Comparing Acute And Non-Acute Care Hospital Admissions

Research Hypothesis

The aim of this report is to determine the number of patients who need acute and nonacute care on admission, including the time during their stay in the hospitals. Thus, the intension is to identify the healthcare’s efficiency to be improved in the specific hospitals.

The main objective of this project is to compare the acute and non-acute care hospital admissions with an increase in the inpatients’ admission. The admissions include med and surg, mental health, orthopedic, chemical dependency, neurology, rehab, and obstetrics admissions. This analysis results that increase in non-acute care should decrease the need for inpatient visits. 

The following are the framed hypotheses:

Hypothesis-1:

H₀: The need for non-acute care on admission is low.

H₁: The need for non-acute care on admission is high. 

Hypothesis-2:

H₀: The number of acute care beds are low during the stay in the hospital among the patients with different medical conditions.

H₁: The number of acute care beds are high during the stay in the hospital among the patients with different medical conditions. 

The following sets of data are analyzed:

• Admissions of care unit:It consists of Hospital characteristics, acute admissions details, non-acute admissions details, and total number of admissions.
• Admissions by payer: It consists of Hospital characteristics, managed care details, non-managed care details, Self-pay admissions, other payer admissions, total acute care admissions, and average length of stay.

This research uses Descriptive Statistics and T-Test methods to analyze the given health care data to compare acute and non-acute care hospital admissions with an increase in inpatients admissions. The descriptive statistics is used to provide the statistical information about the selected variables such as mean, median, standard deviation, skewness, kurtosis, minimum, and maximum (Frost, 2022). T-tests are the hypotheses tests, which examines one or two groups’ means. Hypothesis tests utilize sample data for inferring the entire populations’ properties (“7. The t tests”, 2022). To utilize t-test, get random sample from the target populations. Based on the t-test and its configuration it can determine the following:

• Means of the two groups are different.
• Paired means are unique.
• One mean is unique from the target value.

T-Test is utilized for testing the null hypothesis i.e., if the two populations’ means are equal or not. 

Conduct data analysis to test the hypothesis with the help of T-test analysis on Excel. Before doing T-test, first perform descriptive statistics for the selected variables in the research hypothesis as discussed below.

Descriptive Statistics

The descriptive statistics for the selected variables are presented below:

For Acute Care Admissions,

For Non – Acute Care Admissions,

For Available Beds,

Perform T-test for testing the hypothesis (“How To Perform T-Tests In Excel”, 2022).  

For Hypothesis-1,

• H₀:The need for non-acute care on admission is low.
• H₁:The need for non-acute care on admission is high.

To do this, go to data tab > Data analysis > T test: Two Sample assuming equal variances and then select the input as Total Acute care admissions and Total non–acute care admissions. The output of T test is displayed below:

Dataset Description

For Hypothesis 2,

• H₀:The number of acute care beds are low during the stay in the hospital among the patients with different medical conditions.
• H₁: The number of acute care beds are high during the stay in the hospital among the patients with different medical conditions.

In T-test using the Microsoft Excel, select input as Total Acute care admissions and Available beds. The T-test output for this hypothesis is tabulated below.

In general, let us understand the difference between acute and no-acute admissions in the healthcare system. The acute admission can be referred as a health care level, where the patients’ illness and service intensity are determined based on the inpatients setting (“CMS Data Navigator Glossary of Terms”, 2022). Subsequently, the decision is termed as “admit” by the doctors for providing inpatient care. The non-acute care is the medical admissions which signifies the treatment goals for supporting the patients with participation restrictions, impairment, or activity limitation due to certain health condition.

Here, let us assume that the acute patients belong to the category who need ventilator support, and the non-acute patients are those who don’t need ventilator support, however needs constant nursing support. Hence, based on this concept the following hypotheses are framed to determine the actual result.

Hypothesis-1:

H₀: The need for non-acute care on admission is low.

H₁: The need for non-acute care on admission is high.

The above hypothesis helps in determining the patient’s illness severity in the healthcare to help the physicians decide to provide care for the inpatients. 

Hypothesis 2:

H₀: The number of acute care beds are low during the stay in the hospital among the patients with different medical conditions.

H₁: The number of acute care beds are high during the stay in the hospital among the patients with different medical conditions.

Whereas, the above hypothesis helps in determining whether the inpatients need beds and care in the healthcare or not. 

Hypothesis-1

As per the first hypothesis’ result, the output indicates that the mean for the Total Acute Care Admissions is 3944.027 and for the Total Non-Acute and Nursery Care Admissions is 498.779. The variance shows that the acute and non-acute admissions are not equal (“T TEST in Excel (Formula, Examples) | How to Use T.TEST Function?”, 2022).

Next, P (T<=t) two-tail shows the p-value for the two-tailed form of the t-test. Because our p-value (9.45) is greater than the standard significance level of 0.05. Thus, accepts the null hypothesis and it is not statistically significant. Specifically, the Total Acute Care Admissions is greater than the Total Non-Acute and Nursery Care Admissions mean. Therefore, the need for non-acute care on admission is low, which means acute care admissions is high compared when compared to the non-acute care. Take a look at the below graph to understand this. 

Research Method

The above graph shows that 89% of total acute care and 11% of non-acute care admissions. The following graph depicts the acute care admissions that have high number of admissions (“t-Test in Excel”, 2022):

The above graph represents that med/surg admission and obstetrics admissions to have a higher number of acute admissions when compared with the other acute admissions (“Tail of the Test: Interpreting Excel Data Analysis t-test output”, 2022). 

Hypothesis-2

As per the second hypothesis’ result, the output indicates that the mean for the Total Acute Care Admissions is 3944.027, and the available beds are 85.43448276. The variance show that Acute and available beds are not equal (ZACH, 2022). Next, it shows P (T<=t) two-tail, which is the p-value for the two-tailed form of t-test. Because our p-value (2.31) is greater than the standard significance level of 0.05, thus accepts the null hypothesis and it is not statistically significant. Specifically, the Total Acute Care Admissions is greater than the available beds’ mean. Therefore, the number of acute care beds are low (Afilalo et al., 2015).

The above shown graph represents to have 98% of total acute care admissions and 2% of available beds. Thus, it represents decrease in the acute care admissions. 

Conclusion

This report concludes that in the healthcare the acute care is high and the beds are less. Thus, it is required to decrease the number of acute care patients by providing them effective care i.e., turn the acute care admissions in the healthcare into non-acute care admissions. This will help to improve the number of beds in the hospitals for the inpatients. Therefore, the need for non-acute care admission is low. Further, Med/surg admission and obstetrics admissions have a higher number of acute admissions when compared with the other acute admissions. 

Sanford Westbrook Medical Center have least number of beds, which is a critical access hospital (CAH) present in the in the city named Westbrook, Cottonwood. This has to be improved by offering reimbursement improvements for the CAHs, improve payments of CAH, including the increase in the number of beds, and charge reasonable costs for the clinical lab services for the Medicare beneficiaries, according to the legislation of CAH program such as -“Medicare, Medicaid, and SCHIP Benefits Improvement and Protection Act (BIPA) of 2000”, “Medicare Prescription Drug, Improvement, and Modernization Act (MMA) of 2003” and “Medicare Improvements for Patients and Providers Act (MIPPA) of 2008” (“Critical Access Hospitals (CAHs) Overview – Rural Health Information Hub”, 2021).

References

The t tests. The BMJ | The BMJ: leading general medical journal. Research. Education. (2022). Retrieved 25 January 2022, from https://www.bmj.com/about-bmj/resources-readers/publications/statistics-square-one/7-t-tests.

Afilalo, M., Soucy, N., Xue, X., Colacone, A., Jourdenais, E., & Boivin, J. (2015). Hospital stay on acute care units for non-acute reasons. Healthcare Management Forum, 28(1), 34-39. https://doi.org/10.1177/0840470414551906

CMS Data Navigator Glossary of Terms. Cms.gov. (2022). Retrieved 25 January 2022, from https://www.cms.gov/Research-Statistics-Data-and-Systems/Research/ResearchGenInfo/Downloads/DataNav_Glossary_Alpha.pdf.

Critical Access Hospitals (CAHs) Overview – Rural Health Information Hub. Ruralhealthinfo.org. (2021). Retrieved 25 January 2022, from https://www.ruralhealthinfo.org/topics/critical-access-hospitals.

Frost, J. (2022). How to do t-Tests in Excel. Statistics By Jim. Retrieved 25 January 2022, from https://statisticsbyjim.com/hypothesis-testing/t-tests-excel/.

How To Perform T-Tests In Excel. Top Tip Bio. (2022). Retrieved 25 January 2022, from https://toptipbio.com/t-tests-excel/.

T TEST in Excel (Formula,Examples) | How to Use T.TEST Function?. EDUCBA. (2022). Retrieved 25 January 2022, from https://www.educba.com/t-test-in-excel/.

Tail of the Test: Interpreting Excel Data Analysis t-test output. Dawn Wright. (2022). Retrieved 25 January 2022, from https://www.drdawnwright.com/tail-of-the-test-interpreting-excel-data-analysis-t-test-output/.

t-Test in Excel. Excel-easy.com. (2022). Retrieved 25 January 2022, from https://www.excel-easy.com/examples/t-test.html.

ZACH. (2022). Complete Guide: How to Interpret t-test Results in Excel – Statology. Statology. Retrieved 25 January 2022, from https://www.statology.org/interpret-t-test-results-in-excel/.

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