Case Studies

Clinical Decision Support & Precision Care System

Large Tertiary Hospital

A clinical decision support system for large medical institutions, merging EMR/LIS/PACS multi-modal data to achieve assisted diagnosis and whole-course disease management.

Key Metrics

Project Results

High
Diagnostic Consist.
Strong alignment between AI-assisted suggestions and expert judgment
Effective
Risk Detection
Helps identify drug interactions, contraindications, and other potential risks
0+
Specialized DBs
Built databases for key disciplines like oncology and cardiovascular diseases
Seconds
Decision Response
Real-time CDSS audit delay when doctors issue orders
Core Technology Features

Technical Highlights

Clinical Inference Engine

Fuses evidence-based medicine with data-driven AI for explainable assisted diagnosis

Multi-modal Data Lake

Unified management of text, imaging, and waveform data for a patient holographic view

Medication Safety Guard

Real-time audit of prescription compliance, automatically alerting to drug incompatibilities

Whole-course Management

Connects screening, treatment, and follow-up for full-lifecycle patient care

XLSAI70%<5m
Project Overview

Client Background

This Large Tertiary Hospital handles 20k+ daily outpatients, accumulating massive EMR, lab reports, and imaging data. However, fragmented systems and siloed data made it hard for doctors to retrieve information quickly, leading to heavy reliance on personal experience and risks of misdiagnosis and research data silos.

Technology Stack

CDSSFHIR/HL7Medical KGMulti-modal AI
Transformation

From Challenges to Solutions

Transformation
1Heterogeneous data: EMR, LIS, and PACS standards are incompatible; unstructured medical records make up a high percentage, making computer comprehension difficult
Built a Medical Data Lake for unified collection, cleaning, and standardization (HL7/FHIR) of multi-modal data hospital-wide, breaking data silos
2Clinical decision risk: Complex cases rely on individual experience; lack of real-time evidence-based medicine support tools leads to potential medical errors
Developed a Clinical Inference Engine merging medical guidelines and expert knowledge to build a CDSS system for real-time assisted diagnosis and alerts
3Poor personalization: Lack of comprehensive analysis of patient lifecycle data makes it difficult to formulate precise individualized treatment plans
Created a Precision Treatment Platform using AI to analyze patient genome, imaging, and clinical data to recommend personalized treatments and medication
4Low research conversion: Massive clinical data sleeps in databases without efficient mining and analysis tools to convert it into high-level research results
Established a Research Data Base providing one-stop cohort selection, statistical analysis, and multi-modal mining tools to accelerate research output
Technical Architecture

System Architecture Design

Layer 1
Medical Data Lake

Multi-modal data fusion and standardization of EMR, LIS, and PACS

Multi-modal FusionCleaningInteroperabilityPrivacy
Layer 2
Clinical Inference Engine

Diagnostic inference based on Evidence-Based Medicine knowledge base and CDSS

CDSSKnowledge BaseRisk PredictionInteraction
Layer 3
Precision Care Layer

Personalized treatment recommendation and full-cycle health management

Personalized PlanAlertsChronic MgmtFollow-up
多模态数据融合临床决策推理精准方案推荐全生命周期管理
Implementation Timeline

Phased Implementation

1
Phase 1

Data Governance & Integration

Cleaned and ingested 10 years of historical data; established unified patient 360-views and specialized disease databases

2
Phase 2

CDSS Engine Launch

Built clinical inference models on medical KGs; launched assisted diagnosis in Cardiology and Oncology departments

3
Phase 3

Whole-course Care Loop

Connected intra- and extra-hospital data; launched follow-up and chronic disease management modules for full-cycle coverage

Testimonial

Client Testimonial

The CDSS system is like an experienced professor standing behind me. It not only alerts me to potential diagnostic omissions but also recommends medications based on the latest guidelines, providing valuable reference suggestions for young doctors.

Head of Cardiology

Clinical MD Supervisor

FAQ

Frequently Asked Questions

Do CDSS suggestions have legal validity?
How does the system protect patient privacy?
Can the system adapt to special departmental needs?
How does the AI perform on rare diseases?

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