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Download PDF FileOverview
The rationale of this study has been to investigate the major factors that cause medication errors within the context of Kuwaiti Government Hospitals. In this regard, the study has also been carried out to comprehend the ways of reducing medication errors through clinical vigilance and staff training. For that purpose, the researcher in this study has used the most appropriate methods and tools for collecting, analyzing, and interpreting the data to provide conclusive findings the study. The selection of the right research methods carries huge importance for attaining desired research objectives, as the use of any inappropriate method can make the findings invalid. Hence, the right selection of research methods has been ensured in this study by the nature and requirement of the research topic. This section presents a review of some of the key methods and techniques that have been used in this study for the collection and analysis of data.
Variables of the Study
In this study, the researcher has followed the primary source of data collection for gaining comprehensive information about each variable of this study. Concerning this study, the researcher has incorporated several variables, about which the data has been collected from relevant sources. The key variable of this study includes the efficiency of hospitals, the number of incident reports and complaints, and the evaluation of professionals. To gain the required information about these variables, the researcher has collected the information from six hospitals. The type of data collection method that has been used in this study was an observation, which allows the researcher to gain useful information about the medication errors in different hospitals that work under Kuwait Government. The data about the efficiency of professionals concerning category was collected based on three different categories, which include Dr/Physician, Pharmacist, and Nurses. On the other hand, the data related to the number of incidents reported and complaints were collected concerning different categories of hospitals. Lastly, data for the variables of efficiency of hospitals and efficiency of professionals was categorized by hospitals and scale. The data collection method that has been used in this study has enabled the researcher to easily quantify the data and provide more factual information about the research topic.
Data Processing and Analysis Technique
The processing and analysis of data are considered another crucial part of the research, which determines the authenticity and reliability of research findings (Cole and Trinh, 2017). Therefore, it is important to make the right selection of analysis and processing techniques to accomplish the main research objective. Since the data collected in this study was quantitative, thus researcher has employed different statistical techniques to interpret the quantitative data. Firstly, within each category of variables, the researcher has arranged the total number of observations that were gathered from each of the six hospitals. At the initial stages of data analysis, all the relevant data about different variables were arranged about different hospitals that were under investigation. This allows the researcher to gain important information about the issues and the information that is missing. Following that, the data was analyzed through SPSS software where the researcher imported the data. Moreover, the researcher has done the coding of data concerning each category of different variables including, the efficiency of hospitals, number of incident reports and complaints, the efficiency of professionals, and efficiency of professionals concerning the category. The statistical tests that the researcher has conducted in this study include, descriptive statistics, ANOVA analysis, homogeneity of variances, means plot, and robustness of equality of means.
DATA ANALYSIS
Overview of the Data Analysis
There are different types of techniques that are used for the analysis of data; however, the right selection of analysis technique is highly dependent on the type of data that the researcher looks to analyze (Kumar, 2019). In the context of this study, the main purpose of data analysis has been to evaluate the statistical significance of the collected data. Therefore, the researcher has applied different statistical tests to critically assess the data and to provide clear and factual information about the research topic. In data analysis, the researcher has analyzed differences in mean values, statistical significance, and test homogeneity of variances. Moreover, the researcher has conducted a One-way ANOVA test to determine whether or not the mean value of all the dependent variables is similar for all the groups. Some of the key statistical tests that the researcher has carried out in this study inincludeNOVA, descriptive statistics, a test of homogeneity of variances, means plot, and robustness of equality of means. This section of the study presents the overall outcomes of data analysis of each variable of the study. Moreover, this section also provides a graphical representation of the results of different variables of this study to bring more clarity to the research outcome.
Evaluation of Efficiency of Hospitals
Categorized by Hospitals
The collection of the data is based on the gathering of information from six Kuwaiti government hospitals for evaluating their efficiency along with the problems and common complaints regarding medication error. Based on the collected data, the evaluation of the six Kuwaiti government hospitals is conducted concerning their efficiency. The main purpose is to determine as to where there is a difference in efficiency among the six Kuwaiti government hospitals that affect the medication error. The evaluation of the efficiency of the hospitals is conducted through evaluating the descriptive statistics, testing of homogeneity of variances, ANOVA analysis, and robustness of equality and mean plots.
Table 1 refers to the descriptive statistics of the hospital regarding the hospital’s efficiency based on different aspects. Descriptive analysis is a useful tool that is commonly used for the evaluation of the data by summarizing the data into a means form that is easier for the analyst to interpret (Amrhein, Trafimow, and Greenland, 2019). The descriptive analysis is based on the information that is gathered from each of the six hospitals. While referring to the H1 which is the first hospital, the mean value is computed as 1568.8 in the maximum value is 3000. This indicates that the efficiency of H1 was slightly better than average. The standard deviation value is computed as 691.42 which demonstrates that the efficiency of H1 can either increase or decrease by 681.42. The minimum value of efficiency is computed as 701 whereas the maximum value of efficiency is computed as 2855. While referring to H2, the mean value is computed as 1198.3 which is significantly below the value of 3000 which indicates that the efficiency for error reporting or when a medication error is committed is weak. The standard deviation is computed as 747.08 which indicates that the dispersion of the efficiency can increase or decrease by 747.08 units for H2. The minimum value is computed as 236 whereas the maximum value is calculated as 2615.
While referring to H3, the mean value is computed as 1378.7 which were below the value of 3000 which signifies that the efficiency regarding the error reporting is weak for H3. The standard deviation is computed as 766.6 which demonstrates that the efficiency aspect of the hospital can increase or decline by 766.6 units. The minimum value of efficiency was 428 whereas the maximum value of H3 is computed as 2516. Evaluating the descriptive of H4, the mean value is computed as 1297 which was significantly lower than the overall general scale. The dispersion value for H4 is computed as 627.16 which indicates that the efficiency can either increase or decline by 627.16. While examining H6, the mean value is computed as 1427.6 while the standard deviation of efficiency is identified as 764.75. Lastly, the H6 mean value is computed as 1101.1 which demonstrates the weak efficiency aspect whereas the standard deviation is computed as 905.74. Based on the analysis and reflecting on the mean value, H1 is found to have the highest efficiency in comparison to the other five hospitals.
Test of Homogeneity of Variances
The assumption of homogeneity of variance is a second statistical assumption that requires to be tested while comparing three or more groups on an outcome through ANOVA. The common tool that is used for measuring the assumption of homogeneity of variance is Levene’s test in which the p-value must be above 0.05 for meeting the assumption whereas the value below leads to the violation of the assumption (Jayalath et al., 2017). Based on the results, the significance value is computed as 0.919 which the null hypothesis is accepted. The variance among the different Kuwaiti government hospitals regarding their efficiency is equal.
One-Way ANOVA
Table 3 reflects the table of ANOVA in which its F-statistic and significance value is evaluated. The null hypothesis of the case is that the mean value of the hospital’s efficiency is the same for all groups. Concerning the significance value, it is computed as 0.782 and is above the threshold value of 0.05. This means that the null hypothesis is accepted in that the mean value for the hospital’s efficiency is the same for all the groups.
Robustness of Equality of Means
The robust Test is similar to Levene’s test which is used for testing the equality of the means by using the deviations from the group’s medians (Karagö, and Saraçbasi, T., 2016). The robust test of equality of means has been evaluated through the sig value which is 0.782 (p-value >0.05). Therefore, the null hypothesis is accepted in which the means of all the groups are equal.
Means Plot
Figure 1 represents the mean plots of the efficiency of the six Kuwaiti government hospitals in which it is identified that the H1 has the highest mean value in comparison with the other hospital. This also implies that the H1 has the highest level of efficiency in terms of error reporting compared to the other hospital. On the contrary, H6 has the lowest mean point of efficiency of hospitals which indicates having the least efficiency.
Categorized by Scale
In this section, the evaluation of the efficiency is measured based on the developed scale in which 1 is equal to bad efficiency whereas 10 indicates perfect efficiency. The purpose is to indicate the aspects of efficiency that are employed in the hospital based on the scale.
Descriptive Statistics
Table 5 represents the descriptive analysis of hospital efficiency based on scale ranking where 1 is the worst and 10 is the best. Concerning the analysis, the efficiency of the Kuwaiti hospitals is found to be highest on the 8th scale based on the mean value which is 1768.5 and the standard deviation is computed as 765.90 on the 8th scale which means that it can increase or decrease by 765.90 units. The weakest efficiency of the hospitals is found to be at the 3rd scale which had an efficiency of 994 whereas the standard deviation is computed as 603.64 which illustrates the dispersion of efficiency.
Test of Homogeneity of Variances
Table 6: Levene's Test for Hospital Efficiency
Table 6 reflects the measurement of the assumption of homogeneity of variance through the use of Levene’s Test concerning the ranking of the efficiency. The null hypothesis established for the model is that the variance among the scale rating about the hospital’s efficiency is equal. The significance value is computed as 0.724 which is above the threshold value of 0.05. Therefore, the null hypothesis is accepted where the scale rating of the hospital’s efficiency is equal.
One-Way ANOVA
Table 7 refers to the results of one-way ANOVA in which the hospital’s efficiency is measured concerning the ranking scale. The null hypothesis of the model is that the mean value of the hospital’s efficiency is similar for all the ranking groups. Based on the sig value, it is identified to be 0.397 which led to the acceptance of the null hypothesis. Thus, this implies that the mean value of the efficiency of the hospital same for all the ranking groups.
Robustness of Equality of Means
Table 8 reflects the robust test of equality of means for Hospital efficiency in which the sig value is computed as 0.399 which is above the p-value of 0.05. Thus, the null hypothesis is accepted where the means of all the groups are equal.
Figure 2: Mean Plots of Efficiency of Hospital
Figure 2 represents the mean plots of the efficiency of hospitals based on the scale in which it is identified that the highest efficiency of hospitals was noted at the 8th ranking whereas the least efficiency among the hospital was observed on the 3rd scale. Moreover, it is also identified from the above graph that efficiency has significantly declined on the 9th scale of the Kuwaiti hospital.
Evaluation of Number of Incident Reports and Complaints
Descriptive Statistics
Table 9: Descriptive Analysis of Number of Incident Reports and Complaints
Here it becomes important to mention that there was a total of 6 hospitals were involved in the survey process. Based on the aforementioned table, it can be observed that the sig value has been computed as 2082.67. This suggests that the average number of incident reports and complaints from the concerned hospitals was 2082.67 provided in a particular time frame. While discussing the median, the median value has been obtained as 2302.00. This suggests that 2302, is the middle number when the data set is sorted and distributed between the two extremes. Further, in the context of standard deviation, the value has been computed as 659.381. This value suggests that to this extent the values deviate from the mean value. Besides this, the minimum value has been identified as 1208 from the data set. This suggests that within the collected responses, the lowest number of reports collected was 1208. However, the highest number of reports collected was 2701. This suggests that 2701 were the highest number of reports that were collected from the concerned hospitals in a particular period. Further, the obtained skewness value suggests that distribution exhibits to be left-skewed because the negative value has been obtained. Also, the value of Kurtosis suggests that the data is thin-tailed relative to its normal distribution.
Figure 3 presents the mean plots of the number of incident reports and complaints by hospitals. In this regard, figure 3 outlines all the six hospitals with their respective mean of several incidents and complaints. As per the results, hospital 2 and hospital 5 are found to have the highest mean number of incident complaints and reports. In contrast, the hospital 1 and 6 were identified with the lowest mean of incidents reported.
Evaluation of Efficiency of Professionals
Categorized by Hospitals
The section is based on evaluating the efficiency of the professionals in the different government hospitals of Kuwaiti for evaluating their ability for reporting medical errors and taking a proactive stance in dealing with medical errors.
Descriptive Statistics
Table 9: Descriptive Analysis of Professionals' efficiency
Table 9 reflects the descriptive analysis of the professional’s efficiency based on the six different hospitals. While referring to the results, it is found that H5 had the highest level of professional efficiency due to its mean value being computed as 2018.3 and its standard deviation is computed as 775.313. On the other hand, the hospital that is found to have the lowest professionals efficiency in H1 as its mean value is computed as 1343.3 and the dispersion value is identified as 791.40.
Test of Homogeneity of Variances
Table 10: Levene's Test for efficiency of Professional
Table 10 reflects Levene’s test for evaluating the assumption of homogeneity of variance. The significance value is computed as 0.858 which is above the p-value of 0.05; therefore, the variance among the different Kuwaiti government hospitals concerning the professional efficiency is equal.
One-Way ANOVA
Table 11: One-Way ANOVA for Professional’s efficiency
While reflecting on table 11, its significance value is computed as 0.571 which is lower than the threshold value of 0.05. Thus, the null hypothesis is accepted in the model where there is no mean value difference in the professional’s efficiency among all the groups.
Robustness of Equality of Means
Table 12: Robust Test of Equality of Means for Professional’s efficiency
Table 12 reflects the robust test for equality of means regarding the professional efficiency based on the six hospitals. The significance value is 0.572 which indicates acceptance of the null hypothesis where the means of all the groups are equal.
Means Plot
Figure 3: Mean Plots of Efficiency of Professionals
Figure 3 reflects the mean plots of efficiency of professionals in which the government hospital of Kuwaiti that has been found to have the highest mean plot is H5 followed by H4. On the contrary, the hospital that is found that has the lowest efficiency of professionals is H1.
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Categorized by Scale
The following section is based on the evaluation of the efficiency of the professional which is categorized by the ranking scale from 1 to 10 where 1 reflects bad efficiency and 10 reflects perfectly.
Descriptive Statistics
Table 13: Descriptive Analysis of Professionals' efficiency by scale
Table 13 reflects the descriptive statistics of the professional efficiency based on the scale in which it is determined that the highest level of professional efficiency aspect is observed on the 7th scale where the mean value is computed as 2131.17 and the dispersion value is identified as 993.94. On the other hand, the lowest professional efficiency is observed at the 5th scale as the mean value is computed as 1373.5 for professional efficiency whereas the dispersion value is computed as 314.233.
Test of Homogeneity of Variances
Table 14: Levene's Test for efficiency of Professional
Table 14 reflects Levene’s test which is utilized for measuring the assumption of homogeneity of variance among the groups. The significance value is computed as 0.539 which demonstrates that the variance among the difference scale concerning the professional efficiency is equal.
One-Way ANOVA
Table 15: One-Way ANOVA for Professional’s efficiency
Table 15 refers to the one-way ANOVA test for evaluating the mean value difference among the professional efficiency concerning the different scales. The sig value is 0.901 which is above 0.05; therefore, the null hypothesis is accepted in which the mean value of professional efficiency concerning all groups is equal.
Robustness of Equality of Means
Table 16 is the robust test of equality in which the Brown-Forsythe test is conducted for evaluating the equality of means for the professional’s efficiency concerning the scale. The significance value is 0.90 which is above the value of 0.05; hence, the mean of all the groups is equal concerning professional efficiency.
Means Plot
Figure 4: Mean Plots of Efficiency of Professionals
The mean plots of the efficiency of professionals can be observed in figure four where the highest efficiency is observed at the 7th scale concerning medical error reporting. On the contrary, the lowest professional efficiency is observed in the fifth scale concerning the mean value.
Evaluation of Efficiency of Professionals concerning Category
In the following section of the report, the evaluation has been conducted concerning the categories of professionals working in the hospital. The purpose of this assessment is to evaluate whether or not the efficiency level differs among nurses, Dr/physicians, or pharmacists.
Categorized by Hospitals
Specifically, in this section, the evaluation has been conducted by the six hospitals mentioned earlier.
Descriptive Statistics
The results of descriptive statistics including mean, standard deviation, minimum and maximum have been presented in Table 17. It has been evaluated that the average efficiency of Dr/physicians is computed to be 1,338.3 out of 3,000. In addition, out of 3,000, the average efficiency of the nurses is computed to be 1,446.7 whilst pharmacists are computed to have 2,028.8. Similarly, the standard deviation in terms of efficiency scale in Dr/physicians, nurses, and pharmacists is computed to be 686.3, 698.4, and 1043.09 respectively. This depicts that the highest efficiency is recorded in the category of pharmacists, however, the deviation in efficiency level is also high. The table also depicts maximum and minimum values where it has been found that the minimum efficiency is computed in the category of Dr/physicians whereas, the maximum is computed in the category of pharmacists.
Table 17: Descriptive Statistics of Efficiency of Professionals concerning Category sorted by Hospitals
Test of Homogeneity of Variances
Since it is one of the major assumptions of the one-way ANOVA analysis that the variances should not be heterogeneous, therefore, Levene’s test has been employed. The results should not be significant if a result is to be deemed significant. Considering this, the results presented in Table 18 imply that the variances are homogeneous. The assertion has been drawn based on the sig value which is computed to be 0.062> 0.05. Therefore, the null hypothesis entailing the inference that variances are homogenous has been retained.
Table 18: Homogeneity of Variances of Efficiency of Professionals Category sorted by Hospitals
ANOVA Analysis
To determine the differences amongst the categories of professionals, the results have been presented and interpreted in this section. The results have been illustrated in Table 19 which depicts that the f-statistics is computed to be 2.021 with a p-value of 0.152. Hence, it can be concluded that the efficiency level does not differ among Dr/physicians, nurses, and pharmacists significantly. The findings, in this case, are found to be similar to the study conducted by Laurant et al., (2018) who also found similar efficiency levels between them.
Table 19: ANOVA Analysis of Efficiency of Professionals Category sorted by Hospitals
As the results are insignificant, it can be seen that the equality of means is also not robust in terms of the Brown-Forsythe test. The results have been depicted in Table 20.
Table 20: Robustness of Equality of Means Efficiency of Professionals Category sorted by Hospitals
As the results are insignificant, it can be seen that the equality of means is also not robust in terms of the Brown-Forsythe test. The results have been depicted in Table 20.
Table 20: Robustness of Equality of Means Efficiency of Professionals Category sorted by Hospitals
Figure 5: Means Plot Means Plot of Efficiency of Professionals concerning Category sorted by Hospital
Categorized by Scale
In this specific section, the data has been sorted by the efficiency scale. This has helped in examining which scale is more common in hospitals in terms of efficiency. In addition, it has also assisted in determining the overall efficiency of Dr/physicians, nurses, and pharmacists on the efficiency scale ranging from 1 to 10 implying low efficiency to perfect efficiency.
Descriptive Statistics
In the context of the data sorted by efficiency scale, the results of the descriptive statistics have been presented in Table 21. It has been found that the most concentrated scale score in terms of six hospitals in the 9th score has an average value which means all the professionals have considerably high efficiency. The least concentration of efficiency is found to be in the 7th score having an average value of 857. In addition, the minimum deviation amongst the efficiency in professionals is computed to be the 9th score attributed to a value of 334.6.
Table 21: Descriptive Statistics of Efficiency by Professionals’ Category sorted by Scale
Test of Homogeneity of Variances
Even in this case, the report incorporates homogeneity testing using Levene’s statistic which is computed to be 2.155 with a p-value of 0.73. The p-value is above the threshold of 5%, hence, the null hypothesis entailing the conclusion that variances are not heterogeneous is retained. The results have been depicted in Table 22.
Table 22: Homogeneity Testing of Efficiency of Professionals concerning Category sorted by Scale
ANOVA Analysis
To determine the variation in efficiency scale amongst all the medical professionals, one-way ANOVA concerning scale has been conducted. The results have been presented in Table 23. The f-statistics has been computed to be 1.357 with p-value of 0.271 (p-value> 0.05). Hence, the p-value is implying that there is no difference in the scale efficiency of the professionals working in different hospitals. However, considering the sensitive nature of the profession, the health service sector and the associated practitioners should be highly efficient (WHO, 2016). The statement implies that the average efficiency of all professionals should be high and the model score obtained in this case is 9 which is also high, hence, the findings are consistent.
Table 23: ANOVA Analysis of Efficiency of Professionals concerning Category sorted by Scale
Robustness of Equality of Means
In the same vein, as the ANOVA analysis was insignificant, the Brown-Forsythe test to evaluate the robustness of means equality is also insignificant. The results can be seen in Table 24.
Table 24: Robustness of Equality of Means of Efficiency of Professionals concerning Category sorted by Scale
Means Plot
According to the results of one-way ANOVA, the means plot has been constructed and plotted in Figure 6. It is evident that the variation amongst the scale is present, however, that is statistically insignificant. The means plot is also depicting that the highest point is formed at score 9 which is followed by the 3rd score. However, the lowest concentration is computed to be at the 7th score. In furtherance, from point 4 to point 6, the difference is avidly minimal. Provided this, it can also be seen that some concentration at score 1 depicting poor efficiency is also present. On the contrary, a perfect score which is 10 is also found to be concentrated, however, it is relatively lesser than others.
Figure 6: Means Plot of Efficiency of Professionals concerning Category sorted by Scale
Evaluation of the Overall Hypotheses
In the context of the evaluation conduction in the preceding sections of this report, the evaluation of all the hypotheses has been conducted in this section in a tabular form. The decision of each hypothesis has been taken based on the p-values discussed, interpreted, and evaluated in the preceding sections. In this concern, it has been found that all the hypotheses have been rejected because none of the p-values of the one-way ANOVA table were found to be statistically significant. All the values were above the threshold which was considered to be 5%. The assessment of the hypotheses has been presented in Table 25.
Table 25: Hypotheses Assessment Table
Hypothesis Number | Statement | Decision |
H1a | The efficiency of hospitals varies concerning each hospital significantly | Rejected |
H2a | The efficiency of hospitals varies concerning the efficiency scale significantly | Rejected |
H3a | The efficiency of professionals varies in each hospital significantly | Rejected |
H4a | The efficiency of professionals varies concerning the efficiency scale significantly | Rejected |
H5a | The efficiency of professionals working in hospitals varies concerning their category significantly | Rejected |
H6a | The efficiency of professional’s categories working in hospital vary concerning the efficiency scale significantly | Rejected |
Summary of the Results
The overall analysis of the results provides conclusive findings of each variable of this study. Firstly, concerning the efficiency of each hospital that has been studied in this research, H1 is found to have the highest level of efficiency in comparison with other hospitals. In this context, as per the results of Homogeneity of Variances, the significance value is figured ass 0.919. Based on this, the null hypothesis of this study has been accepted. Similarly, the results of ANOVA also validate these findings. On the other hand, the efficiency of hospitals based on scale ranking is found to be highest on the 8th scale; whereas the weakest efficiency of Kuwaiti hospitals is found to be on the 3rd scale. Moreover, as per the results of ANOVA, the mean value of the efficiency of hospitals is found to be similar for all ranking groups. Concerning the number of incident reports and complaints, petals 5 and 2 were found to have the highest mean number of reported incidents.
The results about the efficiency of professionals amongst all the investigated hospitals, H5 is found to have the highest level of professional efficiency, whereas H1 has the lowest level of professional efficiency. The results of professional efficiency based on scale category identify 7th scale with the highest efficiency of professionals. Lastly, as per the results, the professional category of pharmacists was recorded at the highest efficiency, whereas the DR/Physicians category of professional was found to be the least efficient. Conclusively, the overall findings of this study have rejected all the hypotheses and accepted the null hypothesis.
References
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Jayalath, K.P., Ng, H.K.T., Manage, A.B. and Riggs, K.E., 2017. Improved tests for homogeneity of variances. Communications in Statistics-Simulation and Computation, 46(9), pp.7423-7446.
KARAGÖ, D. and Saraçbasi, T., 2016. Robust Brown-Forsythe and robust modified Brown-Forsythe ANOVA tests under heteroscedasticity for contaminated Weibull distribution. Revista Colombiana de Estadística, 39(1), pp.17-32.
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Appendices
