Congestive Heart Failure and Readmission Rates
Despite advances in heart failure treatments, inpatient readmission rates remain high. Therefore, greater emphasis has been placed on lowering readmission rates and, as a result, identifying patients who are most at risk of readmission (Ryan et al., 2019). O’Connor (2017) argues that hospital readmissions continue to be a problem in treating heart failure patients. Predicting who will be re-hospitalized is challenging, and much remains unknown; therefore, identifying the hospital’s position in today’s health system is critical. Congestive heart failure is a significant global health issue in the United States and worldwide (Zohrabian et al., 2018). Notably, it also has one of the highest rates and expenditures of hospital admission and readmission. Ryan et al. (2019) state that the problem is that congestive heart failure is a chronic condition that impacts more than six million people in the United States per year, with 960 000 new cases reported. Hence, the research proposal highlights the importance of the problem; the purpose of the study is to investigate the evidence for reducing readmissions in patients with congestive heart failure.
The research aims at providing valuable data to contribute to a deeper understanding of congestive heart failure. Essentially, the research question is ‘How to reduce the readmission rates in patients with cognitive heart failure?’ The study’s goals are to identify the factors of the patient’s readmission and investigate the ways to reduce the readmission rates. The research hypothesis is that ‘Reviewing current course and treatment plan, reassessing patient’s needs, and re-educating patient regarding the changes in healthcare plan assist in reducing readmission rates.’ The null hypothesis is ‘The health system’s organizational structure is not crucial in reducing readmission rates.’ The study will try to prove the research hypothesis and disprove the null hypothesis.
The study variables include health systems, the admission criteria of congestive failure patients, the course and treatment, and patients’ re-education regarding changes, among others. In order to operationalize variables, the study presents significant definitions to establish reliability. For instance, O’Conner (2017) informs that health systems are now defined as a network of primary and tertiary hospitals, skilled nursing facilities, ambulatory facilities, primary healthcare networks, subspecialty groups, and transitional care teams. Consequently, several crucial factors influence the admission of a heart failure patient. Initially, the actual admission of heart failure patients is primarily determined by the admission criteria of the health system. The availability of same-day access clinics and emergency room teams are essential services that can regulate whether a patient is hospitalized (O’Conner, 2017). Reducing readmission rates often necessitates significant expenditures. These resources are more likely to be allocated to revenue-generating activities than readmission reductions in hospitals (Zohrabian et al., 2018). Thus, health systems with low admission rates for heart failure patients also have low readmission rates, implying that the health system’s organizational structure is significant.
The course and treatment of the patients during their hospitalization are additional factors to consider in decreasing readmission rates. Evidence-based therapy begun and executed in the hospital, patient education, and socioeconomic obstacles are all responsibilities of the health system (O’Connor, 2017). Moreover, the length of stay and degree of decongestion are essential factors determining readmission risk. Banerjee et al. (2017) acknowledge that numerous healthcare organizations have launched quality-improvement initiatives to minimize heart failure readmissions. Early post-discharge telephone follow-up and post-discharge hospital visits, and medication reconciliation, including the use of risk prediction techniques to detect and indicate individuals at high risk for readmission, are evidence-based therapies targeted at decreasing readmissions (Banerjee et al., 2017). Therefore, tracking the readmission data by health systems is also a study variable that reduces readmission rates.
Banerjee, D., Thompson, C., Kell, C., Shetty, R., Vetteth, Y., Grossman, H., DiBiase, A., & Fowler, M. (2017). An informatics-based approach to reducing heart failure all-cause readmissions: The Stanford heart failure dashboard. Journal of the American Medical Informatics Association, 24(3), 550-555. Web.
O’Connor, C. M. (2017). High heart failure readmission rates: Is it the health system’s fault? JACC: Heart Failure, 5(5), 393-393. Web.
Ryan, C. J., Bierle, R. (Schuetz), & Vuckovic, K. M. (2019). The three Rs for preventing heart failure readmission: Review, reassess, and reeducate. Critical Care Nurse, 39(2), 85–93. Web.
Zohrabian, A., Kapp, J. M., & Simoes, E. J. (2018). The economic case for US hospitals to revise their approach to heart failure readmission reduction. Annals of translational medicine, 6(15). Web.