Now that the holiday dust (and snow!) has settled, the medical research community is buckling down for a busy and productive year. This past week brought forth a flurry of ideas and activity on everything from:
- Evaluating the research implications of HHS’s new rule on what constitutes “meaningful use” of health information technology; to
- Moving the next phase of FDA’s Sentinel Initiative to track product safety forward; to
- Driving more and better collaboration among patient registries and biobanks.
Atul Gawande refers to this systematic exchange of best practices as “process science.” He describes it as applying the same scientific rigor currently placed on the discovery of new medical solutions to delivery of those solutions. But between discovery and delivery, we believe there is another step that is just as critical to this equation – translation.
Translation, the bridge between basic and clinical research, is a seminal process that drives the engine of delivery, but one in which we often lose the most time and resources. Essentially considered phase two of discovery, it is the application of ideas and insights generated through that rigorous science Dr. Gawande talks about to the treatment and prevention of human disease.
Regardless of how exact and scientific the initial discovery process may be, however, if valuable data sets and research models uncovered though that process aren’t shared and systematized, their translation into solutions that providers can deliver will take years longer than necessary.
The NIH is trying to incentivize more translational research through its Roadmap initiative and specific programs such as the Clinical and Translational Science Awards and the new Therapeutics for Rare and Neglected Diseases (TRND) program. But there remains an enormous amount to be done.
This week, we heard some great ideas for getting those research models and data sets flowing in ways that could considerably reduce the time and improve the standard operating procedures between discovery and delivery. They are:
- Supporting the exchange of de-identified patient data through electronic health records (EHRs) for research purposes, and ensuring that the final meaningful use rule includes language to that effect
- Reevaluating the informed consent process to more clearly distinguish between the use of electronic information for public health surveillance versus scientific research
- Building an empirically evaluated methods library for innovative clinical trial models
- Developing standards and guidelines for biospecimen collection and management so those processes can be reproduced
- Identifying and training biospecimen champions at hospitals and health systems where procedures take place -- possibly even establishing a national “concierge resource” for biospecimen collection
- Connecting biorepositories with robust clinical data sets, such as those available through the National Cancer Institute's cancer Human Biobank (caHUB)
- Expanding and improving ORDR’s “registry of registries,” building it out with individual researchers across all rare diseases and making it fully query-able
