Electronic health records offer one of the biggest opportunities for uncovering new insights. The growing availability of real-world data has generated tremendous excitement in health care. By some estimates, health data volumes are increasing by 48% annually, and the last decade has seen a boom in the collection and aggregation of this information. Among these data, electronic health records (EHRs) offer one of the biggest opportunities to produce novel insights and disrupt the current understanding of patient care.But analyzing the EHR data requires tools that can process vast amounts of data in short order. Enter artificial intelligence and, more specifically, machine learning, which is already disrupting fields such as drug discovery and medical imaging but only just beginning to scratch the surface of the possible in health care.Lets look at the case of a pharmaceutical company we worked with. It applied machine learning to EHR and other data to study the characteristics or triggers that presage the need for patients with a type of non-Hodgkins lymphoma to transition to a later line of therapy. The company wanted to better understand the clinical progression of the disease and what treatment best suits patients at each stage of it. The companys story highlights three guiding principles other pharma companies can use to successfully deploy advanced analytics in their own organizations.

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