Description

An onsite recording of this session will be included in the Symposium OnDemand offering. 

Revealing Patterns of Child Maltreatment Policy Differences and Demographic Dynamics using BERT-Networks and Clustering Approach

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

Abstract Keywords: Natural Language Processing, Pediatrics, Health Equity, Evaluation, Population Health, Machine Learning
Primary Track: Policy
Programmatic Theme: Public Health Informatics

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.

Speaker(s):
Zhidi Luo, MS
Northwestern University

Author(s):
Zhidi Luo, MS - Northwestern University; Richard Epstein, Ph.D., M.P.H. - Northwestern University; Rameela Raman, Ph.D. - Vanderbilt University School of Medicine;

Acceptance and Perceptions of Electronic Health Record-based Clinical Decision Support for Obesity in Pediatric Primary Care

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

Abstract Keywords: Clinical Decision Support, Pediatrics, Population Health
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics

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.

Speaker(s):
Mona Sharifi, MD. MPH
Yale School of Medicine

Author(s):
Jessica Ray, PhD - University of Florida; Emily Finn, MPH - Yale School of Medicine; Hollyce Tyrrell, MSSW - Academic Pediatric Association; Jeremy Michel, MD, MHS - The Children's Hospital of Philadelphia, Center for Biomedical Informatics; Randall Grout - Indiana University; Charles Wood, MD, MPH - Duke University; Dean Miner, MD - Duke University; Eliana Perrin, MD, MPH - Johns Hopkins University School of Medicine; Laura Damschroder, MS, MPH; Denise Esserman, PhD - Yale School of Public Health; Mona Sharifi, MD. MPH - Yale School of Medicine;

Development and multi-center validation of a pre-trained language model for predicting neonatal morbidities

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

Abstract Keywords: Pediatrics, Natural Language Processing, Clinical Decision Support, Large Language Models (LLMs), Critical Care
Primary Track: Applications
Programmatic Theme: Clinical Informatics

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.

Speaker(s):
Feng Xie


Author(s):
Feng Xie; Philip Chung, MD, MS; Jonathan Reiss, MD - Stanford University; Erico Tjoa, PhD - Stanford University; Thanaphong Phongpreecha, PhD - Stanford University; William Haberkorn, MS - Stanford University; Dipro Chakraborty, MS - Stanford University; Alan Chang, PhD - Stanford University; Tomin James, PhD - Stanford University; Yeasul Kim, MD - Stanford University; Samson Mataraso; Ivana Maric, PhD - Stanford University; Sayane Shome, PhD - Stanford University; Momsen Reincke, MD - Stanford University; Gary Shaw, DrPH - Stanford University; David Stevenson, MD; Nima Aghaeepour - Stanford University;

Using the Technology Acceptance Model to guide refinements to the Color Me Healthy symptom assessment app for children

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

Abstract Keywords: Pediatrics, Human-computer Interaction, Patient Engagement and Preferences, Tracking and Self-management Systems, Mobile Health, Patient / Person Generated Health Data (Patient Reported Outcomes), Participatory Approach/Science, User-centered Design Methods
Working Group: Nursing Informatics Working Group
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics

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.

Speaker(s):
Lauri Linder, PhD, APRN, CPON, FAAN, FAPHON
University of Utah, Primary Children's Hospital

Author(s):
Haley Utendorfer, Undergraduate student - University of Utah; Brianna Oliveros, Undergraduate student - University of Utah; Sydney Gilliland, RN, BSN - University of Utah; Victoria Tiase, PhD, RN, NI-BC, FAMIA, FAAN, FNAP - University of Utah; Roger Altizer, PhD - University of Utah;

Acceptability of pictographs as a novel patient identifier to improve patient safety in the neonatal intensive care unit

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

Abstract Keywords: Patient Safety, Clinical Decision Support, Qualitative Methods
Primary Track: Applications
Programmatic Theme: Clinical Informatics

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.

Speaker(s):
Hojjat Salmasian, MD, MPH, PhD, FAMIA
Children's Hospital of Philadelphia

Author(s):
Carmina Erdei, MD - Brigham and Women's Hospital; Joanne Applebaum, MPH - Columbia University Irving Medical Center; Danielle Sharon, BS - Brigham and Women's Hospital; Katie Hannon, BS - Brigham and Women's Hospital; Deborah Cuddyer, BSN, RN - Brigham and Women's Hospital; Mary Sawyer, BSN, RN - Brigham and Women's Hospital; Tina Steele, RN, IBCLC - Brigham and Women's Hospital; Yvonne Sheldon, BSN, RN - Brigham and Women's Hospital; I-Fong Lehman, DrPH, MS - Columbia University Irving Medical Center; Joseph Shwartz, PhD - Columbia University Irving Medical Center; Allen Chen, MD, PhD, MS - Johns Hopkins Medicine; Jason Adelman, MD, MS - Columbia University Medical Center; Jason Adelman, MD, MS - Columbia University Irving Medical Center;

Probabilistic Graphical Models for Evaluating the Utility of Data-Driven ICD Code Categories in Pediatric Sepsis

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

Abstract Keywords: Knowledge Representation and Information Modeling, Machine Learning, Pediatrics
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics

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.

Speaker(s):
Lourdes Valdez
Biomedical Informatics Department, University of Utah

Author(s):