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Technology adoption is reinventing the way clinical research professionals collect clinical data from diverse sources. Integrating the clinical data collected from a complex range of sources which include new and emerging data collection solutions to site-level data and data collected from wearables, Electronic Health Records and patient apps is quite the challenge presented today to sponsors in the clinical ecosystem.
As more and more sites adapt to remote monitoring requirements, the need for a platform which integrates all the data collected using diverse data sources into a single-source-of-truth becomes of paramount need.
A CTMS falls short of Expectations
Clinical trials sponsors, CROs, academic medical centers etc. are all used to employing CTMSs to conduct clinical research housing all trial-conduct information. Many of these require dedicated servers while some are cloud-based. They collect all trial-related data, including trials’ milestones, progress, adverse event (AE) notifications, resource management, asset tracking, and the highly configured investigator payments. In the outsourced clinical trials’ ecosystem, each sponsor works with at least 2 or more CROs as well as Niche CROs across their portfolio of trials. As trials get outsourced to CROs, with each CRO maintaining a different CTMS to manage trial conduct, sponsors find their own investment into a CTMS not only redundant but also inconsistent with the updates maintained at/made to CRO-managed CTMSs.
However, none of the information gets standardized, even with the terminology used in milestone tracking, subject or site statuses and deviations in protocol. The data across the studies and various CTMS systems employed would not be consistent, making it difficult if not impossible to share any of it across studies, therapeutic areas or companies. The lack of standardization makes it impossible to plan any process improvements or gain any insights to drive for operational efficiencies. Unlike later technologies which employ Artificial Intelligence and Machine Learning which manage most of the tasks and leave managers to focus on study conduct and risk mitigation and enable greater efficiency and accuracy, the CTMS systems leaves both sponsors and managers struggling to understand how a study is structured and learn the process steps properly to ensure that the clinical trial is on track.
The CTMS is inadequate to unify as well as process data across the diverse eClinical and digital / virtual data collection sources as well as processes of today, which supersede the traditional, centralized approaches. Nor can they handle the data collected in real-time or offer any actionable insights based on reviewing such data. It is also unequipped to deal with gaining access to data from independent solutions which call for an appropriate interface before they allow any access to their data repository. Even if this was achieved, the lack of standardization in a CTMS would only result in data quality issues which put the clinical trial at risk. The trial sponsors would have no way of ensuring the quality of data, reconcile the collected data to weed out data duplication and fix any data quality issues.
Sponsors would do well to be aware of the pitfalls of signing on for such inadequate systems and incurring the costs of setting them up only to lose upwards of 3 months in their trial timeline. They need to think beyond a CTMS to adopting a fully managed oversight platform offering capabilities which are not limited to the operational aspects of trial conduct. With a portfolio of trials spanning outsourced as well as managed trials across diversified partners with a complex set of data sources, sponsors today need a platform which offers them a command center with right vision into the conduct of their trials.
Adopt a Reliable eClinical Platform
MaxisIT’s CTOS (Clinical Trial Oversight System) collects data from disparate sources, integrates and analyzes them in real-time to offer at actionable insights which enable informed decisions across a trial portfolio.
The AI-powered CTOS of MaxisIT uses machine learning to offer capabilities which surpass the CTMS in many different ways, like:
allowing managers to focus on qualitative study conduct, performance management, data quality assessment, and patient safety.
The AI-powered data analytics offered by the CTOS cut through the time and cost of deriving actionable insights using clinical trial data and enable real-time coordination to offer unprecedented efficiencies. Sponsors keen on reducing their time-to-market can achieve first mover advantage in reaching the market, by adopting MaxisIT’s CTOS.
MaxisIT’s Clinical Trials Oversight System (CTOS) enables “data-driven digital transformation” by its complete AI enabled analytics platform from data ingestion, processing, analysis to in-time clinical intelligence by establishing value of data, improve efficiency and empower clinical stakeholders to mitigate risks or seize the opportunity. The CTOS platform helps clinical operations, clinical data management, bio-statistics, and clinical R&D portfolio management by bringing clinical operations and patient data together in a single, central data hub (i.e. single-source of truth). The platform allows self-service analytics and role-based clinical intelligence enabling insights, time & cost efficiency, risk mitigation and the effective management of portfolio, data quality, patient safety, CRO/site performance management. In short, it helps you maintain an ongoing health-check on your portfolio of clinical trials, using real-time data, analytics and visualization to drive rigorous analysis of the entire data set, allowing for proactive trial risk management.
We simplify the monitoring process, feeding all data through a single repository, running robust analytics and ultimately producing visualizations that are fit for human consumption. Because, yes, complex data analysis can produce simple insights. Real-time data ingestion, analytics and visualization empower researchers to identify errors as they occur
7 Feb 2018