The utilization of big data analytics has enabled organizations to develop personalized treatment techniques for the management of chronic diseases.
FREMONT, CA: The emergence of revolutionary technologies such as artificial intelligence (AI), machine learning (ML), and big data is changing the landscape of healthcare. It has enabled healthcare organizations to consolidate the vast troves of structured and unstructured data and gain access to important insights. The capabilities offered by big data have significantly enhanced chronic diseases management.
In Europe, chronic diseases are one of the prominent causes of morbidity and mortality. Chronic patients are often sent to secondary care institutions, which have a significant impact on the efficiency of healthcare. Of all the deaths due to non-communicable diseases in Europe, 59 percent were attributed to cardiovascular disease, kidney disease, respiratory disease, and diabetes.
Big data technology empowers organizations to draw relevant insights and form robust strategies. It improves not only preventive care but also enhances the patient experience. Big data has made giant strides in the field of chronic diseases, including diabetes and mental health. The data gathered from various sources such as IoT devices, social media, medical data, and so on, can help in the development of robust tools designed to generate insights. The insights enable healthcare providers and patients to make informed decisions, ensuring greater efficiency in the process.
Medical organizations across Europe are investing in big data, assessing its potential in enhancing the treatment of chronic diseases. Big data and preventive analytics also show promise in improving the quality of interventions and preventing chronic conditions. By introducing greater efficiency into chronic care management, organizations can not only increase their productivity but also reduce the risk for patients and augment their quality of life.
The generation of analytics from big data empowers organizations to explore new opportunities and develop innovative strategies. Aging process contributes to many of the chronic non-communicable diseases, often as a follow-up for patients with renal transplantation, chronic obstructive pulmonary disease, heart failure, and gestational diabetes.
The implementation of monitoring strategies will enable organizations to gather relevant data from large populations. Appropriate stratification techniques empower organizations to develop robust interventions that go beyond the capabilities of the current clinical practices. It can facilitate higher efficiency in healthcare delivery outcome, while also introducing cost-effectiveness. Big data analytics can significantly reduce the burden on chronic care by enabling organizations to identify critical patients and assigning them to secondary care, whereas non-critical patients can be attended for in primary care.
For instance, a single blood sugar assessment creates hundreds of data points. The insights drawn from this data can be used to customize the procedure according to the habits of the patients, thus introducing greater precision into the treatment. Over 60 million people in Europe are diagnosed with diabetes, which exponentially increases the number of data points. By organizing this data, organizations can generate predictive insights to assist in chronic care research and chronic disease treatment.
Analytics drawn from big data will enable physicians as well as patients to identify the best treatments based on medical, biological, environmental, and socio-economic data. Using ML algorithms can facilitate rapid analysis of patient data, presenting the insights in a format which can be easily understood by the physicians as well as patients. The insights can be used to develop robust decision support tools designed to assist physicians in making informed decisions.
Chronic care management is witnessing new dawn with the rise of big data analytics, enabling healthcare providers to deliver personalized treatment to patients based on their medical needs. The medications and treatments can be tailored according to the anatomy and eating habits of individual patients. The incorporation of bid and analytics into personalized care will not only ensure cost-effectiveness but will also enhance the quality of life for the patients.