scholarly journals Das Treatment Exit Options for Uveitis (TOFU) Register: Einbindung von Patienten in die Evidenzgenerierung

2021 ◽  
Vol 83 (S 01) ◽  
pp. S39-S44
Author(s):  
Jeany Q. Li ◽  
Jennifer Dell ◽  
Tobias Höller ◽  
David Fink ◽  
Matthias Schmid ◽  
...  
Keyword(s):  
Patient Reported Outcomes ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Patient Reported ◽  
Infectious Uveitis ◽  
Exit Options ◽  
Vanderbilt University

ZusammenfassungUveitis ist eine seltenere entzündliche Augenerkrankung, die zu schwerer Sehbehinderung und Blindheit führen kann und besonders Menschen im berufstätigen Alter betrifft. Besonders schwere Verläufe, die meist einer immunmodulierenden Therapie (IMT) bedürfen, treten bei einer Uveitis auf, die die hinteren Teile des Auges oder das ganze Auge betreffen und nicht infektiöser Ursache sind. Für diese Formen der Erkrankung gibt es nur wenig gute Evidenz zum langfristigen Management der Erkrankung und insbesondere zur Beendigung oder Reduktion einer IMT. Das Treatment exit Options For non-infectious Uveitis (TOFU) Register der Sektion Uveitis der Deutschen Ophthalmologischen Gesellschaft (DOG) hat das Ziel, Krankheitsverläufe von Patienten mit nicht-infektiöser nicht-anteriorer Uveitis zu dokumentieren und Empfehlungen zur Beendigung einer IMT zu erarbeiten. Ein wesentlicher Aspekt des TOFU-Registers ist die aktive Einbeziehung von Patienten in die Erfassung Patienten-berichteter Endpunkte über ein Patientenmodul (Patient Reported Outcomes, PROs). Neben seh- und gesundheitsbezogener Lebensqualität werden auch Fragebögen zur Therapieadhärenz, Produktivität und Auswirkungen der Therapien eingesetzt. Die eingesetzten Fragebögen wurden in dieser Kombination in einer Pilotstudie mit Patienten getestet und es hat sich gezeigt, dass die wesentlichen Patienten-relevanten Aspekte der Erkrankung und deren Auswirkungen auf den Alltag erfasst werden. Das Patientenmodul, wie das Register selbst, nutzt zur Dokumentation die electronic data capture (EDC)-Software REDCap (Version 9, Vanderbilt University, USA). Durch die Einbindung von Patienten in sowohl die Konzeption des Registers als auch die fortlaufende Datensammlung wird sichergestellt, dass Patienten-relevante Evidenz für z. B. die Erstellung von Leitlinien und Behandlungsempfehlungen generiert wird.

How Did a Multi-Institutional Trial Show Feasibility of Electronic Data Capture in Older Patients With Cancer? Results From a Multi-Institutional Qualitative Study (Alliance A171902)

2021 ◽  
pp. 442-449
Author(s):  
Nichole A. Martin ◽  
Elizabeth S. Harlos ◽  
Kathryn D. Cook ◽  
Jennifer M. O'Connor ◽  
Andrew Dodge ◽  
...  
Keyword(s):  
Older Patients ◽  
Electronic Devices ◽  
Data Entry ◽  
Data Capture ◽  
Data Availability ◽  
Electronic Data Capture ◽  
Wireless Internet ◽  
Electronic Data ◽  
Patients With Cancer ◽  
Patient Reported

PURPOSE New technology might pose problems for older patients with cancer. This study sought to understand how a trial in older patients with cancer (Alliance A171603) was successful in capturing electronic patient-reported data. METHODS Study personnel were invited via e-mail to participate in semistructured phone interviews, which were audio-recorded and qualitatively analyzed. RESULTS Twenty-four study personnel from the 10 sites were interviewed; three themes emerged. The first was that successful patient-reported electronic data capture shifted work toward patients and toward study personnel at the beginning of the study. One interviewee explained, “I mean it kind of lost all advantages…by being extremely laborious.” Study personnel described how they ensured electronic devices were charged, wireless internet access was up and running, and login codes were available. The second theme was related to the first and dealt with data filtering. Study personnel described high involvement in data gathering; for example, one interviewee described, “I answered on the iPad, whatever they said. They didn't even want to use it at all.” A third theme dealt with advantages of electronic data entry, such as prompt data availability at study completion. Surprisingly, some remarks described how electronic devices brought people together, “Some of the patients, you know, it just gave them a chance to kinda talk about, you know, what was going on.” CONCLUSION High rates of capture of patient-reported electronic data were viewed favorably but occurred in exchange for increased effort from patients and study personnel and in exchange for data that were not always patient-reported in the strictest sense.

scholarly journals Research Electronic Data Capture (REDCap)

2018 ◽  
Vol 106 (1) ◽  
Author(s):  
Emily F. Patridge ◽  
Tania P. Bardyn
Keyword(s):  
Clinical Research ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
Web Based ◽  
Vanderbilt University

Research Electronic Data Capture (REDCap) is a web-based application developed by Vanderbilt University to capture data for clinical research and create databases and projects.

scholarly journals 238. Novel Way to Evaluate Antibiotic Use Appropriateness: Moving Towards the “Never Event” Classification by Electronic Algorithm

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S119-S120
Author(s):  
Twisha S Patel ◽  
Lindsay A Petty ◽  
Jiajun Liu ◽  
Marc H Scheetz ◽  
Nicholas Mercuro ◽  
...  
Keyword(s):  
Urine Culture ◽  
Asymptomatic Bacteriuria ◽  
Antibiotic Use ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Electronic Data ◽  
False Positivity ◽  
Never Event ◽  
Manual Review

Abstract Background Antibiotic use is commonly tracked electronically by antimicrobial stewardship programs (ASPs). Traditionally, evaluating the appropriateness of antibiotic use requires time- and labor-intensive manual review of each drug order. A drug-specific “appropriateness” algorithm applied electronically would improve the efficiency of ASPs. We thus created an antibiotic “never event” (NE) algorithm to evaluate vancomycin use, and sought to determine the performance characteristics of the electronic data capture strategy. Methods An antibiotic NE algorithm was developed to characterize vancomycin use (Figure) at a large academic institution (1/2016–8/2019). Patients were electronically classified according to the NE algorithm using data abstracted from their electronic health record. Type 1 NEs, defined as continued use of vancomycin after a vancomycin non-susceptible pathogen was identified, were the focus of this analysis. Type 1 NEs identified by automated data capture were reviewed manually for accuracy by either an infectious diseases (ID) physician or an ID pharmacist. The positive predictive value (PPV) of the electronic data capture was determined. Antibiotic Never Event (NE) Algorithm to Characterize Vancomycin Use Results A total of 38,774 unique cases of vancomycin use were available for screening. Of these, 0.6% (n=225) had a vancomycin non-susceptible pathogen identified, and 12.4% (28/225) were classified as a Type 1 NE by automated data capture. All 28 cases included vancomycin-resistant Enterococcus spp (VRE). Upon manual review, 11 cases were determined to be true positives resulting in a PPV of 39.3%. Reasons for the 17 false positives are given in Table 1. Asymptomatic bacteriuria (ASB) due to VRE in scenarios where vancomycin was being appropriately used to treat a concomitant vancomycin-susceptible infection was the most common reason for false positivity, accounting for 64.7% of false positive cases. After removing urine culture source (n=15) from the algorithm, PPV improved to 53.8%. Conclusion An automated vancomycin NE algorithm identified 28 Type 1 NEs with a PPV of 39%. ASB was the most common cause of false positivity and removing urine culture as a source from the algorithm improved PPV. Future directions include evaluating Type 2 NEs (Figure) and prospective, real-time application of the algorithm. Disclosures Marc H. Scheetz, PharmD, MSc, Merck and Co. (Grant/Research Support)

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