Pharmacy Practice, Education, and Research in the Era of Big Data: 2014-15 Argus Commission Report

INTRODUCTION The American Association of Colleges of Pharmacy Argus Commission is comprised of AACP's five immediate past presidents and is annually charged by the current President to examine one or more strategic questions related to pharmacy education. The term "Argus" refers to a character from Greek mythology purported to have 100 eyes and be "all seeing". (1) President Patricia Chase charged the 2014-15 Argus Commission with an analysis of how the use of "big data", or bioinformatics sciences using large and diverse data sources, might affect pharmacy education. The Commission met in person in October 2014 and divided the analysis into the distinct study of big data and the primary missions of academic pharmacy: education and specifically the assessment of teaching and learning, patient care practice at the individual and population levels, and research and graduate education. The work was framed by the science plenary keynote by Atul Butte, (2) MD, PhD from Stanford University, presented at the 2014 AACP Annual Meeting, and also included key informant interviews and literature searches. It became clear through these analyses that the availability over the last decade or longer of large databases and super computer computational power has influenced research programs across many of the areas of scholarship in the pharmaceutical and translational sciences (e.g., computational biology, high throughput drug screening, pharmacogenomics and other "--omic" sciences). Education and practice have, for the most part, only begun to be affected by the big data revolution. BIG DATA: WHAT IS IT? In the book, Big Data: A Revolution that Will Transform How We Live, Work and Think (3), Mayer-Schonberger and Cukier note that "there is no rigorous definition of big data". They continue to frame how they approach the topic in their acclaimed book noting that: "big data refers to things one can do at a large scale that cannot be done at a smaller one, to extract new insights or create new forms of value, in ways that change markets, organizations, the relationships between citizens and governments, and more." They continue that this era will require society "to shed its obsession with causality in exchange for simple correlations: not knowing why but only what." They liken the impact of this era as no less than how the telescope and microscope changed our comprehension of the universe and appreciation of the presence of germs, respectively. Perhaps, "big data" should be referred to as our "datascope". The authors continue their clarification by noting that the characterization by some of big data being artificial intelligence is inaccurate. Rather than "teaching computers to think like a human", the analysis of huge data sets allow people to infer probabilities and make predictions. They identify the future potential of the use of big data to diagnose illness and identify treatments, certainly activities that resonate in the realms of pharmacy and health care. The journal Health Affairs published a themed issue on big data in July 2014, and Roski, Bo-Linn, and Andrews (4) described three defining features of this phenomenon as the three V's: volume, variety, and velocity. Volume relates to the availability of massive amounts of data which requires flexible and easily expanded data storage, retrieval, and management systems. Variety refers to the fact that data come in many formats. In health care this is structured and free-text data (e.g., insurance claims, electronic health records (EHRs), diagnostic images, genomic information, social media, personal fitness device data streams.) Velocity refers to the characteristic of the big data infrastructure that makes it possible to manage data more flexibly and quickly. The authors distinguish the use of data from a single large dataset such as Medicare claims data, which they would not characterize as big data analysis, from the combination and analysis of data from the Center for Medicare and Medicaid Services, data from electronic health records (EHRs), and sources of additional data such as a population's fitness or nutrition information for which the authors believe there will be significant advances in health care in the near future. …