Preserving consent-for-consent with feasibility-assessment and recruitment in clinical studies: FARSITE architecture

摘要

Best practice guidance for clinical studies asks investigators to employ the highest possible standards in privacy and consent. When considering the feasibility of a clinical study, issues of privacy extend not only to actual but also to potential study participants. The consent required to access records to determine whether or not an individual might be eligible to participate in a study is sometimes referred to as consent-for-consent. Some initiatives to enhance the efficiency of study-recruitment could compromise consent-for-consent, for example by inviting a patient to take part in a study without the knowledge of their attending clinician. Through iterative working with experts and examination of protocols we explored a range of scenarios for assessing the feasibility of clinical trials and observational studies, and recruiting participants. The main requirement we identified was to speed up feasibility-assessment and recruitment while preserving the patient-clinician trust relationship that is central to consent-for-consent. We present an appropriate information system architecture, FARSITE (Feasibility Assessment and Recruitment System for Improving Trial Efficiency), and show in principle that faster recruitment into clinical studies need not compromise best practice in privacy or consent. We show that FARSITE is a specific instance of an 'e-Lab' architecture for assembling data, methods and expertise around study protocols and defined populations.