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December 15, 2021

Evolution of technology largely hinges on a keen understanding of data and drawing actionable insights from it, ultimately supporting problem solving and decision-making. Technologies such as robotic process automation, artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) can help in standardization and pulling actionable insights from data generated in clinical research, which results in efficient clinical data management. A major challenge pertaining to the clinical research industry today is its inability to integrate big data generated from diverse, siloed sources with speed and volume. In addition, there are a considerable number of roadblocks when it comes to the quality of data and access to clinical trials, resulting in severe delays throughout the drug development lifecycle. Technology has proven to be a key catalyst in addressing these challenges.

Cloud computing

Cloud computing provides a scalable and elastic platform with robust security at a reduced cost for running clinical trials in compliance with ICH-GCP guidelines and 21 CFR Part 11 compliance. It provides unique features including:

1. Audit trail: Centralizing data timely in the cloud facility leading to quick action with robust audit and ensuring    compliance with the regulatory requirements.

2. Centralized data storage: Saving time with centralized cloud management for clinical data with complex logistics such as data cleaning, data engineering, and regulatory submissions.

3. Data security: Providing secure data protection by meeting the regulatory requirements.

Blockchain

Blockchain can improve clinical data quality and patient safety at a reduced cost by enabling automation, data integrity, availability, traceability, and reporting. Blockchain is effectively used in electronic trial master file (eTMF) clinical document management where processes like document creation, review, approval, archival are chain events.

Artificial intelligence

AI helps in improving clinical data management processes by detecting trends, identifying risks, and predicting outcomes. It helps sponsors to understand the effectiveness of drugs, predict the success of a clinical trial, and enhance subject identification and enrollment. Al also enables standardization of clinical data which is subsequently used in analysis and reporting. Listed below are some AI-mapped technologies that can facilitate efficient management of clinical data.

1. Machine learning

ML is applied in several complex clinical trials for clinical data reconciliation and mapping with respect to the Clinical Data Interchange Standards Consortium (CDISC) standards, thereby, expediting the clinical trial study onboarding process and enabling effective utilization of resources and time. For instance, in medical coding, a significant portion of terminologies can be auto-coded using ML-based algorithms.

· Automation-based reconciliation

There are several tasks like designing an electronic case report form (eCRF) and edit checks in clinical data management that are manual, repetitive, and resource-intensive. External data sources like non-eCRF data can be integrated with the electronic data capture (EDC) data, and the non-conformant and reconciliation programs provide all discrepant records which can be managed via the system itself.

· Data analysis

Analysis of data stored in the clinical database is a cumbersome activity. ML technologies allow the identification of hidden patterns and trends with ease, and facilitate the extraction of patterns and correlations from data in a meaningful way thereby supporting proactive analysis, cleaning, and reporting of clinical data.

· Signal detection

Risk-benefit analysis is critical to every clinical trial. ML allows the detection of signals from clinical research data to avoid or highlight potential risks. Effective utilization of machine learning techniques helps in identifying and timely reporting of adverse events.

2. Managing clinical data through AI and big data

The frequency and volume of data generated from a variety of sources like eCRF, external vendor data etc. are considerably high. A data lake architecture allows trial sponsors to efficiently process and manage clinical data produced in running clinical trials and real-world evidence (RWE). Integration of non-eCRF data with eCRF data results in high velocity, variety, and variability. AI and big data can together enable stakeholders to make efficient decisions.

3. Natural language processing

NLP helps build clinical data collection models in many languages. It extracts insights from unstructured data like images, scans, patient's medical records etc. Selection of trial participants can be done by simply scanning the pathological reports of patients. In RWE studies, the analysis of sentiments and prescribing patterns for the medicine can be effectively done by NLP techniques.

Conclusion

The availability of innovative data visualization techniques has been instrumental in transforming the clinical data management process. Effective utilization of data visualization tools can transform big data into useful insights to discover meaningful clinical patterns and trends, and drive smarter decisions in real-time. The next-generation data management technologies enable corresponding sponsors to ingest, aggregate, standardize, and provide secure data access to all stakeholders throughout the clinical organization with cloud-enabled, secured access. Easy, accurate, and quick access to the data provides the organizations with a complete freedom to focus on high-value tasks such as analyzing clinical and operational data to monitor risk and visualize outliers and trends, thereby bringing drugs faster to patients.

Tushar Gadge is a Subject Matter Expert for Clinical Data Management at TCS. In his expansive career spanning over 11 years, Tushar has worked as a Clinical Research Associate, Subject Matter Expert, Clinical Business Analyst and Clinical Data Manager. A Six Sigma Green Belt -certified professional, his interest revolves around automation and implementation of data science technologies in the clinical research domain.

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