Survey: Sponsors and CROs rely on analytics but are limited to traditional and in-house data
Medidata, a global provider of cloud-based technology and data analytics for clinical research, announced results of an industry survey to better understand how global life sciences companies think about and use data in clinical research. Conducted in partnership with PharmaVOICE magazine and titled “Using Data and Analytics in Clinical Development,” the survey revealed that 90% of respondents rely on some form of analytics solution—viewing data as a critical element for achieving their R&D priorities—but are limited to their own data to make strategic decisions.
Garnering insights from nearly 200 life sciences executives across pharmaceutical companies, CROs, biotech companies and medical device developers, the survey indicated that reducing clinical research timelines was the number one concern to their business, regardless of company size and that three key challenges stand in the way of achieving shorter timelines and overall R&D goals:
Patient recruitment: 70% of trials are delayed by more than one month, and recruitment is the leading cause. Such drawn out timelines quickly escalate costs. Further, today’s current approach to recruitment puts the onus on investigators, whose training and focus is on treating patients, not recruiting tactics. Such an issue calls for a solution that relies on an ecosystem of data, analytics, technology, services and provider organizations.
Data quality: Since 2000, the number of participating trial sites outside of the U.S. has doubled, and the breadth of clinical information has expanded to include data from electronic data capture (EDC) systems, medical imaging, labs, electronic health records (EHR), genomics and biomedical sensors. The inbound volume and rate of such data sets are exponentially larger than traditional clinical data; as such, old methods of capturing, cleaning and analyzing are becoming antiquated. As the industry shifts from static to adaptive data management, leveraging machine learning and advanced algorithms (to identify statistical relationships and outliers) is paramount.
Patient retention: Patient drop-out in clinical studies today is estimated to be around 18%, while the cost of recruiting a patient is 12 times greater than those associated with proper engagement tactics to retain them. The introduction of mobile health (mHealth) tools are encouraging more patient-centric research practices—alleviating patient burden and improving compliance and retention. Further, integrated technology platforms, such as mobile apps built on an EDC system, not only create a single source of truth; they enable sponsors to push data back to patients for ongoing feedback loops and communications between researchers and patients.
While respondents’ general data needs differ based on organizational type, roles and size, the survey indicated that real-world evidence (RWE) and historical trial data are the two most sought-after data sets. This is likely attributed to the cost and time-saving implications of leveraging such data during study design and execution. Real-world clinical and commercial data can increase the speed and accuracy of clinical trial site selection, patient recruitment and cohort generation, while tapping into patient-level data from historical clinical trials offer the possibility of early insight during clinical development and reduced failure rates in phase II and III studies.
“The volume of data in healthcare is growing at a massive rate, as the industry enters the exciting realm of RWE, genomics, historical control arms and high-frequency sensor data. But such data yields valuable insight only if it is properly integrated, standardized and analyzed,” said David Lee, Medidata’s chief data officer. “Fortunately, sponsors and CROs are increasingly partnering with technology companies that specialize in integrating new data sources, leveraging machine learning and deploying data science solutions into clinical development workflows. These innovations are already streamlining manual, resource-intensive processes and accelerating timelines to key operational and scientific milestones. Re-architecting pharmaceutical business models to optimize use of data-driven insights will increase the success rate of clinical trials and help improve patients’ lives.”