Optimizing Ermergeny Room Staff Statistics Project

Optimizing Ermergeny Room Staff - Statistics Project ExampleCollected data included age and sex of affected role, particular date and clock cartridge holder patient arrived, date and snip patient discussion began and triage number, Triage number is a scale used in the ER that identifies the urgency of cathexis, standard waiting time, modal(a) length of treatment time and the number of nurses required. See Appendix A.The number of patients was summarized according to a 1-hr time interval of its reaching to the ER. Frequency distribution, time series and regression abridgment were created to determine the trend. See Appendix B.The wait time in minutes was summarized according to a 4-hr interval of the patients arrival. See Appendix C. The 4-hr interval is also identify as the 4-hr work shift of nurses. The distribution of average wait time per month was made to identify the volume of patients having a long wait time in the 4-hr work shift. Analysis of variance was conducted t o determine if there are any significant differences between them with respect to mean waiting time.The treatment time in minutes was also summarized according to a 4-hr time interval of nurses work shift. The treatment time is the average time needed by the nurses to care for patients with respect to its urgency according to the triage number. The distribution of total treatment time per month was made to identify the volume of nurses time in the 4-hr work shift. systema skeletale 1 shows the frequency distribution of the number of patients arriving per month on a 1-hr... Figure 2 shows the time series of the patients arriving per day on a 1-hr time interval. There is a seasonal trend identified per day which further confirms the observation from the frequency diagram.A single factor analysis of variance was conducted development Microsoft Excel Add-In. The results in Table 1 show that the F-value is smaller than the F critical and the P-value is relatively large. The null hypothe sis stating that all means of patient arrival per month is equal and there is no statistical differences between the monthly data. This concurs that the data of patients per month can be summarized into a 24 hr patient arrival behavior.Table 1. Anova Single FactorSUMMARYGroupsCountSumAverageVarianceJUN2432613.583360.3406JUL2430512.708356.1286AUG2436415.166769.0145SEP2436215.083392.5145OCT2429312.208355.6504NOV2433413.916753.9058Source of VariationSSdfMSFP-valueF critBetween Groups175.14535.0280.5420.7442.280Within Groups8913.7513864.592Total9088.889143Figure 3 shows the best pop off line graph of patients arrival from 300 am to 2200 pm. The R-squared value of 0.8839 shows high linearity on the trend. The number of patients increases with time during this period. The coefficient of increase is 0.1148.2. Wait conviction of PatientsThe frequency distribution of wait time is shown in Figure 4. The mean time to wait is 131.11 minutes with a standard deviation of 87.62 minutes. The conf idence level at 95% is 3.85 minutes. The shape of the distribution is skewed to the left. This means that the data may contain outliers with very large waiting time.Figure 5 shows the patients average time