Leveraging Technology To Diagnose Alzheimer's Disease: A Meta Analysis

Abstract

Background: About 50 million people worldwide suffered from dementia in 2018 – two-thirds of those with Alzheimer's Disease (AD). By 2050, this number is expected to rise to 152 million – which is slightly larger than the country of Russia. About 90% of these people are over the age of 65, but early-onset dementia can occur younger ages. The objective of this meta-analysis is to objectively analyze the effectiveness of health information technology to diagnose AD. We analyzed data from studies published over the last 10 years to meet this objective: Cost, efficiency, accuracy, acceptability (by physician and patient), patient satisfaction, and barriers to adoption. Methods: Four research databases were queried (PubMed, CINAHL Ultimate, Web of Science, and ScienceDirect). The study was conducted in accordance with a published protocol, the Kruse Protocol, and reported in accordance with PRISMA (2020). Results: Ten technological interventions were identified to help diagnose AD among older patients, and some involved a combination of methods (such as MRI and PET). The average sample size was 320. These 10 interventions were identified as accurate, non-invasive, non-stressful, in expensive, convenient, and rapid. Only one intervention was identified as ineffective, and this same intervention was used effectively in other studies. Barriers identified were cost, training, expense of travel, and requires physical presence of patient. The weighted average sensitivity was 85.16%, specificity was 88.53, and the weighted average effect size was 0.7339. Conclusion: Technological innovation can accurately diagnose AD, but not all methods are successful. Providers must ensure they have the proper training and familiarity with these interventions to ensure accuracy in diagnosis. While the physical presence of the patient is often required, many interventions are non-invasive, non-stressful, and relatively inexpensive.