Clinical Trial Data managers today are hampered by a number of data gaps that require ﬁlling. Let’s take a quick look at the clinical trial data management areas in which the drug development industry is facing problems. If any of these sound familiar, you might be in need of help!
Data Capturing & Aggregation: The prevalence of ‘data heterogeneity’ in clinical trials is what makes data capturing and aggregation a complex process. Capturing trial data is yet to be perfected because it comes in different forms and formats, thanks to the number of eClinical Systems in use by the various stakeholders. Clinical data today is generated by the multiple devices used by practitioners who follow distinct regulatory protocols at a global level. The cleanliness of clinical data is also subject to issues like non-adherence and data variability which arise due to a different set of on-site challenges. This primary challenge stifles the data lifecycle, affecting analytics and quality of insights.
Data Cleaning & Discrepancy Management: Clinical trials deal with unstructured data all the time. Though digital documents such as EDCs, EHRs and ePROs are embraced in clinical trial projects, use of PCRFs cannot be eliminated completely. This fractional use of PCRFs and multi-source aggregation adds to the complexity of data cleanliness. Clean data, devoid of discrepancies, ensures use of accurate and rational datasets for further analyses.
Data Storage & Data Security: Data storage and security issues always give rise to the debate of ‘on-site or cloud model’ since it is combined with the function of cost. In either case, care must be taken to establish disaster recovery, cost efﬁciency, immunity against security breaches and healthcare speciﬁc compliance with HIPAA security rules.
Data Stewardship & Data Querying: Clinical data has longer shelf life, as it is not just used for current and speciﬁc research, it is archived for future research too. Sometimes, clinical trials also have traces of unutilized datasets that can solve disconnected healthcare issues. Owning and retrieving such data among the large volumes of data repository over time is an area that needs attention. Having historical and secondary data handy can resolve many issues. These also raise red flags through the procedure that pertain to data updation, interoperability and sharing.
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.