Introduction
Nursing practice is very essential to a nation or state’s population in providing satisfactory services especially in the area of acute care hospital services. This makes the nurse-to-patient ratios equally important in assessing the various nursing trends. If one had to consider the general nursing practice statistics, one can come up with many trends. This is the case since different states are struggling with a decreasing supply of nurses while their demand is on the rise. Moreover, the continuous restructuring of hospitals calls for additional numbers of nurses (State of Connecticut Office of Healthcare Access, 2000, p.12).
This paper entails an assessment of the nursing statistical trends in the Connecticut acute care hospitals and the analysis of the statistical significance of the data provided in the report released by the state in 2000 on the Nurse-to-Patient ratio study. The paper begins with a brief discussion on the various statistical methods used in the report followed by an analysis of the conclusions generated in the report. Finally, the paper will embark on testing the statistical significance of the data given.
Analysis of the nurse-to-patient ratio study report for Connecticut’s Acute Care Hospitals
Statistical procedures used in the report
The report entails an analysis of the patient care in the hospitals. This was accomplished by collecting data related to hospital employment trends and the quality within the Acute Care hospital services (State of Connecticut Office of Healthcare Access, 2000, p.7). The report then embarks on identifying the relationship between nursing care and patient response. In this case, different quality indicators are considered. These include; mortality rate, adverse incidents, length of stay in the hospitals, incidences of acute drug reactions, patient satisfaction, and the patients’ adherence to the discharge plan. These indicators relate to patients’ outcomes. Other indicators are those related to the process of nursing. These are; patient education, nurse satisfaction, pain management among others. Finally, the structure of the quality care indicators is considered. This includes measurement of the staffing patterns perceived to affect the quality of nursing (that is) nurse staff injury, ratio of nurse staff to patients, staff continuity, ratio of a registered nurse to total nursing staff among others (State of Connecticut Office of Healthcare Access, 2000, p.8).
The report also includes an assessment of the nursing trends such as nursing statistics and demographics. Furthermore, the quality care indicators and trends are also assessed. These are; patient satisfaction with the overall nursing care, nurse satisfaction, incidences of nosocomial infections, the patient injury rate among others (State of Connecticut Office of Healthcare Access, 2000, p.17).
Conclusions generated by the report
The report indicates that overall, there are no direct connections between quality care in the acute care hospitals and the process of nursing and therefore calls for intensive analysis into scientifically-based relationships. The authors state that “The evidence points toward some linkages between the two, but without further analysis, data standardization and wider data availability, it would be premature to move forward in recommending specific staffing solutions as a means to address the quality of care issue” (State of Connecticut Office of Healthcare Access, 2000, p.34). This is true of the fact that most of the quality care indicators are dependent on individual patients or nurses and can never be assessed from a generalist viewpoint. These factors vary from state to state, hospital to hospital, and individual to individual.
Testing the Statistical Significance of the report’s Findings
According to the report, it is stated that “Connecticut is ranked 12th of the 50 states, having one the lowest ratios of the nurse to a resident with only 2.8 RN’s per 1000 residents compared to the national average of 3.2” (State of Connecticut Office of Healthcare Access, 2000, p.12). In this case, we can take the national average as the population means and the average for Connecticut to be the sample mean. To test for statistical significance, we perform a one-sample z-test. The test is meant to inquire whether the population means are significantly different from the sample mean (Stat Trek, 2009, par.1).
We start by defining the null and alternative hypotheses. Considering the report’s findings we can state that the sample mean is less than or equal to the population mean (x≤M). This represents the null hypothesis. On the other hand, the alternative hypothesis should state that the sample mean is greater than the population means (x>M). The test is then referred to as a one-tailed test (Stat Trek, 2009, par.2). The subsequent step involves the specification of the significance level. In this case, the level of significance is 0.1, taking note of the fact that the paper assumes that a sample size of 100 registered nurses is considered and the population standard deviation is 1.2 RN’s per 1000 residents. In computing the z-value, employ the following formula:
z = (x – M) / [σ / (n)2] (Stat Trek, 2009, par.4).
From the equation, (x) is the sample mean, (M) is the population mean (σ) is the standard deviation, and (n) is the sample size. Using the data provided in the report in computing the value of Z score, the experimental value becomes z=3333.33. The value of the Z score from the z-tables at the level of significance of 0.1 and limited by a standard deviation of 1.2 becomes 0.4015. The calculated z- score is greater than the tabulated value. Therefore, we reject the null hypothesis since the rule of rejection states that reject the null hypothesis if the calculated Z score is greater than or equal to 0.4015. If we reject the null hypothesis, we accept the alternative hypothesis. Therefore, the sample mean is not significantly different from the population mean.
Conclusion
The paper has discussed the various statistical procedures employed in the report in collecting data from the Connecticut acute care hospitals. The data collected was then used in assessing the quality care indicators in the hospitals. The paper has also looked at the significance of the conclusions generated by the report in-depth. Finally, the paper did test the significance of the statistical data provided whereby the data is insignificant since the test has indicated that the national mean for the number of registered nurses is not significantly different from that of the state of Connecticut.
The results thus echo the sentiments of the author is stating that more scientifically based linkages are required in relating the process of nursing to quality care indicators as discussed in the report.
Reference List
State of Connecticut Office of Healthcare Access. Nurse-to-Patient ratio study: a report on the current nursing environment in Connecticut Hospitals. (2000). Hartford: Office of Healthcare Access. Web.
Stat Trek. Statistics and probability glossary: one-sample z-test. (2009). Web.