analysis, provides clinical decision support for physicians, streamlines patient flow, and ensures compliance with healthcare regulations—transforming how they deliver diagnostic care.
The Problem
Diagnostic centers face mounting pressure: increasing patient volumes, radiologist shortages, and the need for faster, more accurate diagnoses. MediCore struggled with inconsistent diagnostic quality across centers, long patient wait times, and fragmented patient records that didn't follow patients across their network.
Key Challenges
Radiologist Workload Crisis
Each radiologist reviewed 100+ scans daily with no AI assistance. Fatigue led to missed findings, especially in complex cases. Second opinions required physical transfer of films between centers. Report turnaround averaged 48 hours for non-urgent cases.
Fragmented Patient Records
Each diagnostic center had its own PACS and EMR system. Patient history wasn't accessible across locations. Physicians repeated tests due to inaccessible prior results. No longitudinal view of patient health trajectory.
Clinical Decision Gaps
Junior physicians lacked decision support for complex cases. Clinical guidelines weren't integrated into workflows. Drug interactions and contraindications checked manually. No alerts for critical findings requiring immediate attention.
Operational Inefficiencies
Patient scheduling was manual and error-prone. Equipment utilization varied wildly between centers. No predictive maintenance for expensive imaging equipment. Billing and insurance claims processed with 15% error rate.
How We Built It
Unified Health Data Platform
Built HIPAA-compliant data infrastructure connecting all 25+ centers. Integrated PACS, EMR, LIS, and billing systems into unified patient record. Implemented FHIR-compliant APIs for interoperability. Created patient-centric longitudinal health view accessible across network.
AI Diagnostic Assistant
Trained medical imaging AI models for X-ray, CT, and MRI analysis. Focused on high-volume studies: chest X-rays, brain CT, mammography, and musculoskeletal MRI. Built radiologist workflow with AI pre-read findings and attention highlights. Implemented confidence scoring and uncertainty quantification.
Clinical Decision Support
Integrated clinical guidelines and protocols into physician workflows. Built drug interaction checker processing prescriptions in real-time. Created critical finding alerts with automated escalation. Developed differential diagnosis assistant for complex cases.
Operations Intelligence
Implemented smart scheduling optimizing equipment utilization and patient wait times. Built predictive models for patient no-shows and overbooking. Created equipment health monitoring for preventive maintenance. Automated insurance eligibility verification and claims processing.
Solution Highlights
AI Radiology Assistant
Computer vision models pre-screen imaging studies, highlighting areas of concern and providing preliminary findings. Radiologists review AI suggestions, reducing reading time by 40% while improving detection of subtle findings.
Unified Patient Records
Complete patient history accessible at any center in the network. Prior studies, lab results, and clinical notes all in one view. Reduces repeated tests and enables better-informed clinical decisions.
Clinical Decision Support
Real-time alerts for drug interactions, contraindications, and critical findings. Guideline-based suggestions integrated into physician workflow. Differential diagnosis assistant for complex presentations.
Smart Operations
AI-optimized scheduling reduces patient wait times. Predictive equipment maintenance prevents costly downtime. Automated claims processing with 95%+ accuracy.
Technical Deep Dive
The radiology AI uses a multi-task learning architecture based on DenseNet-121, trained on 2M+ annotated medical images. Models detect 14 chest X-ray pathologies, 8 brain CT abnormalities, and 12 mammography findings with radiologist-level accuracy. Inference runs on NVIDIA DGX servers with sub-second response times. The clinical decision support system uses a knowledge graph built from medical literature and clinical guidelines, combined with an LLM-based reasoning engine for complex cases. Drug interactions are checked against a comprehensive database with 1M+ interaction pairs. The unified health data platform uses FHIR R4 resources with custom extensions for Indian healthcare requirements, backed by a PostgreSQL database with row-level security for patient data isolation.
AI Capabilities
Medical Image Analysis
AI-assisted reading of X-ray, CT, MRI, and mammography studies
Critical Finding Detection
Automatic identification and escalation of urgent findings
Drug Interaction Checking
Real-time screening of prescriptions for contraindications
Differential Diagnosis
AI-suggested diagnoses based on symptoms and test results
Demand Forecasting
Predicting patient volumes for staffing and resource planning
Equipment Health Monitoring
Predictive maintenance for imaging equipment
Technology Stack
Medical AI
Health Data
Backend
Frontend
Infrastructure
Compliance
Results & Outcomes
+35%
Diagnostic accuracy
AI-assisted detection of subtle findings
-50%
Patient wait times
Smart scheduling and faster reporting
-40%
Report turnaround
AI pre-read reduces radiologist workload
99.2%
System uptime
Reliable platform across 25+ centers
-80%
Billing errors
Automated claims processing
25+
Centers connected
Unified patient records across network
“The AI doesn't replace our radiologists—it makes them superheroes. We're catching findings we would have missed, and our patients get results faster than ever.”
Chief Medical Officer
MediCore Health
Services Used for the Client Product
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