Big Data Analytics Can Play an Important Role in Healthcare

Before you dive into the importance of big data analytics in healthcare, you can learn about the importance of small data vs big data in healthcare.

Global healthcare is in a state of flux with big data analytics emerging as a powerful tool to transform clinical, operational, and administrative functions among others.  The healthcare IT market has grown from basic EMR solutions to specialized hospital information management solutions and healthcare information exchange systems and the Healthcare IT Solutions Market Report predicts growth at a CAGR of 13.57 percent till 2022.  This growth is being fuelled by the increasing role played by big data to manage patient care, reduce costs, and improve quality while keeping one eye steadily focused on operational efficiency.

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Realizing the potential of big data

Probably realizing the value of big data, The Health Information Technology for Economic and Clinical Health (HITECH) Act created a $30 billion federal grant as an incentive to adopt EHRs, which has helped to generate tons of structured and unstructured data.  This data is finding its uses across functions and services.

Value-Added Services

Insurance companies, for example, are changing their models from a fee-for-service to value-based data-driven payments by using electronic health records that enable high-quality patient care. In the value-based model, doctors, hospitals, and insurance work together to deliver care that is measured by patient satisfaction and this model relies on data from EHRs.

Cost Savings

The same data from EHRs have also helped in mitigating fraud thereby increasing cost savings.  For example, the Centers for Medicare and Medicaid Services prevented more than $210.7 million in healthcare fraud in one year alone.  Insurance companies have also experienced a higher return on investment.  United HealthCare generated a 2200% return on investment in a single year.  Big data analytics has helped these companies to take large unstructured information with regard to historical claims and by using machine learning to detect patterns and anomalies.  This has helped to control overutilization of services, patients receiving the same services from multiple hospitals, and filling out identical prescriptions in various locations.

Predictive Patient Care

By analyzing structured and unstructured data, and using predictive modeling on EHR data, it is now possible to diagnose various illnesses which is helping to reduce mortality rates.  To elaborate, devices are helping to monitor patients’ glucose levels, blood pressure etc.  When combined with machine learning IoT, proactive care for patients is a reality.  Advanced big data analytics is able to work with the unstructured data generated by these sensors.

To summarize, evidence-based medicine relies on patient data which is now growing more in availability.  Capturing data is, however, only the first step.  The next one requires analytics which will not only result in better patient care and engagement but also eliminate redundant testing, reduce expensive errors, and help save lives.