CONGESTIVE HEART FAILURE
Patients with Congestive Heart failure and Increased Readmission Rates
Florida National University
Professor Alexander Garcia Salas DNP, MSN, ARNP, FNP-C
Congestive heart failure (CHF), which affects millions of people, especially the elderly, is a significant and expanding public health concern. According to research, CHF accounts for between 12 and 15 million office visits and 6.5 million inpatient days annually (Hollier, 2021). Unfortunately, this approach leads to disease progression and rehospitalizations for many CHF patients because of insufficient care, unclear discharge instructions, and a lack of follow-up visits. These higher rehospitalization rates are driving up expenses and indicating that existing care strategies for CHF are not the most effective. Therefore, evidence-based treatment strategies can lower these patients’ death and readmission rates. These strategies include a comprehensive discharge plan, educational materials, and encouraging counseling.
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Patients over 65 are the known vulnerable group because it has been demonstrated that incidence increases with advancing age. CHF risk factors include hypertension, coronary artery disease, a recent myocardial infarction, diabetes mellitus, a family history of cardiovascular disease, alcohol misuse, pregnancy, sleep apnea, and pollutants (chemotherapy or illicit drugs). More than 6.5 million people in the United States and more than 26 million people globally are affected by incidence rates, according to the American Heart Association (AHA). The ACC/AHA and ESC also advise patient education to encourage self-care and compliance. In addition, both organizations advise close supervision to stop adverse outcomes and illness development, including patient and family surveillance, home visits, telephone support, and remote monitoring. (Malik, 2022). According to recommendations, individuals who continue to develop hyponatremia and show signs of volume overload despite receiving medical treatment should limit their sodium intake to 2-3 g/day and their fluid intake to 2 L/day. Patients also need regular, in-depth education and reassessment to ensure they can follow instructions for the best results.
Review of Literature
Because of sample size constraints in the first literature review, statistical significance was not attained. For instance, patients with hypertension, CAD, diabetes, vascular disease, a history of stroke, and chronic renal disease had more excellent numerical readmission rates than men, current smokers, or patients without any of these conditions. No relationship was seen between better patient self-care practices, understanding of heart failure, and lower readmission rates (Madanat, 2021). Therefore, it was necessary to conduct additional analyses of factors that influence CHF patient admissions, in particular those elements relating to their care, compliance with treatment regimens, and disease awareness at home, even though many of the results regarding comorbid conditions appear to align with previous study findings. The importance of socioeconomic determinants in readmission rates was frequently disregarded, so future attention must be focused on a more thorough patient approach. As a result, it was impossible to stratify patients based solely on the probability of readmission. Individualized patient management and follow-up should have considered social, demographic, and medical factors.
Another study that was looked at showed how a digital platform designed to help doctors monitor their CHF patients more effectively had an impact on their personalized management monitoring. It showed that these patients saw fewer hospital readmissions than the other patients who were not using the platform. With real-time vital statistics and patient health information outside of the hospital, the advanced technology database also demonstrated competent care delivery and cost containment for that group. Finally, the ongoing provision of care showed a slowing of disease progression. The study, however, was a 45-day, 62-participant small group study. The platform allowed users to monitor longitudinal health trends for patient populations and specific patients and information from their medical records, vital signs, symptoms, and prescription schedules. Clinicians used a web portal to access the platform. Along with the patient-generated data, the platform also displayed patient health data from the electronic medical record (EMR). (Gjeka, 2021).
It is encouraging and clear that technology will advance and become increasingly important in patient health promotion, especially for seniors with chronic illnesses that need to be treated by a multidisciplinary team. These patients need ongoing care as well as follow-up sessions. Unfortunately, most aged populations face numerous obstacles to effective and ongoing health promotion. Costs, transportation, cognitive difficulties, and a lack of family support are a few examples. To decrease hospital readmissions or morbidity, creating an individual health promotion program that works with each patient is crucial.
A theoretical model that can direct efforts for promoting health is the health belief model. It is employed to anticipate and justify changes in one’s own health-related behavior. One of the most often used frameworks for understanding health behaviors is this one. The Health Belief Model (HBM) examines how an individual perceives the threat posed by a health issue (susceptibility, severity), the advantages of averting the threat, and the factors that influence their decision to take action (barriers, cues to action, and self-efficacy). Therefore, patients with CHF should refrain from smoking, maintain a healthy weight, engage in regular aerobic exercise (a minimum of three days during the week), refrain fromusing illegal substances such as alcohol and marijuana, and adequately address underlying comorbidities that cause CHF. In addition, the idea to lower the incidence of rehospitalizations caused by a dearth of educational resources, encouraging counseling, and a detailed discharge plan is best served by using this model as the guiding framework.
eferences Gjeka, R. (2021, August 12). Patient engagement with digital disease management and readmission rates: The case of congestive heart failure. Retrieved from Sage Journals: https://journals.sagepub.com/doi/full/10.1177/14604582211030959 Hollier, A. (2021). Heart failure. In A. Hollier, Clinical Guidelines in Primary Care (pp. 68–70). Lafayette, LA: APEA. Madanat, L. (2021). Congestive Heart Failure 30-Day Readmission: Descriptive Study of Demographics, Co-morbidities, Heart Failure Knowledge, and Self-Care. NIH, p. 13. Malik, A. (2022, May 22). Congestive Heart Failure. Retrieved from NIH: https://www.ncbi.nlm.nih.gov/books/NBK430873/