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Table 1: Statistical Summary of Performance Metrics Across Five Key Variables max values among actual non-zero scores. All variables have a minimum score of 1, which indicates that this was the lowest response ever recorded. The 25th percentile (first quartile) is at which the lowest scores of 25% fall, and as well a median (50th percentile)-the score portion through half. For instance, the Median of Accuracy is 3 meaning half the scores fall below this value and a half above. 75th percentile or third quartile- where 75% of the scores fall below a given nu mber, showing us what is happening in the upper-middle range Finally, all variables have a maximum value of 5, whic dataset. This sta view of the distri and variability understanding measures across h is the highest score that appears in the tistical summary offers a comprehensive bution of data in terms of central tendency which constitute a fundamental regarding the performance outcome five different variables. The table above gives a summary of some statistical data for five different variables:Efficiency, Accuracy,Fraud Detection, Compliance and Professional Impact (n=142 observations). Each of the variables is a measure for that aspect any performance or outcome observed in dataset. In the case of each variable, this number represents the average (e.g., 3.12), indicating that grade mean values are being considered on a scale from -2 to +1 Thus one would say that the average efficiency score of all observations is around this value. Where 1.41 is the standard deviation for Efficiency which provides an idea of how spread off from the average all feedbacks are, A standard deviation of 1.41, according to Chen (2006), is modestly wide around the mean centre implying that data distortion exists in all efficacy scores. It also describes the min, 25th percentile (first quartile), average (mean median* from MDDB) and The data analysis based on the responses from 142 for each variable, with results ranging on a Likert scale from 1 (Strongly Disagree) to 5 (Strongly Agree):

Table 1 Statistical Summary of Performance Metrics Across Five Key Variables max values among actual non-zero scores. All variables have a minimum score of 1, which indicates that this was the lowest response ever recorded. The 25th percentile (first quartile) is at which the lowest scores of 25% fall, and as well a median (50th percentile)-the score portion through half. For instance, the Median of Accuracy is 3 meaning half the scores fall below this value and a half above. 75th percentile or third quartile- where 75% of the scores fall below a given nu mber, showing us what is happening in the upper-middle range Finally, all variables have a maximum value of 5, whic dataset. This sta view of the distri and variability understanding measures across h is the highest score that appears in the tistical summary offers a comprehensive bution of data in terms of central tendency which constitute a fundamental regarding the performance outcome five different variables. The table above gives a summary of some statistical data for five different variables:Efficiency, Accuracy,Fraud Detection, Compliance and Professional Impact (n=142 observations). Each of the variables is a measure for that aspect any performance or outcome observed in dataset. In the case of each variable, this number represents the average (e.g., 3.12), indicating that grade mean values are being considered on a scale from -2 to +1 Thus one would say that the average efficiency score of all observations is around this value. Where 1.41 is the standard deviation for Efficiency which provides an idea of how spread off from the average all feedbacks are, A standard deviation of 1.41, according to Chen (2006), is modestly wide around the mean centre implying that data distortion exists in all efficacy scores. It also describes the min, 25th percentile (first quartile), average (mean median* from MDDB) and The data analysis based on the responses from 142 for each variable, with results ranging on a Likert scale from 1 (Strongly Disagree) to 5 (Strongly Agree):

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Table 2: Correlation Analysis (first quartile), average (mean median* from MDDB) and The correlation analysis between the variables provides insights into how these factors relate to each other based on the reennncec fram the accaintante: positive, statistically significant and moderate to strong correlations with Fraud Detection (0.53), Compliance (0. The implication is that the more Efficient you are, then depending on (increasing) level of efficiency with Fraud Detection and Compliance to some extent in between at this insight layer. However, its correlation with Accuracy is still almost zero (0.01), so seems to have no impact on the overall accuracy of the model.boosted logistic regression [Collins et al. Scatter-plot analysis shows Accuracy to have correlations with Fraud Detection (0.53), Compliance (0.
Table 2: Correlation Analysis (first quartile), average (mean median* from MDDB) and The correlation analysis between the variables provides insights into how these factors relate to each other based on the reennncec fram the accaintante: positive, statistically significant and moderate to strong correlations with Fraud Detection (0.53), Compliance (0. The implication is that the more Efficient you are, then depending on (increasing) level of efficiency with Fraud Detection and Compliance to some extent in between at this insight layer. However, its correlation with Accuracy is still almost zero (0.01), so seems to have no impact on the overall accuracy of the model.boosted logistic regression [Collins et al. Scatter-plot analysis shows Accuracy to have correlations with Fraud Detection (0.53), Compliance (0.
Table 3: Regression Table Efficiency-The p-value of 0.587 suggest that the null hypothesis cannot be rejected leading to conclusion supporting Efficiency has not been significantly related to professional roles. Accuracy: As with Efficiency, a p-value of 0.562 indicates insignificance and we fail to reject the null hypothesis Professional Roles: Similarly to fraud detection, there is no evidence from this p-value = 0843 of a significant effect. Compliance: p < 0.05 | The group achieved a non-significant lower than the usual thresholds - but still far from it at 0.408, resulting in null being not rejected In general, these results lead to the conclusion that Efficiency, Accuracy in fraud detection and compliance do not impact professional role significantly according to this examination. he highest positive correlation with Fraud Detection (0.71), urther emphasizing that higher level of accuracy is Ositively associated with improved fraud detection apabilities while negatively correlated for all other metrics, xcept Recall which has an insignificant negative relation. t also demonstrates moderate to strong positive correlations vith Compliance (0.59) and Professional Impact (0.48). ‘his pattern demonstrates a pathway where better Accuracy nay result in more Comprehensive, and hence Professional mpact (since we have seen a decrease in Adherence to uidelines while also increase Payors measures of Quality), hough less Efficient. The correlation of Fraud Detection is n moderate with Compliance (0.39) and strong with rofessional Impact (0.57). Fraud Detection as the most ignificant performance metric correlated with other Efficiency-The p-value of 0.587 suggest that the null financial and auditing services, as they contribute toward outcomes These relationships suggest that increased Fraud Detection is almost always linked to higher levels of Compliance, and Professional Impact (greater proportion palliative care = low). Compliance demonstrates a strong positive (0.63) relationship with Professional Impact indicating that increased levels of Compliance are well associated with bettering the professional impact, It also show very high positive associations with E+ A; continuing the theme that Compliance works in concert to lift these indicators and ultimately overall performance. Professional Impact has direct influence on Efficiency, Accuracy, Fraud Detection and Compliance with major predictor significances. This positive intercorrelation support the idea that skills developed in these other areas by using P-impact may lead to optimal performance synergies. Professional Impact (0.57). Fraud Detection as the most
Table 3: Regression Table Efficiency-The p-value of 0.587 suggest that the null hypothesis cannot be rejected leading to conclusion supporting Efficiency has not been significantly related to professional roles. Accuracy: As with Efficiency, a p-value of 0.562 indicates insignificance and we fail to reject the null hypothesis Professional Roles: Similarly to fraud detection, there is no evidence from this p-value = 0843 of a significant effect. Compliance: p < 0.05 | The group achieved a non-significant lower than the usual thresholds - but still far from it at 0.408, resulting in null being not rejected In general, these results lead to the conclusion that Efficiency, Accuracy in fraud detection and compliance do not impact professional role significantly according to this examination. he highest positive correlation with Fraud Detection (0.71), urther emphasizing that higher level of accuracy is Ositively associated with improved fraud detection apabilities while negatively correlated for all other metrics, xcept Recall which has an insignificant negative relation. t also demonstrates moderate to strong positive correlations vith Compliance (0.59) and Professional Impact (0.48). ‘his pattern demonstrates a pathway where better Accuracy nay result in more Comprehensive, and hence Professional mpact (since we have seen a decrease in Adherence to uidelines while also increase Payors measures of Quality), hough less Efficient. The correlation of Fraud Detection is n moderate with Compliance (0.39) and strong with rofessional Impact (0.57). Fraud Detection as the most ignificant performance metric correlated with other Efficiency-The p-value of 0.587 suggest that the null financial and auditing services, as they contribute toward outcomes These relationships suggest that increased Fraud Detection is almost always linked to higher levels of Compliance, and Professional Impact (greater proportion palliative care = low). Compliance demonstrates a strong positive (0.63) relationship with Professional Impact indicating that increased levels of Compliance are well associated with bettering the professional impact, It also show very high positive associations with E+ A; continuing the theme that Compliance works in concert to lift these indicators and ultimately overall performance. Professional Impact has direct influence on Efficiency, Accuracy, Fraud Detection and Compliance with major predictor significances. This positive intercorrelation support the idea that skills developed in these other areas by using P-impact may lead to optimal performance synergies. Professional Impact (0.57). Fraud Detection as the most
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