Photography: Getty Images Illustration: Paul A. Belci
|
An aging population, the end of the "blockbuster drug era," and mounting drug discovery and development costs are pressuring
organizations to dramatically improve clinical trial efficiency. Innovation in trial design and management to address the
growing pressures requires a new approach to information management—one that is collaborative and allows information to be
aggregated, accessible, and reusable.
Next generation information management also needs comprehensive data mining and analysis tools to support broader and deeper
understanding of the impact of the trial drug on patient populations. Collectively, integrated data management and the associated
new processes, business models, and technology architecture are part of what industry is calling enterprise health intelligence.
Historically, pharmaceutical organizations have excelled at collecting and analyzing vast and complex data required for clinical
trials, while complying with the detailed regulations at many levels. The result, unfortunately, is robust data management
at the study level—creating silos of data, most in different data formats, and using different coding schema. To move beyond
siloed clinical trials data and study focused analysis, an organization needs to start at the top to implement an approach
to enterprise-wide management of internal and external information.
|
Real-world health care data in the form of administrative claims data, lab data, electronic medical records, and the longitudinal
electronic health record (EHR) have important roles to play. In de-identified forms, these data can be incorporated into standard
practice to support all stages of research, development, and postmarket support. Protected health information accessed through
models involving patient consent will play an important role too in areas such as personalized medicine and outcomes management.
When all these changes and technologies are put into place, information becomes a strategic asset that takes time, cost, and
risk out of clinical trials. Following are a few examples of how health intelligence solutions can improve clinical trials:
- Large-scale patient population databases enable study designers to test clinical trial protocol inclusion/exclusion criteria
to optimize protocol design.
- Research sponsors can easily identify investigators from sites known to have eligible patients, which speeds patient recruitment.
- Existing business relationships with organizations having HIPAA and IRB compliant business processes for identifying and recruiting
patients reduces clinical trial time and costs.
- Broader clinical insights can be derived from studies that include noninterventional study control groups' data.
