B. Bell Grove Medical Center: Case Study

Bell Grove, Medical care center is an example of an urgent care center that deals with patients with no appointments. The patient walks in and out of the hospital now and then, seeking medical attention. Such hospitals are usually less expensive for emergencies as they treat less life-threatening ailments, such as simple bone fractures, insect bites, fever, and cuts. Several centers, amongst them, run essential laboratory services and X-rays and can also run analytic checks and give medicaments (Baier et al., 2019). Emergency rooms in the center are meant for the patients reported to have the most severe problems; Patients with less complicated issues can wait for a while before seeing the caregiver. Patients receive medication on arrival, depending on the nature of the illness.


Patients have been experiencing devastating situations when attempting to access emergency health care in the Bell Medical Center. They are forced to wait for long to acquire treatment though most patients do not require serious medical attention. It is attributed to the shortages and poor planning for the available nurses within the center and reduced working days due to the exemption of weekends in the work schedule. Ms. G. Dempsey, the manager of the Bell center, analyzed and tabulated the data of patients received per day to help her plan for the resources to satisfy the patient’s demands. Ownership and the management of urgent care centers can be based on hospitals or doctors, who annually gain a great deal of revenue. The centers are usually composed of a nurse, a physician assistant, and at least one certified board physician whose main purpose is to deliver quality health care to the patients (Majamanda et al.,2021). MS Gene D being an emergency center manager at the B. Bell G.M.C is responsible for ensuring enough nursing amenities that match the several patients that need care urgently within the entire week.

Case analysis

Ms. G. Dempsey, the manager of the urgent care center, while in his job collected and tabulated the number of patients the emergency center had received over a number of weeks. Provided that the center operates only on weekdays from Monday to Friday, the data strictly covers Monday to Friday. Therefore, to guarantee the effectiveness of health delivery in the health center, M.S. Gene needs to analyze the data based on the day critically and the number of patients received and established the variance in the number of patients served daily. This will help her come up with an appropriate strategy indicating the number of nurses required per day to meet the demand of the patients (Montgomery, 2017). In addition, the analysis will help identify the particular days within the week registering high numbers of patients, therefore, assigning them more staff.

Date Day Patients
9/29/2006 Mon 38
9/30/2006 Tue 28
10/1/2006 Wed 28
10/2/2006 Thu 30
10/3/2006 Fri 35
10/6/2006 Mon 35
10/7/2006 Tue 25
10/8/2006 Wed 22
10/9/2006 Thu 21
10/10/2006 Fri 32
10/13/2006 Mon 37
10/14/2006 Tue 29
10/15/2006 Wed 27
10/16/2006 Thu 28
10/17/2006 Fri 35
10/20/2006 Mon 37
10/21/2006 Tue 26
10/22/2006 Wed 28
10/23/2006 Thu 23
10/24/2006 Fri 33


In this scenario, the researcher applied the ANOVA analysis technique, which is involved in comparing three or more means for statistical significance. First, she uses F distribution to conduct a hypothesis test to determine the quality of two or more standards. The data tabulated by MS Dempsey in four weeks then need to be classified into five logical groups (Warne, 2020). Next, she tabulates the data based on rows and columns for the four weeks whereby; on the column, she has total samples, followed by group mean, and finally the overall variation for every observation shown by the subtotal of square variance between each observation and comprehensive mean.

Mondays and Tuesdays

No. of patients (X-mean) (X-mean)2 Date
No, of patients (X-mean) (X- mean)2
9/29/2006 38 1.25 1.563 9/30/2006 28 1 1
10/6/2006 35 -1.75 3.063 10/7/2006 25 -2 4
10/13/2006 37 0.25 0.0625 10/14/2006 29 2 4
10/20/2006 37 0.25 0.0625 10/21/2006 26 -1 1
Total 147 36.75 4.750 Total 108 27 10

Wednesdays and Thursdays

No, of patients (X-mean) (X-mean)2 Date
No, of patients (X-mean) (X-mean)2
10/1/2006 28 1.750 3.063 10/2/2006 30 4.5 20.25
10/8/2006 22 -4.250 18,06 10/9/2006 21 -4.5 20.25
10/15/2006 27 0.750 0.5625 10/16/2006 28 2.5 6.25
10/22/2006 28 1,750 3.063 10/23/2006 23 -2.5 6.25
Total 105 26.25 24.75 102 25.5 53


No, of patients (X-mean) (X-mean)2
10/3/2006 35 1.250 1.563
10/10/2006 32 -1.750 3.063
10/17/2006 35 1.250 1.563
10/24/2006 33 -0.750 0.5625
Total 135 33.75 6.75

From the tables, the variation of the means for each day is apparent. However, using the mean differences alone does not provide absolute certainty that the means are significantly different; thus, the statistical significance can be established by using the hypothesis and an alternative hypothesis. This test should be a two-tailed analysis because the researchers consider a significant variation between any two days. Due to the uncertainty of the results, the researcher applies the null hypothesis that every day means the difference is similar to the other four days. Alternative hypothesis; every day means the difference is not identical to the other four days (Kelter, 2020). A significance value of 0.5 (F-critical) was selected for the hypothesis test, which is to be compared with F (statistical test) for deciding on accepting or rejecting the hypothesis. After detailed analysis, the value of F (statistical) is found to be 15.54. The researcher rejects the hypothesis since the (F- critical) value is less than the (F statistical). Rejecting the hypothesis implies that there exist significant differences in daily sample means.


The ANOVA test is applied to prove the null hypothesis of a sample of two or more groups from the given population with similar mean values to establish the hypothesis two approximations based on various population variance assumptions. The ANOVA provides the F- the statistical ratio of the calculated variance among the mean to the variance within the samples. ANOVA also generalizes the research of impacts of several factors because the research entails observations at all levels of every element, thus referred to as factorial (Baril et al., 2020). Factorial experiments are more efficient than a sequence of single-factor experiments, and the efficacy increases as the number of factors increases. The ANOVA test is preferred because it has a practical test; it is strong beside numerous alternative distributions.


From the ANOVA analysis, it is evident that a significant variance exists in the number of patients registered every day of the week at the B. Bell grove center. Mrs. Gene is correct in her supposition from the data collected that there is a substantial difference in the number of patients assisted by day of the week. It is also seen that Mondays and Fridays are the busiest days compared to other days. Tuesday somehow in overall also can be seen having relatively a greater number of patients than Wednesday and Thursday. Mrs. Dempsey, the center manager, should then go back to the drawing board and plan the healthcare resources for the best of the patient and her entire staff. Mrs. Dempsey should ensure that more staff is assigned duties on Mondays and Fridays to cater to the patients’ high demand. The manager should also ensure that more medical resources are deployed on Friday and Monday to ensure health delivery equity.


Baier, N., Geissler, A., Bech, M., Bernstein, D., Cowling, T. E., Jackson, T., & Quentin, W. (2019). Emergency and urgent care systems in Australia, Denmark, England, France, Germany and the Netherlands–Analyzing organization, payment and reforms. Health Policy, 123(1), 1-10.

Baril, C., Gascon, V., & Miller, J. (2020). Design of experiments and discrete-event simulation to study oncology nurse workload. IISE Transactions on Healthcare Systems Engineering, 10(1), 74-86.

Kelter, R. (2020). Bayesian alternatives to null hypothesis significance testing in biomedical research: a non-technical introduction to Bayesian inference with JASP. B.M.C. MEDICAL RESEARCH METHODOLOG.20, 1-12.

Majamanda, M. D., Joshua Gondwe, M., O’Byrne, T., Makwero, M., Chalira, A., Lufesi, N.,… & Desmond, N. (2021). Capacity Building for Health Care Workers and Support Staff in Pediatric Emergency Triage Assessment and Treatment (ETAT) at Primary Health Care Level in Resource-Limited Settings: Experiences from Malawi. Comprehensive Child and Adolescent Nursing, 1-16.

Montgomery, D. C. (2017). Design and analysis of experiments. John WILEY & SONS.

Warne, R. T. (2020). Statistics for the social sciences: A general linear model approach. Cambridge University Press.

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