The strategic and operational aspects of clinical data management (CDM) are rapidly changing with advances in exponential technologies, evolving shift from disease-centric to patient-centric approaches, and increasingly stringent compliance requirements. The traditional data collection processes are not robust enough to support the volume and velocity of data generated during the product development life cycle.
The proliferation of wearables results in gigabytes of real-time data that requires dynamic coding and reconciliation while ensuring data integrity. Secure data storing and enabling effective processing to provide regulatory-compliant useful data is also a challenge.
With non-interoperable legacy enterprise systems it is difficult to capture, process, analyze, archive and exchange data in an efficient and cost-effective manner. Manual transfer of different types of clinical data e.g. demographic, safety and visit is cumbersome and prone to errors when the exchange involves numerous stakeholders within the clinical trial ecosystem. Furthermore, merger and acquisition activities and intensive re-engineering has increased the complexity of operations with multiple versions of data management processes and disparate technologies used across in-house, outsourcing, and offshore operational models. Data confidence is a constant battle and the companies need to ascertain that the data collected is standardized and trustworthy.
Data gathered from various sources including electronic clinical outcome assessments, wearables, sensors, and electronic case report forms is subject to watertight regulatory mandates. For instance, recently, the U.S. FDA launched the Medical Device Safety Action Plan focusing on advancing medical device cybersecurity. If clinical data is not managed in a streamlined fashion, pharmaceutical companies run the risk of non-compliance, leading to hefty fines and loss of reputation. Data transparency and inspection readiness is critical for achieving compliance, and there is a need to improve traceability and auditability of data through end-to-end digitization of clinical data.
Achieving this would require pharmaceutical companies to analyze existing data management workflows, identify and bridge operational and technological gaps, adopt new-age technologies, and manage all those changes effectively.
Navigating the CDM Transformation Journey
Pharmaceutical companies looking to modernize CDM processes must pay attention to the following areas:
- IT simplification: Drive holistic programs focused on process reengineering and IT rationalization, and make the IT landscape leaner, smarter, scalable and future-ready.
- Data centricity: Implement enterprise data lakes, invest in building capabilities in advanced analytics, and improve data quality and accuracy by adopting standard approaches and next-generation technologies.
- Better collaboration: Create synergies between ecosystem partners such as technology vendors, CROs, etc. for better trial outcomes.
- Change management: Adopt forward-looking change and risk management solutions, and redefine and formalize data management policies, procedures and SOPs etc. Invest heavily in onboarding and training to incubate new skillsets such as big data, artificial intelligence, machine learning, cloud infrastructure management etc.
- Cybersecurity: Build foolproof data governance structures to ensure privacy, security and ethical handling of clinical data including genomics data, HEOR data, and data collected via Bring Your Own Devices and other mobile devices.
Achieving Business 4.0™ Transformation
Disruptive innovations in next-generation technologies will be key to powering CDM transformation within regulatory limits. For example:
- Intelligent automation: Technologies such as robotic process automation, artificial intelligence, machine learning, and natural language processing can be leveraged to augment data standardization. Smart automation can enable organizations to achieve greater efficiency, ensure compliance and better utilize their resources.
- Cloud: Adoption of cloud computing can help sponsors and CROs reduce the total cost of ownership while ensuring high scalability, flexibility and agility. Faster and high memory computing power will enable real-time analysis of data to derive clinical insights at an early stage and help informed decision making on future course of action and plan of study.
- Data visualization: Next generation data visualization tools can enable end-to-end traceability and auditability of data by transforming large volumes of disparate data into knowledge. Automated simulation of data using semantics, smart and intuitive charting components and techniques, and granular dashboards will help discover meaningful clinical patterns and trends and drive smarter decisions in real-time.
- Blockchain: Distributed ledger technology can enable visibility across data handling through chain of custody and provide exceptional record-keeping capabilities. It can also offer benefits like immutability, efficiency and secure data sharing as well as promote collaboration among ecosystem partners.
The pharmaceutical industry needs to accelerate digital transformation and modernize its legacy CDM systems to better process large volumes of data captured from multiple sources in real-time. In addition to achieving regulatory compliance and promoting data-centric strategic decision-making, there is need to significantly switch focus towards automation and machine intelligence. Digitization and advanced predictive analytics across the clinical data value chain will effectively reduce the time taken in the process of data capturing to reporting, accelerate regulatory submissions and approval process, and eventually lead reduce the time and cost of bringing innovative drugs and devices to the market.