Ways Pharmaceutical companies are using Data Analytics to drive innovation & Value

Optimizing and improving the efficacy of clinical trials are costly and time-consuming to run and pharmaceutical companies want to ensure that they have the right mix of patients for a given trial. Big Data can assist in identifying the appropriate patients to participate in a trial (through analysis of demographic and historical data), remote patient monitoring, reviewing previous clinical trial events, and even helping to identify potential side effects before they become a reality. Global management consultancy McKinsey says that patient big data could also help pharmaceutical companies take into account more factors, such as genetic information, to help companies identify niche patient populations to help speed up and reduce costs of trials

Target specific patient populations more effectively With information from genomic sequencing, medical sensor data (a device that can, for instance, be worn and track physical changes in an individual during treatment), and electronic medical records more readily available than ever before, pharmaceutical companies are able to dig into the root causes of specific pathologies and realizing that one size truly does not fit all. Within any disease or condition, different patients will respond differently to treatments – for a host of reasons. Combing through the data from these different sources can allow drug companies to spot trends and patterns that will allow them to come up with more targeted medications for patients that share common features. For instance, Pfizer is combing data from electronic medical records, clinical trials, and genomic data to spot opportunities for “drugs for specific patient populations”. Using this approach the company was able to identify that a small subset of lung cancer patients had a specific genomic defect – a mutation in their ALK gene. Using this insight, Pfizer developed 6 Ways Pharmaceutical Companies are Using Big Data to Drive Innovation & Value Page | 6 Xalkori specifically for lung cancer patients with the ALK gene mutation, which was approved for use by the Food and Drug Administration in 2011. “Had this compound been tested against a broad spectrum of lung cancer patients, it likely would not have been found to be effective," Pfizer CIO Jeff Keisling was quoted as saying in Information Week. "With this analytics-based approach, it was found to be very effective, but we had to be able to identify a subset of cancer patients with a specific gene mutation who previously did not have this treatment option."

Better insight into patient behavior to improve drug delivery and effectiveness and healthcare outcomes Greater amounts of data that companies can tap – including information from remote sensor devices - coupled with advanced analytic models, mean that pharmaceutical manufacturers can gain much greater insight into existing patient behavior. The company can then use that information to design services targeted to different demographics or at-risk patient groups in order to improve the efficacy of treatment. For instance, one ontological drug manufacturer found that many patients were forgetting to take their medicine at the required time, according to the founder of MediSafe writing for Wired magazine. The manufacturer came up with a cloud-based mobile solution that would push messages directly to patients’ mobile phones reminding them to take their medication, thus improving adherence to drug protocols. Meanwhile, according to Medtronic’s VP & GM Deep Brain Stimulation Lothar Krinke, his company has been working on designing technology that can “interpret certain electrophysiological parameters and management data.” He cites how Medtronic’s Neuromodulation business unit uses this technology to detect a spinal patient’s posture, which can then help physicians determine the amount of stimulation the patient requires.