Data Management Best Practices
By Jonathan Andrus, M.S., CQA, CCDM, BioClinica, Inc.
Electronic Data Capture (EDC) systems should be more than just a means to an end. Quality EDC systems can enable the entire clinical trials information management process. However, if the system enables, it is the data managers who drive. They are burdened with designing processes to make the transition from paper to EDC efficient while maintaining the integrity of the data. And while such efficiency sounds simple in theory, it requires extensive preparation prior to the start of an electronic clinical study. Preparation is the most integral and significant function of a data manager, and electronic studies actually require more upfront preparation than paper studies. Because I believe preparation is so vital to the success of an electronic study, I have outlined several basic, but vital best practices for the start-up phase of a clinical study. When such best practices are administered, the result is a more efficient study with far fewer errors and complications during the critical latter phases. Your diligence in implementing thorough preparation — including edit check specifications and standardized eCRF design — will improve the efficiency of your study, and help you avoid some of the most common pitfalls even an experienced data manager can encounter.
Best Practice #1
It all starts with a plan — doubly so for an electronic study. World-leading personal-time-management expert, Alan Lakein, defined planning as “bringing the future into the present so that you can do something about it now.” Whether approaching EDC for the first time or changing to a new EDC vendor, the most crucial element of “the plan” actually takes place before the plan. Setting achievable goals is the most fundamental component of the study’s start-up phase. To frame realistic expectations, standard operating procedures (SOPs) must be updated — including identifying metrics and performance targets, as well as performing a gap analysis between current SOPs and requirements for the new system.
Ideally, the performance targets that are set for EDC projects will be based on the sponsor’s foundational reasons for switching to EDC, and should represent the first level objectives for EDC projects. The next set of objectives can be developed during rollout of the EDC solution and should include feedback from all stakeholders.
Data management must also identify any additional metrics that may not be applicable for paper-based studies, but will be used for EDC projects. Examples of EDC metrics may include average time for discrepancy resolution by site, average number and severity of help desk calls, and percent of EDC system downtime.
Data management may also establish goals for EDC projects based on calculated ROI. However, most organizations will find it necessary to modify their processes to accommodate EDC during the start-up phase. It should be expected that the start-up phase will be iterative, and therefore be impacted by many variables. The development of a clear set of realistic expectations will be influenced by...
Read the entire guide to Data Management Strategies here.