Big Data Revolution!!!

“Prevention is better than cure”. Big Data in HealthCare justifies this.

Big Data Symposium held at University of Arizona Medical Center on October 20^th, 2014 was focused on how big data can be leveraged in Healthcare. These talks inspired us to write a blog on Big Data in HealthCare. We wanted to explore more on what is happening in the industry in terms of Big Data.

Let us listen to what Nicolaus Henke, McKinsey director has to say about how Data Analytics is changing the practice of Medicine.

According to McKinsey&Company the use of Big Data in Healthcare is called as “Big Data Revolution”. . But why is it a revolution? Some real world examples of how Big Data has been useful in Healthcare:

·  Kaiser Permanente has fully implemented a new computer system, HealthConnect, to ensure data exchange across all medical facilities and promote the use of electronic health records. The integrated system has improved outcomes in cardiovascular disease and achieved an estimated $1 billion in savings from reduced office visits and lab tests.

·   Blue Shield of California, in partnership with NantHealth, is improving health-care delivery and patient outcomes by developing an integrated technology system that will allow doctors, hospitals, and health plans to deliver evidence-based care that is more coordinated and personalized. This will help improve performance in a number of areas, including prevention and care coordination.

·   AstraZeneca established a four-year partnership with WellPoint’s data and analytics subsidiary, HealthCore, to conduct real-world studies to determine the most effective and economical treatments for some chronic illnesses and common diseases. AstraZeneca will use HealthCore data, together with its own clinical-trial data, to guide R&D investment decisions. The company is also in talks with payors about providing coverage for drugs already on the market, again using HealthCore data as evidence.

Data is everywhere!! For instance, Asthmapolis has created a GPS-enabled tracker that records inhaler usage by asthmatics. The information is ported to a central database and used to identify individual, group, and population-based trends. The data are then merged with Centers for Disease Control and Prevention information about known asthma catalysts (such as high pollen counts in the Northeast or volcanic fog in Hawaii). Together, the information helps physicians develop personalized treatment plans and spot prevention opportunities. 

This is in correlation with the lecture given by Professor Sudha Ram. She was able to predict Asthma related emergencies using big data from EHR, Social Media and Sensor Datasets. Tweets based on “Asthma” related keywords were collected to perform the research. Based on this data, they developed a predictive model. Research in this field is happening in every part of the world. Hence it is known as a revolution.

The goal of using data is to be develop predictive models. These models can be applied against patients to monitor their health, diseases and better management of hospitals. American Healthways says it has identified 30 "impact conditions" that affect people's lives but can be prevented, or at least ameliorated, by timely interventions. American Healthways calculates that these 30 conditions accounted for half of the $1.4 trillion spent on direct medical expenses in the United States last year. Predictive modeling, combined with care enhancement programs, could save 20 percent of these costs, or $140 billion. If 70 percent of the savings is kept by health plans, that leaves $42 billion in fees to be garnered by American Healthways and its competitors. No wonder predictive modeling is hot, hot, hot!!

References:

[1] http://www.mckinsey.com/insights/health_systems_and_services/the_big-data_revolution_in_us_health_care
[2] http://www.managedcaremag.com/archives/0109/0109.predictive.html
[3] https://www.facebook.com/BusinessIntelligenceAndAnalyticsCenter