A wonderful case study came out last week of how Sequoia Hospital is using business analytics software to inform heart surgery doctors on when to operate and what is the best post- and pre-op care option.
The hospital is using IBM SPSS predictive analytics software to sift through heavy data spanning healthcare databases, medical precedents and real-world medical cases:
"For instance, the software revealed that an anticoagulant drug often given to patients after a heart attack dramatically increases the chances of serious postoperative bleeding. Based on that information, Sequoia was able to put a protocol in place to stop the drug at least five days prior to surgery to allow the patient's platelets to recover and significantly reduce bleeding events."
Why can business analytics play such an important role in healthcare?
Statistics is at the core of modern medicine. Whether it is measuring the response of a sample group against a control group to decide whether a new drug outperforms the placebo, or whether it's tracking through a national database of asthma sufferers in search of factors leading to increased instances of attacks, the medical profession relies heavily on statistics-based predictions. Software such as IBM SPSS solutions have the tooling to crawl through this data and help medical professionals make more informed decisions.
As more hospitals and medical facilities switch to electronic record systems, the amount of medical data that computing systems can access mushrooms. This requires more sophisticated, powerful applications (such as those that can tie together unstructured data from various sources). The payback is a larger sample group, diminished margin of error, and a performance increase in the delivery of healthcare. Whereas in the past a hospital would have only had its own records as evidence when deciding on a course of action, now state-wide or nation-wide information can be mined.