Many facets of a clinical trial are unpredictable. Patient enrollment, a critical and often troublesome step, can vary widely within regions, countries and sites, as well as, across therapeutic areas. This unpredictability increases the complexity of clinical trial planning.
Key assumptions are made at the outset of setting up a clinical trial surrounding country and site selection. What if those assumptions shift? Do you know with a certain level of confidence how changes in those assumptions will impact the execution of the study?
This is why scenario planning is so valuable, and critical to helping to manage the variability in clinical trial setup decisions. Scenario planning attempts to eliminate the two most common errors made in any strategic analysis – overprediction and underprediction. Scenario planning puts the power back into the hands of clinical managers by providing them with an opportunity to explore possible outcomes based on a specific combination of events. In essence, it enables them to find the best strategy while balancing risks, resource allocations, and costs, as well as, giving them the insights needed to discuss with internal stakeholders the impact assessment of various plausible scenarios.
It is this scenario planning based on a wide range of possibilities with specific detailing that can help clinical managers define a series of scenarios that will help reduce errors in the optimal setup of a clinical trial.
Oracle’s study planning tool available in Oracle Health Sciences Activate Cloud Service, provides clinical managers the ability to automatically plan and forecast key study startup dates, generate initial bid estimates for outsourced studies in the case of a CRO organization, and engage in scenario planning for critical study analysis.
Key study attributes (e.g., countries, anticipated number of sites, therapeutic area, site characteristics, etc.) are entered into the study planning tool and a generated list of anticipated dates for key milestones, as configured by the organization, are automatically generated based on machine learning algorithms (see Fig 1). Based on these insights, clinical managers can adjust parameters to find the right balance to ensure on-time completion of milestones within resource constraints.
Figure 1: Forecasting key Study Startup dates
These algorithms have been built using hundreds of thousands of data points gathered over a decade of industry experience gained through the activation of over 170,000 clinical sites in thousands of global studies – and they continue to learn!
Machine learning provides critical operational insights, allowing organizations to learn and adapt. Ultimately, these insights allow organizations to transition away from subjective decisions to data-driven decisions, by optimizing activities in the planning and execution of clinical trials.
Alternatively, clinical managers can choose to leverage milestone dependency maps that provide plans tailored to specific organizational workflow processes, which allows for intuitive scenario modeling. Forecasted dates can be accepted or overridden as needed to support specific planning needs (see Fig 2).
Reporting and visualizations allow for easy export of the summarized data and quick incorporation for sponsor or executive review.
Figure 2: Intuitive visualizations to support study startup planning
With patient recruitment broadly acknowledged amongst clinical professionals as a critical trial success factor, many clinical managers are recognizing the need for greater proactive planning measures. This is particularly acute during study startup and essential in order to mitigate risks. The study planning tool is a valuable addition to the clinical managers planning arsenal.
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