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.
Multi-state Modeling of Pressure Injury Staging Transition Trajectories
This study was conducted to evaluate the time-sensitive progression trajectory of pressure injury stages based on real-world electronic health record (EHR) datasets. Clinical databases within the Mass General Brigham (MGB) Healthcare system was used as data source. Both pressure injury anatomical locations and staging values were obtained through EHR flowsheets. Our results suggested that early intervention, especially for patients with stage 1 can be a very important strategy to prevent severe pressure injury.
Learning Outcomes
- Conduct time-series analysis of pressure injury stage records.
- Evaluate clinical significance of trajectory patterns in pressure injuries based on a real-world dataset.
Speakers
- Wenyu Song, PhD, Brigham and Women's Hospital, Harvard Medical School
Nurses’ Visual Attention in EHR Nursing Summaries through Eye-Tracking Study
This study assessed how nurses allocate their visual attention when reading EHR nursing summaries and examined with information volume. Conducted with 33 nurses from a university hospital using eye-tracking simulations, findings revealed a predominant focus on ""Orders"" and ""Sidebar"" information across patient acuity levels, due in part to its large volume of information. Our results highlight the need for EHR nursing summary redesign by removing less important information-types.
Learning Outcomes
- Identify key types of information that nurses focus on in EHR nursing summaries.
- Explain how this information contributes to EHR redesign efforts.
Speakers
- Suhyun Park, PhD, RN, UTHealth Houston Cizik School of Nursing
Preemptive Forecasting of Symptom Escalation in Cancer Patients Undergoing Chemotherapy
This study evaluates the utility of machine learning (ML) algorithms in early forecasting of total symptom score changes from daily self-reports of 339 chemotherapy patients. The dataset comprised 12 specific symptoms, with severity and distress for each symptom rated on a 1 to 10 scale, generating a ""total symptom score"" ranging from 0 to 230. To address the challenge of an unbalanced original dataset, where Class I (score change ≥ 5) and Class II (score change 5) were unevenly represented, we created a balanced dataset specifically for model training. Using the MATLAB® Classification Learner application, we investigated nine ML models with various classifiers. The objective was to predict the total symptom score change based on the preceding 3 to 5 days' symptom data. Models were trained on the balanced dataset to mitigate the original imbalance's impact, with comparative evaluations also conducted on the unbalanced data to assess performance differences. The analysis revealed that certain classifiers, delivered optimal performance on the unbalanced dataset, with an accuracy rate peaking at 82%. Yet, these models tended to frequently misclassify Class I as Class II. In contrast, the Ensemble algorithm equipped with the RUSBoost classifier demonstrated exceptional skill in accurately classifying both classes on both datasets, achieving accuracies of 59%, 59.3%, and 59.4% for data from 3, 4, and 5 days prior, respectively. Notably, these figures slightly improved to 61.16%, 58.41%, and 60.05% upon utilizing the balanced dataset for training.
Learning Outcomes
- Understand how machine learning models can be used to predict symptom escalation in chemotherapy patients using patient-reported data.
Speakers
- Aref Smiley, PhD, The University of Utah
Nursing Workload and Overcrowding: Patient Safety Role in Emergency Department
This study evaluates nursing workload and the National Overcrowding Score (NEDOCS) in emergency departments (ED) by analyzing electronic health records. It finds weak correlation between workload and NEDOCS (r=0.346) demonstrating their distinct roles in the ED, but a significant link between antibiotic administration for pneumonia patients, NEDOCS (r=0.823), and workload (r=0.952). Highlighting the importance of nurse workload management, it suggests this focus can improve patient safety and care quality in challenging ED settings.
Learning Outcomes
- Know which indicator has a direct impact on patient safety when managing the Emergency Department.
Speakers
- Junhyuk Seo, Registered Nurse, Samsung Medical Center
Using a Healthcare Process Modeling Approach to Understand Electronic Health Records-based Pressure Injury Data and to Support Development of a Standardized Pressure Injury Phenotyping Pipeline
The complexity of health care processes present significant challenges for using Electronic Health Records (EHR) data to build high fidelity phenotypes. This study leverages a healthcare process modeling (HPM) approach to enable understanding of EHR-based pressure injury (PrI) data patterns needed for building a standardized PrI phenotyping pipeline. The PrI HPM was developed and validated using mixed methods, including exploratory sequential design, through interdisciplinary collaboration among clinical experts, data scientists, database analysts, and informaticians. The qualitative analysis identified the dynamics between PrI care and the associated clinical documentation processes. The quantitative analysis identified inherent challenges and limitations of the PrI data. The PrI HPM includes three moderating factors: system configuration, hospital policy, and nurse's individual workflow. We further incorporated the HPM into the PrI phenotype development process to address phenotyping challenges. Moreover, we suggested a set of standardizable recommendations to address PrI phenotyping challenges.
Learning Outcomes
- Understand the moderating factors of pressure injury healthcare process model.
Speakers
- Luwei Liu, Master of Biomedical Informatics, Brigham and Women's Hospital
A Human Factors Approach to Designing for Human-AI Teaming: The Case of an Emergency Department-based Clinical Decision Support Tool to Prevent Community Falls of Older Adults
Human Factors and Cognitive Systems Engineering (HF/CSE) has long been successfully applied in safety-critical industries such as nuclear power and aviation to achieve remarkable augmentation of human operators. To demonstrate the application of HF/CSE in healthcare, we present our work developing an emergency department-based clinical decision support tool to prevent future falls of older adults. We share specific HF/CSE principles leveraged in design and implementation and reflect on the implications for designing for Human-AI teaming.
Learning Outcomes
- Describe four principles of human-AI teaming and how they were applied to the design of an ED-based clinical decision support tool.
Speakers
- Hanna Barton, PhD, University of Wisconsin-Madison
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.