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What's the Role of a Data Manager in a Risk-Based Model?

Risk-based monitoring (RBM) is finally starting to gain momentum as the standard for trial execution within the industry. In fact, a few of the TransCelerate companies are already working in a model where RBM is deployed across all trials, and RBM is also finally starting to take off with mid-size to small pharma. Before long, RBM will become the de facto standard for trial execution. Because on-site monitoring practices were the first to change in the move toward an RBM-based strategy, the initial impact has been absorbed by clinical operations teams. As companies become more sophisticated in their approach to RBM and recognize that RBM extends beyond just on-site monitoring to all types of data monitoring, there is a group of individuals who are critical to the process and cannot be left out of the conversations—your data managers, who ultimately are the steward of data quality and data integrity in a trial.

Historically, data managers have not had a large voice at the table during discussions and decisions about protocol design, risk assessments and monitoring strategies. However, for organizations to successfully implement RBM, they need to ensure data managers are included in the process. If one of the main objectives of implementing a quality risk management approach (inclusive of RBM) is to achieve higher data quality, how can we do this without the critical input of the data manager?

Per recent guidance, we know that the risk-assessment process should occur BEFORE protocol finalization. Therefore, the first conversations in which you should involve your data managers are those around protocol design and risk assessment. During protocol design, we can gain insight as to which data elements might bring no value and complicate execution. During the risk-assessment process, critical data and processes are discussed, and data managers can provide valuable insight about the trial elements that will result in the highest risk to the data. They will also be able to determine which non-critical data elements support critical data and to assess the detectability of risks based on the data being collected. Lastly, they should be responsible for contributing to the selection of the appropriate KRIs to monitor site performance related to data collection.

During the trial, data managers need to participate in ongoing risk reviews. Because data managers look at the data through a different lens than other groups, they will be instrumental in identifying data trends or patterns that differ from those identified by central monitors. They identify data trends that might be indicative of poor trial design, lack of site training, design issues with the case report form, etc. In a risk-based model, data managers will also be instrumental in supporting and ensuring an adequate level of risk surveillance and quality control and that findings are shared cross-functionally.

At the end of the study, data managers should participate in lessons-learned discussions regarding the successes and failures of the RBM model and implementation strategy so the organization can continue to refine its RBM approach.

As you build out your process and organizational structure for implementing RBM, don't forgot that your data management team needs to have a prominent seat at the table in order to achieve organizational success.

If you would like to learn more about how Bioclinica's ClearSite product helps data managers in a risk-based model, please contact me at

RBM 101: What is Risk-Based Monitoring?


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