Paper List
-
Emergent Spatiotemporal Dynamics in Large-Scale Brain Networks with Next Generation Neural Mass Models
This work addresses the core challenge of understanding how complex, brain-wide spatiotemporal patterns emerge from the interaction of biophysically d...
-
Human-Centred Evaluation of Text-to-Image Generation Models for Self-expression of Mental Distress: A Dataset Based on GPT-4o
This paper addresses the critical gap in evaluating how AI-generated images can effectively support cross-cultural mental distress communication, part...
-
GOPHER: Optimization-based Phenotype Randomization for Genome-Wide Association Studies with Differential Privacy
This paper addresses the core challenge of balancing rigorous privacy protection with data utility when releasing full GWAS summary statistics, overco...
-
Real-time Cricket Sorting By Sex A low-cost embedded solution using YOLOv8 and Raspberry Pi
This paper addresses the critical bottleneck in industrial insect farming: the lack of automated, real-time sex sorting systems for Acheta domesticus ...
-
Collective adsorption of pheromones at the water-air interface
This paper addresses the core challenge of understanding how amphiphilic pheromones, previously assumed to be transported in the gas phase, can be sta...
-
pHapCompass: Probabilistic Assembly and Uncertainty Quantification of Polyploid Haplotype Phase
This paper addresses the core challenge of accurately assembling polyploid haplotypes from sequencing data, where read assignment ambiguity and an exp...
-
Setting up for failure: automatic discovery of the neural mechanisms of cognitive errors
This paper addresses the core challenge of automating the discovery of biologically plausible recurrent neural network (RNN) dynamics that can replica...
-
Influence of Object Affordance on Action Language Understanding: Evidence from Dynamic Causal Modeling Analysis
This study addresses the core challenge of moving beyond correlational evidence to establish the *causal direction* and *temporal dynamics* of how obj...
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.