Menu ≡ ╳
There has been a collective realization throughout the industry that the key clinical trial data management processes must show efficiency, continuous compliance, scalability, sustainability and measurable productivity gains that, over time, will transform the research and development processes used to develop new drugs.
Industry leaders are addressing this problem by delivering solutions that can address various needs ranging from study design to data integration to clinical process optimization to targeted therapies to benefit-risks assessments to cost reduction.
Primarily clinical data management solutions empower the ecosystem with decision-making abilities backed by precise, accurate and timely insights. Some best practices culminate in building an integrated clinical data management solution that works for current processes and the future of clinical trials.
Integrated Data Management Platform: There is no single software to capture all kinds of clinical data. During clinical trials, practitioners use multiple software, devices and apparatus to capture data. Though EDCs are fast catching up, globally, some local circumstances and low capital-intensive projects still rely on paper-based case reports and ePROs. Some data is also in the form of X-rays, and scans which are a different format altogether. Therefore, industry needs a platform that allows integration across these diverse sources of data, and further provides all of the data in a single clinical data repository.
Real-time Data processing and insights: For a long time, information churned out of large data sets have been processed in isolation causing complexities. But today, we see a pressing need to use real-time analytics with integrated data from devices, health systems, payers, patients, providers and other systems and participants to deliver services.
Metadata-driven process: To deal with entropy in clinical data, efficient CDMs use metadata. Metadata facilitates information flow, improves retrievals, helps in discovery and provides context by applying metadata principles to information. It establishes some of the most lacking comforts for a data scientist trying to arrive at meaningful insights. Some of those benefits include user friendliness with a simple and intuitive interface.
Reporting Tools: Seldom clinical data management solutions are integrated with reporting tools that can configure, execute, and review reports with built-in analytical algorithms in support of analyzing the data quality, clinical significance, operational performance, as well as reviewing genetics and proteomics data.
Risk-based Monitoring & Assessment: Solution that helps mitigate risk by allowing organizations to focus on critical study parameters. A robust risk monitoring & assessment process needs to be empowered with analytical and reporting capabilities, third-party integrations with open APIs, workflow engine and a knowledge base.
Regulatory and Compliance: An integrated Data Management solution should be configurable to support different types of clinical trial data, clinical trial protocols, results and subject-level data. This configurability comes with an inherent need of maintaining regulatory compliance & audit traceability. Such a solution should be robust enough to configure study specific needs and translate clinical data to meet the regulatory and compliance standards both globally and locally.
The increasing volume, variety in data and the velocity at which clinical data is being produced, a recommended solution needs to be agile and robust to respond to such versatile dynamism.
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 data, allows efficient 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 Analytics 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.”
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.
Since the first few citations of registered and structured clinical data in the 1940s, the methods of capturing clinical data, use and application of clinical data, global inclusions, and role of payers, users and regulators have evolved.
At present, the Clinical Data universe comprises the siloed internal and external data sources within clinical development processes. There’s a need to integrate all such data with source agnosticism, which is a conceivable evolution for actionable insights into the performance of a clinical trial portfolio. Currently, every organization in the industry is looking for this ability.
This resultant data universe is primarily necessary to support catch and mitigate errors in real-time to save time and get to market early as strategic decisions are made in the interest of business to manage cost & revenue pressures, to enable focus on core R&D and management, and to ensure continuous compliance with dynamically evolving regulatory guidance.
Staying focused on the core R&D business and continually improving operational & clinical performance have always been the topmost priorities among the business stakeholders; but, a collective and timely insight across the horizontally spread data and information hasn’t always been possible. In such cases, the business decisions have often been delayed, or had to rely on outdated information or lacked any cross-functional impact.
Other reasons for this evolution could also be that for a long time, the conduct of clinical trials and healthcare initiatives have been carried out by separate functional silos within an organization using separate “legacy” applications & cookie-cutter solutions. Such approaches are typically focused on a specific process or function. This has resulted in multiple, disparate, and inefficient solutions that may not work well together. This has deprived organizations of the ability to make timely decisions, as well as increase efficiencies and overall productivity at the corporate portfolio level while controlling costs & mitigating risks involved at a specific study or at a functional level or portfolio level.
However, the adoption of digital means such as electronic patient reported outcome (ePRO) and electronic data capture (EDC) systems in lieu of paper-based case report form (PCRF) has changed the landscape of clinical data. It has reduced the time taken to collect data and to relay it into the next stage of the process from five days to fifteen minutes. Also, it has managed to expedite the process of developing drugs.
Another milestone is the evolution of data structures. Big Data has allowed the management of large volumes of data and its conversion into comprehensible and insightful visualizations. To realize such benefits, clinical data management processes are compelled to use standardized fields, formats, and forms. This requires the application of a metadata-driven process.
The uprise of Clinical Trial Globalization has eliminated gaps between global study expectations and national standard protocols that lead to many operational complexities. Regulatory bodies like FDA and EMA have come together to synchronize requirements on a protocol-by-protocol basis.
To control financial risks in managing patient populations and to provide continuous access to electronic data, private networks have started building centralized data repositories. This can be further used for a range of analytics, including predictive modeling, quality benchmarking, and risk stratification.
These breakthroughs have eased out many challenges in implementing clinical trials but have also made it the responsibility of CROs and Clinical data managers to upgrade and standardize their systems in response to these progressive developments.
At MaxisIT, we believe that complex data analysis can produce simple yet powerful insights.
MaxisIT’s Clinical Trial Oversight System (CTOS) is a purpose-built command center designed to manage biopharma and life sciences clinical trials as mission-critical business processes. With its complete AI-enabled analytics platform, the CTOS enables “data-driven digital transformation” from data ingestion, processing, and analysis to in-time clinical intelligence. Its real-time data ingestion, analytics, and visualization empower researchers to identify and address errors as they occur.
The CTOS elevates the value of data, improves efficiency, and empowers clinical stakeholders to seize new opportunities. The platform brings clinical operations and patient data together in a single, central data hub as a single-source-of-truth to help clinical operations, clinical data management, biostatistics, and clinical R&D portfolio management.
A complete self-service, AI-enabled analytics platform, the CTOS unifies trial data from disparate eClinical systems to support study planning, clinical data quality, clinical review, patient safety, clinical operations, CRO performance, risk-assessment, portfolio management, compliance, and submission.
From study setup to data ingestion, clinical trial stakeholders can manage clinical development processes with insight into the study conduct and take proactive actions to reduce costs, mitigate risks, and ensure compliance.
MaxisIT’s CTOS 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 dataset, allowing for proactive trial risk management.