The high prevalence of chronic diseases like heart failure is a significant threat to public health. Heart failure affects many Medicare patients and costs the government approximately $33 billion in medical expenses (Sun et al., 2012). Although the current heart failure mitigation strategies have reduced the rate of hospitalization in the country, cost savings generated through this approach are merely modest. Undoubtedly, there is an urgent need to identify and implement more effective methods of controlling the burden of heart failure. Early detection of chronic diseases is arguably the key for early intervention to prevent its progression to a dangerous stage (Farmakis et al., 2015). Therefore, the article proposes the integration of existing knowledge on hypertension and insights derived from data research to facilitate early detection and control of the disease.
Electronic Health Records (EHR) is the primary source of information on heart failure. EHR store data on previous diagnosis, laboratory results, prescriptions, and clinical notes collected on a patient. Therefore, it is possible to obtain a vast magnitude of information to predict the early signs and symptoms of heart failure (Sun et al., 2012). However, there are two disadvantages to using the EHR data drive approach. The problems include the generation of massive data that requires a lot of time to analyze and challenges in interpreting the final results.
Delegate your assignment to our experts and they will do the rest.
The article designs a practical framework for examining the effectiveness of combining the data-driven approach and knowledge base to combat heart failure. Gathering knowledge about heart failure begins with the identification of conventional risk factors and using the information to and collecting comprehensive literature on the subjects (Sun et al., 2012). However, the data-driven approach requires the identification of additional factors that increase the risk of heart failure (Stubbs et al., 2015). The next step is the augmentation of the identified factors, and the process ends with the combination of the knowledge base and the understandings gained through the data-driven approach.
Undoubtedly, the integration of knowledge and discernment from the data-driven research helps in the identification of heart failure risks from the EHR. The study used the Geisinger Health System, which contains massive hospital records of patients residing in over 31 countries (Sun et al., 2012). Through the application of the new methodology, the study determines that coronary disease was the most significant cause of heart failure followed by diabetes, hypertension, and other comorbidities respectively. Therefore, the EHR contains a wealth of information that can only be derived through the use of knowledge and data research
From a personal perspective, the adoption of the suggested framework for predicting the emergence of heart failure will significantly reduce the cost of the disease in the population. Although there is a general knowledge of factors predisposing people to heart failure including poor weight management, kidney diseases, and cardiovascular illnesses, it is difficult for doctors to rank these risks in the correct order (Mills et al., 2015). However, through the combination of the data-driven analysis of all the commodities associated with heart failure, the three major risk factors were ranked by order of priority.
Biblical teachings also support the development of new knowledge that will help in the early treatment of heart failure. Proverbs 1: 7 argues that people should strive to improve their knowledge and wisdom every day to make the right progress in life. However, it is impossible to obtain knowledge without proper research. For example in John: 6-13 Jesus asked the disciples to find something for people to eat and used their findings to perform a miracle for the people.
The evolution of the information age has improved the medical response to chronic diseases affecting millions of people today. EHR is arguably one of the most important technological advancement in the healthcare sector that carries a lot of information on different diseases. Researching through the EHR helps to identify disease risk factors and confirms or disapproves the existing knowledge on various medical conditions.
References
Farmakis, D., Parissis, J., Lekakis, J., & Filippatos, G. (2015). Acute heart failure: epidemiology, risk factors, and prevention. Revista Española de Cardiología (English Edition), 68(3), 245-248.
Mills, K. T., Xu, Y., Zhang, W., Bundy, J. D., Chen, C. S., Kelly, T. N., ... & He, J. (2015). A systematic analysis of worldwide population-based data on the global burden of chronic kidney disease in 2010. Kidney international, 88(5), 950-957.
Stubbs, A., Kotfila, C., Xu, H., & Uzuner, Ö. (2015). Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2. Journal of biomedical informatics, 58, S67-S77.
Sun, J., Hu, J., Luo, D., Markatou, M., Wang, F., Edabollahi, S., ... & Stewart, W. F. (2012). Combining knowledge and data-driven insights for identifying risk factors using electronic health records. In AMIA Annual Symposium Proceedings (Vol. 2012, p. 901). American Medical Informatics Association.
