Every year in the United States, about 209,000 hospital patients suffer from cardiac arrest while they are in the hospital. Less than 25% of these patients live long enough to eventually be discharged. Although survival rates have been improving over the past decade, cardiac arrests in hospitals are still extremely lethal.
Now, rather than trying to limit the damage from cardiac arrests, medical experts are working to prevent them from happening in the first place. Researchers are working to use electronic health records in order to predict and prevent cardiac arrests.
Such data modeling is extremely popular in many industries. Currently, Netflix has 800 engineers developing algorithms for their personalized viewing suggestions. Credit card companies are also using information to predict individuals who are most likely to default on a payment. Now the healthcare industry is getting in on the prediction action.
One such health care expert involved is pediatric oncologist Samuel Volchenboum. The doctor and his team have used electronic health records dating back to 2006 in order to establish the Clinical Research Data Warehouse. This giant compilation of medical data will be used for research purposes. Volchenboum believes that predictive algorithms and the new age of big data will be able to make hospitals a safer place for patients.
By using electronic data, doctors can obtain information faster than ever before. In the past, doctors were required to painstakingly mine through thousands of written notes. Already, Volchenboum and his team have constructed a mathematical model known as eCART to predict the patients who are most likely to suffer from a cardiac arrest.
When it is discovered that a patient is particularly prone to a cardiac arrest, doctors will immediately respond in an effort to fix the contributing factors. The system has already been in place for several months, and so far it has been quite effective.
For now, Volchenboum is working to use the electronic data to develop new types of algorithms. He is currently trying to develop a model that will predict which ICU patients are most likely to become infected with sepsis.
Data modeling has been used in business to help maximize efficiency. Now it is even saving lives.