[0:13]We uh protocol feasibility and child planning is traditionally approached is uh research team planning teams usually starts with benchmark studies.
[0:23]And uh what they do is they try to find the benchmark that's closest to the study in question.
[0:29]They have a research team that goes at incidence prevalence, that goes to look at the reports that are published, looks at historical data to determine what the protocol feasibility would be, what the rate of recruitment would be, and how long it will take, uh to run the child.
[0:46]Now, the process is very labor intensive, it's very time consuming, and by the time the research team is done with their analysis, the research team has already made another change to inclusion or exclusion, and they have to go at the whole process all over again.
[1:03]What we have also seen is the accuracy of the results that they have come up with is also very low.
[1:10]And reason being, they're missing it misses the temporal impact because the benchmark may have been done three years ago, five years ago, and we live in a very different world today.
[1:20]So, what we did at knowledgent was we said, what if we were to reimagine uh protocol feasibility and child planning to be specific with all of the technology that's available to us and all of the data that's available to us, how would we approach it?
[1:37]And what we did was we, um, we brought it to our lab.
[1:41]Uh, we had a team of data scientists, team of researchers go at it.
[1:47]Uh, we broke the problem down to its first principle to truly understand what's really driving the rate of recruitment, what's really driving the feasibility here.
[1:57]And, uh what we saw was we saw that there are four key drivers, uh that are playing in.
[2:02]One, it's the inclusion, exclusion criteria of the study. Two, it's the incidence prevalence. Three, it's the competitive landscape and four, it's the investigator enthusiasm about it.
[2:20]The end product was our intelligent child planning application, uh where you can in real time put in inclusion, exclusion criteria, select your indication and see what the rate of recruitment would be at the study level, and then go down and look at what the the recruitment will be at the country level and site level, and it has options in enough flexibility where you can plug and play customize, uh, customize it to to things, uh that are specific and unique to you or unique to the site, uh to help you better plan and, uh better optimize your clinical trials so you can get to cure faster.
