Use of Percentiles and Run Charts: Laralex Hospital Case Study

Posted: August 25th, 2021

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Use of Percentiles and Run Charts: Laralex Hospital Case Study

Question 1: Explain What Is Wrong With Using Percentiles to Compare Hospitals

The problem with using percentiles for hospital comparison is that they fail to give an equal measurement unit. For instance, a four (4) percentile point attained between two individual variable cores exhibits a difference in their meanings depending on the existing position of condition on the percentage scale. The reason is that a percentage scale is likely to exaggerate discrepancies close to the average while collapsing differences at the extreme ends of the mean. As such, percentiles are ineffective when comparing hospitals.

As revealed in the case, a comparison of the situation of twelve hospitals that were analogically served a similar population and identically designed and managed, it is shown that despite the similarities like these hospitals, x-ray reports randomly vary from one hospital to the other. Hence, it implies that performance among these hospitals is different and subject to unique variation overtime. However, when percentiles are used to compare the performance, it assumes an equal likelihood despite the variation on ranking. Thus, percentiles are the wrong comparison as it fails to detail the differences in the individual factors that influence the performance of hospitals. By illustration, during a survey, a health ranking of hospitals is used based on publicly declared data from a sample of about 4,000 hospitals. When hospital-specific performances particularly on care quality, it is quickly discovered that there are extreme variations. Thus, percentile ranking is limited in this case since due to differences in factors such as patient types that each hospital serves, measures and clinical topics that ultimately influence ranking precision.

Question 2: Show Short-Run Charts and Statistically Evaluate Each Run Chart Using Runs Rules Interpret Each Run Chart for the Outcomes Analyzed In the Case

Run charts refer to the graphical display of sequence plots showing the data that have been observed over time. Usually, the data presented in a run-chart shows the performance of an organization process. The tool can also be used to study and track information besides predicting patterns or trends. The following are of run-charts established when assessing Laralex Hospital performance

Figure 1: Run Chart for Hospital Acquired Infections

The run chart under figure 1 shows that the number of infections that have been reported in the hospital over twenty-four (24) months. The chart also displays variation exhibited per month.

Figure 2: Run Chart for Discrepant X-Rays

Figure 2 displays discrepancies in the x-rays performed over time within twenty-four months. Mostly, the run chart shows the trend and patterns of occurrence of each discrepancy across the months.

Figure 3: Run Chart for Patients who leave the ED Prior to Treatment

Figure 3 is a summary of the trend in the number of patients leaving ED prior to treatment. The run pattern exhibits an increasing trend over twenty-four (24) months.

Question 3: Explain the Use of Run Charts

Purpose of run charts. Run charts are used in studying the trends and patterns of information that have been observed in a specified period with attention given on critical changes in the process or direction. However, run charts cannot display or permit the use of all the analytic techniques as in the control charts. Besides, they do not exhibit the processes control limits.

Construction of run charts. Run charts are constructed by first calculating the mean or median of the data collected over time. Collected data is then plotted on a graph with vertical or y-axis containing the population data while the horizontal or x-axis contains periodical or monthly data. The run is plotted to display the distribution of data from the calculated mean.

Methods of Interpretation. Interpretation is based on the nature of the runs. Of interest are the run points, which are compared with the median or mean scores to ascertain a pattern, trend, or distribution in the information.