With CAMD as an exemplar of the opportunities available, Kush delineated priorities for clinical research to bring such successes to scale across the field. Data quality should be built into the clinical research system from the beginning, and those individuals involved in the research process (including site personnel, the project team, reviewers, and auditors) should be trained and educated to incorporate data quality measures, including standards for data collection, into their work. Data collection should be simplified with well-defined requirements for the necessary data set and standardized formats. Data should be handled only the minimum amount throughout the process, thereby reducing potential errors due to transcription or reentry. Additionally, Kush noted, data quality measures should be considered and incorporated throughout the postmarketing process.
In her final comments, Kush emphasized that greater standardization offers considerable promise for clinical research moving forward, particularly in leveraging EHRs for research. As exemplified by CAMD and similar efforts, standardization facilitates both data sharing and data aggregation, presenting the opportunity for groundbreaking research efforts to identify new treatments and therapies with larger, standardized datasets.
In her discussion of translational informatics, Mia Levy focused on her experience at the Vanderbilt-Ingram Cancer Center, where genome-directed cancer treatment is the focus of the Center’s work. Currently, Levy noted, genomics is playing an ever more important role in the care of patients across the cancer continuum; cancer diagnosis, treatment selection, and care are all experiencing an era of genomics.
Traditionally, cancers have been categorized and treated according to the site of their origin and their histology. Now, the molecular subtypes of cancers are determining the course of care, and the molecular variance being discovered in these subtypes is vast. Levy noted that for those patients with characterized molecular subtypes, their mutations are considered actionable. Either an FDA-approved, standard-of-care therapy is available to treat the subtype of their cancer or a medication for their specific mutation is in the clinical research pipeline. However, in this genomic era, even patients for whom a mutation has not been identified are also considered to be actionable, in that they are spared from receiving ineffective, costly, and potentially harmful treatments. These developments hold great promise for the field of cancer treatment, but the process of implementing a system capable of processing and managing this information poses an entirely new range of challenges to those involved in translational informatics.