Promises and Challenges of Big Data Analytics in Healthcare

Big Data Analytics
Big Data Analytics
Image Credit:

Using big data analytics in healthcare delivers both advantages and impediments as it relies heavily on patient data which is sensitive in nature.

Big data analytics has been evolved at an unprecedented rate in the last few years as its potentials support not only in the smooth business delivery, but also the process of care delivery and disease exploration. As the healthcare industry face heterogeneous amount of data related to patients, processing and analyzing this voluminous data requires big data system. Leveraging this technology, health professionals are now able to glean large sets of data and look for best strategies for better care delivery.

In general, big data is used to assess vast quantities of information generated by the digitization of everything that gets consolidated and evaluated by specific technologies in order to derive meaningful insights. In healthcare, it uses explicit health data of a population and potentially assists in curing diseases at a rational cost.

Applications of Big Data Analytics in Healthcare

Big data analytics in healthcare involves the incorporation of a massive amount of data, data quality control, analysis, modeling, interpretation and validation. Its applications in the field of care delivery provide inclusive ideas and knowledge discovering from the existing data.

Predictive Analytics: It has the ability to support population health management, financial management, and better outcomes across the value-based care continuum. Predictive analytics envisage the possibilities of a future outcome based on patterns in the historical data. This enables clinicians, financial experts, and administrative staff to get alerts about potential events before they happen, so they can make more informed decisions to proceed further.

Real-Time Health Monitoring: In order to keep monitoring and informed about patients’ health, healthcare systems is actively looking at solutions that can give them real-time health data. The emergence of new facilities and advanced tools provide health service providers and their patients more choices to readily access and leverage health information, and amass and store real-time data. This has majorly driven the access of monitoring health signs at home and enables the consultation with doctors without visiting healthcare centers.

Electronic Health Records (EHR): It aims at making vital patients data that involves medical history and general information readily available to medical personal, healthcare organizations, government, and other entities. Since every patient has their own medical records such as laboratory tests results, medical reports, lists of medicines, and others, the use of EHRs make it easier to manage and maintain the data and deliver easy access to such data.

Patient Engagement: Big data analytics tools have the potential to assist stakeholders across the health care gamut to enhance and build patient engagement in clinical research and care. With the availability of low-cost tools and improved analytics, medical professionals these days have enormous quantities of medical and healthcare data. By making use of AI, machine learning, NLP, and other technologies, healthcare organizations can enable patients to keenly participate in their own care, which will lead to improved care delivery and health outcomes.

Challenges to a Prevalent use of Big Data Analytics in Healthcare

Undeniably, big data analytics in the field of healthcare enables analysis of massive datasets from a large number of patients, recognizing clusters and relationship between datasets. It also builds predictive models using data mining techniques for the future healthcare research. Unfortunately, as big data analytics delivers vast promises to healthcare, it also poses a set of challenges. One of the foremost barriers to the widespread big data analytics in healthcare is the nature of the decisions and the data themselves.

Unlike other industries, decisions in care delivery deal with immensely sensitive information require information and action quickly, as medical professionals face life or death consequences. The other challenge care service providers face while using big data analytics is how medical data is disseminate across various sources governed by diverse states, healthcare centers, and administrative departments. Thus, integration of these data sources would require to develop a new infrastructure where all data providers collaborate with each other.