| | Jan-March 201719For the sake of patients, the health care system, and a company's bottom line, prevention of DDIs is keyIT and informatics systems which are not interoperable, it's not surprising there is no single complete source of DDI information for either doctors or patients to consult. Big data techniques can reveal `hidden' DDIsEfforts are also underway to use technology to reveal previously undetected or "unseen" DDIs. This approach involves using text and data mining, backed by techniques perfected in big data analysis, to identify possible DDIs. For example, one study used sophisticated algorithms to analyze the FDA's Adverse Event Reporting System. The algorithms looked for DDIs that might prolong the QT interval ­ a heart condition that can lead to a potentially fatal arrhythmia. Once identified, their DDI predictions were validated against electrocardiogram data from patient electronic health records (EHRs). The result was the discovery of eight distinct drug pairs that increase the risk of acquired long QT syndrome (LQTS), which had been previously unknown. Incorporating `real world' data Much like any big data analysis, `real world' data and evidence is critical to improving the accuracy of results. In drug safety, one of the most important sources of this real world evidence is EHRs. Within these records, clinicians document their findings from patient evaluation, as well as their prescribing of a drug and monitoring of the patient afterward. This is important because whether or not a potential DDI becomes an actual DDI depends on patient-specific factors ­ such as age, gender and what other medications they take. This is valuable real world evidence, and as shown in the LQTS example mentioned above, the availability of accurate EHRs--and systems that can mine these records-- will enable companies to identify and reduce DDIs, and ensure drug safety.Other approaches to capturing real world evidence already being tested include informatics-driven approaches to process input from multiple big data sources, including social media, and even employing machine learning techniques. Currently, these approaches are hard to replicate for many pharma CIOs, as they require deep analytics skills and capabilities. In addition, data standardization remains a challenge. Many pharma CIOs look forward to the day when DDI data is standardized and compiled into a universally available resource. But, as this day may be some way off, CIOs must evaluate what they can do to improve PV today. Drug safety is absolutely critical to both patients and manufacturers, so CIOs must embrace new techniques to improve the efficiency and accuracy of DDI monitoring and prediction. SCChristy Wilson
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