Managing a clinical trial of any size and complexity requires efficient trial management. Yet for the past three decades, life sciences companies have built and then continuously added to inefficient processes, often cobbling together critical system support functions. At the same time, the business environment and regulatory factors have made the clinical trial landscape progressively more complex. A focus on outsourcing partnerships, in addition to a significant number of mergers and acquisitions over the last 10 years, has added to this complexity. In 2011 alone, despite uncertainties on both economic and regulatory fronts, over $200 billion in healthcare related M&A deals were announced.
Simultaneously, health authorities are demanding better, faster disclosure of data. In September 2012, the U.S. Secretary of Health and Human Services delegated its clinical trial monitoring authority to the U.S. Food and Drug Administration (FDA). Though the full implications of this change are not yet known, increased GCP inspections and more stringent compliance requirements are
expected. Drug development teams are also faced with ensuring smart strategies for the emerging challenges, including mobile health, social media, electronic health record data, genomics information and the tsunami of big data.
In order to meet these challenges, life sciences companies must improve the efficiency of internal clinical management processes, reduce manual reporting and human intervention, and provide better visibility across the clinical trial landscape. Conducting a clinical architecture assessment allows companies to get a full picture of see what current systems and processes they have, understand what is working, and more importantly critically identify the gaps of what is not working and what is missing. It provides the team with a path roadmap forward to synchronized improvement in both improving their systems and their operations, enabling effective change in the near and long term. When these two facets of systems and operations are optimized and in sync, the resulting improvements are greater than the sum of optimizing each individually.