A recent influx of digital technology has unlocked tremendous potential for clinical trials. For an industry that has traditionally relied on patients’ paper intake forms and manual data entry into clinical systems, digitized processes present new opportunities to enhance the clinical experience for investigator staff and patients alike. While innovative solutions that solve specific problems—e.g., virtual visits or eConsent applications—have driven positive change, adding disconnected tools increases complexity and costs, making it harder to get a holistic view of trial data.
Despite companies now being able to capture data using various technologies from sensors to wearables, accessibility of information remains a key challenge. While more data can lead to greater insights, it can also overwhelm and confuse research sites and data managers if managed incorrectly.
As the industry moves toward more connected insights and digital trials, it must first lay ground rules for robust data management, facilitating quick data capture, ingestion, and cleaning. Here are some key challenges facing the industry today—and recommendations for overcoming them.
Taming data overload
A typical Phase III trial uses close to 10 data sources and generates an average of 3.6 million data points, three times the amount reported 10 years ago. The number of data sources is expanding as health information tools—from patient surveys and electronic health records to remote patient monitoring devices and smartwatches—become more widely adopted.
While the boom of patient information presents many opportunities, more data does not automatically equate to better or faster insights. Source data needs to be attributable, legible, contemporaneous, original, and accurate while also meeting the regulatory requirements for record-keeping. Without clarity from regulatory agencies on data ownership, privacy, and security around evidence from devices, using this data in a clinical trial requires additional work and verification.
Currently, information from smartphones and other sources is managed independently and must be verified against the research facility’s electronic data capture systems. The overwhelming number of data sources and lack of access to the data originator make it difficult for study teams to determine which information to use and how to use it.
Enabling standardization
Standardization may seem like it would address some of the industry’s data management problems, but it’s not a quick and easy fix. Without the proper measures and systems in place, standardization would require constant monitoring and updating for alignment across stakeholders.
Research sites must ensure data is measured, captured, and interpreted consistently across all sources for every trial participant. By using connected clinical systems that enable easy information sharing, companies can work toward standardization to ensure all data is being ingested and read in the same way, improving accuracy and reliability. As long as the industry adapts to market changes, opportunities to leverage new data sources and drive long-term improvements in trial execution will not be in jeopardy.
Accelerating information flow for timely adjustments
The time involved in managing and sharing data across systems can negatively impact trial participants. For instance, suppose a patient’s diagnostic readings indicate that they are experiencing adverse side effects because a dose is too strong. In such a case, changes should be made immediately for all patients at risk of having the same reaction. Delays caused by manual data processes, however, could slow these necessary dosage adjustments.
Centralizing and consistently formatting data from all sources makes information usable faster. Establishing connections across systems can drive further improvements by enabling research data collected in various source systems to seamlessly and automatically flow into the tools used by research sites and sponsors. This is where data can be aggregated, cleaned, and used immediately, delivering greater insights for more informed decision-making and increased trial effectiveness.
The solution: Establishing a data foundation for digital trials
By working together to standardize data documentation processes and leverage advanced systems, sponsors, CROs, and research sites can access and interpret data faster and better than ever. Aggregating data in a central clinical data management system (CDMS) is a crucial first step since advances in technology can easily facilitate data being centralized and consolidated into one clinical platform. A CDMS gathers information (e.g., heart rate levels) from different sources, stores it, and verifies it within a single system. This expedites data collection and reduces human error, enabling processes to be automated and reconciled efficiently.
With a CDMS, companies can use metadata to help organize trial data without completely mapping, transforming, or adhering to a standard. Metadata auto-mapping makes the same data point from different sources equivalent (e.g., a blood pressure reading from a site visit versus one taken from a device at home). With this method, data can quickly be made accessible to other stakeholders in the organization and beyond for faster insights.
With centralized data and strong standards, organizations can enable digital clinical trials that link sponsors, CROs, research sites, and patients for seamless information sharing. Digital trials unleash new possibilities for the industry in how patients access research and how new therapies are brought to market.
To maximize the digital clinical trial opportunity, it is imperative to establish a solid foundation of data collection and management best practices and capitalize on the advancements in data management technologies. Only then will we see the true potential of medical innovation as new treatments get to patients at an unprecedented pace.
Photo: Deidre Blackman, Getty Images
