Managing clinical trials is an enormously complex endeavor and requires sufficient business acumen and rigorous decision making.
With high financial stakes and increasingly aggressive timelines, decision making becomes exceedingly vital. The impact of decisions can make the difference between successful clinical trials versus trials that fail; therefore, decisions must be thoughtful and methodically approached, and deal less with gut instinct and be based more on solid data and complex modeling. Enormous sets of data constantly must be analyzed from different manual and technical systems. Obtaining the data needed for analysis is only half the chore—integrating the systems and making sense of huge amounts of data require interpretation and a trusted partner.
Having enormous amounts of data is fine, but the data must be generated into usable metrics for them to be useful tools for decision making. Multiple-point solutions that don’t integrate are similar to a room full of people, all of whom speak different languages. Without interpretation or system integration, the data is useless.
On average, there are seven eClinical systems used for a clinical study. These systems collect data imperative to the conduct of a study, including IVRS, eTMF, remote monitoring, document management and EDC. There is great value to these systems, but while they may solve individual work flow issues in their own space, they actually may create other issues in the larger picture. Savvy pharmaceutical companies and CROs are starting to recognize the major challenges spreadsheets and single-point solutions pose and are exploring how and when to transition to a technical platform solution. Regrettably, the single-point solution model still exists without considering the long-term impact it will have, in terms of integration or the amount of manual work needed.
In addition to having too many individual solutions, many companies have access to data but do not understand how to integrate and leverage those data for smart decision making. For example, data analysis can revolutionize patient recruitment by using statistical modeling to estimate the cost and time required to randomize a certain number of patients. This type of interpretative analysis can be leveraged to show the probability of meeting target recruitment goals in multiple scenarios. If you do nothing, your probability of success looks one way over a set period of time.
To change the probability, you have multiple options. Plotting out several options and seeing how each option increases your success and shortens your timeline is a valuable decision-making tool. If meeting your timeline is most important, you can see the option that best fits. If the goal is to shorten the timeline, a more aggressive recruitment option is needed. By having the options plotted out, the decisions can be influenced by actual data and will not rely as heavily on guesswork.
Making sense of data is essential to making data valuable for decision making. But even more important is who you partner with to evaluate those data. Clinical research is a complex industry that requires deep domain expertise and a genuine passion for helping accelerate approval of new medications. This takes experienced people who truly understand the global clinical research environment and who, over the years, have developed “time proven” and “field tested” technology that will scale to meet the rigor of globally conducted clinical trials.
A trusted partner will know what works and, most importantly, what won’t work—especially when it comes to developing and implementing technical platform solutions that make an impact on decision making, which very often determines the success or failure of a clinical trial.
Ashley Tointon directs patient engagement programs at ePharmaSolutions, combining traditional methods with innovative, data-driven techniques to provide sponsors high-impact, cost-effective recruitment and retention programs. She has more than 18 years of patient recruitment and project management experience supporting clinical trials and the pharmaceutical industry.
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