SmartState: An Automated Research Protocol Adherence System
Developing and enforcing study protocols is crucial in medical research, especially as interactions with participants become more intricate. Traditional rules-based systems struggle to provide the automation and flexibility required for real-time, personalized data collection. We introduce SmartState, a state-based system designed to act as a personal agent for each participant, continuously managing and tracking their unique interactions. Unlike traditional reporting systems, SmartState enables real-time, automated data collection with minimal oversight. By integrating large language models to distill conversations into structured data, SmartState reduces errors and safeguards data integrity through built-in protocol and participant auditing. We demonstrate its utility in research trials involving time-dependent participant interactions, addressing the increasing need for reliable automation in complex clinical studies.
Learning Objectives
- Understand how SmartState can be applied to their research
- Describe the benefits of state machines in improving research study compliance and verification
- Utilize large language models to interpret and extract conversational intent
Speaker
- Samuel Armstrong, MS (University of Kentucky)
Human-Centered Design of the Vanderbilt Algorithmovigilance Monitoring and Operations System
As AI adoption in healthcare grows, there is an increasing need for continuous monitoring after implementation, known as algorithmovigilance. While existing tools provide some support, few systems enable comprehensive proactive oversight and governance of AI across a healthcare system. This study outlines the human-centered design process used to develop the Vanderbilt Algorithmovigilance Monitoring and Operations System (VAMOS). We describe key insights and design recommendations to guide the development of robust algorithmovigilance tools for healthcare institutions.
Learning Objectives
- Describe key features and end-user needs for AI monitoring systems.
Speaker
- Megan Salwei, PhD (Vanderbilt University Medical Center)
From Scanner to Science: Reusing Clinically Acquired Medical Images for Research
Growth in the field of medical imaging research has revealed a need for larger volume and variety in available data. This need could be met using curated clinically acquired data, but the process for getting this data from the scanners to the scientists is complex and lengthy. We present a manifest-driven modular Extract, Transform, and Load (ETL) process named Locutus designed to appropriately handle difficulties present in the process of reusing clinically acquired medical imaging data. Based on four foundational assumptions about medical data, research data, and communication, Locutus presents a five-phase workflow for downloading, de-identifying, and delivering unique requests for imaging data. To date, this workflow has been used to process over 27,000 imaging accessions for research use. This number is expected to grow as technical challenges are addressed and the role of humans is expected to shift from frequent intervention to regular monitoring.
Learning Objectives
- Describe the challenges involved in reusing clinically acquired medical imaging data for research purposes.
- Explain the modular Extract, Transform, and Load (ETL) process used by Locutus to facilitate the secure transfer of imaging data from scanners to scientists.
- Outline the five-phase workflow of Locutus for downloading, de-identifying, and delivering imaging data.
Speaker
- Remo M. S. Williams, MS (Children's Hospital of Philadelphia)
Empowering Precision Medicine for Rare Diseases through Cloud Infrastructure Refactoring
Rare diseases affect approximately 1 in 11 Americans, yet their diagnosis remains challenging due to limited clinical evidence, low awareness, and lack of definitive treatments. Our project aims to accelerate rare disease diagnosis by developing a comprehensive informatics framework leveraging data mining, semantic web technologies, deep learning, and graph-based embedding techniques. However, our on-premises computational infrastructure faces significant challenges in scalability, maintenance, and collaboration. This study focuses on developing and evaluating a cloud-based computing infrastructure to address these challenges. By migrating to a scalable, secure, and collaborative cloud environment, we aim to enhance data integration, support advanced predictive modeling for differential diagnoses, and facilitate widespread dissemination of research findings to stakeholders, the research community, and the public and also proposed a facilitated through a reliable, standardized workflow designed to ensure minimal disruption and maintain data integrity for existing research project.
Learning Objectives
- Explain the challenges in diagnosing rare diseases and the role of informatics in improving diagnosis.
- Describe how cloud-based computing enhances scalability, security, and collaboration in rare disease research.
Speaker
- Hui Li, Phd (University of Texas Health Science Center at Houston)
Data Governance for a Novel Pet-Patient Data Registry
Significant opportunities for understanding disease co-occurrence across species in coincident households remain untapped. We determined the feasibility of creating a pet-patient registry for analysis of health data from UCHealth patients and their pets who received care at the geographically-adjacent Veterinary Teaching Hospital (CSU-VTH). 12,115 matches were identified, indicating 29% of CSU-VTH clients or a household member were UCHealth patients. Given the favorable linkage results, we describe data governance considerations for establishing secure pet-patient registries.
Learning Objectives
- Determine key data governance considerations necessary for establishing and maintaining secure pet-patient registries.
- Identify the components of a data registry team and a governance team for EHR linkage.
- Understand registry oversight mechanisms and appropriate data management for to ensuring the integrity, security, and accessibility of the information within a pet-patient registry.
Speaker
- Nadia Saklou, DVM, PhD (Colorado State University)
A Standardized Guideline for Assessing Extracted Electronic Health Records Cohorts: A Scoping Review
Assessing how accurately a cohort extracted from Electronic Health Records (EHR) represents the intended target population, or cohort fitness, is critical but often overlooked in secondary EHR data use. This scoping review aimed to (1) identify guidelines for assessing cohort fitness and (2) determine their thoroughness by examining whether they offer sufficient detail and computable methods for researchers. This scoping review follows the JBI guidance for scoping reviews and is refined based on the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) checklists. Searches were performed in Medline, Embase, and Scopus. From 1,904 results, 30 articles and 2 additional references were reviewed. Nine articles (28.13%) include a framework for evaluating cohort fitness but only 5 (15.63%) contain sufficient details and quantitative methodologies. Overall, a more comprehensive guideline that provides best practices for measuring the cohort fitness is still needed.
Learning Objectives
- Summarize the current state of literature on guidelines for evaluating Electronic Health Record (EHR) cohort fitness.
Speaker
- Nattanit Songthangtham, PhD Health Informatics (University of Minnesota Twin Cities)