“Prevention is better
than cure”. Big Data in HealthCare justifies this.
Big Data Symposium held at University of Arizona Medical
Center on October 20th, 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
