The geographic proximity of patients’ homes to research sites, number of site visits, the amount of time required to participate in a trial, and the pandemic are some of the reasons why patients are apprehensive about participating in a clinical trial. These factors impede patients from visiting study sites, receiving scheduled dosing, or attending necessary on-site screenings. The site staff can often access study records, collect patient lab samples or conduct patient interviews, limiting site and data monitoring. Ideally, decentralized clinical trials approaches are selected and homogenized into a clinical protocol long before recruitment begins during the study design phase. The COVID-19 crisis pushed the sponsors to consider alternative patient-centric approaches to ensure the continuity of trials.
Identifying patients within and beyond the site’s database has been essential to support site-based recruitment and patient engagement. These capabilities directly affect individual studies and overall delivery strategies. Optimizing patient enrollments by improving patient recruitment rates offers a clear advantage that reduces the time to launch.
The Decentralized Clinical Trials (DCTs) model or the site‐less or virtual study model is a patient-centric trial aiming to eliminate the need for patients to travel to an investigational site. DCTs are a relatively new yet underutilized method of conducting clinical research taking full advantage of mobile applications, electronic monitoring devices, and online social engagements. This patient‐centric model is getting popular for helping to reduce the number of sites and study-staff, thus lowering operating costs, reducing patient dropout rates, and the time associated with site development and patient recruitment phase.
Adoption of cloud technologies can revolutionize a pharmaceutical company’s clinical trial outcomes. The benefits of a centralized collaboration platform are clear to see during the protocol development phase. Archiving the latest document with the most recent amendments in one central location can circumvent the confusion that ensues when everyone is trying to keep track of the newest version on their own. A Centralized cloud management system can save time for life science organizations conducting clinical trials with complex logistics such as multiple sites enrollment and remotely monitored participants.
Cloud services facilitate data standardization, presentation, and visualization that help clinical research organizations and other partner organizations integrate, share, and analyze data from clinical trials, adhering to data security, compliance, and flexibility.
The scalability of cloud data management services can be swiftly modified to cater to drug testing on new groups and larger populations. In the years ahead, both pre-and post-approval trials will only expand in size and scope as more patients join in. With the right cloud-managed services partner, pharmaceutical companies and businesses will serve patients to enter and succeed in this promising new landscape for clinical trials.
Addressing the problem
There is a need for an intelligent, data-driven integrated analytics platform that leverages insights from a huge pool of external and internal data sources. This platform should also facilitate a sponsor with data-driven insights to effectively assess the investigator, site, and protocol feasibility during clinical trial planning. Sponsors also need a solution that performs a what-if analysis during the study and suggests mitigating strategies to avoid cost overruns and overshot timelines.
Cognitive technologies such as artificial intelligence (AI) and machine learning are evolving with a promise of solving current industry challenges. These challenges include faster marketing of new treatment solutions, accelerating clinical development activities, including clinical trial planning and risk management using a data-driven analytical approach to perform a feasibility analysis of clinical trials. The platform can also minimize time delays by identifying suitable patient pools, sites, investigators, and KOLs for the study. This platform can also mitigate risks across the trial process by objective analysis of earlier trials and patient enrollment data. By analyzing the competitor data, manufacturers can gain more visibility on the previous trials conducted at sites by various sponsors in specific therapeutic areas. Recent research suggests that AI and automation could cut the cost of drug development by as much as 70 percent.
The solution implements a next-generation intelligent clinical trials feasibility AI platform with solid data, knowledge, and cognitive capabilities. The platform can facilitate sponsors with data-driven insights to effectively assess investigator, site, and protocol feasibility with harmonized internal and external data during the clinical trial planning phase. It can also enable sponsors to look beyond their internal databases by leveraging real-time insights from a vast pool of external data sources, identifying the risks, and making a go or no-go decision much earlier. Implementing this solution can result in fewer protocol amendments, providing a view of the best-performing sites & investigators, eliminating under or no patient enrollments sites and investigators, and rendering a bird’s eye view of the competitor trial landscape.
The platform can also analyze the data collected during the clinical study phases to provide a what-if analysis and suggests mitigation strategies to avoid extended budgets and timelines.
The platform derives its knowledge from a variety of ingested datasets that can provide real-time insights and facilitate data-driven decisions: