'Blinding' involves concealing knowledge of which trial participants received the interventions from participants themselves and other trial personnel throughout the trial. Blinding reduces bias... Show more'Blinding' involves concealing knowledge of which trial participants received the interventions from participants themselves and other trial personnel throughout the trial. Blinding reduces bias arising from the beliefs and expectations of these groups. It is agreed that where possible, blinding should be attempted, for example by ensuring that experimental and control treatments look the same. However, there is a debate about if we should measure whether blinding has been successful, this manuscript will discuss this controversy, including the benefits and risks of measuring blinding within the randomised controlled trial. (c) 2021 Elsevier Inc. All rights reserved. Show less
Dijk, W.B. van; Fiolet, A.T.L.; Schuit, E.; Sammani, A.; Groenhof, T.K.J.; Graaf, R. van der; ... ; Mosterd, A. 2021
Objective: This study aimed to validate trial patient eligibility screening and baseline data collection using text-mining in electronic healthcare records (EHRs), comparing the results to those of... Show moreObjective: This study aimed to validate trial patient eligibility screening and baseline data collection using text-mining in electronic healthcare records (EHRs), comparing the results to those of an international trial.Study Design and Setting: In three medical centers with different EHR vendors, EHR-based text-mining was used to automatically screen patients for trial eligibility and extract baseline data on nineteen characteristics. First, the yield of screening with automated EHR text-mining search was compared with manual screening by research personnel. Second, the accuracy of extracted baseline data by EHR text mining was compared to manual data entry by research personnel.Results: Of the 92,466 patients visiting the out-patient cardiology departments, 568 (0.6%) were enrolled in the trial during its recruitment period using manual screening methods. Automated EHR data screening of all patients showed that the number of patients needed to screen could be reduced by 73,863 (79.9%). The remaining 18,603 (20.1%) contained 458 of the actual participants (82.4% of participants). In trial participants, automated EHR text-mining missed a median of 2.8% (Interquartile range [IQR] across all variables 0.4-8.5%) of all data points compared to manually collected data. The overall accuracy of automatically extracted data was 88.0% (IQR 84.7-92.8%).Conclusion: Automatically extracting data from EHRs using text-mining can be used to identify trial participants and to collect baseline information. (C) 2020 The Authors. Published by Elsevier Inc. Show less
Spencer-Bonilla, G.; Thota, A.; Organick, P.; Ponce, O.J.; Kunneman, M.; Giblon, R.; ... ; Shared Decision Making Atrial Fibr 2020
BackgroundShared decision making (SDM) implementation remains challenging. The factors that promote or hinder implementation of SDM tools for use during the consultation, including contextual... Show moreBackgroundShared decision making (SDM) implementation remains challenging. The factors that promote or hinder implementation of SDM tools for use during the consultation, including contextual factors such as clinician burnout and organizational support, remain unclear. We explored these factors in the context of a practical multicenter randomized trial evaluating the effectiveness of an SDM conversation tool for patients with atrial fibrillation considering anticoagulation therapy.MethodsIn this cross-sectional study, we recruited clinicians who were regularly involved in conversations with patients regarding anticoagulation for atrial fibrillation. Clinicians reported their characteristics and burnout symptoms using the two-item Maslach Burnout Inventory. Clinicians were trained in using the SDM tool, and they recorded their perceptions of the tool's normalization potential using the Normalization MeAsure Development (NoMAD) survey instrument and verbally reflected on their answers to these survey questions. When possible, the training sessions and clinicians' verbal responses to the conversation tool were recorded.ResultsOur study comprised 183 clinicians recruited into the trial (168 with survey responses and 112 with recordings). Overall, clinicians gave high scores to the normalization potential of the intervention; they endorsed all domains of normalization to the same extent, regardless of site, clinician characteristics, or burnout ratings. In interviews, clinicians paid significant attention to making sense of the tool. Tool buy-in seemed to depend heavily on their ability to see the tool as accurate and "evidence-based" and their perceptions of having time in the consultation to use it.ConclusionsWhile time in the consultation remains a barrier, we did not find a significant association between burnout symptoms and normalization of an SDM conversation tool. Possible areas for improving the normalization of SDM conversation tools in clinical practice include enabling collaboration among clinicians to implement the tool and reporting how clinicians elsewhere use the tool. Direct measures of normalization (i.e., observing how often clinicians access the tool in practice outside of the clinical trial) may further elucidate the role that contextual factors, such as clinician burnout, play in the implementation of SDM.Trial registrationClinicalTrials.gov, NCT02905032. Registered on 9 September 2016. Show less
