Care Evolution

Improving recruitment and screening

Participant recruitment and screening for eligibility are traditionally inefficient processes that require a significant amount of time. Trial failures are often attributed to recruitment and screening issues—90% of studies end up doubling their recruitment timelines after launch to reach their enrollment targets, and 50% of trials aren’t completed as a result of insufficient enrollment. [3, 10, 13] To illustrate the impact of these issues, Advarra recently summarized the results of a “leaky pipe” analysis performed by Clinical Performance Partners, Inc.

Ultimately, this leaky pipe yields only 7% of known and available potential participants that complete a study, where the majority of the “leakiness” occurs prior to participant randomization, in the recruitment and screening phase. [11]

Reduce participant travel burden
Perceived participant burden, including travel and financial cost to participate, contributes to screening failures and loss of potential participants. This results in extended trial timelines and often requires staff to over-recruit in anticipation of high attrition rates. In particular, burden related to travel has a large impact on study sample diversity.

A recent investigation by Sanofi found that 70% of potential participants live at least two hours away from their nearest study site. [12]. In addition, clinical trial and research sites are frequently selected based on investigator location (e.g., affiliation with local academic medical center) rather than communities that suffer disproportionately and would benefit the most.

DCTR platforms enable low lift, web-based recruitment and screening methods that not only reduce staff burden but further support an “anyone, anywhere” model of participation. With a decentralized study design, access to a breadth of outreach methods—social media posts, automated email campaigns, QR codes posted in popular community locations, public websites, and more—expand the reach of a clinical research study.

By removing location barriers and performing multi-channel outreach, studies may achieve more diverse participant populations and more generalizable results while reducing recruitment time. Automated, electronic screening methods also free up staff time by enabling participants to complete screening surveys remotely on their own time.

Enhance recruitment efficiency and decrease screening failures
Further, these screening methods increase recruitment efficiency and limit screening failures with sophisticated branching logic implemented in the surveys that maximizes identification of eligible participants. This branching logic also enables powerful precision recruitment which makes it significantly easier to target very specific cohorts of patients, especially in multi-arm studies. Alleviating the burden on staff to perform more repetitive tasks like pre-screening ultimately allows their efforts to be redirected toward more complex tasks.

Obtaining real-world data (ePROs, wearables, EHR)

Traditional clinical study designs are limited to episodic data collection that lacks breadth. In many cases, valuable data from wearable devices, electronic health records (EHR), and contextualized patient-reported outcomes (PROs) are altogether inaccessible in conventional study models or limited to information acquired through expensive contracting with payers, providers, or other organizations. This conventional, confined dataset ultimately limits data analysis and the potential impact of the study results.

Access novel, broader datasets
DCTR technology unlocks a broader set of data that can be participant-mediated and efficiently accessed throughout the duration of a study. EHR and wearable devices each provide a particularly robust set of clinical data that results in a more holistic digital phenotype.

Information additionally shared through electronic collections of PROs (ePROs) help fill in gaps allowing for a multi-modal approach in data collection. Participants can login and connect their wearable devices and EHR to the DCTR platform—providing data from multiple sources—and submit their data to the study. Collating these data in the DCTR platform allows it to serve as a personal health record, which can also facilitate participant retention.

Improve data accuracy with digitally collected EMAs
ePROS can also enhance a study’s dataset by providing temporal context to the information, commonly assessed through ecological momentary assessments (EMAs). Participant responses to EMAs are impacted less by recall bias than conventionally collected answers, as they are evaluated in the moment and can be contextualized by supplemental data like that derived from wearable devices.

Gain immediate access to actionable data
The value of ePROs, in addition to data from wearable devices and EHR, is further increased by the accessibility of the data. Traditional episodic data collection delays the accessibility of the information, often relying on transcription of the data or integration steps before it becomes available to the study team for use.

The wealth of data collected through DCTR technology is immediately accessible, however, and the participant can elect to share data from a multitude of sources through a decentralized study model. These result in the potential for the data to inform the study as it progresses rather than solely being available for analysis at the end of the study.

Eliminate the need for source data verification
Beyond breadth and accessibility of data, traditional clinical study designs require extensive source data verification (SDV). This process is typically conducted during periodic site monitoring visits, in which data for analysis is confirmed to accurately reflect the data collected at the clinic site. Site monitoring visits to accomplish this process require significant dedicated staff time to complete. [3, 10]

DCTR platforms collect ready-to-analyze participant-provided information directly from the source, thereby greatly reducing or eliminating the need for SDV. This direct-from-the-source data collection method improves data quality by reducing inefficiencies and inaccuracies associated with transcription of data from paper to electronic records.