The influx of big data analytics has disrupted every industry and has transformed the way we manage, analyze and leverage data. But one of the most promising areas where big data can drive huge change is the health care industry.
Today, Healthcare analytics is disrupting how medical professionals approach treatments.
It now has the potential to reduce treatment costs, increase human lifespan and improve the quality of life, predict the outbreak of epidemics and prevent diseases.
Just like business entrepreneurs, medical professionals are able to amass a large amount of data and employ predictive and prescriptive analytics to provide personalized medicine, automated health check-ups, clinical risk intervention and so on, thanks to healthcare analytics.
The larger challenge lies in managing this humongous amount of information: precise and
efficient storage; centralization; and employing the right tools for accurate analysis.
Moreover, as health care costs are escalating worldwide, the need to introduce big data in healthcare is even more paramount. A McKinsey report states that healthcare expenses now represent 17.6 percent of GDP —nearly $600 billion more than the expected benchmark for a
nation of the United States’ size and wealth.
More than ever, there are now financial incentives to centralize big data analytics in the healthcare industry. Earlier, healthcare providers lacked the incentive to share the information they had gathered from one another. Now, since they are getting paid on an incentive basis, they have a financial motive to share the information that can be further be used make better decisions for patients, all the while effectively cutting the cost of the insurance companies.
Hurdles along the way
Problems in any business arise when there is a lack of coordination and communication.
The data in the healthcare sector is spread across various sources governed by different states
and institutions. To integrate them, a new infrastructure would need to be developed where all data units can collaborate with each other.
While we come across new medical facilities and treatments every day, the same can’t be said for its information sharing capabilities. The healthcare industry needs to catch with other industries in the market in order to make full use of the potential of data analytics.
Big data changing the healthcare world
One of the major problems that any health care unit faces is staffing. How do you know how many staff members do you need per shift? If there are too many workers, the institution might face addition labor costs. If the staff members are less, the people working will be burdened, compromising their ability to work efficiently- which can be fatal to the patients they are tending. To solve this problem, Bigdata Analytics gathered info from all the hospitals that are a part of Assistance Publique-Hôpitaux de Paris, to see how many patients are expected at each hospital daily and staff accordingly.
Electronic Health Records (EHRs) are perhaps one of the best things to happen to the healthcare sector ever. These little devices record a variety of info about the patients like their demographics, medical history, allergies, laboratory test results etc. These results are then shared via a secure information system that is available to both public and private sector healthcare providers.
It is one of the most prime examples of how big data’s usefulness.
Real-time alerting is another of the most useful uses of big data. In hospitals Clinical Decision Support (CDS) software analyze medical data on the spot, providing health practitioners with advice about prescriptive decisions. However, personal analytic devices are soon gaining traction. These wearables gather data about the patient and send it to cloud storage, which can later be accessed by various healthcare domains. The data gathered then helps the doctors to customize the treatment plan for each patient, according to their socioeconomic context, for effective delivery strategies.
A miraculous breakthrough of big data analytics is in the field of opioid abuse in U.S. Using years of insurance data, researchers have managed to identify 742 factors that contribute to the
development of opioid addiction. Now combine it with data from public domains and social media and people who are at high risk can be identified and strategies made to prevent them from falling prey to drug abuse.
The experience of using predictive analysis is just getting better and better. Optum Labs have gathered data from EHRs of over 30 million patients, with the sole aim to refine the data, which will later be used to facilitate doctors in making discussion within seconds, improving the delivery of healthcare services.
Big data might just be the key to helping researchers cure cancer. Big data will allow researchers to use large amounts of data on treatment plans and recovery rates of cancer patients to see which treatments work the best in what conditions, thus taking a step forward in the battle against cancer.
For more info on big data services in the healthcare sector, contact us at Datahut.