As a doctor, we would like to know what is best care possible for each patient. However, as a patient one of our biggest concern is the cost of healthcare. By reducing the medical cost, not only it will reduce the burden of the patients, but it can also allow patients with minimum income to enjoy treatments that they previously could not afford. According to Versel (2015), by using big data analytic Beaufort Memorial Hospital in South Carolina estimate that they could approximately save $435,000 by allowing patients to discharge half a day earlier. So how Beaufort Memorial Hospital managed to accomplished this? Their IT department analyzes 180 parameters and report on important key data points. The purpose of all these important key data points are to plan prescriptions, wheelchair transportation, follow-up-visits and room cleaning. By setting mini daily goals for the staffs such as assigning patients to their bed/room within 10 minutes will reduce the average amount of stay length and this would eventually help Beaufort Memorial Hospital save cost (Versel, 2015).
Another way on how Big Data can help reduce medical cost is to reduce emergency room visits. A research by the Minnesota Department of Health recently found out that every year there are 1.3 million unnecessary trips to the emergency room. With this 1.3 million unnecessary visits, it cost the medical facilities $2 Billion (Health.state.mn.us, 2016). After this discovery, the state government of Minnesota started using Big Data analytic to help reduce unnecessary emergency room visits. For example, the state distinguishes 50,000 local residents that had at least four preventable emergency room visits due to chronic diseases. Instead of assigning them to the emergency room before receiving primary medical care, the state ensures that these 50,000 local immediately receive primary medical care by working closely with doctors, nurses and hospital staffs (Health.state.mn.us, 2016). This will not only help the hospital to save cost, it can also decrease the severity of an illness due to early admissions.
With Big Data analysis, St. Louis Children’s hospital was able to eliminate unnecessary number of tests for Dravet Syndrome (a rare form of epilepsy) which cost $6,000 per test. These test are often carried out on infants with seizures. However, according to Dr.Nephi Walton uses data analytic to determine the effectiveness of the Dravet Syndrome Test and 100% of the time the test result is negative for the last 5 years (Healthcare Finance News, 2015). One of the easiest and effective way of saving money is to reduce small resource waste that add up over time.
Using Business Intelligent and analytic tools, hospital can monitor essential hardware to predict and prevent breakdowns. By ensuring all these medical equipments are working at optimal level not only save cost, it can also ensure patients care and safety. This can be accomplished by remotely sensing data from equipment, correlating data obtain from equipment with business information to predict future malfunctions, and also ensuring maintenance work is performed at the right time using the prediction generated from the data collected. As a result, hospitals that practice this preventive and predictive maintenance technique achieve 17% lower annual maintenance cost, 44% lower unplanned downtime and 28% higher return on assets. (Humphries, 2015).
Nearly 45,000 deaths are correlated with the lack of health insurance in the America itself (Cecere, 2009). However, with the help of big data analysis, we can help reduce medical cost. With the reduced cost, more patients can afford to pay for a better medical treatment. Hence, saving more lives.
REFERENCE
Cecere, D. (2009). New study finds 45,000 deaths annually linked to lack of health coverage. [online] Harvard Gazette. Available at: http://news.harvard.edu/gazette/story/2009/09/new-study-finds-45000-deaths-annually-linked-to-lack-of-health-coverage/ [Accessed 3 Jun. 2016].
Healthcare Finance News. (2015). Healthcare providers show success with predictive analytics at Boston symposium. [online] Available at: http://www.healthcarefinancenews.com/news/healthcare-providers-so-success-predictive-analytics-boston-symposium [Accessed 6 Jun. 2016].
Humphries, D. (2015). How Health Care Analytics Boosts Efficiency & Reduces Costs. [online] Software Advice. Available at: http://www.softwareadvice.com/resources/bi-how-data-analytics-solutions-reduce-costs/ [Accessed 3 Jun. 2016].
Health.state.mn.us. (2016). News release: Novel MDH study yields first statewide estimate of potentially preventable health care events. [online] Available at: http://www.health.state.mn.us/news/pressrel/2015/hcevents.html [Accessed 4 Jun. 2016].
Versel, N. (2015). Cleveland Clinic, Other Health Groups Use Data to Boost Patient Satisfaction. [online] US News & World Report. Available at: http://health.usnews.com/health-news/hospital-of-tomorrow/articles/2015/01/16/cleveland-clinic-other-health-systems-use-analytics-to-boost-patient-satisfaction [Accessed 1 Jun. 2016].
Prepared by Soam Wei Jie
BIG DATA ON HEALTHCARE 