The government has established the readmission rate as a significant marker of hospital performance. Examination of Vital Community Care performance readmission rates against the government set benchmark will help to determine the effectiveness of the facility and the way the economic abilities of the clients can determine the rate of readmission among patients classified as Medicare, Medicaid, and all payers. The study will be conducted in three weeks. An understanding of how economic factor affect readmission is significant as it can assist the facility in coordinating its operations, lower fines by the government, increase profits through savings and customer satisfaction, and finally enhance corporation between clients and the hospital.
The examination of Readmission Rate is a significant marker for the performance of a health facility. Determining readmission rates has been used traditionally by hospitals to gauge their performance. However, this has been limited by the existence of different readmission strategies used in various hospitals or regions (Khan et al., 2015; Fischer et al., 2014). This notwithstanding, a review of a readmission rate within the same hospital will generate a statistically significant result owing to the elimination of confounding factors such as measures from different facilities. This study will examine quantitative data of Vital Community Care facility in Michigan, Southfield and that of the government and compare the performance rate of Vital Community Care against the government’s standard. The data will be classified based on three main categories; Medicare, Medicaid, and all-payer, which reflect the economic status of the post-accident surgical readmission cases. This will help to determine how economic factors affect or determine the quality of care services that one could receive after discharge to avoid readmission (Herrin et al., 2015; Henke et al., 2015). This project will enable me to understand and develop recommendations on how a facility can offer equal care to financially stable and unstable people in the community regardless of other defining factors such as geographical location and demographic factors.
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Statement of the problem
There is variation in readmission rates among patients of different economic backgrounds accessing health service at a facility; the readmission rates for post-accident cases at vital community care vary between financially stable and unstable patients. Economically stable patients have a readmission rate within the government-established rate that is equal to 20% or below 20% while patients that depend on government support for care have percentages above 20%. How does the economic status of the patients contribute to their varied readmission rates and therefore their quality of health?
Background
A low readmission rate is attributed to good performance. This was the initial intent of the government in establishing a standard readmission rate and fine for hospitals that had higher readmissions within 30 days (Birmingham & Oglesby, 2018). The established mark should be easier for private owned hospitals to meet as compared to government hospitals; their management structures focuses on a few facilities as compared to complex government management. This notwithstanding, the readmission rates are high in privately-owned hospitals as compared to government-owned facilities (Mittal et al., 2018; Bergethon et al., 2016). The performance of hospitals varies with geographical locations. The main determinate is the presence of healthcare facilities in the various locations that enable one health facility to perform better than another (Nuckols, 2015; Hekkert et al., 2018; Cui et al., 2015). This explains why Vital Community Care in Michigan would have lower readmission rates than another facility and vice versa.
At a different scale, the management at the hospital level in the two facilities would explain why such differences could occur. Horwitz et al. (2017) noted that facility managers are influential in determining readmissions rates. The discrepancy in response strategies could explain a possible variation at the facility levels. Of great significance is the patients’ economic characteristics affect the rate of readmissions, and this has been established as a major contributor to readmission cases; this will be observed in examining vital community care data for patients discharged and readmitted due to post-surgical complication resulting from chest, abdomen, skull, limbs and neck damages (Barnett, Hsu & McWilliams, 2015). The data will be presented through graphs and pie chart. These graphical presentations will enhance the comparison of the data for the three payment options (Medicare, Medicaid, and all-payer)
Graphics
Graphs
Two graphs will be used in the data presentation
Admission and Readmission Rates
The Admission and Readmission Rates graph will plot the readmission rates for the three payment plans, all-payers, Medicare and Medicaid, for six medical conditions. The values will be percentages for each case. This plot will enhance the comparison for each condition in the three payment options and postulate why patients with low income would have higher readmission rates and more importantly under which condition.
Total readmissions
Total readmission graph will plot the actual figures in terms of population count for face, limb, skull, abdomen chest and spine. The plot will aid in comparing the readmission totals for these conditions and determine which one is the highest.
Pie-Charts
The pie chart will be a comparison of the percentage contribution of each of the three payment options to a total of 100. It is designed to provide a closer comparison of the three percentages .
Client Value Proposition
Balanced scorecard
Table 1 Balanced scorecard
|
Business Operations |
Finance |
Customer Service |
Organizational Learning and Growth |
|---|---|---|---|
| Will improve health care service coordination between the various branches of Vital Community Care by 50% | Will increase savings by minimizing loses made to government fines by 25% | Improve customer satisfaction scores by 60% in the first quarter of the next financial year | Will improve organization and patient understanding and corporation in discharging quality care and meeting the government's set quality mark |
Expected Outcomes and Precise Performance Measurement
The study will be based on quantitative analysis of data obtained from Vital Community Care facilities in Southfield, Michigan and government standards. The margin in the rates will be used as an indicator of how the facility performs against government set standards and the quality of services offered. The payment plan for the Medicare, Medicaid, and all-payers will be used as a standard for their economic status and thereby a determinate of readmission rate. The values will be compared using bar graphs and pie chart to provide a visual impression of the differences: the heights of the graphs and the area covered in the pie chart for the three variables will be an indicator of their variations.
Leadership Component
Throughout the study, my role will be to apply for meeting with the facilities, obtain data, and analyze and present conclusions.
Timeline
The research will be accomplished throughout three weeks as outlined in table 2 below
Table 2 Timeline
| Week | Activity |
| 1 | Data collection |
| 2 | Data analysis |
| 3 | Research compilation |
References
Barnett, M. L., Hsu, J., & McWilliams, J. M. (2015).Patient characteristics and differences in hospital readmission rates. JAMAInternal Medicine, 175 (11): 1803-1812.
Bergethon, K. E., Ju, C., DeVore, A. D., Hardy, N. C., Fonarow, G. C., Yancy, C. W., ... & Hernandez, A. F. (2016). Trends in 30-day readmission rates for patients hospitalized with heart failure: findings from the Get with the Guidelines-Heart Failure Registry. Circulation: Heart Failure, 9 (6): e002594.
Birmingham, L. E., & Oglesby, W. H. (2018). Readmission rates in not-for-profit vs. proprietary hospitals before and after the hospital readmission reduction program implementation. BMC Health Services Research, 18 (1): 31.
Cui, Y., Torabi, M., Forget, E. L., Metge, C., Ye, X., Moffatt, M., & Oppenheimer, L. (2015). Geographical variation analysis of all-cause hospital readmission cases in Winnipeg, Canada. BMC Health Services Research, 15 (1): 129.
Fischer, C., Lingsma, H. F., Marang-van de Mheen, P. J., Kringos, D. S., Klazinga, N. S., &Steyerberg, E. W. (2014). Is the readmission rate a valid quality indicator? A review of the evidence. PloS one, 9 (11): e112282.
Hekkert, K., Kool, R. B., Rake, E., Cihangir, S., Borghans, I., Atsma, F., &Westert, G. (2018). To what degree can variations in readmission rates be explained on the level of the hospital? A multilevel study using a large Dutch database. BMC Health Services Research, 18(1): 999.
Henke, R. M., Karaca, Z., Lin, H., Wier, L. M., Marder, W., & Wong, H. S. (2015).Patient factors are contributing to variation in same-hospital readmission rate. Medical Care Research and Review, 72 (3): 338-358.
Herrin, J., St. Andre, J., Kenward, K., Joshi, M. S., Audet, A. M. J., & Hines, S. C. (2015). Community factors and hospital readmission rates. Health Services Research, 50 (1): 20-39.
Khan, A., Nakamura, M. M., Zaslavsky, A. M., Jang, J., Berry, J. G., Feng, J. Y., & Schuster, M. A. (2015). Same-hospital readmission rates as a measure of the pediatric quality of care. JAMA Pediatrics, 169 (10): 905-912.
Mittal, M., Wang, C. H. E., Goben, A. H., & Boyd, A. D. (2018).Proprietary management and higher readmission rates: A correlation. PloS one, 13 (9), e0204272.
Nuckols, T. K. (2015). County-level variation in readmission rates: implications for the hospital readmission reduction program's potential to succeed. Health Services Research, 50 (1): 12.
Solomon, L. S., Zaslavsky, A. M., Landon, B. E., & Cleary, P. D. (2002).Variation in patient-reported quality among health care organizations. Health Care Financing Review, 23 (4), 85.
