Paper List
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Exactly Solvable Population Model with Square-Root Growth Noise and Cell-Size Regulation
This paper addresses the fundamental gap in understanding how microscopic growth fluctuations, specifically those with size-dependent (square-root) no...
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Assessment of Simulation-based Inference Methods for Stochastic Compartmental Models
This paper addresses the core challenge of performing accurate Bayesian parameter inference for stochastic epidemic models when the likelihood functio...
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Realistic Transition Paths for Large Biomolecular Systems: A Langevin Bridge Approach
This paper addresses the core challenge of generating physically realistic and computationally efficient transition paths between distinct protein con...
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MoRSAIK: Sequence Motif Reactor Simulation, Analysis and Inference Kit in Python
This work addresses the computational bottleneck in simulating prebiotic RNA reactor dynamics by developing a Python package that tracks sequence moti...
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The BEAT-CF Causal Model: A model for guiding the design of trials and observational analyses of cystic fibrosis exacerbations
This paper addresses the critical gap in cystic fibrosis exacerbation management by providing a formal causal framework that integrates expert knowled...
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A Theoretical Framework for the Formation of Large Animal Groups: Topological Coordination, Subgroup Merging, and Velocity Inheritance
This paper addresses the core problem of how large, coordinated animal groups form in nature, challenging the classical view of gradual aggregation by...
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ANNE Apnea Paper
This paper addresses the core challenge of achieving accurate, event-level sleep apnea detection and characterization using a non-intrusive, multimoda...
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DeeDeeExperiment: Building an infrastructure for integrating and managing omics data analysis results in R/Bioconductor
This paper addresses the critical bottleneck of managing and organizing the growing volume of differential expression and functional enrichment analys...
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
The 30-Second View
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.
Innovation (TL;DR)
- 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.
Key conclusions
- 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.
Abstract: 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.