Care Sync - Clinical Narrative Drift Detector

A hallucination-safe, enterprise-oriented AI system that detects clinical narrative drift across hospital roles (physicians, nurses, allied health). This demonstration uses synthetic data; the system is designed to integrate with hospital information systems like Oracle Health (Cerner), Epic, and other EHR platforms.

📖 For detailed documentation, visit the project website documentation page

What Inspired the Project

Through personal experience dealing with doctors, nurses, and specialists, I've witnessed a recurring problem: all parties don't have the same information, aren't on the same page, or don't consult with each other before speaking with patients or their families. This has led to miscommunication between staff members and confusion for patients and families.

My family members who work in healthcare have repeatedly expressed frustration with this issue - a problem that has persisted without a clear solution. In busy hospital environments, different clinicians document patient status at different times, and without a system to catch inconsistencies, critical information gaps can go unnoticed until they cause problems.

Care Sync was built to solve this problem by analyzing and alerting on notes as they come in, in real-time. The system automatically detects when different clinicians document conflicting information or when critical updates aren't acknowledged across the care team.

Impact: By providing real-time alerts and creating an audit trail of documentation inconsistencies, Care Sync helps reduce hospital mishaps, supports accountability, and can even help prevent malpractice by ensuring all care team members have access to the same information.

The Idea

Care Sync is an AI-powered clinical decision support system that analyzes and alerts on notes as they come in, in real-time. The system:

Real-Time Analysis: Analyzes notes as they are entered into the EHR system, providing immediate alerts when contradictions are detected
LLM-Based Detection: Uses GPT-5.1 to extract clinical facts and detect contradictions across roles and time windows
Prioritized Alerts: Creates alerts (CRITICAL, HIGH, MEDIUM, LOW) based on clinical severity and potential safety impact
Evidence Traceability: Links each alert to verbatim source quotes from conflicting notes
Audit Trail: Documents all detected inconsistencies for accountability and quality improvement

Important: This system does NOT perform diagnosis, prognosis, or treatment recommendations. It only flags documentation inconsistencies for clinical review.

How I Built It

Tech Stack

Frontend: Next.js 14, React 18, TypeScript, Cerner-style UI
AI/ML: OpenAI GPT-5.1, LLM-based drift detection, Clinical guardrails
Backend: Next.js API Routes, Serverless functions, Vercel deployment
Architecture: Multi-layer caching, change detection, EHR integration ready

Dataset Structure

Demonstration Dataset: This project uses a synthetic dataset structured as JSON with the following format:

{
  "patients": [
    {
      "patient_id": "DX-401",
      "patient_name": "Marcus Lee",
      "mrn": "MRN-904112",
      "admission_date": "2026-02-22",
      "diagnosis": "...",
      "notes": [
        {
          "note_id": "DX-401-n-001",
          "timestamp": "2026-02-22T06:18:00",
          "author_role": "ED_MD",
          "note_text": "ED Provider Note: ..."
        }
      ]
    }
  ]
}

Each note includes:

note_id: Unique identifier for the note
timestamp: ISO 8601 timestamp of when the note was created
author_role: Clinical role (ED_MD, RN, HOSPITALIST_MD, RT, SURGERY_RESIDENT_MD, etc.)
note_text: Full text content of the clinical note

Real-World Integration: For production deployment, clinical notes from EHR systems (Oracle Health/Cerner, Epic, etc.) can be extracted and converted to this JSON format. EHR systems typically provide HL7 FHIR resources, database queries, API endpoints, or HL7 v2 messages that can be mapped to this structure.

Key Challenges & Solutions

False Positives: Added clinical guardrails and post-processing filters to distinguish true contradictions from normal progression
Quote Accuracy: Implemented strict validation ensuring verbatim quotes from source notes
Performance: Multi-layer caching (localStorage + server-side) with change detection to minimize redundant API calls
Severity Classification: Defined clear clinical criteria (e.g., CRITICAL requires objective instability like SpO\(_2\) < 88, HR > 120, SBP < 90)

Impact Vision

Early Detection: Identify documentation inconsistencies before they lead to adverse events
Care Coordination: Help care teams reconcile conflicting documentation
Patient Safety: Prevent discharge safety conflicts and unacknowledged deterioration
Accountability: Create audit trails for quality improvement and risk management

UN SDG Alignment

🎯 SDG 3: Good Health and Well-being

Target 3.8: Achieve universal health coverage, including financial risk protection, access to quality essential health-care services, and access to safe, effective, quality, and affordable essential medicines and vaccines for all.

How Care Sync Contributes:

Improves Quality of Care: By detecting documentation inconsistencies, Care Sync helps ensure accurate communication between care providers, reducing medical errors and improving patient outcomes
Enhances Patient Safety: Early detection of unacknowledged deterioration, discharge safety conflicts, and treatment plan misalignments prevents adverse events
Supports Healthcare Workers: Reduces cognitive load on clinicians by automatically flagging potential issues, allowing them to focus on direct patient care
Promotes Health Equity: By standardizing documentation review across all patients, Care Sync helps ensure consistent quality of care regardless of patient demographics or care team composition

Additional Alignment: Care Sync also supports SDG 9 (Industry, Innovation, and Infrastructure) by demonstrating how AI can be safely and effectively integrated into healthcare systems to improve infrastructure and service delivery.