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This study chapter investigates the efficiencies of Kuwait government Hospitals and their healthcare professionals in coordination with the frequency of medication errors taking place and relevant complaints. Data analysis builds a direct relationship between efficiencies and the number of medication errors, whereas the results may assist Kuwait Government hospitals in reducing these errors. Considering the rationale, this study has used the most appropriate methods and tools for collecting, analyzing, and interpreting the data to provide conclusive findings. Selecting suitable research methods carries massive importance for attaining desired research objectives, as using any inappropriate method can invalidate the findings. Hence, the proper selection of research methods has been ensured in this study by the nature and requirement of the research topic. The following section reviews some key methods and techniques used in this study to collect and analyze data.
Variables of the Study
In this study, the primary source of data collection has been used for gaining comprehensive information about each underlying variable. The researcher has used relevant sources to collect appropriate data. The key variables targeted in this study include the efficiency of hospitals, the number of incident reports and complaints, and the evaluation of professionals. The researcher targeted six hospitals to gain the required information about these variables. The data collection method was initially through observation, which allowed the researcher to gain useful information about medication errors in different hospitals under Kuwait Government. Three professional categories mainly focused on evaluating their efficiency levels, including Dr/Physicians, Pharmacists, and Nurses.
On the other hand, the data related to the number of medication error incidents and complaints were collected from different hospitals. Lastly, hospitals and standard scale categorized data for the efficiency variables of hospitals and professionals. The data collection method used in this study enabled the researcher to easily quantify the data and provide more factual information about the research topic.
Data Processing and Analysis Techniques
Data processing and analysis are considered another crucial part of the research, which determines the authenticity and reliability of research findings (Cole and Trinh, 2017). Therefore, selecting analysis and processing techniques is crucial to accomplish the primary research objective. Since the data collected in this study was quantitative, thus researcher employed different statistical techniques to interpret the quantitative data. Firstly, the researcher has arranged the total number of observations obtained from each of the six hospitals within each category of variables. At the initial stages of data analysis, all the relevant data about different variables investigated from different hospitals were arranged. This allows the researcher to highlight issues with the selected data and possible missing information.
The data was then analyzed through SPSS software, where the data was imported. Moreover, the researcher has coded data concerning each category of different variables, including the efficiency of hospitals, the number of reported incidents and complaints, and the efficiency of professionals. 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
Although different techniques are used for data analysis, the right selection of analysis technique is highly dependent on the type of data the researcher is determined to analyze (Kumar, 2019). In this study, the primary 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 provide clear 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 critical statistical tests the researcher has carried out in this study include NOVA descriptive statistics, a test of homogeneity of variances, a means plot, and robustness of equality of means. This section of the study presents the overall data analysis outcomes for each study variable. Moreover, this section also provides a graphical representation of the results of different variables to bring more clarity to the research outcome.
Evaluation of Efficiency of Hospitals
Categorized by Hospitals
The data collection is based on gathering information from six Kuwaiti government hospitals to evaluate their efficiency along with the medication errors reported there and common complaints. The six Kuwaiti government hospitals are evaluated concerning their efficiency based on the collected data. The main purpose is to determine whether there is a difference in efficiency among the six Kuwaiti government hospitals that affect the incidents of medication error. The evaluation of the efficiency of the hospitals is conducted through 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 commonly used to evaluate the data by summarizing it into means form, which becomes easier for the analyst to interpret (Amrhein, Trafimow, and Greenland, 2019). The descriptive analysis for this study is based on the information gathered from each of the six hospitals. While referring to H1, the first hospital, the mean value is computed as 1568.8, whereas 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, demonstrating that the efficiency of H1 can either increase or decrease by 681.42. The minimum efficiency value 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 quite below the value of 3000, indicating the possibility of reporting medication errors with lower efficiency. The standard deviation is computed as 747.08, suggesting that the efficiency dispersion 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.
Moving on to the H3, the mean value is computed as 1378.7, 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, demonstrating that the hospital's efficiency aspect can increase or decline by 766.6 units. The minimum efficiency value was 428, whereas the maximum value of H3 is computed as 2516. Evaluating the data for 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, whereas the standard deviation is computed as 905.74, demonstrating weak efficiency. 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 standard tool used for measuring the assumption of homogeneity of variance is Levene's Test, in which the p-value must be above 0.05 to meet the assumption. In contrast, the value below violates the assumption (Jayalath et al., 2017). Based on the results, the significance value is computed as 0.919, and 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, 2016). The robust equality test of means has been evaluated through the sig value of 0.782 (p-value >0.05). Therefore, the null hypothesis is accepted because all the groups' means are equal.
Means Plot
Figure 1 represents the mean plots of the efficiency of the six Kuwaiti government hospitals. It is identified that the H1 has the highest mean value compared with the other hospital. This also implies that the H1 has the highest efficiency level in error reporting compared to the other hospital. On the contrary, H6 has the lowest mean point of efficiency of hospitals, indicating having the least efficiency.
Categorized by Scale
This section measures efficiency based on the developed scale where 1 equals bad efficiency, and 10 indicates perfect efficiency. The purpose is to demonstrate the aspects of efficiency employed in the hospital based on the scale.
Descriptive Statistics
Table 5 represents the descriptive analysis of hospital efficiency based on a 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 means that it can increase or decrease by 765.90 units. The weakest efficiency of the hospitals is found to be on the 3rd scale, which had an efficiency of 994. In contrast, the standard deviation is computed as 603.64, illustrating the efficiency dispersion.
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 Levene's Test concerning the efficiency ranking. 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, 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 as 0.397, which led to the acceptance of the null hypothesis. Thus, this implies that the mean value of the efficiency of the hospital is the 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. In contrast, 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 the Number of Incident Reports and Complaints
Descriptive Statistics
Table 9: Descriptive Analysis of the Number of Incident Reports and Complaints
Here it becomes necessary to mention that a total of 6 hospitals were involved in the survey process. Based on the table mentioned above, 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.
Furthermore, 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 indicates that 2701 was the highest number of reports 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 indicates that the data is thin-tailed relative to its normal distribution.
Figure 3 presents the mean plots of hospital incident reports and complaints. In this regard, figure 3 outlines all 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, hospitals 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 Kuwait to evaluate their ability to report medical errors and take a proactive stance in dealing with them.
Descriptive Statistics
Table 9: Descriptive Analysis of Professionals' efficiency
Table 9 reflects the descriptive analysis of the Professional's efficiency based on the six hospitals. While referring to the results, it is found that H5 had the highest level of professional efficiency because its mean value was computed as 2018.3, and its standard deviation was calculated as 775.313. On the other hand, the hospital was found to have the lowest professional 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 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 no mean value difference exists 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 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 with 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. The dispersion value is identified as 993.94. On the other hand, the lowest professional efficiency is observed on the 5th scale, as the mean value is computed as 1373.5 for professional efficiency, whereas the dispersion value is calculated as 314.233.
Test of Homogeneity of Variances
Table 14: Levene's Test for efficiency of Professional
Table 14 reflects Levene’s test, which measures the assumption of homogeneity of variance among the groups. The significance value is computed as 0.539, demonstrating that the variance among the difference scale concerning 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 to evaluate the equality of means for the professional’s efficiency concerning the scale. The significance value is 0.90, 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. This assessment aims to evaluate whether or not the efficiency level differs among nurses, Dr/physicians, or pharmacists.
Categorized by Hospitals
Specifically, in this section, the six hospitals mentioned earlier have conducted the evaluation.
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 calculated 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 illustrates maximum and minimum values where it has been found that the minimum efficiency is computed in the category of Dr/physicians. In contrast, 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, Levene’s test has been employed. The results presented in Table 18 imply that the variances are homogeneous. The assertion was drawn based on the sig value, which was 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
The results have been presented and interpreted in this section to determine the differences among the categories of professionals. 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 significantly differ among Dr/physicians, nurses, and pharmacists. In this case, the findings are 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
The efficiency scale has sorted the data in this specific section. This has helped examine which scale is more common in hospitals regarding 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 six hospitals in the 9th score has an average value, meaning all the professionals have considerably high efficiency. The least efficiency concentration is found in the 7th score with 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 at 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 implies 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 also depicts that the highest point is formed at score 9, followed by the 3rd score. However, the lowest concentration is computed to be at the 7th score. Furthermore, the difference is avidly minimal from point 4 to point 6. 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 of 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
All the evaluations conducted in the preceding sections of this report are presented in tabular form in this section. 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 was 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 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 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 hospitals' efficiency is 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 professional efficiency, whereas H1 has the lowest professional efficiency. The results of professional efficiency based on scale category identify the 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.
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References
Amrhein, V., Trafimow, D. and Greenland, S., 2019. Inferential statistics as descriptive statistics: There is no replication crisis if we don’t expect replication. The American Statistician, 73(sup1), pp.262-270.
Cole, A.P. and Trinh, Q.D., 2017. Secondary data analysis: techniques for comparing interventions and their limitations. Current opinion in urology, 27(4), pp.354-359.
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.
Kumar, R., 2019. Research methodology: A step-by-step guide for beginners. Sage Publications Limited.
Laurant, M., van der Biezen, M., Wijers, N., Watananirun, K., Kontopantelis, E. and van Vught, A.J., 2018. Nurses as substitutes for doctors in primary care. Cochrane Database of Systematic Reviews, (7).
World Health Organization, 2016. Health system efficiency: how to make measurement matter for policy and management. World Health Organization. Regional Office for Europe.
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