scholarly journals Multimedia Appendix Correction: A Computer-Assisted Personal Interview App in Research Electronic Data Capture for Administering Time Trade-off Surveys (REDCap): Development and Pretest

10.2196/11436 ◽  
2018 ◽  
Vol 2 (2) ◽  
pp. e11436
Author(s):  
Mark Oremus ◽  
Anis Sharafoddini ◽  
Gian Paolo Morgano ◽  
Xuejing Jin ◽  
Feng Xie
Keyword(s):  
Data Capture ◽  
Computer Assisted ◽  
Electronic Data Capture ◽  
Personal Interview ◽  
Electronic Data ◽  
Trade Off

scholarly journals A Computer-Assisted Personal Interview App in Research Electronic Data Capture for Administering Time Trade-off Surveys (REDCap): Development and Pretest (Preprint)

2017 ◽  
Author(s):  
Mark Oremus ◽  
Anis Sharafoddini ◽  
Gian Paolo Morgano ◽  
Xuejing Jin ◽  
Feng Xie
Keyword(s):  
Quality Of Life ◽  
General Public ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Health Related Quality ◽  
Electronic Data ◽  
Trade Off ◽  
Related Quality ◽  
Health Related

BACKGROUND The time trade-off (TTO) task is a method of eliciting health utility scores, which range from 0 (equivalent to death) to 1 (equivalent to perfect health). These scores numerically represent a person’s health-related quality of life. Software apps exist to administer the TTO task; however, most of these apps are poorly documented and unavailable to researchers. OBJECTIVE To fill the void, we developed an online app to administer the TTO task for a research study that is examining general public proxy health-related quality of life estimates for persons with Alzheimer’s disease. This manuscript describes the development and pretest of the app. METHODS We used Research Electronic Data Capture (REDCap) to build the TTO app. The app’s modular structure and REDCap’s object-oriented environment facilitated development. After the TTO app was built, we recruited a purposive sample of 11 members of the general public to pretest its functionality and ease of use. RESULTS Feedback from the pretest group was positive. Minor modifications included clarity enhancements, such as rearranging some paragraph text into bullet points, labeling the app to delineate different question sections, and revising or deleting text. We also added a research question to enable the identification of respondents who know someone with Alzheimer’s disease. CONCLUSIONS We developed an online app to administer the TTO task. Other researchers may access and customize the app for their own research purposes.

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)

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.

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