Real-world healthcare resource utilization of Alzheimer's disease in the early and advanced stages: a retrospective cohort study.

Aims: To compare all-cause and Alzheimer's disease (AD)-related healthcare resource utilization (HCRU) by cognitive stage.

Methods And Materials: This retrospective study analyzed insurance claims data linked to electronic health records (01/01/2015-12/31/2021). Patients with ≥1 cognitive assessment (Mini-Mental State Examination or Montreal Cognitive Assessment) and ≥1 medical or pharmacy claim for an AD diagnosis or AD medications were included.

Real-world diagnostic, referral, and treatment patterns in early Alzheimer's disease among community-based practices in the United States.

Background: Over 90% of individuals with mild cognitive impairment (MCI) may not receive a timely diagnosis. Understanding community-based practice patterns, where most individuals are seen, is critical to improving patient care.

Objective: To understand how patients with MCI and mild dementia due to Alzheimer's disease (AD) are diagnosed and managed in community-based settings, including the use of clinical and cognitive assessments, referrals to dementia-related specialties, and receipt of treatment.

Prediction of Healing Trajectory of Chronic Wounds Using a Machine Learning Approach.

New treatment options are emerging for chronic wounds, which represent a growing problem because of population ageing and increasing burden of chronic disease. While promising, the existing evidence for advanced modalities is commonly derived from small and/or poorly controlled studies and clear criteria for selecting patients, who are likely to benefit from these expensive options are lacking. In this study, we develop and validate a machine learning model to predict if a chronic wound, independent of etiology, is expected to heal within 12 weeks to identify cases in potential need of advanced treatment options.