The emergence of big data, as well its advancement approaches and technology, is providing pharmaceutical companies with an opportunity to gain novel insights that can enhance and accelerate drug development. It will increasingly help government health agencies, payers, and providers to make decisions about such issues as drug discovery, patient access, and marketing. From our unique vantage points at Genentech, a leading biotechnology company with a major data sciences practice, and The Data Incubator, a datascience education company that places and trains data scientists, we have seen how the pharmaceuticals industry has leveraged bigdata for some potentially revolutionary advances and the challenges it has faced along the way.
For the industry, the biggest challenge by far has been talent: upgrading skill sets from those sufficient to analyze relatively small amounts of clinical trial information to those required to gain insights from the vast amount of real-world data, including unstructured data such as physicians’ notes, scans and images, and pathology reports. The pharmaceuticals industry has seen an explosion in the amount of available data beyond that collected from traditional, tightly controlled clinical trial environments. To be sure, anonymized insurance-claims data and electronic health record (EHR) data has been accessed and analyzed for many years. But in the past, EHR indformation was often limited to a single research institution or provider network, and obtaining the data needed to help answer a specific research question usually involved a tedious and inefficient process. While much still needs to be done to create standardized methods for sharing and making sense of anonymized EHR and genomic information across providers, it is now possible to link different data sources, which allows complex research questions to be addressed.
For example, the analysis of comprehensive EHR patient information collected in real time during doctor or hospital visits provides an opportunity to better understand diseases, treatment patterns, and clinical outcomes in an uncontrolled, real-world setting. These valuable insights complement those gained from clinical trials and can provide an opportunity to assess a wider spectrum of patients that are traditionally excluded from clinical trials (e.g., elderly, frail, or immobile patients, as well as people with rare indications and diseases not yet studied in clinical trials). It also allows companies to assess real-world challenges that cannot be observed in a clinical trial, such as drug compliance and the utilization of health care resources.
While these advances are generating great opportunities, they also pose resourcing and capability development challenges. One of the biggest is how to make the transition from legacy technology and analytical competence to more-powerful and sophisticated analytical tools and analysis methodologies.
Historically, the pharmaceutical industry has recruited SAS programmers who have executed well-defined analyses of clinical trials in a standardized, efficient manner. This worked well, given that clinical trials have been designed to answer questions about efficacy and safety with clean data sets in an industry-standard structure with few missing values.Read More On hbr.org