BIG DATA TRENDS TRANSFORMING THE HEALTHCARE INDUSTRY

Big data is a revolution that is under way in the healthcare sector, and it is here to stay. Over the last decade, there’s been a remarkable increase in the supply of information followed by years of research and development. The use of big data technologies along with advanced analytics not only helps reduces cost but also increase patient outcomes. While the price of treatments, diagnosis and medication become lower, the advantage is in using predictive analytics to anticipate the outbreak of epidemic and endemic diseases.

Health systems are now using big data solutions to execute analytics in multiple data streams including unstructured and structured data. Risk models are being built with the expectation of predicting undesirable outcomes a patient may experience. This may include negligibility, being readmitted post discharge or even being affected with an infection while hospitalized. This analysis can further be used to enhance patient outcomes and gain actionable healthcare insights that improve patient care.

A McKinsey report states that healthcare currently represents 17.6% of the country’s GDP, a steady increase, more than 20 years.

Other industries are aggressively creating new analytics tools, data application and strategies to reap higher benefits such as predictive analysis, machine learning and graph. The healthcare industry is now looking at acute data analysis and larger application methods to progress and collaborate with each other for business success. As the healthcare industry experiences a drastic change in data technology and transformation, let’s take a closer look at big data trends that are quickly changing the healthcare industry for the better.

Individual Patient Care

With the rise of big data, patient focused care is steadily becoming a priority. Doctors and staff are able to serve patients better as they can quickly analyze and locate patient related data. The value of data is benefitting patients as well as hospitals as it is improving the overall quality of the experience. The structured approach can greatly reduce the net of healthcare costs while improving patient outcomes.

Hospitals are able to provide proactive patient care with real-time monitoring. The vital signs can be continually monitored, analyzed and instantly alert representatives in case the patient’s condition deteriorates. Processing these real-time events along with machine learning algorithms gives doctors the much-needed insight to make life-saving decision.

In a 2016 PWC survey, majority of people participating already agree that they would be excited to experience wearable technology. 65 percent voted yes to using wearables from doctors, 62 percent from hospitals and health insurance company

The trend of wearable technology is quickly becoming a norm. These wearable devices and sensors allow nurses and care givers to interact with a patient in a more convenient way. Devices can be used to remotely monitor weight and track changes in a patient battling a heart disease. These applications can go as far as detecting fluid medication to check if hospitalization is required. The end result is capturing extensive data that allows for superior patient engagement and patient care coordination that is personalized to each individual patient needs.

Reducing Waste, Fraud And Abuse

The spiraling healthcare costs in the United States are majorly caused by the fraud, abuse and waste costs in the healthcare sector. Countering this notion, big data analytics is a huge game changer. In a predictive modeling environment, big data solutions and Hadoop can be used to identify inaccurate claims that are systematic and repeatable. However, a large number of the healthcare data that is stored remains unstructured. Using machine learning algorithms, patterns and anomalies across historical claims can be detected leading to preventing fraudulent occurrences.

Individual data is collected from sources such as claims, pharmacy, EMR, notes, logo, clinical and third party data which makes way for personalized experience. Implementing big data solutions in the data hub serves as a medium to detect fraud, waste and abuse in the healthcare industry. For example, analytics can be used to model the flag to certain charges and raise a red flag. This makes it easier to prevent many fraudulent insurance claims or medical claims across the industry and the country.

The Centers for Medicare and Medicaid Services prevented more than $210.7 million in healthcare fraud in one year alone using predictive analysis.

Apart from this, healthcare sector can analyze billing and patient records to identify irregularities and notify in case of excess utilization of services in a short notice. This is possible across healthcare services across locations and organizations consecutively. The data is filtered clearly leaving very little room for error in information such as identical prescriptions that could be filed for the same patient in multiple locations.

Continued in Part 2

Author Bio – Matt Wilson – A Healthcare Expert, is working with Aegis Health Tech as senior developer from last 5 years. He has extensive experience in patient management system, EMR & EHR Development, Implementation and Integration.