Reusable Generic Clinical Decision Support System Module for Immunization Recommendations in Resource-Constraint Settings

Presentation Time: 03:30 PM - 03:45 PM

Abstract Keywords: Clinical Decision Support for Translational/Data Science Interventions, Data/System Integration, Standardization and Interoperability, Data Sharing/Interoperability
Primary Track: Clinical Research Informatics
Programmatic Theme: Emerging Best Practices for Clinical Research Informatics Operations

Clinical decision support systems (CDSS) are routinely employed in clinical settings to improve quality of care, ensure patient safety, and deliver consistent medical care. However, rule-based CDSS, currently available, do not feature reusable rules. In this study, we present CDSS with reusable rules. Our solution includes a common CDSS module, electronic medical record (EMR) specific adapters, CDSS rules written in the clinical quality language (CQL) (derived from CDC immunization recommendations), and patient records in fast healthcare interoperability resources (FHIR) format. The proposed CDSS is entirely browser-based and reachable within the user’s EMR interface at the client-side. This helps to avoid the transmission of patient data and privacy breaches. Additionally, we propose to provide means of managing and maintaining CDSS rules to allow the end users to modify them independently. Successful implementation and deployment were achieved in OpenMRS and OpenEMR during initial testing.
Speaker(s):
Samuil Orlioglu, MS
Clemson University

Author(s):
Samuil Orlioglu, MS - Clemson University; Akash Shanmugam Boobalan, Ms - Clemson; Kojo Abanyie, PharmD/Post-doctoral Scholar - University of PIttsburgh; Richard Boyce, PhD - University of Pittsburgh; Hua Min, PhD - George Mason University; Yang Gong, MD, PhD - UTHealth Houston; Dean Sittig, PhD - University of Texas Health Science Center at Houston; Paul Biondich, MD, MS - Regenstrief Institute; Adam Wright, PhD - Vanderbilt University Medical Center; Christian Nøhr, PhD - Aalborg University; Timothy Law, DO - Ohio University; Nina Hubig, PhD - IT:U; Ronald Gimbel - Clemson University; Xia Jing, MD, PhD - Clemson University; David Robinson, MD - Independent Consultant; Arild Faxvaag, PhD - Norwegian University of Science and Technology; Lior Rennert, PhD - Clemson Univserity;

RD-LIVES: A Living Evidence Synthesis System for Rare Disease Treatment Efficacy and Safety

Presentation Time: 03:45 PM - 04:00 PM

Abstract Keywords: Informatics Research/Biomedical Informatics Research Methods, Drug Discovery, Repurposing, and Side-effect Discovery, Machine Learning, Generative AI, and Predictive Modeling, Biomedical Informatics and Data Science Workforce Education, Data-Driven Research and Discovery, Data Sharing/Interoperability, Natural Language Processing, Implementation Science and Deployment
Working Group: Pharmacoinformatics Working Group
Primary Track: Data Science/Artificial Intelligence
Programmatic Theme: Harnessing the Power of Large Language Models in Health Data Science

Although rare diseases (RD) are gaining priority in healthcare worldwide, developing research policies for studying them in public settings remains challenging due to the limited evidence available. Evidence generation is crucial for rare diseases, requiring systematic assessment of study quality across multiple sources. Given the scarcity of patients, literature and clinical trial data for orphan drugs, we developed RD-LIVES—a tool designed to automatically accelerate evidence collection from literature and clinical trials for systematic reviews and meta-analyses. This tool enhances our understanding of treatment outcomes, determines appropriate follow-up durations, and informs the required treatment impact size for new drugs. Using Idiopathic Pulmonary Fibrosis (IPF) as an example, we demonstrate how RD-LIVES automates evidence collection and element extraction. The results indicate that RD-LIVES plays a vital role in designing costly prospective trials and has the potential to increase the likelihood of successful trial outcomes
Speaker(s):
jinlian wang, PhD
UTHealth

Author(s):

Performance changes in ACCEPT model for predicting COPD exacerbations when applied to patients with and without exacerbation history

Presentation Time: 04:00 PM - 04:15 PM

Abstract Keywords: Machine Learning, Generative AI, and Predictive Modeling, Secondary Use of EHR Data, Clinical Decision Support for Translational/Data Science Interventions, Real-World Evidence and Policy Making, Data-Driven Research and Discovery
Primary Track: Data Science/Artificial Intelligence
Programmatic Theme: Real-World Evidence in Informatics: Bridging the Gap between Research and Practice

Most tools for predicting COPD exacerbation require complex feature inputs and are explicitly trained on high-risk patient cohorts with an exacerbation history. This study evaluated the performance decline when a previously validated model for predicting COPD exacerbations was deployed on a cohort with no COPD exacerbation history and limiting the model to features readily captured as structured fields in the EHR. Results demonstrate the need for additional features to predict COPD exacerbation among low-risk populations.
Speaker(s):
Iben Sullivan, PhD, MPH
Dartmouth College

Author(s):
Iben Sullivan, PhD, MPH - Dartmouth College; Sharon Davis, PhD - Vanderbilt University Medical Center; Todd Mackenzie, Ph.D. - Dartmouth College; Michael Matheny, MD, MS, MPH, FACMI, FAMIA - Vanderbilt University Medical Center; Laura Paulin, MD MHS; Thomas Lasko, MD, PhD - Vanderbilt University Medical Center; Bradley Richmond, MD, Ph.D - Vanderbilt University Medical Center; Adrienne Congers, MD - Vanderbilt University Medical Center; Mohammed Al-Garadi, PhD - VUMC; Ruth Reeves - Tennessee Valley Health Care System, US Veterans' Affairs; Jeremiah Brown, PhD, MS - The Dartmouth Institute;

Development and Implementation of a Clinical Decision Support System for Reduction in Unhealthy Alcohol Use

Presentation Time: 04:15 PM - 04:30 PM

Abstract Keywords: Clinical Decision Support for Translational/Data Science Interventions, Implementation Science and Deployment, Learning Healthcare System
Primary Track: Clinical Research Informatics
Programmatic Theme: Implementation Science and Deployment in Informatics: Enabling Clinical and Translational Research

Unhealthy alcohol use is a leading cause of preventable mortality and a risk factor for an array of social and health problems with an estimated annual economic impact of $249 billion. Despite the significant public health impact of unhealthy alcohol use, rates of screening across the US remain low. Effective screening and treatment in small practices are limited by a lack of clinician education around screening and management, low rates of prescribing medications for alcohol use disorder, poor availability of referral programs, and difficulty integrating interventions into existing clinical workflows. Checklist-based screening tools present a simple, easy-to-understand strategy and have been successfully applied within the healthcare setting. The Chronic Care Model emphasizes that practices use clinical decision support (CDS) to create prepared and proactive care teams and informed and motivated patients, leading to improved health outcomes. This study was embedded within the Intervention in Small primary care Practices to Implement Reduction in unhealthy alcohol usE (INSPIRE) Program (R18HS027088) and aimed to develop a standards-based, interoperable alcohol screening and brief intervention (ASBI) CDS system (i.e., actionable medical knowledge such as clinical practice guidelines and local best practices, translated into computable and interoperable CDS logic expressions) that can help decrease unhealthy alcohol use.
Speaker(s):
Author(s):

Clinical and Economic Impact of an Informatics-Enabled, Reward-Based, Behavior Change Program for Medicare Members with Gaps in Care

Presentation Time: 04:30 PM - 04:45 PM

Abstract Keywords: Machine Learning, Generative AI, and Predictive Modeling, Measuring Outcomes, Data/System Integration, Standardization and Interoperability, Collaborative Workflow Systems, Influence of Informatics on Pharma and Insurance Industry
Primary Track: Translation Bioinformatics/Precision Medicine
Programmatic Theme: Real-World Evidence in Informatics: Bridging the Gap between Research and Practice

We describe key components and outcomes of the Healthy You Rewards framework; a proprietary, payor-led, behavior change rewards program. Building on informatics, transdisciplinary expertise, tools, and capabilities are operationalized to effectively deliver actionable, contextualized, and personalized evidence-based clinical actions to Medicare Advantage members, at scale.
Speaker(s):
Kelly Jean Craig, PhD
CVS Health

Author(s):
Amanda Zaleski, PhD, MS - Aetna; Francesco Abate, PhD - CVS Health; Kelly Jean Craig, PhD - CVS Health; Eleanor Beltz, PhD, ATC - Aetna Medical Affairs, CVS Health; Ranjitha Korrapati, MS - CVS Health; Anirud Vem, MS - CVS Health; Jimmy Cao, BA - CVS Health; Parsa Rastin, BA - CVS Health; Hanying Ji, MA - CVS Health; Lukas Hansen, MBA - CVS Health; Dorothea Verbrugge, MD - CVS Health; Laure Salomon, MBA - CVS Health;

Assessing intervention implementation and sustainability for familial hypercholesterolemia screening

Presentation Time: 04:45 PM - 05:00 PM

Abstract Keywords: Implementation Science and Deployment, Health Information and Biomedical Data Dissemination Strategies, Clinical Decision Support for Translational/Data Science Interventions, Patient-centered Research and Care
Primary Track: Clinical Research Informatics
Programmatic Theme: Implementation Science and Deployment in Informatics: Enabling Clinical and Translational Research

We used implementation science and human-centered design to inform an implementation strategy package including electronic health record (EHR) informatics interventions to increase rates of screening and diagnosis of familial hypercholesterolemia (FH) as part of a clinical trial. FH is a genetic condition that increases cholesterol levels. To prepare for sustainability, we iteratively assessed the use of best practice alerts (BPAs) and other interventions at each phase of the trial and identified opportunities to improve.
Speaker(s):
Katrina Romagnoli, PhD, MS, MLIS
Geisinger

Author(s):
Ana Morales, MS, CGC - Geisinger Health System; Zachary Salvati, MS, CBC - Geisinger Health System; Laney Jones, PharmD, MPH - Amgen; Marc Williams, MD - Marc S. Williams; Seneca Harberger, MD - Geisinger; Maria Kobylinski, MD - Geisinger Health System; David Rolston, MD - Geisinger Health System; Matthew Nelson, DO - Geisinger Health System; Samuel Gidding, MD - Geisinger Health System;