Artificial intelligence for dementia research methods optimization

Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care

A Literature Review of Medication Errors in the United States of America

This study is a review fifteen articles selected from 395 articles of medication error in the United States of America between the year 2000 and 2015 from the CINAHL and Aca-demic Search Elite databases. This study explored existing literature on medication error with the aim of providing knowledge about safe medical care. The goal of the study was to shed light to the following questions: (1) What factors contribute to the medication errors? (2). What can be done to mitigate these errors? Broadly speaking, deficits in knowledge and performance, lack of resources, tiredness, work environment, documentation, lack of information and failure to use available information, policy violation, product similarity and inexperience were identified as the causes of medication errors. The study identified education and training, improving the work environment, employing full-time unit based pharmacist, use of technology and encouraged error reporting as tested strategies that can reduce medication errors. This study reveals that management strategies that can better reduce errors should focus on the system as a whole and not just on individuals. It is evident in this study that medication errors are still quite common and are made by all categories of care professionals. This study is faced with the limitation to fifteen articles selected from two databases. The findings might be affected if a larger sample size is used. This work was commissioned by Arcada university of Applied sciences