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.
Revealing Patterns of Child Maltreatment Policy Differences and Demographic Dynamics using BERT-Networks and Clustering Approach
Examining child abuse and neglect policies is crucial for shaping child health outcomes. 411 policy items were organized using Siamese BERT-Networks. 52 U.S. territories were categorized into 4 clusters primarily by mandated reporting and differential response policies. Race, gender, and economic status show significant differences among the 4 clusters. Sub-analysis on fatality-related policies revealed significant impact of fatality definitions on outcomes. These findings underscore the necessity of precise policy formulation for improving child outcomes.
Learning Outcomes
- Describe the role of various data analysis methods in transforming policy data into actionable insights regarding child maltreatment outcomes.
Speakers
- Zhidi Luo, MS, Northwestern University
Acceptance and Perceptions of Electronic Health Record-based Clinical Decision Support for Obesity in Pediatric Primary Care
We surveyed 245 clinicians at 84 primary care practices within three US health systems in a cluster-randomized trial of a clinical decision support (CDS) intervention. Clinicians in intervention vs. control sites had higher odds of perceived ease of providing patient materials and subjective norms regarding CDS use and lower odds of intention to use future CDS tools. Our findings highlight opportunities and challenges of CDS to address clinicians’ preferences within healthcare and EHR system constraints.
Learning Outcomes
- Gain exposure to application of the technology Acceptance Model in evaluating the implementation of clinical decision support tools.
Speakers
- Mona Sharifi, MD. MPH, Yale School of Medicine
Development and multi-center validation of a pre-trained language model for predicting neonatal morbidities
We present work in developing, training, and validating NeonatalBERT, a pre-trained language model to automatically predict neonatal diseases at birth from unstructured clinical notes based on a large dataset with over 30,000 newborns. We perform both internal and external validation on a comprehensive list of neonatal morbidities and demonstrate strong performance across hospitals and patient populations. NeonatalBERT has a great degree of flexibility and paves the way for various future applications in neonatal care.
Learning Outcomes
- Gain exposure to application of the technology Acceptance Model in evaluating the implementation of clinical decision support tools.
Speakers
- Feng Xie
Using the Technology Acceptance Model to guide refinements to the Color Me Healthy symptom assessment app for children
We describe revisions to the Color Me Healthy app for children and evaluation of its usability. Fourteen children with cancer and their parents participated in cognitive walkthrough evaluations. Observations of children and parents’ ability to complete key tasks and analysis of qualitative data supported the app’s perceived ease of use and perceived usefulness. Future directions include incorporating Color Me Healthy in clinical care to support monitoring trends in children’s symptoms and facilitating timely interventions.
Learning Outcomes
- Describe application of the constructs within the technology Acceptance Model in evaluating the usability of the revised Color Me Healthy symptom assessment app by children with cancer and their parents.
- Describe the use of cognitive walkthrough interviews as a strategy to evaluate the usability of digital health resources with targe end users.
Speakers
- Lauri Linder, PhD, APRN, CPON, FAAN, FAPHON, University of Utah, Primary Children's Hospital
Acceptability of pictographs as a novel patient identifier to improve patient safety in the neonatal intensive care unit
As part of a randomized controlled trial on the use of pictographs (images used in lieu of a patient photo) embedded in the electronic health record to reduce wrong-patient errors in the neonatal intensive care unit (NICU), we conducted a series of surveys of parents, providers and nurses in the NICU. Data from survey responses were thematically analyzed and categorized. We found that in all groups, there was very high awareness of the intended purpose of the pictographs; however, the perception of providers and nurses about the effectiveness of pictographs was not as strong. While several providers and nurses acknowledged that pictographs can or have helped them avoid wrong-patient errors when caring for multiple birth infants (such as twins), many nurses believed that their current practice of the use of two patient identifiers was sufficient, and pictographs were not useful. Parents reported that pictographs improved their experience of care.
Learning Outcomes
- Describe the challenges with patient identification in the neonatal intensive care setting.
Speakers
- Hojjat Salmasian, MD, MPH, PhD, FAMIA, Children's Hospital of Philadelphia
Probabilistic Graphical Models for Evaluating the Utility of Data-Driven ICD Code Categories in Pediatric Sepsis
Electronic health records (EHRs) are digitalized medical charts and the standard method of clinical data collection. They have emerged as valuable sources of data for outcomes research, offering vast repositories of patient information for analysis. Definitions for pediatric sepsis diagnosis are ambiguous, resulting in delayed diagnosis and treatment, highlighting the need for precise and efficient patient categorizing techniques. Nevertheless, the use of EHRs in research poses challenges. EHRs, although originally created to document patient encounters, are now primarily used to satisfy billing requirements. As a result, EHR data may lack granularity, potentially leading to misclassification and incomplete representation of patient conditions. We compared data-driven ICD code categories to chart review using probabilistic graphical models (PGMs) due to their ability to handle uncertainty and incorporate prior knowledge. Overall, this paper demonstrates the potential of using PGMs to address these challenges and improve the analysis of ICD codes for sepsis outcomes research.
Learning Outcomes
- Identify our approach to classifying and validating ICD codes for exploring the pediatric sepsis population.
Speaker
- Lourdes Valdez, Biomedical Informatics Department, University of Utah"
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.