This was found to be a key factor in releasing patients early, when they might have benefited from a longer hospital stay (Bueno, Ross, & Wang et al., 2010; Capelastegui, A,, Espana, P., & Quintana, J. et al., 2008), This factor will have to be considered as a potential barrier to the study. It may be that insurance companies and Medicaid/Medicare reimbursements are a factor in early release of patients rather than hospital practices. Factors that were identified in other studies of hospital 30-day readmissions included the presence of deep vein thrombosis and pulmonary embolism (Spencer, Gore, & Lessard et al., 2008). Severity scores such as those for community-acquired pneumonia were found to be predictive of clinical outcomes. These initial scores were found to be predictive of the initial length of stay for those patients (Yandola, Capelastegui, & Quintana et al., 2009). Numerous studies were found that examine a number of risk factors that might influence early release and the potential for 30-day readmission. These will be explored at greater length as part of the final research project.

Theoretical Summary

Sufficient evidence was found in a preliminary literature review to support the primary theoretical and conceptual model used for the study. Other researchers were found to have used this conceptual model as the basis for their research into the same area. Many of the studies were based on the model introduced by Ludke and Booth that 30-day readmissions could be used to determine quality of care. Since that time, this theory has been expanded to include the concept that 30-day readmissions can be used as a tool to uncover weaknesses in the system so that they can be addressed. This results in the ability to use the 30-day risk factor as a means to improve the quality of care at hospitals.

Empirical Summary

A majority of the studies used statistical methods to arrive at their results. The most common types of studies were longitudinal and comparative. Some of them used a start point and measured data from that point to a predetermined point in the future. However, using historical data was another common study method. For this type of study, historical data was found to be appropriate, as that is the same type of data used to measure 30-day readmissions by the hospitals themselves.

The use of historical data is being used to assess how well they performed in the past. It is not consistent with the ability to predict outcomes in the future, but it is useful when determined data trends in retrospect, as is the case with the current research study. Using historical data to predict future trends in often difficult, as unforeseen circumstances in the future can change the expected outcome. Using historical data in the proposed type of study will allow the results obtained to be similar to the situation in which they will be used in the real world.

Section 111: Methodology

This study will use a chart review of 40 subjects. Data will be gathered using admission data. Many of these types of studies suffer from a small sample size. Often the number of readmissions is low. This study will take place over a period of 2 years. The sample number was determined by inquiring at the local hospital where the study will be conducted, taken as a reasonable average over the past two years. This sample does not reflect the entire patient load, only those that are readmitted.

The sample population will consist of elderly patients aged 65 and older who are readmitted within 30 days of discharge....

They will only include those who are released to long-term care facilities and nursing homes. It will not include those who are released to a residential home, whether assisted living or not. This sample population was chosen for several reasons. The first is that this population is expected to have a higher readmission rate than younger populations, simply based on the affects of aging. Another reason is that the more common problems that are associated with aging are relatively well-known and easily accounted for in the study.
The tools that will be used in the study include those used for data collection and analysis. These include spreadsheets and statistical programs used to organize, analyze and present the data. Data will be collected by asking permission to examine the charts of the sample population from January of 2007 through January of 2010. The data will be broken into subsets of 6-month time spans. This data will be categorized and analyzed using descriptive statistical techniques. The study will examine the number of patients admitted under the determined parameters using frequency distribution and correlation coefficients. The results and analysis will be presented in graphical format with an explanatory narrative.