Using Data to Design and Implement Patient-Centric Enrollment and Retention Strategies
What sponsor doesn’t want more successful clinical trials – on time, on budget, with quality data?
After shining the spotlight on sites for decades, sponsors are placing more emphasis on patients and the role they play in effective and efficient clinical trials. As hopeful eyes shift toward patient centricity as the new “fix,” we must keep in mind that as with any marketing effort, successful patient (customer) acquisition and retention requires a deep understanding of: 1) who your targets are; 2) what their needs and desires are; and 3) how they will behave. Qualitative insight offers a glimpse at what patients think they will do as trial participants, but it’s the quantitative experiential data that can statistically predict what they will actually do.
The traditional site-centric model of patient enrollment is failing more frequently as inclusion and exclusion criteria become more restrictive, protocols more burdensome and competition for patients more fierce. According to Ken Getz, director of sponsored research at Tufts Center for the Study of Drug Development (CSDD), “The results of our recent study paint a complex picture of global practices and their effectiveness, and characterize a very high level of investigative site performance risk.”1
So, it isn’t at all surprising that while interest in patient-centricity is increasing, overall site performance continues to be an issue. Industry articles and lectures are emerging everywhere, with most focused on the importance of involving patients in the clinical trial process and understanding patient and caregiver needs in order to identify potential barriers to study participation. Better insight into patient convenience is important, but it is only the first step. True patient centricity requires a multilevel process that combines good data with sophisticated analytics to help sponsors make fact-based decisions in planning and implementing more effective and cost-efficient enrollment and retention strategies.
What may be surprising are the abundance and variety of data available to sponsors if they know where to look. Central recruitment specialists have been tracking and harvesting enrollment metrics from clinical trials for many years. For example, Acurian’s proprietary patient database contains more than 70million people with self-reported health related conditions who have opted-in to be contacted for clinical trials. Sponsors want to make sure that those databases also contain results metrics from central recruitment programs across disease states, studies, protocols, drug categories and indications.
So, we know the data exist. How can we put them to work?
Pre-Protocol Design: Patient input is step one toward designing a more patient-friendly protocol with fewer enrollment and retention obstacles. Analysis of historical data from like-studies, however, can also help accurately predict how the protocol will impact patient participation in terms of enrollment and retention metrics.
Protocol Feasibility and Patient Engagement: Even when it’s not practicable or feasible to make protocol logistics entirely patient-friendly, metrics from trial databases can be used to predict enrollment attrition rates and flag potential impediments to patient participation. Armed with this information, sponsors can decide early on how to allocate budgets based on objective data that determine the ease or difficulty of enrolling a specific protocol.
Site Selection: According to research from Tufts CSDD, only half of all sites meet or exceed their enrollment targets in any given trial. With the help of a central recruitment site database, sponsors can identify and select sites that are most productive at enrolling centrally recruited patients and located in geographic areas with the highest disease prevalence. This selection process helps ensure that virtually all sites have the potential to meet or exceed their enrollment goals on time.
Central Recruitment Feasibility and Planning: Before any program begins, metrics from like studies, disease prevalence, patient population idiosyncrasies, patient motivation, protocol challenges, I/E criteria and site performance can be used in predictive modeling to more precisely calculate the number of respondents needed to yield every one randomized patient and the cost.
Similarly, data and analytics from a range of sources can be used to create a highly effective, centralized marketing campaign that targets potential patients with focus and precision – people who live within a reasonable distance of the participating investigator sites; patients who have indicated they or someone in their household have a specific condition; and people who have expressed an interest in receiving new health care and clinical trials information.
The data exist. The analytical capability exists. When fully armed, any sponsor can effectively plan and implement a highly successful enrollment and retention strategy with the patient at the center.
Scott Connor is Vice President, Marketing at Acurian. If you want to hear more on the important topics he discussed, join the Patient-Centered Clinical Trials conference on September 4-5, 2014, in Boston.
Sources: 1) New Research from Tufts [CSDD] Characterizes Effectiveness and Variability of Patient Recruitment and Retention Practices, January 15, 2013
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