The current clinical trials reduce the cost and development time and failures in research, and support data-driven decision-making
Clinical data management is an important phase of clinical research that collects reliable, high-quality, and statistically sound data from clinical trials. It, thus, saves a lot of time in the development and marketing of drugs.
Data generated by trials is becoming complicated with time, resulting in overflowing data. Researchers prefer adaptive clinical design over fixed clinical design because of its ability to modify according to the data accumulated by subjects. Oncology drugs and biologics can be developed swiftly through adaptive studies which include basket, umbrella, and platform designs.
Master study protocol uses dynamic and flexible database builds and is used to conduct multi-arm, cohort, and decision-tree studies.
There is abundant data for the trials, including diverse data sources (e.g., wearable devices or sensors), increased data volume and precision, risk-based quality control techniques, decentralized clinical trials, and adaptive designs.
Clinical trials are focusing more on patient experience and the value they provide, which further complicates the trial. Therefore, it is becoming impossible for clinical data managers to implement and enable old-school, heads-down data cleaning methods.
Clinical data managers can utilise modernised processes and technologies to support their roles and responsibilities, and adapt to the latest domain of data management in clinical trials.
Earlier, clinical data management was centralised and collected using paper CRFs. Furthermore, they were translated into the database by manual data entry.
The clinical managers in the pre-CDM era were skilled and could review patient data by conducting cross-from reviews and using SQL queries to get the output and check the validity of the data. The managers could also review listings and by using the knowledge of therapeutic areas and indications, could raise the issue back to the investigation site.
However, this procedure was dependent on the skill and experience of the reviewer. Therefore, it was difficult to achieve consistency in reviewing and validation of data, which resulted in the use of inefficient and time-consuming quality control steps.
Presently, multiple software programs are supporting smart data management, which allows data managers to multitask and use various tools. There are multiple solutions for different studies when it comes to streamlining and flexibility. The biggest innovation of data management is centralization.
Sponsors are centralising their technology along with centralizing their data management systems. The functional service providers (FSPs) in smaller companies are outsourcing their work to keep everyone on the same level. The right software and partnership with an FSP can produce an efficient and productive working environment.
A good data management system makes an organized and analytics-based approach possible. Data sources such as Electronic Data Capture (EDC), Electronic Clinical Outcome Assessment (eCOA), central laboratory data, and electrocardiograms (ECGs) are used in CDM. In recent years, we have witnessed the use of biosensors, wearable devices, and Electronic Health Records (EHRs). It helps in enabling real-time access to participant statistics.
The current clinical trials are dependent on real-time analytics, modeling, and simulation. This, in turn, reduces the cost and development time and failures in research, and supports data-driven decision-making.
The traditional methods to acquire and manage clinical trials have not seen much advancement as compared to the technology. All these methods were considered inefficient and the healthcare industry needs to find new methods of clinical development in the future to create new and better medicines in the future.
Technology is advancing rapidly and the clinical data management process finds it difficult to keep up with it. High-quality medical data is growing fiercely and at a rapid rate, therefore, it is difficult to keep up with it without the usage of modern tools.
It is possible, that the future of clinical data management will use AI and machine learning to understand the pattern of the data and predict the outcome from it. These automated tools and intelligence will decrease the risk factor and Quality by Design (QOD) and the clinical database system will help in the actionable analysis of the data.
The modern clinical data management approach uses systematic, prioritized, and risk-based quality management approaches for clinical trial monitoring. To adapt to the modern clinical data management paradigm, it is important to establish central monitoring strategies to read the data provided by electronic data capture (EDC) and other sources. The sponsors and CROs must avoid post-database lock delays to identify and mitigate the negative trends in data after the database is locked.
(The author is Co-founder, DPHS Pvt. Ltd)
