clinical-trials-2017-scientifictracks-abstracts

37 / 46

Page 80
Notes:
conferenceseries.com
Volume 7, Issue 5 (Suppl) J Clin Trials, an open access journal
ISSN:2167-0870 Clinical Trials 2017
September 11-13, 2017
September 11-13, 2017 San Antonio, USA
4th International Conference on
Cl inical Tr ial s
Future visions in clinical trials: The role of artificial intelligence and how it changes clinical research
Kim Walpole
USA
The synergy of advances in diverse areas of artificial intelligence holds considerable promise for improving the efficiency
and efficacy of clinical trials. Of particular interest to is improving the design and execution of protocols. Poorly
constructed protocols lead to poor research and costly amendments, protocol deviations, delays in obtained appropriate data
and more.
Opportunities for Artificial Intelligence to Improve Outcomes: The system is designed to accept a protocol
document, scan it and assess for opportunities and threats. This includes, for example, consistency between the time and
events (T&E) table and the in-text description of events, and consistency between the synopsis and the body of the protocol.
Natural language processing tools are helpful in this regard. Additionally, AI tools are used to scan completed protocols based
on a similarity. Similar protocols coupled with published and internal data are mined to predict the degree of success from the
proposed design as well as identify any problematic items.
Addressing Enrollment and Retention: Clinical trials often fail because of poor enrollment. can warn the study
designer, for example, that particular inclusion/exclusion criteria may be too limiting for subject enrollment can save the
sponsor considerable funds and time that can be invested in a better clinical design or an alternative effort. Clinical trials also
fail because of poor subject retention. To address this, we have created and are refining what we call a Patient Burden Index
(PBI), which is an AI-derived quantitative measure of the impact that the protocol design has on the subject. Protocols can
then be scored based on their PBI, and alternative designs can be explored. We believe this is an entirely novel approach to
improving subject retention and cooperation at all stages of a clinical trial.
Executing the Trial More Effectively and Efficiently: Once the trial has started, AI will play additional roles to help ensure a
best outcome. In our case, our client partners provide a protocol that is uploaded into our system. The system then maps the
T&E schedule into a dashboard-driven user interface that shows when each event is to be completed. With available personnel
assignments and schedules, the system can also identify who is to do which task in support of which patient at which time.
Practitioners are alerted ahead of time for events, which helps to reduce protocol deviations.
Biography
is Kim’s 3rd company yet her passion for fixing logistical problems with clinical trials drives her as if it were her first. Her expertise in organizational
development enabled her build Corporate Development Programs for companies like Pfizer, Merck and Wyeth Ayerst. Ms Walpole understands the complex clinical
trials space and the needs of sponsors, CROs and sites. Her goal is to use technology to as a catalyst for helping organizations in study design and execution so
that patients can be exposed to better quality treatments, faster.
Kim Walpole, J Clin Trials 2017, 7:5 (Suppl)
DOI: 10.4172/2167-0870-C1-019