Improving clinical efficiency in screening for cognitive impairment due to Alzheimer's

INTRODUCTION

To reduce demands on expert time and improve clinical efficiency, we developed a framework to evaluate whether inexpensive, accessible data could accurately classify Alzheimer's disease (AD) clinical diagnosis and predict the likelihood of progression.

METHODS

We stratified relevant data into three tiers: obtainable at primary care (low-cost), mostly available at specialty visits (medium-cost), and research-only (high-cost). We trained several machine learning models, including a hierarchical model, an ensemble model, and a clustering model, to distinguish between diagnoses of cognitively unimpaired, mild cognitive impairment, and dementia due to AD.

RESULTS

All models showed viable classification, but the hierarchical and ensemble models outperformed the conventional model. Classifier "error" was predictive of progression rates, and cluster membership identified subgroups with high and low risk of progression within 1.5 to 3 years.

DISCUSSION

Accessible, inexpensive clinical data can be used to guide AD diagnosis and are predictive of current and future disease states.

HIGHLIGHTS

Classification performance using cost-effective features was accurate and robustHierarchical classification outperformed conventional multinomial classificationClassification labels indicated significant changes in conversion risk at follow-upA clustering-classification method identified subgroups at high risk of decline.

Conversations about wellness: A qualitative analysis of patient narratives post annual wellness visit

Support for successful aging in place requires an understanding of the factors that impede or support health promotion behaviors. Nurse-led monthly follow-up calls between annual wellness visits (AWV) offer the opportunity to both provide support and collect data which can inform development of effective health promotion strategies. The aim of this research was to provide ongoing support for health promotion efforts and explore factors playing a role in meeting AWV recommendations. A total of 144 visit summaries for 47 Medicare beneficiaries (65+) who had completed an AWV within the last year were analyzed using a thematic approach. Themes were organized into 7 categories with findings indicating the complexity of factors which influence health promotion behaviors. The importance of ensuring personalization of health promotion recommendations using data collected during the AWV and provision of ongoing support between visits may enhance health outcomes.