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.
Choose Your Format
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 $144*
Nonmembers: $255 $204*
*Includes 20% discount through March 31. Log in to see your discount.
Empowering Patient-Centric Data Management in Healthcare Using Blockchain-based Self-Sovereign Identity and Non-Fungible Tokens
Patient tokenization, leveraging non-fungible tokens and self-sovereign identity on blockchain technology, represents a transformative approach for secure, anonymous patient data linkage across diverse healthcare domains, including medical, dental, and beyond. This study demonstrates the feasibility of this innovative system through a case study involving over three million transactions, showcasing its potential to fundamentally reshape identity management and health information exchange in a patient-centric manner. This work showcases its transformative potential across various healthcare domains.
Learning Outcomes
- Explain how blockchain-based self-sovereign identity (SSI) and non-fungible tokens (NFTs) enhance patient control and data privacy in health information exchange.
- Describe the role of NFTs in securely linking patient records across multiple healthcare facilities while maintaining anonymity and compliance with privacy regulations.
- Analyze the potential challenges and opportunities of integrating blockchain-based patient tokenization with existing healthcare frameworks and interoperability standards.
Speakers
- Yan Zhuang, PhD, Indiana University
Enhancing Wearable Sensor Data Classification Through Novel Modified-Recurrent Plot-Based Image Representation and Mixup Augmentation
Deep learning advancements have revolutionized scalable classification in many domains including computer vision, healthcare and Natural Language Processing (NLP). However, when it comes to classification and domain adaptation based on wearables, it suffers from persistent underperformance, largely due to the scarcity of pre-trained deep learning models that are abundantly available for computer vision and NLP. This is primarily because wearable sensor data need sensor-specific preprocessing, architectural modification, and extensive data collection. We present a novel modified-recurrent plot-based image representation that seamlessly integrates both temporal and frequency domain information. We employ an efficient Fourier Transform-based frequency domain angular difference estimation scheme in conjunction with existing temporal recurrent plots. We validated proposed method in two different domains: accelerometer-based activity-recognition and real-time glucose level prediction from wearable sensors. Our findings demonstrated the method we developed not only improves accuracy at recognizing activity but also makes a big leap in glucose level prediction.
Learning Outcomes
- Understand how modified recurrence plots can improve wearable sensor data classification.
- Identify the advantages of combining temporal and frequency domain information in wearable data analysis.
- Recognize the potential of mix-up augmentation in enhancing classification accuracy.
Speakers
- Mohammad Arif Ul Alam, PhD, University of Massachusetts Lowell
Voice-Activated Self-Monitoring Application (VoiS): Perspectives from People with Diabetes and Hypertension
This paper describes the development and usability test processes of the voice-activated self-monitoring (VoiS) application which is purposed to support the self-management of people with both diabetes and hypertension. VoiS is an innovative, theory-driven mobile app on a smart speaker platform to support people with coexisting diabetes and hypertension to self-monitor blood pressures, glucose levels, and health behaviors routinely and conveniently, and to improve the quality of communication with healthcare providers. The prototype of VoiS includes voice interaction with Amazon Alexa and data representation using smartphones (iOS and Android). A total of 14 people with coexisting diabetes and hypertension participated in usability testing. After completing a range of tasks individually, testers participated in group interviews. We used a survey based on the Technology Acceptance Model to measure the ease of use and perceived usefulness of VoiS. All interviews were recorded and transcribed, and then common themes were extracted. Participants found VoiS to be easy to use and useful.
Learning Outcomes
- Describe what VoiS is.
- Recognize the purpose of VoiS.
- Demonstrate the process of usability test.
Speakers
- Li Yang, MSM, University of Wisconsin-Milwaukee
Integrating Remote Patient Monitoring Data into Machine Learning Models for Predicting Emergency Department Utilization
The integration of Remote Patient Monitoring (RPM) data into risk stratification models has emerged as a promising approach for improving healthcare delivery and patient outcomes. In this work, we explore the integration of RPM features – including at home monitoring of body weight, blood pressure, and blood oxygen – into a machine learning model that uses EHR data to predict the likelihood of emergency department (ED) visits or unplanned inpatient admissions within the next 30 days. Through exploratory data analysis, feature engineering, model training, and evaluation of a dataset with 913 patients, we found that RPM data has signal to predict unplanned utilization, and combining RPM data with EHR data improves the predictive power of the model, compared with either data source alone. We discuss the transformative potential of RPM data to augment predictive analytics capabilities in care management settings.
Learning Outcome
- Be able to integrate remote patient monitoring data (RPM) with EHR to predict emergency department visits and unplanned utilization.
Speakers
- Ashika Farzana, MS, Geisinger
Detection of Short-Form Video Addiction with Wearable Sensors via Temporally-Coherent Domain Adaptation
Short-form Video Addiction (SVA), a novel digital addiction of the modern world, proliferates among young adults and is not formally diagnosable. SVA detection from resulting bio-signals is crucial to prevent its adverse impacts. Existing formal methods involve large and expensive neuro-imaging devices in laboratory setups that are intrusive and not feasible to use in daily life. A possible non-intrusive solution can be using wearable sensors which is challenging due to the resulting noisy and faint signals. To address this problem, we investigate multi-modal wearable sensing technology to detect SVA in a non-intrusive fashion. However, fusing multi-modal sensors effectively presents different challenges due to the presence of signal heterogeneity. In this study, we propose a novel multi-modal temporally coherent domain adaptation method to effectively detect SVA using Electroencephalogram (EEG) and Electrodermal Activity (EDA) sensors. We also investigate the nature and properties of SVA with the help of different components of EEG and EDA signals. We evaluate our proposed method for SVA detection and fatigue assessment tasks. Experimental evaluation posits the proposed model's superior performance (10% accuracy) over state-of-art domain adaptation models.
Learning Outcomes
- Discuss the use of wearable sensors in detecting cognitive fatigues resulting from short-form video addiction.
- Explain the advantage of using multi-modal sensors with time-domain alignments
Speakers
Mahmudur Rahman, PhD, University of Wisconsin-Madison
“I worry we’ll blow right by it” Barriers to Uptake of the STRATIFY CDSS for ED Discharge in Acute Heart Failure
We recently implemented a clinical decision support system (CDSS) to identify patients in the emergency department (ED) with acute heart failure that may be safe for discharge instead of the typical costly hospitalization. Despite user-centered-design initial tool uptake was low. To explore barriers to use we interviewed 10 ED clinicians with a case-simulation. Usability issues around tool launch, instead of the tool itself, along with low familiarity of evidence supporting the CDSS drove low uptake.
Speakers
- Matthew Christensen, MD, Vanderbilt University Medical Center
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.