Problem 177 Are Errors Less Likely with Elec... [FREE SOLUTION] (2024)

Expert verified

When we talk about the proportion of errors in a study, we're referring to the ratio of the number of errors to the total number of observations. In the realm of medical prescriptions, this metric is crucial because it helps measure the accuracy and safety of the prescribing process. For example, in the study mentioned, finding the proportion of errors for electronic prescriptions involved dividing the number of errors (254) by the total prescriptions (3848), resulting in about 0.066 or 6.6%. This indicates that 6.6% of electronic prescriptions had errors.

Similarly, for written prescriptions, the proportion was significantly higher: 1478 errors out of 3848 prescriptions, or approximately 0.384 or 38.4%. Understanding these proportions is fundamental as they offer the basis for making comparisons and ultimately determining if one method of prescribing is superior to the other in terms of reducing errors.

The debate of electronic versus written prescriptions concerns the method healthcare providers use to issue prescriptions. Electronic prescriptions are generated through a digital platform and are intended to reduce human error by eliminating issues such as poor handwriting or unclear dosages. On the other hand, written prescriptions are the traditional method, which involves pen and paper.

The comparison is significant because it relates to patient safety, healthcare efficiency, and the potential for errors. In the exercise, an educational determination was pursued to ascertain whether error rates in prescribing could be decreased by using electronic systems. This kind of research can have broad implications for practices and policies in the healthcare industry, potentially leading to widespread changes in how prescriptions are generated and managed.

The difference in proportions is a statistical tool used to compare two proportions to see if they are statistically significantly different from each other. In the context of our study on prescription errors, we subtract the proportion of errors from electronic prescriptions from that of the written prescriptions. In this case, the difference is approximately 0.318 or 31.8%.

This large difference indicates that the error rate for written prescriptions is much higher than for electronic ones. When assessing such a difference, it's not only important to note the magnitude but also to consider the practical significance. If the difference in proportions is deemed statistically significant, it suggests that these two methods of prescribing have different impacts on error rates, which can lead to important interventions to improve patient safety.

In statistics, the standard error measures the precision of an estimated value, such as a sample mean or, in our case, the difference between two proportions. It quantifies the variability of the estimate if the study were to be repeated multiple times under the same conditions. The smaller the standard error, the more precise the estimate is considered to be.

To calculate the standard error of the difference in proportions, we use a formula that takes into account the size of both samples and the proportion of each group. For the prescription study, the standard error was found to be 0.013063. This value is critical for constructing confidence intervals; it's what we multiply by the z-score (which corresponds to our confidence level) to determine the margin of error for our interval estimate. Understanding the standard error is key in interpreting the results and trustworthiness of the study's conclusions.