The Gap Between Clinical Research and Practice

There is a growing gap between oncology research and practice, particularly when clinical data is siloed. Creating a continuous learning environment requires clinicians who have access to guidance that is relevant, timely, useful and evidence-based. The challenge comes with the growing complexity of healthcare data. The knowledge base for medicine is extensive and continues to expand, meaning that accessing the right data at the right time for the right patient is an arduous task.

We have been witness to an explosion in the volume of medical knowledge, and the pace of discovery is expected to continue. The doubling time of new medical knowledge in 1950 was estimated at 50 years; by 2010, it was estimated that it was occurring in as little as 3.5 years. The projection for 2020 is that medical knowledge could be doubling in just 73 days. Not only is the volume of data growing, but also the variety. With increased linkage of private health records with public health records, registries, and clinical trials, health organizations have massive amounts of intel at their disposal. It is pushing the limit of practitioners’ ability to manage new information and apply it to regular care.

Another cause for the gap between research and practice stems from differences between patients who participate in clinical trials and those that do not. For oncologists who treat cancer patients, clinical trials are the largest source of information to advance care and treatment. However, only 3 to 5% of the people diagnosed with cancer participate in a clinical trial. Furthermore, these patients are disproportionately younger, and their cases are less complex with fewer comorbidities and rare cancers than the full range of patients. This means the information gleaned from these trials is essentially built in a vacuum and may not be useful outside of the scope of the trials.

As a result, much of the activity in the creation of Learning Healthcare Systems (LHCS) has focused on the discipline of oncology. LHCS are already shrinking the gap. LHCS are built by leveraging shared data and insights across boundaries to drive more efficient medical practice and patient care. For LHCS to be effective, privacy must be managed when sharing data. As a great example, the American Society of Clinical Oncology (ASCO) has developed a big data solution to provide practitioners with real-time guidance to deliver optimal results.

Essential to creating a LHCS is the incorporation of high quality de-identified data which maintains granularity without compromising patient privacy. Only with de-identified data can healthcare organizations truly develop an efficient, effective LHCS. Learn more in our latest white paper, Opportunities Found in Learning Healthcare Systems.

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