Case Studies

Clinical Decision Support & Precision Care System

Large Tertiary Hospital

This large hospital adopted FIM One to run clinical decision support, merging EMR/LIS/PACS multi-modal data to deliver assisted diagnosis and whole-course disease management.

Key Metrics

Business Impact

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
Adoption Overview

Customer Context

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 Pain Points to Adoption

Transformation
1Heterogeneous data: EMR, LIS, and PACS standards are incompatible; unstructured medical records make up a high percentage, making computer comprehension difficult
Used FIM One to build 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
Turned on the Clinical Inference Engine, merging medical guidelines and expert knowledge into a CDSS 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
Leveraged the Precision Treatment Platform to analyze patient genome, imaging, and clinical data and 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
Enabled the Research Data Base, giving teams 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
Adoption Journey

Phased Implementation

1
Evaluation

Data Governance & Integration

The hospital cleaned and ingested 10 years of historical data, establishing unified patient 360-views and specialized disease databases on FIM One

2
Pilot

CDSS Engine Pilot

The hospital configured clinical inference models on medical KGs and turned on assisted diagnosis in Cardiology and Oncology departments

3
Scale-out

Whole-course Care Loop

The hospital connected intra- and extra-hospital data, rolling out follow-up and chronic disease management modules for full-cycle coverage

Testimonial

Customer Voice

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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