Ways to Innovate Clinical Data Management in an Evolving Clinical Trial Landscape

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Clinical trials have changed over the previous decade to incorporate a growing number of data sources (e.g., wearable devices or sensors), greater data volume and precision, risk-based quality management methodologies, decentralized clinical trials, and adaptable trial designs. 

Clinical trials have also been more focused on the patient experience and value distinction. These variables have resulted in increased complexity in clinical trial designs, as well as an exponential growth in the number of data collected. In the previous 20 years, the volume of clinical trial data has increased sevenfold, and a typical Phase III research currently generates an average of 3.6 million data points. Currently, phase II and III protocols comprise 263 operations per patient, with around 20 objectives supported.

These significant shifts in the data-collecting environment present new problems for data management and monitoring teams. Aggregating and reconciling data from various and new sources using systems that are not built to manage them is time-consuming and inefficient. These data sources also contribute to data silos, making it more difficult for study teams to acquire clean, accurate data, influencing decision-making.

To achieve patient-centricity, clinical data management must be modernized.

With more data being collected directly from patients, such as wearable devices, electronic clinical outcome assessments (eCOA), and telehealth platforms, monitoring and data management processes must adapt to how and where the data is collected, as well as scale to detect and clean issues without the need for additional resources. 

As a result, there is an urgent need to move our thinking away from reactive methods and toward proactive planning and technology-based solutions to replace query and listing-based trial data assessments.

Clinical data managers in conventional clinical trials are entrusted with manually analyzing patterns and data abnormalities using data lists, dashboards, and home-grown systems that frequently lack interoperability. Modernized methods and technology, on the other hand, are becoming more available to assist clinical data managers as they adapt to the new domain of data management in clinical trials, including the benefits associated with risk-based quality management approaches led by RBQM/ICH E6 guidelines.

The challenge for those in data management leadership and those working directly on trials is how to best incorporate an ever-expanding list of data sources, novel data types, analytic tools, and existing personnel in a risk-based environment to execute the core function of data management, which is ensuring that clinical data is collected 'fit for purpose'. As previously stated, while developing and delivering interoperable technology-based solutions to replace manual query and listing-based evaluations, modern clinical data managers will need to be increasingly proactive. In brief, clinical data managers are becoming the core for bringing together all of the diverse data to present a full patient's journey in a harmonic manner.

The future of data management has arrived

The old methods will not operate in the new, modernized world of clinical data administration. Clinical data management organizations will need to prioritize clinical data manager upskilling and devote time and resources to expanding their analytical mindsets, clinical skills, risk and mitigation processes, understanding and use of real-world evidence, data trends, and new clinical endpoints based on the deployment of edc in clinical data management. In today's ever-changing clinical trial world, here are three approaches to improving clinical data management:  

1. Unify Datasets to Capture a Complete View of Patient and Study Data

Clinical trial data is currently recorded in siloed systems. This necessitates human programming to aggregate and reconcile the data, which takes time, money, and resources.

You can simply visualize and analyze data in a unified fashion to drive the value demonstration of your new medication or technology by combining heterogeneous datasets through a unified, intelligent, and secure platform—one that is built to automatically process data through a single model. Any of these datasets is complimentary, and combining them allows you to create very detailed patient profiles that are more revealing about how medications and illnesses impact people.

Octalsoft's EDC for clinical data, for example, gives a comprehensive, centralized view of patient and research data. Without the need for programming or reconciliation, data acquired from any source instantly integrates inside and between studies on a single platform. From ongoing insights into patient disease progression or regression, to detecting inconsistent, anomalous, or missing data early on, and on to the delivery of data ready for analysis on demand. Octalsoft’s eClinical suite offers the essential structure for current trial designs to reach the pace and scale required.

2. Automate Tasks & Workflows

Data is frequently acquired from various sources and put via a single data management procedure nowadays. Clinical data capture systems help in examining, cleaning, and locking data. Data that is manually combined by programmers or data that is funneled via an organization's bespoke solutions to aggregate and manage data is not ideal; both procedures are resource- and time-intensive, and they add the possibility of inaccuracies.

Automation, intelligence, and a user experience that works on the whole research and patient dataset regardless of its source will improve your workflow in general. Workflows adapt to where and how data is acquired in Octalsoft's next-generation clinical data management approach, and processes are automated or aided where feasible using machine learning. This eliminates the need to grow data management resources in response to data volume, redirects your workflows away from tedious, manual chores and towards higher-value analysis, and enables more effective data cleaning and quicker database lock.

3. Deliver High-Quality Data, Faster

When it comes to doing an extensive data review, doing it manually is wasteful and counterproductive. This isn't scalable in the face of rapidly increasing data quantities because adding resources doesn't deliver a return on investment. However, you must supply higher-quality data more quickly.

Identify data issues and surface them using sophisticated analytics with the aid of automation and professional services professionals. This removes or drastically decreases the requirement for programmer resources while also improving patient safety.

If clinical data management commits to this cultural shift and we augment our daily activities with a new data governance toolkit that includes specialized tools for real-time analytics, and quality control in clinical data management, data managers will be better equipped to provide engagement in any phase of a decentralized trial, from protocol design to data visualization development, as well as patient-centric technology solutions.

Want to know more about how Octalsoft’s comprehensive eClinical suite can help your clinical data management processes evolve? Book a demo with us today!