A study to investigate the Impact of Automated Drug Dispensing Cabinets on Patient Safety

Abstract

This research proposal paves way for a full study on the impact of automated drug dispensing cabinets on patients. particularly, the study is anchored on the idea that While there have been various studies testing the effectiveness of automated drug dispensing machines before, these studies have largely yielded inconsistent results. Hence the proposed study aims to fill this gap by identifying whether automated drug dispensing cabinets medication omission, dose duplication, and medication administration errors. The study intends to apply the systematic literature review methodology, with data synthesis as the main method of data analysis.

Introduction

The key elements of safe drug handling include administering the right medicine to the right patient, at the right time and through the right method. According to Risor et al (2016), this helps in reducing adverse events related to poor medical administration – which are estimated to be among the most frequent causes of harm to hospitalised patients. Yet, systematic reviews such as (Carayon et al (2014) and Kanjanarat et al (2005) have indicated that nearly half of these adverse events may be preventable if safe and effective medication administration is adopted. Against this backdrop, the importance of reducing medication administration errors can never be underestimated.

The need to address the problem of adverse medical events emanating from medical administration errors has been highlighted by various statistics which shows that medical errors have caused harm to more than 1.5 million people and led to the death of 7000 people yearly in the United States (Keers et al, 2013). Studies in other countries such as Denmark indicate that medication errors were experienced in at least 41% of medication administration instances with an estimation that at least 30% of these medication errors could have contributed to adverse events.

Statement of the Problem

The plight of medication administration errors and the harm they cause to patients within the medical setting has turned the attention of researchers to alternative methods of medication administration that are safer and more efficient. Particularly, automated drug dispensing machines have been of greater interest to practitioners and researcher alike – with most of them attempting to establish its effectiveness in reducing medication administration errors (Lis et al, 2005).

While there have been various studies testing the effectiveness of automated drug dispensing machines before, these studies have largely yielded inconsistent results. For instance, whereas the study by Chapius et al (2010) and Cousein et al (2014) found a reduction in the number of medication errors with the use of automated drug dispensing machines, a recent study by Tsao et al (2014) found that automated drug dispensing machines had insignificant potentiality in reducing medication errors and that the effectiveness of such devices was highly dependent on the particular institution within which they are used.

Another technique that has largely been of interest to scholars is barcode-assisted medication administration (BCMA). Particularly, studies focusing on BCMA have suggested that its use can be effective in reducing cases of medication administration errors (Poon et al 2010; and Payton et al, 2007). However, according to Rissor et al (2016), there is a paucity of knowledge regarding the issue of medication administration errors and whether the use of various technologies such as BCMAs and electronic medication administration records can help in reducing medication administration errors. Therefore, the main aim of this study is to make a deeper inquiry into the effectiveness of automated drug dispensing cabinets and how they can help reduce medication errors such as medication omission, dose duplication, and assist in controlling high alert medications.

Objectives of the Study

1. To identify why automated drug dispensing cabinets can reduce medication omission
2. To identify why automated drug dispensing cabinets can reduce medication dose duplication
3. To explore the role of automated drug dispensing cabinets in gaining more control on high alert medications
4. To explore the effectiveness of electronic health system integration in reducing medication administration errors

Research Questions

1. Why can automated drug dispensing cabinets reduce medication omission?
2. Why can automated drug dispensing cabinets reduce medication dose duplication?
3. What is the role of automated drug dispensing cabinets in gaining more control on high alert medications?
4. What is the effectiveness of electronic health system integration in reducing medication administration errors?

Significance of the Study

This study is important because it will add to the already existing knowledge base on medication errors and how automated medication administration can help reduce these errors. This will, in turn, promote evidence-based practice, a phenomenon that promotes positive health outcome and increases the quality of life of patients. Besides, with the current technological advancement within the medical equipment and Medication management outside the pharmacy, it is of great importance to ascertain whether various technological advancements and innovative medication administration techniques (e.g. BCMA, automated dispensing systems and unit drug dose distribution) are effective and worth investing in. Ultimately, this study plays a significant role in addressing the crucial issue of medication errors and their role in medication-related deaths.

Literature Review

Several studies have attempted to examine whether an automated medication administration system is effective in reducing medication errors within the hospital setting. For instance, Risor et al (2016) were interested in establishing whether an automated medication system installed in a Danish hospital was effective in reducing errors in the process of medication administration. The randomised control trial was conducted in two Danish hospitals within a three-week period and was majorly focused on identifying the number of medication errors occurring within that period. Upon conducting a logistic regression analysis to identify any changes in error rates, the study found a negative change in the number of medication errors incidences from a rate of 0.35 incidences at baseline to 0.17 in the intervention ward, and from 0.37 to 0.35 in the control ward. The study found an overall reduction of risk of errors of 57% in the intervention ward compared to the control ward. However, it is important to note that the study was exposed to some level of bias due to the fact that the pharmaceutical staff who performed the observations were not blinded, leading to an overestimation of drug dispensing technology because they could have had an interest in the positive effects of dispensing technology. There is, therefore, a need for a less biased study that can give a more accurate conclusion over the effectiveness of an automated drug dispensing system in reducing medication errors.

Another study by Chaipus et al (2010) also attempted to identify the effectiveness of automated drug dispensing systems in reducing medication errors albeit with a specific focus in the intensive care setting. The pre-intervention and post-intervention study were administered in a 200-bed hospital involving two groups – the intervention and control group. Particularly, the study found that upon introducing an automated dispensing system, there was a reduced percentage (p <.05) of total likeliness of error occurrence in the intervention group compared to the control group. Further analysis of the results informed a conclusion that the implementation of automated drug dispensing systems had a significant contribution in reducing the opportunities for drug administration errors occurring within the intensive care setting. However, there is a need to conduct further studies to ascertain or validate these findings for purposes of promoting evidence-based practice.

Study Methodology

The study will apply a systematic literature review methodology. Booth (2001) defines systematic literature review as the process of collecting and analyzing data from various research studies that have targeted the same research topic. Bryman (2001) also says that it provides an exhaustive summary of existing literature on a particular topic, especially when the researcher needs to use evidence-based answers to specific research questions. On the same note, opines that systematic literature reviews are effective research methodologies that assist in evaluating existing interventions because they objectively analyse existing data to answer the existing research question – within minimum time and budget.

According to Gough & Elboune (2002), systematic literature review methodology can be used to derive both qualitative and quantitative data so as to achieve complete and transparent data for further interpretation and analysis. Against this backdrop, the current study will adopt the systematic literature review methodology to investigate the impact of automated drug dispensing cabinets on patient safety. The methodology will enable the researcher to summarize existing evidence on the topic of automated drug dispensing cabinets with the aim of developing effective and conclusive results. In doing so, the researcher will review several primary and secondary research on the topic while engaging several elements of the methodology such as search strategy, quality assessment, data extraction and selection of studies for inclusion and exclusion.

Data Collection

The data collection process will involve two reviewers using a pre-piloted standardized form. In doing so, the researcher will identify important information about the research design of the included study, the nature of the intervention, the inclusion criteria, the population of study and the effects of the intervention in regards to specific outcomes. All the collected data will be recorded and stored electronically. Specifically, the journal articles will be gathered from electronic databases such as EBSCO and JSTOR. This is because according to evidence by Pawson et al (2003), using online databases enables the researcher to integrate the search process with specificity and relevance because the search process involves the use of specifically predetermined search terms. Besides, the use of online databases makes it easier to duplicate the study, compared to a physical search of the literature material from a library. The two databases, EBSCO and JSTOR, have been chosen for the current study because they contain abundant literary materials (i.e. journal articles) that are relevant to research in nursing and allied health sciences (Pawson, 2002). Nonetheless, the selection process of the literary materials will be anchored on predetermined inclusion/exclusion criteria.

A key element of data analysis that will be included in the proposed study is data extraction. During the process, the researcher will read the full text of each selected articles and extract data through predetermined and standardized extraction criteria. However, the researcher expects to develop the data extraction criteria to be as sort or as long as necessary to enable the option of computer coding if necessary. Besides, during the data extraction, the researcher will include a field for identifying the study quality

Data Analysis

The systematic literature method will consider various data analysis and synthesis methods to analyse the collected data. In the data analysis section, the researcher will present the main findings of the systematic review, especially if the findings of the reviewed studies are significantly different (heterogeneous) (Bryman, 2001). However, if there is homogeneity in the results of the reviewed studies, the researcher may use a meta-analysis. According to Gough & Elboune (2002), meta-analysis involves the use of statistical methods to collect similar results from different studies. It is, however, important to note that if the studies included in the review have different research designs, then it will be impossible to conduct a meta-analysis, even if they have similar results (Pawson, 2002). In such a scenario, the researcher may use a narrative synthesis data analysis technique to synthesize data.

Reference

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