Data analysis

Data analysis for this research consisted of both quantitative and qualitative methods due to the data collection techniques employed; direct observation, observational notes (made by the researcher) and semi-structured interviews with nurses and pharmacists.

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2.6.1 Observations: statistical data analysis (quantitative)

During the observations, a previously-devised and printed data collection observation form was used in order to record the details of MAEs. After data collection, quantitative data (observations) were analysed by employing descriptive data analysis (e.g. the number of OEs, followed by the number of MAEs and the error rates) using Microsoft Office Excel. As well as that, inferential statistics was used to determine verifiable relationships between the number of errors found and a number of potential contributing factors using IBM SPSS (version 21). Microsoft Office Excel supports different chart formats, such as bar graphs and the generation of numerical tables which can be easier to read; meaning that the data was also described by using numbers and percentages, as well as visually presented, to provide an overview and add depth to the findings. One of the distinct features of inferential statistics is that it allows the researcher to decipher whether any statically significant relationships exist. These are discussed in terms of the details of the modelling used to examine the MAEs in Chapter 3 (Section 3.3).

The medication administration errors were divided into procedural errors and clinical errors. The ‘Procedural errors’ is failure to follow the medication administration procedure that includes expiry errors, given but unsigned errors, and other reason errors. The ‘Clinical errors’ are any other errors in conduct or judgement in the clinical environment that include extra dose errors, formulation errors, almost not given errors, omitted and unsigned errors, omitted but signed, un-prescribed drug errors, wrong dose errors, and wrong time errors. In this thesis, these two types were separated to acquire more accurate results, in order to increase the quality of the data findings, whereas the combination between the two types might illustrate inaccurate findings.

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In addition, after the MAEs were recorded the potential for harm from these was judged and rated by the researcher, and one of the Trust clinical pharmacists using inter-rater reliability (Haw and Cahill, 2011). The researcher gathered the 367 error cases including the notes of how those errors happened, then, the first 20 medication error cases were rated by the researcher and a copy was sent separately to the clinical pharmacist. After that, the researcher and the clinical pharmacist met to check the results and to calculate their agreement rating which was 60% matched cases and 40% was not. This might be due to different experiences of the researcher and the clinical pharmacist as some errors appeared potentially harm for one and not for the other. Differences of judgment between healthcare professionals might happen and can be resolved through discussion (Chua et al., 2010). As a result, the researcher and the pharmacist completed an analysis of harm for the rest of the 367 error cases after agreeing the rating process and discussing the differences then arranged for a second meeting to compare and agree on all the rated results.

2.6.2 Observation notes and interviews: data analysis (qualitative)

Qualitative research analysis contained three stages; first, defining the data (for example, interviews or observation), second, organising the collected data by the researcher which is also known as the coding process and the final stage which was writing the report (Miles et al., 2013).

In this study, the qualitative approach was adopted while analysing the observation notes and the transcripts of the semi-structured interviews. The researcher applied the organisational accident causation model (Taylor-Adams and Vincent, 2004) (see Figure 2.3), and the framework developed by Vincent et al. (1998) (see Table 2.1). Both are adapted from James Reason’s model of organisational accidents (Reason, 1997).

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The model is useful to manage the data from the observational notes and nurses and pharmacists’ interview transcripts. By using this framework, the researcher started to be familiarised with the data and gain a sense to divide them into sections and classifying the data into frequent codes and categories. This was completed by highlighting a section of the data and coding with labels from the framework which were then categorised in more detail. Thematic analysis was carried out in the qualitative data where the themes form: recurrent topics, ideas or statements identified across the corpus of data. Also, the thematic analysis allows these themes to be mapped against a theoretical framework within a deductive approach.

To begin with the observational notes, the data were transcribed from the researcher’s notebook to a Microsoft Office Word document, where these were divided according to the ward name that was involved where an error was found on the relevant visit (see Appendix 11).

The researcher’s notes (labelled as quotes) were organised in relation to the mode of administration under each type of error; these quotes were then categorised to an active failure category i.e. slips, lapses, violations, mistakes etc. Following this, the error- producing conditions and latent conditions were categorised according to the researcher’s perception in relation to the administration round and wards setting at the time. The medication administration errors were divided into procedural errors and clinical errors.

Furthermore, collected data from the semi-structured interviews for the nurses and the pharmacists were analysed in a similar way in relation to the adopted framework. The nurses’ and pharmacists’ quotes were organised in relation to the type of error. These quotes were classified according to the nurses and pharmacists’ point of view about the

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reasons behind these errors. Next, the quotes were categorised into an active failure category i.e. slips, lapses, violations, mistakes etc., followed by the error-producing condition and latent condition.

The observational notes and interviews analysis examples are provided in (Appendix 12). The codes and categories will be discussed in more detail in Chapter 4 (see Table 4.3 and Table 4.4). All data were compared and contrasted the opinions of the researcher, nurses and pharmacists about specific errors.