Big Data Use Cases in Healthcare
If you’re in the healthcare business, by now you would have heard the words “Big Data” being used quite often. Whether it’s a healthcare provider like clinics, hospitals, or rehabilitation centers, big data is already having a significant impact in each segment. As a result, the whole healthcare industry is changing and is becoming increasingly evidence-based (unlike in the past when it was basically a trial and error approach). First health related mobile apps entered the healthcare domain, so it’s only natural that big data soon followed.
Health Information Organizations have digitized their operations and are helping the industry reduce costs. Further, they also help providers improve patient experience by analyzing and managing a huge amount of (clinical, financial, operational, and genomic) data that’s continuously collected. This vast amount of information covers a wide spectrum within the industry. For example, Electronic Health Records (EHRs) have already impacted patient diagnosis times by providing quick access to patient data.
There is an extremely high volume of data, but it’s picking out the “good data” that matters. Building analytical platform to make sense of this information gives you a unique insight that is beyond basic clinical data. This in turn enables providers to enhance their standard of care while cutting costs at the same time. By incorporating predictive analytics, more and more healthcare institutions are coming up with ways to utilize this information and it’s paying dividends.
By incorporating big data analytics and genomics into a hospital or clinic, healthcare providers can now enabled to provide personalized treatment. As big data can be used to make predictions on the course of the disease based on the patient’s genetic makeup, the insights gained from this information can save lives. Predictive analytics has already had a significant impact in the fight against cancer. Further, this tool is also being used to develop preventative measures to combat diabetes and heart disease.
Enhanced Hospital Safety & Quality
Big data predictive analytics is quickly growing in importance in the ICU. Further, with the Internet of Things (IoT) making its way into hospitals and medical devices that are found by the bed are becoming “smarter.” As a result, these devices can be used to detect plummeting vital signs and predict sudden downturns caused by sepsis or infections. This is a great breakthrough as sepsis is the main contributor to 40% of the mortality rate.
At the present time, an analytics system known as QPID has been helping staff at the Massachusetts General Hospital ensure that critical patient data is included during admission and throughout the course of treatment. Further, this big data analytics tool is also being used by surgeons to predict surgical risk. According to the Associate Medical Director for Information Systems at the Massachusetts General Physicians Organization, Dr. David Ting, “surgeons, even the world-renown surgeons, do not want to operate on a patient who’s going to die on the table. The last thing they want to do is do harm to a patient or do something inappropriately. The system automates searches using national guidelines, and then it essentially shows the results in a dashboard with a red, yellow, or green risk indicator for the surgeon to see.”
Decrease Unnecessary Readmissions
For years hospitals have been trying to reduce their readmissions rates. That’s why providers have now started to look towards big data analytics to find a solution to this problem. Access to this information will allow the system to make predictions based on prior hospitalization histories and flag those who return more often within 30 days of treatment.
By having real-time access to EHRs, practitioners are now able to easily identify and target patients who are at a higher risk of readmission. As a result, more focused care can be administered and more effort can be made to coordinate the care that patients receive. Properly harnessing this data will also assist healthcare providers to ascertain which readmissions are preventable and which are avertable.
Big data in healthcare is also working in a macro sense to manage the health of the public as a whole. By conducting macro predictive analytics algorithms, practitioners can be ready to treat the patient when they arrive. For example, patients can be automatically reminded to refill their prescription thereby avoiding a trip to the emergency room. As past behaviors can be used to predict future events, this is increasingly becoming a vital part of the industry.
Predictive analytics will essentially help eliminate holdups in the emergency room. With analysis of this information, providers can also be ready with the necessary staff to provide care. Beyond the clinical aspect of predictive analytics, providers can also use big data in other aspects of their business:
· Health insurance fraud detection
· Brand management
· Device and pharma supply-chain management
· Real-time targeted offers
· Calculate patient lifetime value
· Development of new drugs
· Positive patient engagement (e.g., shared decision-making and patient decision aid engines; telemedicine)
· Prescription fulfillment and adherence
· Behavior modification (promote healthy habits via health tracking applications for wearable gadgets)
· Increase productivity and efficiency
Big data is expected to grow in importance and play an even bigger role in the healthcare industry. As the technology evolves, it will become increasingly necessary for healthcare providers across the country and around the world to incorporate robust predictive analytics systems that will soon be industry standard.