How AI Models Drive Breakthroughs in Cross-EHR Intelligent Clinical Record Assistants Background and Challenges
- Wang Suhong
- Nov 20, 2024
- 4 min read
As artificial intelligence (AI) continues to advance alongside evidence-based medicine, clinical record assistants (CRAs) have become essential for improving patient care and streamlining healthcare workflows. These tools, ranging from basic documentation support to advanced platforms with predictive analytics, often face limitations tied to specific EHR systems. Such challenges hinder cross-platform integration, inflate costs, and slow the adoption of innovative solutions, creating a need for more adaptable and intelligent CRAs.
To address these challenges, the AIExPro platform was created. As a cloud-based intelligent clinical record assistant, AIExPro provides an all-in-one solution encompassing Build a Custom Chatbot, Develop Your Own AI Model, and Model Training & Fine-Tuning. The platform focuses on optimizing workflows, enhancing data-driven decision-making, and delivering smarter AI solutions for healthcare institutions.
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Core Strengths of AIExPro
1. Flexible AI Customization
AIExPro supports users in Developing Your Own AI Model and quickly building AI solutions, such as predictive models and diagnostic tools, tailored to specific clinical needs. Additionally, users can leverage Model Training & Fine-Tuning to further enhance the performance of their AI models.
2. Cross-EHR Platform Integration
The platform is designed to seamlessly integrate with various EHR systems, breaking free from the dependence on a single platform. This eliminates the need for repeated development across systems, significantly reducing deployment costs and increasing adaptability across multiple clinical scenarios.
3. Data Labeling and Training
AIExPro includes a built-in Label Your Training Data feature, simplifying data preprocessing and providing high-quality data for training accurate AI models. This approach not only improves the reliability of models but also accelerates the transition from concept to application.
4. Workflow Optimization
AIExPro ensures real-time communication with EHR systems, automatically extracting patient information and populating relevant CDS tools, thus reducing manual input errors. Additionally, the Build a Custom Chatbot feature offers real-time support for healthcare professionals, further improving workflow efficiency.
AIExPro places data at its core, providing continuous optimization of CDS tools through real-time feedback and intelligent analytics. For instance, the IMPROVE-DD tool achieved an adoption rate of 77.8% across two tertiary hospitals, benefiting 4,083 out of 5,249 patients by significantly improving venous thromboembolism (VTE) prevention in hospitalized patients.
Data and Workflow Optimization
1. Intelligent Workflow Analysis
AIExPro deeply integrates into clinical workflows during development. For example, to optimize the application of the Wells scoring tool, the platform identified the optimal trigger points in emergency department (ED) workflows through real-time observation and interviews with healthcare providers, ensuring efficient integration into real-world scenarios.
2. User Experience and Behavioral Nudges
Based on user feedback, AIExPro refines its tools. For instance, the initial version of the IMPROVE-DD tool displayed numerical calculations, which was later updated to absolute risk percentages for better clinical relevance, significantly improving user satisfaction. Behavioral nudges also actively prompt clinicians to use relevant tools, further boosting adoption rates.
3. Real-Time Data Flow and Tool Interaction
AIExPro uses APIs to extract real-time patient data from EHR systems (e.g., lab results and vital signs) and return analysis results to the EHR front end. This process reduces clinical operation complexity while enabling rapid, precise AI-assisted decision-making. For example, the IMPROVE-DD tool seamlessly integrates into inpatient workflows, from physician order entry to discharge summaries.
4. Flexible Multi-Scenario Deployment
AIExPro supports the development and deployment of tools across various clinical scenarios:
Emergency Departments: The Wells scoring tool intelligently aids clinicians in diagnosing pulmonary embolism.
Inpatient Wards: The IMPROVE-DD tool provides personalized anticoagulation therapy recommendations for VTE prevention.
Public Health Emergencies: The NOCOS tool predicts patient survival rates during COVID-19, supporting medical resource allocation.
Case Studies
1. Tackling Public Health Emergencies
During the COVID-19 pandemic, AIExPro rapidly developed the NOCOS tool to predict inpatient survival rates. While medical resources were not fully depleted, the tool demonstrated the platform's ability to quickly develop, deploy, and optimize AI solutions under urgent circumstances.
2. Precision VTE Prevention
In two tertiary hospitals, the IMPROVE-DD tool optimized VTE prevention strategies using automated scoring systems and D-dimer testing. With an adoption rate of 77.8%, the tool covered over 4,000 hospitalized patients.
3. Intelligent Pulmonary Embolism Diagnosis
After optimization through AIExPro, the Wells scoring tool incorporated behavioral nudges, significantly enhancing its clinical applicability and reducing cognitive workload for physicians during diagnostic workflows.
The Significance of AIExPro for Clinical Practice
AIExPro serves as a comprehensive and flexible AI solution platform for healthcare institutions, enabling rapid AI model development, cross-system integration, real-time data interaction, and dynamic optimization. By adopting a data-driven approach, the platform continuously improves the performance and usability of clinical decision support (CDS) tools and clinical record assistants.
Additionally, the Build a Custom Chatbot feature offers real-time support for healthcare professionals, enhancing their productivity and streamlining workflows. From emergency departments and inpatient wards to public health emergency settings, AIExPro provides intelligent solutions that empower medical teams and optimize the functionality of clinical record assistant systems.
