Data Integrity – Foundational building block for medical research

Time and again we have all heard that having the right information at the right time is the key to meeting one’s objectives. The healthcare and pharmaceutical industries rely heavily on having the most genuine, authenticated data, as they work with patients’ lives.

According to numerous studies, the price of developing a new medicine range from $985 million to $2.8 billion, depending on the therapeutic area. On average, introducing a new drug in today’s market is thought to cost around $1.3 billion. Because it takes so much money and time to create a new treatment and bring it to the market, the research data obtained across the timeline is crucial for making key decisions.

Managing Data Integrity today

To guarantee the quality, safety, and efficacy of medicines, as well as the ability of regulatory bodies to safeguard the public’s health, the pharmaceutical sector has a duty to ensure data integrity. From the point at which the data is first created, through any stages of transfers, replication, or reporting, maintaining data integrity is essential. Organisations must have systems and procedures in place to ensure data integrity, as data is essential for decision-making.

The World Health Organisation (WHO)’s draft data integrity guideline suggests governance control measures based on sound risk management concepts. It is crucial to have procedural papers available for data management, such as policies, Standard Operating Procedures (SOPs), and risk management strategies, along with clear training and awareness of responsibilities for preserving data integrity throughout the drug development lifecycle.

Risk-Based Quality Management approach to Data Integrity

A Risk-Based Quality Management (RBQM) method, which scans data as it accumulates and evaluates it comprehensively to find problems or patterns that need more investigation, covers the clinical trial continuum. Key principles of successful RBQM include:

Hierarchy of data: Not all data is created equal. In order to give a comprehensive evaluation, RBQM creates risk hierarchies that prioritise essential data up front, such as primary, secondary, and safety endpoints, before moving on to performance and other data. In other words, RBQM is about using the appropriate data under the supervision of the appropriate individuals at the appropriate moment.

Technology: RBQM uses tools with robust statistical capacity to identify absolute and relative values as well as anomalies in data. These tools also provide built-in pressure testing for models to confirm their robustness and accuracy.

Integration: RBQM unifies central, remote, and on-site monitoring into integrated platforms, swapping out compartmentalised functions and spreadsheets for a comprehensive, cross-functional strategy that aligns people, processes, and technology.

Transparency: With team access to a real-time data dashboard that promotes group ownership and accountability to swiftly analyse indicators of risk and course correct at any and every junction along the road, RBQM creates transparency between CROs, sponsors, and sites.

RBQM and Decentralised Clinical Trials

Successful Decentralised Clinical Trials (DCT), which produce copious amounts of data from various sources, are built on the foundation of RBQM. Performing a rigorous risk analysis at the protocol stage offers important insights into subject safety and data integrity, as well as determining whether the trial will use a hybrid model or be totally remote.

The fundamental ideas, procedures, and technologies of RBQM still hold true even though DCT introduces cutting-edge new mechanisms and technologies for research conduct. Key Risk Indicators (KRIs) and Quality Tolerance Limits (QTLs) must be predetermined by sponsors and CROs at the beginning of a trial. Then, robust statistical analyses must be applied to the body of data to identify discrepancies or deviations that could indicate errors, noncompliance, equipment failures, performance problems, or other issues. In other words, the foundation of both DCT and conventional trials is identifying and monitoring essential data and critical processes.

Sanjay Vyas
Executive VP and Country Head, Parexel India

RBQM as the solution to data integrity

The capacity to perform RBQM effectively is supported by technology, but organisations must be dedicated to adopting this attitude for RBQM to be successfully used throughout all clinical trials. Through the use of data exploration tools and customised visualisation dashboards, RBQM should be viewed as a business intelligence solution that enables teams to explore choices, test theories, and make crucial decisions at various stages of the trial.

Disclaimer: The views expressed in this article are those of the author and do not necessarily reflect the views of the Economic Times – ET Edge Insights, its management, or its members

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