Improving Preventative Care and Health Outcomes for Patients with Chronic Diseases using Big Data-Driven Insights and Predictive Modeling
Kavita Singhania and Arjun Reddy
Abstract
Chronic diseases such as diabetes, cardiovascular disease, and cancer are leading causes of disability and death worldwide. With aging populations and unhealthy lifestyles becoming more prevalent, the burden of chronic disease continues to grow. Preventative care and proactive disease management are critical to improving health outcomes for these patients. However, traditional reactive approaches fail to detect risks early enough. This paper proposes leveraging big data analytics, insights, and predictive modeling to enable personalized and precision care that empowers patients and providers to get ahead of chronic diseases. Specifically, advanced analytics can integrate diverse digital data from wearables, medical records, claims, social determinants of health, genomics, and other sources to uncover risks, predict adverse events, and prescribe interventions tailored to each individual. When combined with education and support programs, data-driven precision care can significantly improve preventative care, disease management, health outcomes, and quality of life for chronic disease patients while lowering costs. This paper reviews applications of big data analytics for chronic disease management, examines key technologies and solutions, identifies challenges and limitations, and provides recommendations to fully realize the potential of big data-driven care. With thoughtful design and responsible implementation, advanced analytics of disparate data can enable a learning health system optimized for preventative and personalized management of chronic diseases.
Author Biography
Kavita Singhania and Arjun Reddy
