AMIA's Annual Symposium is the premier learning and networking conference attended by more than 2,500 health informaticians from across the world. Now, you can access full presentations and slides from the live event at your convenience while earning CME/CNE online.
AMIA 2024 Annual Symposium On Demand is designed to provide you with the very latest health informatics content with maximum value and convenience. Revisit one or all top 20 sessions from the conference, featuring leading voices from across the informatics field. Choose the format that fits your preferred learning style. Take up to two years to claim your education credits. Recorded at AMIA’s Annual Symposium, held November 9-13, 2024, in San Francisco, CA.
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Presenter, Slides, and Audio
Members: $225 $180*
Nonmembers: $315 $252*
*Includes 20% discount through March 31. Log in to see your discount.
Slides and Audio
Members: $180
Nonmembers: $255
*Includes 20% discount through March 31. Log in to see your discount.
Exploring the Utilization of Synthetic Data in Unsupervised Clustering for Opioid Misuse Analysis
Privacy and security restrictions on medical data pose challenges to collaborative research, making synthetic data an increasingly attractive solution. Recent advancements in Generative AI technologies, like GAN models, have improved synthetic data generation. This study investigates the use of synthetic data in clustering models for opioid misuse analysis, generating a dataset that replicates real-world data from 2017 to 2019, including demographics and diagnosis codes. By maintaining patient privacy, we enable comprehensive analysis without compromising security. We developed unsupervised clustering models to identify opioid misuse patterns and assessed the effectiveness of synthetic data across four scenarios: training on real dataset and testing on real dataset (TRTR), training on real dataset and testing on synthetic dataset (TRTS), TSTR, and TSTS. Results demonstrate that synthetic data can replicate real data distributions and clustering characteristics as a training set, offering significant potential for collaborative model development and optimization without exposing privacy or security risks.
Learning Outcomes
- Describe why synthetic data demonstrates a promising pathway for advancing medical research under privacy constraints and data scarcity challenges.
- Explain how the overall alignment between TRTR and TSTR in demographic distributions an the ability to replicate key trends in clustering tasks affirms the potential of building training models on synthetic data and testing on real data.
- State the direction of future research based on the presentation.
Speakers
- Yili Zhang, PhD, Georgetown University
Generating Synthetic Test Data Using LLMs for Automated Testing of a Patient-Focused, Survey-Based System: Does Generative AI Live Up to the Hype?
The excitement around the possibilities of generative AI is infinite, but can it live up to the hype when the goal is to generate quality test data quickly and efficiently as part of an automated testing process? In the context of a patient-focused, survey-based system, we demonstrated the potential of generative AI to create custom synthetic data using 2 different large language models (GPT 3.5 and Flan T5-XL) in AWS and Azure environments. While we improved test coverage and efficiency by synthetically generating many test cases, the experience included technical and communication challenges as well as complexities associated with balancing the desire for high utility and realism in the data with the available testing resources. Recommendations range from defining and gaining consensus on evaluation metrics early in the process as it influences technical questions like persona creation and prompt-engineering to encouraging test teams to build flexible frameworks from the start.
Learning Outcomes
- Understand the differences in synthetic data generated by GPT 3.5 and Flan T5-XL for the purposes of testing a patient-focused, survey-based system.
Speakers
- Catherine Anderson, PhD, Accenture
Clinician Perceptions of Generative Artificial Intelligence Tools and Clinical Workflows: Potential Uses, Motivations for Adoption, and Sentiments on Impact
Successful integration of Generative Artificial Intelligence (AI) into healthcare requires understanding of health professionals’ perspectives, ideally through data-driven approaches. In this study, we use a semi-structured survey and mixed methods analyses to explore clinicians’ perceptions on the utility of generative AI for all types of clinical tasks, familiarity and competency with generative AI tools, and sentiments regarding the potential impact of generative AI on healthcare. Analysis of 116 clinician responses found differing perceptions regarding the usefulness of generative AI across clinical workflows, with information gathering from external sources rated highest and communication rated lowest. Clinician-generated prompt suggestions focused most often on clinician decision making and were of mixed quality, with participants more familiar with generative AI suggesting more high-quality prompts. Sentiments regarding the impact of generative AI varied, particularly regarding trustworthiness and impact on bias. Thematic analysis of open-ended comments highlighted concerns about patient care and the role of clinicians.
Learning Outcomes
- 1. Understand the perspectives of clinicians regarding the potential usefulness of generative AI tools in different types of clinical workflows.
- Understand the ability of clinicians to suggest quality prompts.
- Understand which factors would motivate clinicians to adopt a generative AI tool.
- Understand the distribution of sentiments on the potential impact of generative AI on clinical practice.
Speakers
- Elise Ruan, MD, MPH, Columbia University Department of Biomedical Informatics
A framework for evaluating the value of generative AI in healthcare
In response to the burgeoning integration of Large Language Models (LLMs) and generative AI (Gen AI) within healthcare, our study at the Children's Hospital of Philadelphia introduces a pioneering framework to assess the value of Gen AI applications. This framework, designed through our pilot within the Epic EHR system, transcends traditional evaluation metrics to encompass a holistic array of dimensions including efficiency, user experience, scalability, reliability, and relatability, alongside an in-depth cost analysis. Our pilot revealed notable insights: clinicians and support staff experienced efficiency gains with reduced response times by leveraging LLM-generated drafts for patient communication. Specifically, clinicians saw a 14-second reduction in response times, with a corresponding 17-second reduction observed among support staff. Despite these efficiencies, a nuanced cost-benefit analysis underscored the complexity of justifying Gen AI implementation based solely on cost savings. Our findings indicate the multifaceted value of Gen AI extends beyond immediate financial gains, enhancing healthcare delivery through improved interaction quality and enabling tasks previously unfeasible without Gen AI. This study underscores the imperative of a comprehensive, multidimensional approach to evaluating Gen AI in healthcare. By presenting a framework that captures the broad spectrum of Gen AI's value, we aim to foster a deeper understanding of its potential to transform healthcare delivery, encouraging further exploration and refinement in the assessment of Gen AI's true value in healthcare settings.
Learning Outcomes
- Identify and evaluate the key dimensions of value when implementing generative AI solutions in healthcare settings beyond traditional ROI metrics.
Speakers
- Stephon Proctor, PhD, ABPP, Children's Hospital of Philadelphia
Exploring the Impact of Explainable AI on Clinicians’ Acceptance of AI-Generated Results in Healthcare
The adoption of advanced artificial intelligence (AI) techniques is rapidly advancing in healthcare. Despite AI's expanding role, challenges associated with transparency issues became a significant barrier to its full adoption in clinical practice. Explainable AI (XAI) emerged as a solution to improve clinicians’ acceptance of AI produced results by clarifying the inference process. Yet, the impact of XAI methods on clinicians’ acceptance of AI results has not been fully explored. This study explored how XAI affected clinicians’ mental models of decision making, trustworthiness, and satisfaction towards AI-generated results.
Learning Outcomes
- Discuss the need for continual refinement of XAI explanations to align with clinician feedback and support their evolving understanding, ensuring effective integration of AI systems in clinical practice.
Speakers
- Jinsun Jung, Master, Seoul National University
Neural Mosaics: Detecting Aberrant Brain Interactions using Algebraic Topology and Generative Artificial Intelligence
Epilepsy is a neurological disorder affecting more than 50 million worldwide with up to 30% of patients remaining refractory to medications. Accurate seizure detection is crucial for surgical planning and successful outcomes. Neurophysiological signal-based seizure detection methods are complicated, computationally expensive, and time consuming due to the large volumes of data from long periods of seizure monitoring and the complex methods for feature extraction from signal data. Moreover, these methods fail to capture multifocal interactions between brain regions. Persistent homology offers robust representations of complex brain interaction patterns. We propose a novel approach to classifying persistent homology structures representing brain interaction dynamics in epilepsy using the Google Gemini Pro Vision large language model (LLM). Using intracranial electroencephalography (iEEG) from refractory epilepsy patients, we apply persistent homology to one-second epochs during seizure and non-seizure periods and generate persistence diagrams to visualize the results. We introduce new prompting template for Gemini 1.0 Pro-Vision model to classify these diagrams, distinguishing multifocal brain interactions from seizure and non-seizure activity. To our knowledge, this is the first study to use persistence diagrams as input to a foundational model for analyzing aberrant brain interaction dynamics. In contrast to traditional approaches of using machine learning algorithms for EEG classification that require hand crafted feature engineered data, large volume of representative training data, and brittle hyperparameter tuning, our approach is a robust method that combines recent advances in algebraic topology and LLMs to analyze large-scale EEG data for seizure detection without requiring large volumes of training data.
Learning Outcomes
- Understand promises and limitations of using in-context learning with Goodle's Gemini 1.0 Pro-Vision model to classify persistence diagrams generated from intracranial electroencephalography (iEEG) signals to differentiate multifocal brain interactions during seizure and non-seizure events.
Speakers
- Katrina Prantzalos, MS, Case Western Reserve University
Continuing Education Credit
Physicians
The American Medical Informatics Association is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
The American Medical Informatics Association designates this online enduring material for 1.5 AMA PRA Category 1™ credits. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
Claim credit no later than January 20, 2028 or within two years of your purchase date, whichever is sooner. No credit will be issued after January 20, 2028.
Nurses
The American Medical Informatics Association is accredited as a provider of nursing continuing professional development by the American Nurses Credentialing Center’s Commission on Accreditation.
- Approved Contact Hours: 1.5 participant maximum
- Nurse planner for this activity: Jenna Thate, PhD, RN, CNE
- Jenna Thate discloses that she has no financial relationships with ACCME/ANCC-defined ineligible companies.
Upon completion of each video and corresponding evaluation portion of this activity, all learners will be able to download the appropriate credit certificate, or a certificate of participation.
Claim credit no later than January 20, 2028 or within two years of your purchase date, whichever is sooner. No credit will be issued after January 20, 2028.
ACHIPsTM
AMIA Health Informatics Certified ProfessionalsTM (ACHIPsTM) can earn 1 professional development unit (PDU) per contact hour.
ACHIPsTM may use CME/CNE certificates or the ACHIPsTM Recertification Log to report 2024 Symposium sessions attended for ACHIPsTM Recertification.
Claim credit no later than January 20, 2028 or within two years of your purchase date, whichever is sooner. No credit will be issued after January 20, 2028.