27 Feb 2020 Blog
More manpower = less development time? Not necessarily.

Hiring more manpower is not the answer to getting your clinical trials’ timelines back on track. If anything can help, that would be the adoption of technology and more specifically, AI. The new way to cut the timelines of clinical trials short is to leverage artificial intelligence to drive the processes.

Manage Data to Manage your Trial

Let’s look at how AI can be of help to manage clinical trials which need to adhere to regulatory compliance requirements as well as regulator-approved data standards & reporting. Not to forget the challenges with patient recruitment, data sources and data collection and aggregation. During a clinical trial, a large amount of data gets collected across the variety of eClinical systems, wearable and external sources, requiring the ability to manage, ingest and standardize it without delays.

Clinical trials stay compliant only when the system can check all the input data, verify its source, standardize it and ensure its quality and accuracy as per protocol parameters. AI not only helps to standardize the data but also learns to detect any deviations as it processes the data from the various sources to create a single source of truth which is compliant with regulatory standards.

Most clinical studies also face challenges with time and cost overruns, while study quality itself refuses to be maintained at an optimum level. All these challenges can be resolved and the success of a study can be ensured when we have access to and oversight of ready and relevant information. Apart from ensuring the success of the study, such a capability would make it possible for trial managers to not be overwhelmed by copious amounts of data and to quickly respond to any crucial red flags and prevent costly delays by resolving their root causes.

Development Time manages itself

When data gets captured and integrated with all the other data in the data repository, clinical data management processes turn seamless and on time. Quick access to reliable data helps all the study stakeholders to focus on clinical tasks of higher value and regularly monitor their KPIs to coordinate all stages of the trial. AI helps here too by matching the real time data to historic data as a benchmark and flag any issues in real-time, making immediate corrective action possible. As AI helps the study to stay on track and meet its milestones, it can also help with the site and patient outcomes by finding the most relevant populations.

With AI, we can also enjoy end-to-end automation and faster clinical trials along with its automated analytics which offer actionable insights which power further improvements in the clinical trails’ processes. All these would help to keep the development on schedule and the clinical trial on track.

About MaxisIT

At MaxisIT, we clearly understand strategic priorities within clinical R&D, and we can resonate that well with our similar experiences of implementing solutions for improving Clinical Development Portfolio via an integrated platform-based approach; which delivers timely access to study specific as well as standardized and aggregated clinical trial operations as well as patient data, allows efficient trial oversight via remote monitoring, statistically assessed controls, data quality management, clinical reviews, and statistical computing. Moreover, it provides capabilities for planned vs. actual trending, optimization, as well as for fraud detection and risk-based monitoring. MaxisIT’s Integrated Technology Platform is a purpose-built solution, which helps Pharmaceutical & Life sciences industry by “Empowering Business Stakeholders with Integrated Computing, and Self-service Dashboards in the strategically externalized enterprise environment with major focus on the core clinical operations data as well as clinical information assets; which allows improved control over externalized, CROs and partners driven, clinical ecosystem; and enable in-time decision support, continuous monitoring over regulatory compliance, and greater operational efficiency at a measurable rate”.

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