Paper List
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Autonomous Agents Coordinating Distributed Discovery Through Emergent Artifact Exchange
This paper addresses the fundamental limitation of current AI-assisted scientific research by enabling truly autonomous, decentralized investigation w...
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D-MEM: Dopamine-Gated Agentic Memory via Reward Prediction Error Routing
This paper addresses the fundamental scalability bottleneck in LLM agentic memory systems: the O(N²) computational complexity and unbounded API token ...
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Countershading coloration in blue shark skin emerges from hierarchically organized and spatially tuned photonic architectures inside skin denticles
This paper solves the core problem of how blue sharks achieve their striking dorsoventral countershading camouflage, revealing that coloration origina...
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Human-like Object Grouping in Self-supervised Vision Transformers
This paper addresses the core challenge of quantifying how well self-supervised vision models capture human-like object grouping in natural scenes, br...
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Hierarchical pp-Adic Framework for Gene Regulatory Networks: Theory and Stability Analysis
This paper addresses the core challenge of mathematically capturing the inherent hierarchical organization and multi-scale stability of gene regulator...
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Towards unified brain-to-text decoding across speech production and perception
This paper addresses the core challenge of developing a unified brain-to-text decoding framework that works across both speech production and percepti...
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Dual-Laws Model for a theory of artificial consciousness
This paper addresses the core challenge of developing a comprehensive, testable theory of consciousness that bridges biological and artificial systems...
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Pulse desynchronization of neural populations by targeting the centroid of the limit cycle in phase space
This work addresses the core challenge of determining optimal pulse timing and intensity for desynchronizing pathological neural oscillations when the...
An AI Implementation Science Study to Improve Trustworthy Data in a Large Healthcare System
Georgia Institute of Technology, Atlanta, GA, USA | Shriners Hospitals for Children, Tampa, FL, USA
30秒速读
IN SHORT: This paper addresses the critical gap between theoretical AI research and real-world clinical implementation by providing a practical framework for assessing and improving healthcare data quality using trustworthy AI principles.
核心创新
- Methodology Developed a Python-based extension of OHDSI's Data Quality Dashboard (DQD) that integrates the METRIC framework for trustworthy AI assessment, addressing informative missingness, timeliness, and distribution consistency.
- Methodology Implemented a real-world case study modernizing a large pediatric healthcare system's Research Data Warehouse from OMOP CDM v5.1/5.2 to v5.4 within Microsoft Fabric, achieving 4% improvement in data quality test success rate (84.78% to 88.88%).
- Biology Demonstrated that data harmonization using OMOP CDM concept codes does not significantly impact AI model performance (mean AUROC: 71.3% with source codes vs. 70.0% with OMOP codes) while increasing interoperability for Craniofacial Microsomia case study.
主要结论
- Modernizing SC's OMOP CDM database from v5.1/5.2 to v5.4 improved overall data quality by 4% (84.78% to 88.88% success rate) and conformance by 8% (80.73% to 88.09%).
- Data harmonization using OMOP CDM concept codes maintained comparable AI model performance (mean AUROC difference: 1.3%) while enabling better interoperability across healthcare systems.
- Only 50% of ICD-9 codes shared common mappings with ICD-10 codes, revealing significant vocabulary transition challenges that could degrade AI model performance when encountering mixed coding systems.
摘要: The rapid growth of Artificial Intelligence (AI) in healthcare has sparked interest in Trustworthy AI and AI Implementation Science, both of which are essential for accelerating clinical adoption. Yet, barriers such as strict regulations, gaps between research and clinical settings, and challenges in evaluating AI systems hinder real-world implementation. This study presents an AI implementation case study within Shriners Children’s (SC), a large multisite pediatric system, showcasing the modernization of SC’s Research Data Warehouse (RDW) to OMOP CDM v5.4 within a secure Microsoft Fabric environment. We introduce a Python-based data quality assessment tool compatible with SC’s infrastructure, an extension of OHDSI’s R/Java-based Data Quality Dashboard (DQD) that integrates Trustworthy AI principles using the METRIC framework. This extension enhances data quality evaluation by addressing informative missingness, redundancy, timeliness, and distributional consistency. We also compare systematic and case-specific AI implementation strategies for Craniofacial Microsomia (CFM) using the FHIR standard. Our contributions include a real-world evaluation of AI implementations, integration of Trustworthy AI in data quality assessment, and evidence-based insights into hybrid implementation strategies, highlighting the need to blend systematic infrastructure with use-case-driven approaches to advance AI in healthcare.