Information Preferences Following ADRD Biomarker Testing

Original Title: Dementia Care Research and Psychosocial Factors

Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association

DOI: 10.1002/alz70858_099100

Overview

This study investigates how individuals with cognitive symptoms and their care partners prefer to receive and share health information after undergoing biomarker testing for Alzheimer's disease and related dementias. Utilizing a mixed-methods approach, researchers analyzed data from 50 symptomatic participants with a mean age of 72.6 years and 36 care partners with a mean age of 67.6 years. The cohort was diverse, including 18.6% Black and 14% Hispanic/Latino individuals. Quantitative results indicated a preference for traditional communication; 84% of participants and 69.4% of care partners favored receiving results via paper copies. Email was also highly ranked, selected by 66% of participants and 72.2% of care partners. In contrast, patient portals were less popular, preferred by only 38% of participants and 47.2% of care partners. Regarding social sharing, 92% of participants and 97.2% of care partners expressed comfort in disclosing results to family and friends, while nearly 98% of respondents were willing to share information with healthcare providers.

Novelty

The study provides a detailed characterization of attitudes toward emerging technologies, such as Artificial Intelligence and virtual platforms, within the context of dementia diagnostics. While previous research often focuses on the clinical accuracy of biomarkers, this work examines the psychosocial dimension of information seeking and the subsequent communication of results. A distinct finding is the persistent demand for physical documentation despite the increasing availability of digital health records. The qualitative focus group discussions highlighted a spectrum of attitudes toward AI-driven tools, ranging from proactive adoption to significant mistrust. This research identifies a specific dissatisfaction with existing information delivery systems, emphasizing that the transition to virtual formats often lacks the person-centered approach required for sensitive cognitive health data. By comparing the preferences of both patients and care partners, the study clarifies how these two groups may have diverging needs, particularly regarding the use of telemedicine and patient portals for complex diagnostic feedback.

Potential Clinical / Research Applications

These findings can inform the design of clinical workflows by ensuring that biomarker results are provided in multiple formats to suit diverse literacy and technological comfort levels. Healthcare systems could implement hybrid delivery models that combine in-person consultation with physical summary documents and follow-up emails. For researchers developing AI-driven educational tools, the data suggests a need for interfaces that prioritize simplicity and build trust through clear explanations of how conclusions are reached. There is also potential to improve patient portals by making them more intuitive for care partners, who showed a higher interest in digital access compared to patients. Additionally, the high comfort level with sharing results among family members supports the development of collaborative digital platforms where patients can easily grant access to their support network. This would facilitate better-coordinated care and help manage the psychosocial impact of an Alzheimer's-related diagnosis through shared understanding and data-driven family discussions.

Similar Posts

  • Interpretable Survival Analysis for Alzheimer’s Progression

    Original Title: Basic Science and Pathogenesis Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association DOI: 10.1002/alz70855_107083 Overview This research addresses the challenge of predicting the progression of Alzheimer’s disease and related dementias using survival analysis. While deep learning models offer high predictive performance, their complex architectures often obscure the biological factors driving their outputs. To resolve this, the authors introduce the Neural Additive Deep Clustering Survival Machines (NADCSM) framework. This model utilizes data from the Alzheimer’s Disease Neuroimaging Initiative, specifically focusing on AV45 Florbetapir PET imaging, genotyping, and demographic information to track the transition from mild cognitive impairment to early Alzheimer’s disease. The framework models survival times…

  • Supervised Contrastive Learning for Lacune Detection in MRI

    Original Title: Biomarkers Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association DOI: 10.1002/alz70856_099645 Overview Lacunes are small, deep brain infarcts that indicate vascular disease and increase the risk of cognitive decline. Detecting these features manually is time-consuming and prone to error due to their small size and similarity to other structures like perivascular spaces. This study presents a deep learning framework designed to automate the segmentation of lacunes using 2D T2-FLAIR MRI scans. The researchers utilized a dataset of 427 images, which underwent preprocessing to segment intracranial volume and white matter hyperintensities. The core architecture employed is an Attention U-Net. To address the challenge of imbalanced data…

  • Robust CRC Diagnosis via Causal and Uncertainty-Aware AI

    Original Title: Uncertainty-aware and causal test-time adaptive foundation model for robust colorectal cancer pathology diagnosis Journal: NPJ digital medicine DOI: 10.1038/s41746-025-02149-1 Overview Colorectal cancer remains a major global health challenge, requiring precise histopathological analysis for effective treatment. While computational pathology has advanced with the use of large-scale foundation models, these systems frequently encounter obstacles when deployed in real-world clinical settings. Key issues include domain shifts caused by variations in staining protocols and scanner hardware, as well as the tendency for models to provide overconfident yet incorrect predictions. This paper introduces UAD-FM, an uncertainty-aware and causally adaptive foundation model designed to address these limitations. The framework integrates a variational Bayesian approach…

  • AI for Cancer Risk Assessment in Oral Disorders

    Original Title: Artificial Intelligence in cancer risk assessment of oral potentially malignant disorders: applications and challenges Journal: International journal of surgery (London, England) DOI: 10.1097/JS9.0000000000003363 Overview This article examines the role of artificial intelligence in evaluating the risk of malignant transformation in oral potentially malignant disorders. Traditionally, clinicians rely on oral epithelial dysplasia grading to determine cancer risk. However, this method is often limited by human subjectivity and an inability to incorporate various risk factors simultaneously. Artificial intelligence offers a method to integrate diverse datasets, including demographic information, smoking history, clinical images, and histopathology slides. By analyzing both structured and unstructured data, these computational models can provide an objective assessment…

  • Learning interpretable network dynamics via universal neural symbolic regression

    Unveiling System Dynamics with Neural Symbolic Regression One-Sentence Summary The paper introduces a computational tool, Learning Law of Changes (LLC), that combines neural networks and symbolic regression to automatically discover the mathematical equations governing complex network dynamics from observational data. Overview Understanding the behavior of complex systems, such as biological networks or epidemic spreads, is a fundamental challenge in science. These systems are often governed by underlying mathematical rules, typically in the form of differential equations. However, identifying these exact equations from data alone is notoriously difficult. This paper presents a novel computational framework named LLC designed to tackle this problem. The method first employs neural networks to learn the…

  • Identifying Rare Pathogenic Cells with GARDEN

    Original Title: Robust characterization and interpretation of rare pathogenic cell populations from spatial omics using GARDEN Journal: Nature communications DOI: 10.1038/s41467-026-68500-6 Overview Spatial omics technologies map gene expression within the architectural context of tissues. Identifying rare cell populations that drive disease remains difficult because standard clustering often overlooks these groups or misclassifies them as noise. This paper introduces GARDEN, a framework designed to detect rare pathogenic cells using graph-based anomaly detection. GARDEN models spatial transcriptomics as a graph where nodes represent cells and edges represent proximity. By training an encoder-decoder to reconstruct healthy cell features, it identifies pathogenic cells as anomalies with high reconstruction errors. In breast cancer datasets, GARDEN…

Leave a Reply

Your email address will not be published. Required fields are marked *

CAPTCHA