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

On nonparametric predictive inference and abjective Bayesianism

This paper consists of three main parts. First, we give an introduction to Hill’s assumption A (n) and to theory of interval probability, and an overview of recently developed theory and methods for nonparametric predictive inference (NPI), which is based on A (n) and uses interval probability to quantify uncertainty. Thereafter, we illustrate NPI by introducing a variation to the assumption A (n), suitable for inference based on circular data, with applications to several data sets from the literature. This includes attention to comparison of two groups of circular data, and to grouped data. We briefly discuss such inference for multiple future observations. We end the paper with a discussion of NPI and objective Bayesianism