Lessons learned from the integration of a FHIR based questionnaire application to assess cognitive decline during annual wellness visits.

Presentation Time: 08:00 AM - 08:15 AM

Abstract Keywords: Informatics Implementation, Patient / Person Generated Health Data (Patient Reported Outcomes), Data Transformation/ETL
Primary Track: Applications
Programmatic Theme: Clinical Informatics

Cognitive impairment (CI), Alzheimer’s disease, and related dementias are significant societal concerns, especially for older adults. Early detection of CI has benefits but is challenging to achieve. Most adults ages 65 to 75 have at least annual contact with ambulatory health care professionals. We implemented a cognitive screener based on two tests from the NIH Toolbox® for Assessment of Neurological and Behavioral Function that are administered during the Annual Wellness Visit.

Speaker(s):
Michael Bass
Northwestern University

Author(s):
Michael Bass - Northwestern University; Stephanie Young, PhD - Northwestern University; Julia Yoshino-Benavente, MPH - Northwestern University; Laura Curtis, MS - Northwestern University; Lihua Yao, PHD - Northwestern University Feinberg School of Medicine Department of Medical Social Sciences; Maria Varela Diaz, MS - Northwestern University; Zahra Hosseinian, MA - Northwestern University; Andrew Cooper, MS, MPH - Northwestern University; Greg Byrne, MA - Northwestern University; Richard Gershon, PhD - Northwestern University; Michael Wolf, PhD - Northwestern University; Cindy Nowinski, MD, PhD - Northwestern University Feinberg School of Medicine;

Assessing the Seasonality of Lab Tests Among Patients with Alzheimer’s Disease and Related Dementias in OneFlorida Data Trust

Presentation Time: 08:15 AM - 08:30 AM

Abstract Keywords: Precision Medicine, Aging in Place, Chronic Care Management, Clinical Decision Support, Population Health
Primary Track: Applications
Programmatic Theme: Clinical Informatics

About 1 in 9 older adults over 65 has Alzheimer’s disease (AD), many of whom also have multiple other chronic conditions such as hypertension and diabetes, necessitating careful monitoring through laboratory tests. Understanding the patterns of laboratory tests in this population aids our understanding and management of these chronic conditions along with AD. In this study, we used an unimodal cosinor model to assess the seasonality of lab tests using electronic health record (EHR) data from 34,303 AD patients from the OneFlorida+ Clinical Research Consortium. We observed significant seasonal fluctuations—higher in winter in lab tests such as glucose, neutrophils per 100 white blood cells (WBC), and WBC. Notably, certain leukocyte types like eosinophils, lymphocytes, and monocytes are elevated during summer, likely reflecting seasonal respiratory diseases and allergens. Seasonality is more pronounced in older patients and varies by gender. Our findings suggest that recognizing these patterns and adjusting reference intervals for seasonality would allow healthcare providers to enhance diagnostic precision, tailor care, and potentially improve patient outcomes.

Speaker(s):
Wenshan Han, MS
Florida State University

Author(s):
Wenshan Han, MS - Florida State University; Balu Bhasuran, Ph.D - Florida State University; Victorine Patricia Muse, PhD - University of Copenhagen; Søren Brunak, PhD - University of Copenhagen; Lifeng Lin, PhD - University of Arizona; Karim Hanna, MD - University of South Florida Health; Yu Huang, Ph.D.; Jiang Bian, PhD - University of Florida; Zhe He, PhD, FAMIA - Florida State University;

Patterns of telemedicine use in primary care for people with dementia in the post-pandemic period: Evidence from two health systems

Presentation Time: 08:30 AM - 08:45 AM

Abstract Keywords: Telemedicine, Internal Medicine or Medical Subspecialty, Patient Engagement and Preferences
Primary Track: Applications
Programmatic Theme: Clinical Informatics

The COVID-19 pandemic caused primary care practices to rapidly expand access to telemedicine. However this expansion was not designed to support complex care for people with dementia (PWD). This study leverages data from two large health systems to understand, (1) what modalities of care are utilized by PWD and how has this changed pre-to-post pandemic (2) what demographic characteristics are associated with telemedicine versus in-person modality in the post-pandemic period.

Speaker(s):
Julia Adler-Milstein, PhD
UCSF School of Medicine

Author(s):
Julia Adler-Milstein, PhD - UCSF School of Medicine; Anjali Gopalan, MD - Kaiser Permanente Northern California Division of Research; Jie Huang, Ph.D; Christopher Toretsky, MPH - University of California, San Francisco; Mary Reed, DrPH - Kaiser Permanente Division of Research;

Analyzing Dementia Caregivers’ Experiences on Twitter: A Term-Weighted Topic Modeling Approach

Presentation Time: 08:45 AM - 09:00 AM

Abstract Keywords: Social Media and Connected Health, Natural Language Processing, Bioinformatics, Data Mining, Machine Learning, Large Language Models (LLMs)
Primary Track: Applications
Programmatic Theme: Public Health Informatics

Dementia significantly impacts affected individuals and their families, making it essential to understand the experiences and concerns of family caregivers for enhanced support and care. This study introduces a novel approach for analyzing tweets from individuals with family members suffering from dementia. By collecting data from Twitter (now called X), we applied advanced natural language processing techniques, including spam removal, lemmatization, stopword removal, compound word segmentation, and spell checking, to preprocess the data. We enhanced conventional topic model—Gibbs Sampling Dirichlet Multinomial Mixture Model (GSDMM)—with two term-weighting strategies, “Log” and “BDC”, to mitigate the impact of common and topic-specific stopwords, respectively. This enhanced approach enabled the identification of key topics among dementia-affected families, offering semantically rich and contextually coherent topics, demonstrating that our method outperforms the state-of-the-art BERTopic model in clarity and consistency. We further leveraged ChatGPT 4, alongside two human experts, to interpret these topics. Our findings illuminate the multifaceted challenges faced by dementia caregivers. This work aims to provide healthcare professionals, researchers, and support organizations with a valuable tool to better understand and address the needs of caregivers impacted by dementia.

Speaker(s):
Bojian Hou, PhD
University of Pennsylvania

Author(s):
Yanbo Feng, MSE in Bioengineering - University of Pennsylvania; Bojian Hou, PhD - University of Pennsylvania; Ari Klein - University of Pennsylvania; Karen O'Connor, Master of Science - University of Pennsylvania; Jiong Chen; Andres Mondragon, High School - University of Pennsylvania; Shu Yang; Graciela Gonzalez-Hernandez, PhD - Cedars-Sinai Medical Center; Li Shen, Ph.D. - University of Pennsylvania;

Analyzing Chronic Disease Patterns Prior to the Onset of Mild Cognitive Impairment through Hypergraph Clustering

Presentation Time: 09:00 AM - 09:15 AM

Abstract Keywords: Information Retrieval, Data Mining, Information Extraction
Primary Track: Applications

As the global prevalence of dementia is projected to triple by 2050, understanding the prodromal stage of mild cognitive impairment (MCI) is crucial for developing interventions and optimizing clinical trial selection. Limited research has explored the historical sequences of medical conditions leading to MCI, particularly patterns that may signal progression to cognitive impairment or reversion to normal cognition. We use the Mayo Clinic Study of Aging cohort and analyzed 15 common chronic diseases five years before MCI diagnosis, categorizing participants into two groups based on MCI outcome (progression or reversion). We observe frequence sequences and likelihood of progression.

Speaker(s):
Muskan Garg, Postdoctoral Research Fellow
Mayo Clinic

Author(s):