In an increasingly complex clinical trial landscape, it’s becoming more and more critical for research organizations to evaluate and enhance their trial practices. Many organizations now measure their operational performance to gain an objective view of their clinical trial processes and more easily identify areas in need of improvement.
However, while tracking operational performance metrics can be incredibly valuable, it can be difficult to find the right starting point. Organizations may want to get started making large improvements immediately, but it’s important to consider all of the necessary steps to effectively measure operational performance before getting started.
Doing your due diligence before you begin tracking will help you avoid making unnecessary mistakes that can lead to an unfocused analytics strategy. In this excerpt from our eBook, Analytics in Clinical Research: Using Data to Inform Your Research Operations, we address three common mistakes organizations make when measuring operational performance.
1. The metric is not clearly defined
Organizations often struggle to clearly identify what they want to measure and for how long. Your data can easily become murky without clearly defined parameters for collection. Take the time upfront to define the question you’re trying to answer, the metric that will answer your question, a start and end date for collection and an effective methodology for measurement.
2. Lack of communication between stakeholders
Measuring operational performance often requires teamwork, particularly when tracking staff effort. Without proper communication, data can be skewed by individuals who unknowingly enter the wrong information or are not motivated to enter the adequate amount of information in the correct format.
Inadequate communication with the organizations funding your research can also lead to trial error and strained relationships. It’s essential to keep lines of communication open with the funding bodies to ensure their goals and timelines are feasible for your operations. For example, if you’re working with a sponsor and they ask that the trial is opened in 100 days, communicate with them upfront about any concerns or questions you have with their requested timeline (e.g. do they mean calendar days, or business days?).
3. Change is not universal for every protocol
One of the areas that can be particularly difficult for an organization is considering all of the confounding factors that affect each different protocol. Many organizations often use a trial and error approach to tracking metrics by looking at specific problem protocols to find an amplification of a larger problem. While this can sometimes work, it can also lead to a lot of wasted time and inconclusive and/or frustrating results. Ideally, it’s best to have a statistician analyze past protocols, assess the many factors that influence protocol performance, categorize the protocols by type and communicate their findings to all stakeholders.
Avoid these mistakes with an effective strategy
It’s essential for research organizations to dedicate time upfront to evaluate and define the steps necessary to measure the right data in the most effective manner. Download our free eBook, Analytics in Clinical Research: Using Data to Inform Your Research Operations, to learn best practices for starting your analytics strategy, as well as tips to generate buy in for organizational stakeholders and metrics that can answer critical performance questions. Get your copy today!