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Successful project management requires efficiency in both identifying and addressing bottlenecks before they impact study timelines. This article examines how to design data collection criteria for optimal performance.
It is not uncommon for sponsors to collect screen failure information from study sites in order to optimize recruitment and enrollment. The most common way for capturing this information involves embedding data collection criteria, specifically, requiring sites providing rationale in Electronic Data Capture (EDC) systems during screen failures. The literature suggests that inclusion/exclusion criteria and population changes represent 16% of all changes made in protocol amendments1, with safety assessments (12%) and general information(i.e., protocol title, names, addresses, etc.) (10%) accordingly.
While inclusion/exclusion criteria represent the majority of screen failures, it is seemingly uncommon for sponsors to investigate other enrollment impediments, such as site operational aspects (i.e., staff expert availability/approved and trained staff, facilities malfunctions, etc.), patient refusal, and unknown factors impacting participation during screening. In this article, we will recommend a framework on how to design data collection criteria to identify enrollment bottlenecks during screening, and optimize enrollment performance.
Step 1: Define Enrollment Impediments at Sites during Screening
The first and most important aspect towards identifying site operational enrollment impediments involves defining what data you will be collecting from sites to uncover these impediments. The following questions may help with defining potential enrollment impediments:
Investigating these questions with your study team can help you identify and address a broad range of potential enrollment impediments during screening.
Step 2: Structure and Automate Your Data Collection
Once you have identified enrollment impediments during screening, it is important to categorize screen failure criteria, so that you can more easily aggregate your data during analysis. You can typically categorize screen failure reasons into the following buckets:
Step 3: Identify the Bottlenecks via Data Analysis
Once you’ve gathered enough data on reasons for screen failure from study sites, start analyzing the data and identifying bottlenecks. Figure 1 illustrates data from a large medical device study conducted at a medical research institution.
Naturally, inclusion/exclusion criteria represented the majority of screen failures, however more than 30% of screen failures were operational in nature (including patient refusal and other reasons). Figure 2 dissects identifiable operational impediments.
Figure 2 shows that nearly half of operational impediments towards enrollment involved a lack of presence for trained staff to conduct study visits. Further, this analysis suggests the importance of collecting information on why patients have refused participation, as these factors can be resolved through site engagement initiatives.
Step 4: Break Down the Bottlenecks
Uncovering operational impediments can help with optimizing study design during protocol amendments. However, operational impediments are study site related and require some form of intervention by the sponsor. In the aforementioned case, staff training limitations impeded enrollment, and needed to be streamlined through business process reform.
Project managers always experience unexpected outcomes during clinical operations. Successful project management requires both efficiency in identifying bottlenecks, and addressing them before they balloon into larger problems that ultimately impact study timelines. Leveraging this data-driven framework can help study teams identify additional factors impeding enrollment outside of the common inclusion/exclusion criteria, and improve study performance.