Last month, Perficient attended the Oracle OpenWorld conference and presented at several sessions. One of those sessions was on how clinical trial management systems (CTMS) can support risk-based monitoring (RBM) strategies at life sciences companies.
This guide analyzes how artificial intelligence – including machine learning – can be used by pharmaceutical and medical device companies to improve the clinical data review and cleansing process.
In addition to depicting the ultimate IT landscape for RBM, Param Singh, Perficient’s Director of Clinical Operations Solutions, shared specific scenarios on how Siebel CTMS can be used to alert sponsors and CROs of potential issues with sites, such as subject enrollment and protocol deviations.
While the session did dispel some myths and misconceptions about RBM, based on the audience’s questions and comments, it was evident that organizations are struggling to develop their own RBM strategies. According to the attendees, there is a lack of clarity from regulatory agencies about the use of RBM, and frankly, not a good enough understanding of RBM within the industry itself.
If you too are finding yourself perplexed about RBM or are interested in learning how you can leverage your clinical trial systems to mitigate risks, please reach out to see how we can help.