Posted: July 5th, 2022

How using Big data can improve clinical outcomes in cardiovascular disease

How using Big data can improve clinical outcomes in cardiovascular disease


Cardiovascular diseases are the leading cause of the majority of cases of hospitalization and death. Significant advances have been made to improve the treatment and prevention of cardiovascular diseases. Medical data has been utilized to measure the result of healthcare by indicating how cardiovascular treatment is delivered in clinical practice (Chin and Upshur,2018). The study also analyses the patients’ outcome and costs, thus presenting patient cases that indicate the value of cardiovascular care that has been increasing over time. However, according to (Murdoch and Detsky, 2013), an increase in costs does not mean that the quality of care is better. The studies often demonstrate gaps presented by the patient’s case-mix that reflects the quality of care. Efforts have been deployed to solve the differences in quality and outcome variability in cardiovascular care. The paper presented will analyze how big data in healthcare can improve the outcomes of cardiovascular disease.
Effective use of data is essential in the development and learning of the health care system. The evidence derived is used to inform practice while practice informs evidence used for the optimal knowledge of the health care system. Study shows that the amount of data available for the health care system is at zeta byte levels (Silverio,2019). With the integration of IT in the health care system, electronic tools are used to record patients’ reported outcomes, genomic information, and electronic health records. The increase in data availability has led to big data analytics that can support the diverse data sets and rapid analysis of extensive data (Murdoch and Detsky,2013). BDA has been established to improve health care quality and analysis of the capabilities of the system. BDA in cardiovascular health combines the data and its analytical properties to come up with information that can improve the quality of care in this department. BDA can be applied to reduce the resources that can be used to increase efficiency by determining the specific steps that can be taken to improve the process.
Big data analysis offers predictive models that are aimed at improving healthcare delivery. Big data and predictive analytics provide quantitative approaches to health improvement. The data can produce predictive analytics information that can predict the future and unknown health occurrences. The statistical techniques can be derived to generate predictions for data mining that can identify patterns through machine learning systems that analyze large amounts of data. The analysis results in algorithms that construct predictive models. In cardiovascular healthcare, BDA is executed through models that search for data directed towards the problem to determine previously recorded information for future decisions (Silverio,2019). The BDA model can be based on the specific variable that presents accurate data of optimized statistical models that can be utilized for predictions (Bates et al.2016). However, the study shows that using BDA for cardiovascular care can be beneficial, but it may fail to translate into better quality care due to challenges such as unstable associations between the terms and results.
BDA in cardiovascular studies comprises of rich biomedical and omics data that provides access to large scales databases such as disease registry and electronic health records. The wide data sets derived is used to design prognosis algorithms that can predict the history and evolution of cardiovascular disease based on the hospitalization, medical, and country-specific statistics. For example (Rumsfeld et al.2016) the Europe significant data initiative for the cardiovascular disease brought together nineteen stakeholders under the medicine initiative that was launched on March 17. The BDA was applied to the common cardiovascular diseases in Europe such as the acute coronary system (ACS), heart failure (HF) and Atrial fibrillation (AF) that was aimed at improving the patients’ outcome (Krumholz,2014). Applying the data found was intended at bridge the fugal and the health domain through trial data and the imaging data that would represent the mismatches that may be present in the algorithms vocabulary result (Bates et al.2016). Deriving accurate outcomes is the end goal of the initiative to ensure better cardiovascular healthcare. Integrating reliable vocabulary in the system should enable new interventions and medications that can be directed towards improvement and the management of the patient’s outcome. Definitions such as ACS, AF, and HF should be outlined accurately to impact the clinical trial design hence contributing to personalized medicine and ensuring feasible economics in cardiovascular health (Silverio,2019).
BDA should be applied in cardiovascular practice published data that present information on population management such as case finding the application that focuses on the population monitoring in the health system. Applying the proactive methods of big data analysis can change the outcome of cardiovascular medical care. The standardized definitions that will result from improved classification can be used in the health care system as consented sources. Integrating big data tools such as machine learning will enable proper information about the clinical guidance of cardiovascular healthcare (Rumsfeld et al.2016). BDA should be implemented to generate reliable, ESC clinical guidelines that are up to date with the sources and analytics. Medical algorithms from the reliable analysis will improve the personalized medicine approaches and contribute to drug development. Through case studies and patients’ medical history analysis, the system can come up with genomic approaches and new drug discovery for cardiovascular diseases. Understanding the costs of cardiovascular care via BDA can strengthen industrial leadership through better use of resources and drugs. The overall initiative will improve patients’ health and wellbeing (Islam,2018).
Big data analysis presents promising opportunities and improvement in the healthcare system. However, some nuances surround data mining and machine learning in cardiovascular health solutions. The machines often require supervised and unsupervised machine learning. The process may require regular preprogramming whenever there is raw data input, which is costly and requires efficient management (Rumsfeld et al.2016). The system involves programming such as inductive logic and artificial neural network programming. The programming enables the coordination of human neurons and enables external stimuli. The deep learning programming can be utilized to learn the algorithms that can detect cardiovascular conditions through images and can determine the patients’ longevity (Rumsfeld et al.2016). Big data analysis in cardiovascular treatment will provide predictive analytics that can replace the physicians with complex calculus performed by an intelligent computer. Monitoring a diagnosis that is performed by physicians can be deployed to the algorithms patterns. However, such a statement can be dismissed as dubious because the BDA can only facilitate the physicians’ diagnostic accuracy as opposed to facing them off.
Other challenges posed by BDA include the uncertainty and limitations of big data. Research shows that significant data uncertainty cannot be eliminated by predictive analytics. The uncertainty presents different information of the class and the individual cases that result in uncertain conclusions. The uncertainty can be mitigated by mechanistic reasoning and clinical experience. Also, the uncertainty can be slim if the predictive analysis uses personalized data. Big data analysis cannot provide a perfect prediction because the approaches and data used cannot be accurately applied to predict unobserved cases in the future. As (Bates et al.2016) remark, the depth and sophistication of BDA can provide useful interpolation, but they may fail to explore beyond their training domain. Too much information and lack of accurate information causes uncertainty because the integrated machines can experience cognitive overload. Linguistic uncertainty is a significant challenge in the cardiovascular encounter. The ambiguity of the vocabulary can foster under specificity that can limit clarity (Rumsfeld et al.2016). The BDA should dilute the words to fit the narrative approaches that can provide an individual’s lifeworld for better understanding. Contextual understanding is essential because most of the conclusion emanates from context-dependence even when the data-driven language may have issues. In end, big data has great potential in improving the quality of cardiovascular care. The data required to enable the practice continues to grow tremendously. Significant data approaches can help with data exploitation through analytical tools that can lead to effective and efficient care. However, managing and clearly defining the sheer amount of information to points of perfect predictions and solutions can be a challenge. Regardless, the methods and tools will evolve, and the evidence-based implementations in cardiovascular care will grow.

Rumsfeld, J. S., Joynt, K. E., & Maddox, T. Melly G. (2016). Big data analytics to improve cardiovascular care: promise and challenges. Nature Reviews Cardiology, 13(6), 350.
Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014). Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Affairs, 33(7), 1123-1131.
Krumholz, H. M. (2014). Big data and new knowledge in medicine: the thinking, training, and tools needed for a learning health system. Health Affairs, 33(7), 1163-1170.
Chin‐Yee, B., & Upshur, R. (2018). Clinical judgement in the era of big data and predictive analytics. Journal of evaluation in clinical practice, 24(3), 638-645.
Islam, M., Hasan, M., Wang, X., & Germack, H. (2018, June). A systematic review on healthcare analytics: Application and theoretical perspective of data mining. In Healthcare (Vol. 6, No. 2, p. 54). Multidisciplinary Digital Publishing Institute.
Silverio, A., Cavallo, P., De Rosa, R., & Galasso, G. (2019). Big health data and cardiovascular diseases: a challenge for research, an opportunity for clinical care. Frontiers in medicine, 6.
Murdoch, T. B., & Detsky, A. S. (2013). The inevitable application of big data to health care. Jama, 309(13), 1351-1352.


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