Big Data Analytics in Healthcare: Data, Math, and Number to Provide a Better Healthcare

Jul 30, 2017

patient engagement analysis

Big data analytics has been the most trending topic in recent years due to its ability to drive efficiency and productivity in its application areas. The rising expenditure on healthcare has urged the medical industry to make the best use of big data analytics to provide qualitative and affordable health care. The digitization of healthcare records across various countries has helped provide quality inputs for big data analytics to generate meaningful insights in the healthcare sectors. There are numerous ways in which big data analytics has been helping the healthcare industry attain maximum efficiency.

Move Towards Value Based Healthcare

In some parts of the world, insurance companies have revised their reimbursement model from fee-for-service to value based healthcare compensation to prioritize outcomes. Value based healthcare compensation compels the medical professionals to improve the quality of service. Big data analytics outlines the compensation model by pointing out clinical inefficiencies, identify at-risk patient population, and incorporating quality reporting program.

Speak to an analytics expert to know more about the role of big data in healthcare.

The Healthcare Internet of Things (IoT)

Patient monitoring devices and medical wearables such as pacemakers, heart rate monitors, glucose monitors, and electrocardiograms record detailed data relating to the patient’s health, which is then analyzed during a physician visit. But with the use of big data analytics, these machines can communicate with each other to refine this process. It assists the healthcare practitioners to remotely monitor patient’s health, activity levels, and reactions to treatments and provide solutions using telemedicine.

Predictive Analytics to Drive Outcome Efficiency

Adoption of Electronic Health Data (EDH) makes it possible to capture a large amount of structured and unstructured data, ranging from patient’s health to healthcare facilities. For example, by considering historical medical admissions and recent trends, the algorithm can predict the future medical admissions so that hospitals can efficiently allocate resources and personnel. Predictive analytics uses various prediction models to prioritize clinical workflow, reduce system waste, increase diagnosis accuracy, and improve patient treatment outcomes.

Precision Medicine for Better Treatment

The majority of the traditional medicines are sometimes ineffective due to differences between an individual’s genomes, proteome, and body flora. Big data analytics is changing the way treatment trials are being administered due to growing popularity of personalized medicines which can reduce cost and improve outcome.

 For more insights on the trends in big data analytics, request for more information from our experts.

Reduction of Fraud, Waste, and Abuse

Fraud, waste, and abuse of medicines and medical facilities are costing healthcare end users millions of dollars. Big data analytics can be used along with various machine learning algorithms and predictive models to dwell through a large set of historical data and detect anomalies and patterns. The trigger of a particular kind of event in itself may not mean a lot, but a particular combination such as frequent doctor visits, multiple conditions, specific zip code, and individual prescription could signal a possible case of abuse.

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