1 Business School, Business Analytics, University Of Colorado Denver, Denver, Colorado, United States.
2 College of Engineering, Computer Information Systems, Prairie View A&M University, Prairie View, Texas, United States.
Research Article
World Journal of Advanced Research and Reviews, 2024, 24(01), 2159–2176
Received on 10 September 2024; revised on 19 October 2024; accepted on 21 October 2024
Big data analytics has emerged as an incredibly valuable tool in understanding population health trends and improving the effectiveness of healthcare delivery systems. By leveraging big data sources from various domains, including the patients’ electronic health records, claims data, wearables, and social media accounts, healthcare organizations can obtain novel and rich insights regarding population health characteristics, predisposing factors, and disease progression. The arrival of big data has transformed healthcare organizations by providing a scientific population health management and health system enhancement tool. Using superior analytical tools, healthcare entrepreneurs and policymakers can discover relations, rates, and patterns that are concealed in large data sets. Such knowledge can be used for early detection of possible interventions, management of resources, and prevention measures, thus leading to better health and less spending on health issues. Moreover, big data analytics helps in early diagnostics and the development of management strategies for high-risk groups, which in turn improves the functioning and effectiveness of healthcare systems. It is also important to note that big data solutions in healthcare are not limited to population health management, but also include functional aspects of healthcare organizations. Additionally, using data about patient movements, resource consumption, and clinical activity, it is possible to determine inefficiencies and opportunities to improve processes in healthcare organizations. This approach of collecting and analyzing data helps in decision making thus reducing time and improving patient flow and experience. However, incorporating real-time data into the clinical decision support systems can improve diagnostic capabilities, treatments offered, and patient tracking resulting in improved quality of services delivered.
Big data analytics; Population health trends; Electronic health records (EHR); Machine learning; Internet of Things (IoT); Omics data; Natural language processing (NLP); Artificial intelligence; Medical imaging; Quantum computing; Telemedicine
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Olayanju Adedoyin Zainab and Toochukwu Juliet Mgbole. Utilization of big data analytics to identify population health trends and optimize healthcare delivery system efficiency. World Journal of Advanced Research and Reviews, 2024, 24(1), 2159-2176. Article DOI: https://doi.org/10.30574/wjarr.2024.24.1.3198