Big data is saving lives, and that’s not a fairytale. It has been changing lives in many ways. Big data is bringing a welcome shift in the healthcare sectors. Healthcare analytics cannot only help reduce the cost of healthcare facilities including treatments, medication, and diagnosis. Analytics in this area can also contribute to predicting the outbreak of endemic and epidemic diseases like SAARS and the Flu.

According to a recent McKinsey report, more than 20 years of steady increase, health care now represents 17.6% of the country’s total GDP. This is $600 million over the reasonable upper limit for the USA. Healthcare costs have skyrocketed in the last 20 years. No new presidential policies or health insurance regulations have been able to dampen that growth.

However, right now, healthcare officials are finding a new incentive to share patient information with one another. There are medical platforms for the interaction of patients as well as professional platforms for exchange of information for the medical professionals. This is possible by prioritizing individual patient health over the number of patients. Right now, real decisions are more evidence-based than ever. There are pools of research and clinical data that these doctors can directly access. Professionals now have all the assistance they need, thanks to secure applications that allow them to access and utilize the data pool.

Data-driven healthcare has its own set of obstacles. Medical data encompasses different hospitals, districts, and states. They include several administrative systems. This call for the necessity of a new tool that can help data providers and data users collaborate with each other. This is why the creation of new analytics tools, strategies, and data applications are significant right now. Healthcare needs acute data analysis in the forms of the graph, machine learning and predictive analysis that other industries are already enjoying.

What are the biggest big data trends of healthcare in 2017?

1. Prioritization of patient-oriented care

As we already stated before, patient care quality is on the rise. More doctors are finding incentives in providing individual patient-related data. The quality of data relates to the quality of patient healthcare. This is becoming beneficial for the patient since it is improving the quality of healthcare that is consistent with the professional experience and knowledge. It has also reduced the health care costs and provided support for payment structures.

This means the practice is gradually moving away from fee-for-service to fee based on the quality of service. This will not only improve patient outcomes, but it will also reduce the net healthcare costs.

2. IoT and healthcare

The Internet of things is the new LBD of data technology. Industrial Internet or IoT describes the fast increase in the volumes of data each connected device and the individual user generates. IoT is going to be worth about $120 billion in the next two years.

Interestingly, most of the data from the healthcare IoT is unstructured. This means, data engineers can find the widespread potential use of Hadoop and Kafka-like analytics and framework.

The variety of services can range from storing individual patient data to pulling data related to a particular subset of symptoms that come from thousands of patients from all 50 states.

This opens a range of employment opportunities for those interested in Six Sigma Green Belt Training. Knowledge of management services will help the data scientists in the better structuring of inflowing data from the very beginning. Healthcare IoT already generates gargantuan qualities of data each waking moment. If the data is unstructured for long, it might become too chaotic to implement any analytics program and structure.

3. Better management and monitoring

You can imagine the data lake as a fathomless body of data flowing in from 7 other sources. Claims, clinical, pharmacy, EMR, logs, and notes, third party data and additional data, all contribute to the healthcare data lake. This serves as the data hub for all health related departments including fraud prevention and management. It is now possible to detect over 20% cases of fraud, waste, and abuse in the claims department(s) of the hospital(s).

The Centers for Medicare and Medicaid Services now uses predictive analysis to assign risk scores to individual claims and their providers. This helps the model to flag certain charges automatically. The predictive mode can compare charges against profiles that may be fraud and raise a red flag in the system. This helps prevention of fraudulent insurance claims and medical aid claims across the USA.

4. Predictive analysis can improve outcomes

The adoption of EHR or Electronic Health Records is helping the volume of patient data grow exponentially. The $30 billion stimuli from the federal government catalyzed the use of EHR.

The new EHR policies can now combine and analyze data from several data sources. Multiple medical boards, administrations, and practitioners can now enjoy the benefit of a predictive data analytics model derived from data from the EHRs.

At this moment, Congestive Heart Failure or CHF accounts for most spends in healthcare. The patients and, sometimes, the doctors neglect most early symptoms too. It can be treated and kept under control if detected early. However, the lack of enough information on the symptoms makes it very challenging for physicians to diagnose it early. Machine learning can take into account number of factors. The algorithms can factor in additional features that physicians cannot see or detect. This increases the chances of correct, early diagnosis of the patients. This model will distinguish people who have CHF from the healthy population.

Machine learning and big data have applications contribution in the monitoring of post-trauma and post-op patients. From heartbeats, blood pressure, breathing to brain activity – big data can collect, store and structure all data. Real-time monitoring is a big pro with big data.