Congestive Heart Failure and Readmission Rates

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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 patients’ readmission and investigate the ways to reduce the readmission rates. The research hypothesis is ‘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, 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.

Literature Review

Congestive heart failure is a severe chronic disease associated with high readmission risks. With nearly a million new cases reported annually, it is essential to improve the existing frameworks of identification and treatment of the problem (Ryan et al., 2019). Many experts propose new prevention methods and innovative solutions for predicting the consequent health complications. From these considerations, it is crucial to analyze the existing academic research to identify the potential knowledge gap in the study of readmission rates. Ultimately, the current paper provides a comprehensive overview of the contemporary literature on congestive heart failure and the reduction of readmission rates.

Readmission Prediction and Prevention

At present, there is an extensive list of methods used to reduce readmission rates. Some of the notable frameworks include the implementation of digital health monitoring, biomarkers, and the overall improvement of healthcare services (Ryan et al., 2019; Park et al., 2019). Furthermore, experts utilize statistical and computational methods, such as deep learning neural networks, to calculate the risk of readmission (Liu et al., 2019). Lastly, the research suggests that a person-centered treatment approach with comprehensive patient education might significantly reduce the readmission rates (Wan et al., 2019). Nevertheless, some methods demonstrate questionable effectiveness, and it is essential to analyze contemporary research to identify the knowledge gaps.

Health Monitoring

Health monitoring generally refers to telemonitoring and complex digital health monitoring, which implies the remote transmission of the patient’s vital health data to the hospital. Aronow and Shamliyan (2018) suggest that simple telemonitoring, such as patient surveys, provides the same results as the common treatment. On the other hand, Aronow and Shamliyan (2018) and Park et al. (2019) demonstrate that complex digital health monitoring is highly beneficial and might reduce readmission rates by approximately 15%. The primary advantage of the method is the immediate interpretation of health data and consequent prescription of the appropriate medications (Park et al., 2019). Ultimately, complex health monitoring is an effective method of reducing readmission rates.

Computational Methods

Consequently, computational methods, such as neural networks, are primarily utilized to predict readmissions. Liu et al. (2019) have found that convolutional neural networks (CNN) have an approximately 10% higher prediction rate of 30-day readmissions than the existing frameworks. Furthermore, the authors report that other CNN methods might be even more productive, requiring further research (Liu et al., 2019). Therefore, it is essential to take advantage of contemporary computational technologies to improve the quality of healthcare.

Biomarkers

Biomarkers generally refer to the measurements of the patient’s vital health data. Sudharshan et al. (2017) emphasize B-type Natriuretic Peptide (BNP), N-terminal pro BNP, and galectin-3 (Gal-3) among the biomarkers that can identify the need for future readmission. Paul and Harshaw-Ellis (2019) also demonstrate the effectiveness of cardiac biomarkers to diagnose and manage heart failure diseases. As a result, contemporary research suggests that the intelligent usage of biomarkers might significantly reduce the number of readmissions for patients with congestive heart failure.

Person-Centered Approach

Lastly, hospitals need to ensure the continually evolving quality of healthcare and focus on the person-centered approach. The research transparently demonstrates that the patient’s awareness of the problem, including methods of treatment and lifestyle change recommendations, is the most significant psychosocial factor for reducing readmissions (Ryan et al., 2019). Other strategies in the person-centered approach include phone calls from the hospital, the development of home-based exercise programs, post-discharge education, and dietary recommendations (Wan et al., 2019). Ultimately, the current literature review has identified four primary areas of readmission prediction and prevention: health monitoring, computational methods, biomarkers, and the person-centered approach.

Theoretical Framework

Congestive heart failure is a complex disorder that constantly leads to repeated hospitalizations or deaths. In order to study the reasons for this course of the disease and its grounds, the project will apply the situation-specific theory of heart failure self-care. It focuses on the patients’ behavior and supervision outside the hospital, which becomes the principal reason for the improvement or deterioration of their health condition. Self-care is a natural decision-making process that concerns determining conduct that maintains physiological stability and responding to symptoms when they occur (Hooker et al., 2018). The theory contends the two primary aspects of treatment – maintenance and competent health management. It emphasizes the value of long-term monitoring and evaluating the patient’s necessity for further intervention. Other guiding concepts concern symptom recognition, which means the primer abnormalities detection, which is the foundation of the resultative treatment (Son et el., 2020). Furthermore, the need for primary skills and knowledge for physicians and patients takes a meaningful place in the theory.

The Heart Failure Self-Care is explicitly developed to assist individuals with this disorder, which justifies its application. Out-of-hospital behavior is a crucial variable in the in-depth study of the course of the disease and the causes of readmission (Son et el., 2020). The theory is aimed to identify shortcomings in the patients’ actions based on which it will be feasible to comprehend how to prevent regression. Its application will consist of examining the characteristics of distinct individuals, observing their actions and environment in order to make judgments about the factors that cause deterioration. The Heart Failure Self-Care will contribute to the understanding of gaps in the knowledge and enable modifications in the treatment approaches. The results will be analyzed for compliance with the patients’ needs, which will allow to conclude the relevance of some medical prescriptions.

Methodology

The analysis will take place at a local central hospital in the city. It will be conducted to find out what effect early comprehensive geriatric screening has on the rate of readmission in the acute geriatric unit. A preliminary hypothesis is that this intervention would reduce the 30-day rate of repeat hospitalizations in elderly patients. The study will enroll adult patients 18 years of age or older who are admitted for unplanned hospitalization for at least one night after a previous hospitalization within 30 days. If a patient is readmitted to the hospital more than once during the study period, only the first readmission will be included. Patients re-hospitalized for procedures or chemotherapy, patients readmitted to a non-medical specialty, hospitalized because of pregnancy, and minors are excluded from the study. Also, those patients who stayed less than one night and those who were admitted to another hospital would not be used in the experiment.

The medical departments whose patients will be analyzed will be cardiology, gastronomy, neurology, and oncology. It is possible to increase the number of departments if the capacity of the selected clinic allows it. Data will be obtained directly from patients, clinical records of previous appointments, and discharge records. In addition, information from treating physicians, nurses, and direct caregivers will be collected. Quasi-experiments are a kind of compromise between reality and methodological rigor (Rogers & Revesz, 2019). They are aimed at testing causal hypotheses, but they lack a preliminary procedure for equating groups. When this method is used, the control group can be replaced by repeated testing of participants before and after exposure (Lin et al., 2019). This approach provides a more thorough evaluation in a situation where there are large-scale changes in practice when randomization is not possible.

Methodology

Extraneous Variables

In order to make sure that the effects that independent variables have on dependent ones are determined correctly, it is essential to consider extraneous variables. The latter can have such an influence on the study that the results may have alternative explanations (Flannelly et al., 2018). There are some extraneous variables related to this particular study, and they may be described as participant ones. Generally, it is essential to remember that all patients are different, and their personal characteristics like previous diseases, lifestyles, or behavior after a previous hospitalization within 30 days have to be considered. Consequently, it will be necessary to monitor these variables and make sure that they do not affect the overall picture and results (Flannelly et al., 2018). Further, since there will be interviews with caregivers, nurses, and physicians, it is vital to control the conditions in which the interviews are conducted. In other words, the participants are in an equally comfortable setting to describe all details and provide relevant information without being disturbed.

Instruments

One instrument chosen for this study is the conduction of interviews with participants, including patients and healthcare providers. In order to make sure that this instrument is valid and reliable, it will be essential to check whether the questions are accurate and will allow to gather the necessary information. The second instrument is observation, and the researchers will have to ensure that all the elements needed to draw conclusions are accurate and the data gathered by several different observers are similar.

Description of the Intervention

The intervention will take place at a local central hospital in the city. Its purpose is to determine whether early comprehensive geriatric screening can reduce the number of patients admitted for unplanned hospitalization for at least one night after a previous hospitalization within 30 days. Consequently, the intervention will take at least a month (30 days) of observation and analysis. Researchers will not interfere with the work of healthcare practitioners, and interviews with them will be conducted in the physicians’ and nurses’ free time. Patients who do not fit all the necessary characteristics will not take part in the study.

Data Collection Procedures

There is a specific purpose that this paper aims to achieve and a particular question it tries to answer. Unfortunately, the literature review is not enough for identifying the factors of the patients’ readmission and investigating the ways to reduce the readmission rates. Therefore, precise data collection procedures that will take into consideration all goals of the study and incorporate the most suitable method are required. The data collection plan is outlined in the following paragraphs.

To begin with, three types of procedures should be used to collect the necessary information: observation, secondary data analysis, and interviews. The first one refers to the analysis that will take place at a local central hospital in the city. Adult patients who fit all of the characteristics mentioned above will be observed in order to determine whether early comprehensive geriatric screening can reduce the rate of readmission in the acute geriatric unit. Further, the second procedure is the analysis of the already existing literature in order to identify additional gaps or find relevant and useful information that will help during the observation (Maher et al., 2019). What is more, patients’ clinical records and discharge records will also be considered. Finally, direct caregivers, treating physicians, and nurses will be interviewed. The information they will provide is likely to have a significant effect on the process and outcomes of the analysis.

References

Aronow, W. S., & Shamliyan, T. A. (2018). Comparative effectiveness of disease management with information communication technology for preventing hospitalization and readmission in adults with chronic congestive heart failure. Journal of the American Medical Directors Association, 19(6), 472-479.

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.

Flannelly, K. J., Flannelly, L. T., & Jankowski, K. R. (2018). Threats to the internal validity of experimental and quasi-experimental research in healthcare. Journal of Health Care Chaplaincy, 24(3), 107-130.

Hooker, S. A., Schmiege, S. J., Trivedi, R. B., Amoyal, N. R., & Bekelman, D. B. (2018). Mutuality and heart failure self-care in patients and their informal caregivers. European journal of cardiovascular nursing, 17(2), 102-113.

Lin, K., & Chen, J.-H., & Lu, F.-P., Wen, C.-J., & Chan, D.-C. (2019). The impact of early comprehensive geriatric screening on the readmission rate in an acute geriatric ward: a quasi-experimental study. BMC Geriatrics, 19, 1-9.

Liu, X., Chen, Y., Bae, J., Li, H., Johnston, J., & Sanger, T. (2019). Predicting heart failure readmission from clinical notes using deep learning. In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 2642-2648). IEEE.

Maher, N. A., Senders, J. T., Hulsbergen, A. F., Lamba, N., Parker, M., Onnela, J. P., Bredenoord, A. L., Smith, T. R., & Broekman, M. L. (2019). Passive data collection and use in healthcare: A systematic review of ethical issues. International Journal of Medical Informatics, 129, 242-247.

O’Connor, C. M. (2017). High heart failure readmission rates: Is it the health system’s fault? JACC: Heart Failure, 5(5), 393-393.

Park, C., Otobo, E., Ullman, J., Rogers, J., Fasihuddin, F., Garg, S.,… & Atreja, A. (2019). Impact on readmission reduction among heart failure patients using digital health monitoring: feasibility and adoptability study. JMIR medical informatics, 7(4), e13353.

Paul, S., & Harshaw-Ellis, K. (2019). Evolving use of biomarkers in the management of heart failure. Cardiology in review, 27(3), 153-159.

Rogers, J., & Revesz, A. (2019). Experimental and quasi-experimental designs. Routledge.

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.

Son, Y. J., Choi, J., & Lee, H. J. (2020). Effectiveness of nurse-led heart failure self-care education on health outcomes of heart failure patients: a systematic review and meta-analysis. International journal of environmental research and public health, 17(18), 6559. 6559.

Sudharshan, S., Novak, E., Hock, K., Scott, M. G., & Geltman, E. M. (2017). Use of biomarkers to predict readmission for congestive heart failure. The American journal of cardiology, 119(3), 445-451.

Wan, T. T. (2018). Strategies to modify the risk for heart failure readmission: A systematic review and meta-analysis. Population Health Management for Poly Chronic Conditions, 85-112.

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).

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NursingBird. (2022, December 21). Congestive Heart Failure and Readmission Rates. Retrieved from https://nursingbird.com/congestive-heart-failure-and-readmission-rates-essay-examples-2/

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NursingBird. (2022, December 21). Congestive Heart Failure and Readmission Rates. https://nursingbird.com/congestive-heart-failure-and-readmission-rates-essay-examples-2/

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"Congestive Heart Failure and Readmission Rates." NursingBird, 21 Dec. 2022, nursingbird.com/congestive-heart-failure-and-readmission-rates-essay-examples-2/.

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NursingBird. 2022. "Congestive Heart Failure and Readmission Rates." December 21, 2022. https://nursingbird.com/congestive-heart-failure-and-readmission-rates-essay-examples-2/.

1. NursingBird. "Congestive Heart Failure and Readmission Rates." December 21, 2022. https://nursingbird.com/congestive-heart-failure-and-readmission-rates-essay-examples-2/.


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NursingBird. "Congestive Heart Failure and Readmission Rates." December 21, 2022. https://nursingbird.com/congestive-heart-failure-and-readmission-rates-essay-examples-2/.