Developing Standardized Data: Connecting the Silos

Abstract

We live in an electronic age where information is captured every second, and we have all learned to accept standards as a regular part of our lives. For example, the letters that we send through the postal service would likely never reach their final destination without a standardized format for addresses. In addition, when fire engines come from other municipalities to put out a fire in our neighborhood, we aren’t concerned with whether or not their trucks will be able to hook up to the local fire hydrants, because all connections are standard. Contrast this with the Great Baltimore Fire of 1904 where thousands of fire fighters from the surrounding cities and states were unable to assist as the fire raged for 30 hours, simply because their fire hoses were not compatible with the Baltimore hydrant connections. At present, we are witnessing an ever-increasing and soon to be overwhelming stream of data in our regulatory review pathways from electronic health records and clinical trials. Regulatory stakeholders, much like the fire fighters from 1904, need to develop those standard connections that will allow us to capture and fully utilize the wealth of available data in order to assist in addressing our most challenging health problems. While information capture typically calls to mind consumertargeted data collection, the medical and regulatory review settings also acquire massive amounts of data. Patients may be continuously monitored, and those data may be collected, stored, and used immediately for clinical decision making or used later for analysis of various metrics such as outcomes. Ideally, a patient’s medical information could be easily and automatically available to health care providers, regardless of the point of care; however, data from electronic health records captured in one medical facility are often not easily transferred to another. This clearly presents a hurdle in leveraging all available patient data in the health care setting. A similar hurdle exists for the data gathered from clinical trials where much of the data exist in ‘‘silos’’ because they are not collected consistently and may need to be converted from one form to another before submission to the regulatory agencies. Even essential data variables may be formatted differently between trials for a single product, across drug trials within a class, and for trials across drug classes. If these ‘‘silos’’ were connected, regulatory stakeholders would be empowered to more efficiently review data and ask more probing questions. One of the simplest examples of data standards challenges that regulatory agencies encounter is the representation of males versus females in clinical trials. The data may be presented in various ways such as ‘‘male and female,’’ ‘‘M and F,’’ ‘‘1 and 2,’’ or ‘‘0 and 1.’’ As one can imagine, inconsistencies of this sort can cause a host of problems when trying to group data from multiple clinical studies together and make assessments within and across therapeutic areas nearly impossible. Early efforts are under way at FDA to address these emerging data standardization needs. Several initiatives launched within the FDA’s Center for Drug Evaluation and Research (CDER) are aimed at the development of data standards and management of clinical data across specific therapeutic areas. For example, the Office of Clinical Pharmacology in the Office of Translational Sciences in CDER has developed an infrastructure for pharmacometric knowledge management in a subset of disease areas. This infrastructure includes data standards development, queryable databases, libraries of modeling tools, and archives of analysis results. One example of the success of this approach is the