Why India is poised for Clinical Data Management

Clinical trials are envisioned to resolve the questions related to research activities by generating data to hypothesise the facts. The quality of data generated plays a significant role in the outcome of the study. Clinical Data Management (CDM) is the process of collection, analysing, and management of clinical trial data in compliance with regulatory standards. The data collected should be attributable, legible, contemporaneous, original, and accurate. To maintain data integrity, Clinical data managers design a case report form (CRF) which describes and specify the type of data to be collected in accordance with CRF completion guidelines (i.e., instructions for filling in data). Coded terms are used to annotate the variables. CFR tracking, data entry, validation, discrepancy management, medical coding, and database locking are subsequent stages in the process of clinical data management. CDM procedure is aimed to convey an accurate, valid, and statistically sound database. As a result, the CDM process starts even before the finalization of the study protocol.

India is one of the desired locations for outsourcing clinical research services due to the availability of human subjects for enrolment, skilled clinical research professionals, cost-effective and comprehensive databases for data management.  A decade before, many industries are managing clinical data manually but now the availability of data technologies allowed them to control the data management process which was otherwise very chaotic.

Clinical Data is usually stored in a data repository known as Clinical Data Repository, which stores data mostly in a patient-centric fashion, accumulating data from multiple sources. Clinical data repositories may form Clinical Data Warehouses when the data stored in them is specifically organised for analytics purposes. Commonly used Clinical Data Management tools are Oracle Clinical, Clintrail, Macro, Rave, eClinical Suite.

Continuous demand from pharmaceutical industries to accelerate the drug development process and from the regulatory authorities generate of high-quality data for drug evaluation. To satisfy this criterion, electronic systems of data management is introduced. Advancement in the technology front has positively influenced the CDM process, systems, thus leading to produce positive results rapidly, and generate high-quality data.

From the regulatory and industry perspective, clinical data management is facing some challenges like standardization of data management process, and to develop data standards and regulations. On the other hand forecasting and implementation of data management systems, with the rapid evolution of technology that outdates the existing infrastructure. The growth of IT industry in India lend a hand for the evolution of CDM  and to generate effective and high-quality clinical research data, and maintain a balance between the expectations and restraints in the existing systems. In a country like India that is driven by technological developments and business demands, this all is possible

From the regulatory and industry perspective, clinical data management is facing some challenges like standardization of data management process, and to develop data standards and regulations. On the other hand forecasting and implementation of data management systems, with the rapid evolution of technology that outdates the existing infrastructure

Pharmaceutical companies in India have streamlined CDM processes by joining hands with IT Industry. They have initiated holistic programs focused on process reengineering and IT validation, and make the IT landscape slenderer, smoother, accessible and future-ready. Implementation of enterprise data lakes, and investing in building capabilities in advanced analytics, and be focused to improve data quality and accuracy by implementing standard methodologies and next-generation technologies. Pharmaceutical industries have also collaborated with technology vendors, CROs, for better trial outcomes. They have also adopted progressive and risk management strategies and redefined and formalized data management policies, procedures and SOPs etc. Invested comprehensively and nurtured new skillsets such as big data, artificial intelligence, machine learning, cloud infrastructure management etc. They have also built full proof data governance structures to ensure privacy, security and ethical handling of clinical data including genomics data, Health economics and outcomes research (HEOR) data.

The pharmaceutical industry has accelerated the digital transformation of CDM systems to better process large volumes of data captured from multiple sources. Digitization and advanced predictive analytics across the clinical data will effectively reduce the time taken in the process of data capturing to reporting, fast-track the regulatory submissions and approval process, and ultimately lessen the time and cost of bringing new drugs and devices to the market.