Data Collection in a Multisite Project: Tele<i>form</i>™

Hardin, J. Michael; Woodby, Lesa L.; Crawford, Myra A.; Windsor, Richard A.; Miller, Thomas

doi:10.1111/j.0737-1209.2005.220410.x

Cited by 16 publications

(9 citation statements)

References 10 publications

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“…TeleForm is a paper based data capture system that uses ICR/OCR/OMR/BCR software, called RecoFlex, to convert marks on scanned forms into data which then can be evaluated and entered into a database. The mechanics of Teleform processing have been extensively described elsewhere [3, 7]. The software package is comprised of several applications, the core being: Designer, Scan Station, Reader, and Verifier.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of a Teleform-based data collection system: A multi-center obesity research case study

Jenkins

Boyce

Akers

et al. 2014

Computers in Biology and Medicine

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Utilizing electronic data capture (EDC) systems in data collection and management allows automated validation programs to preemptively identify and correct data errors. For our multi-center, prospective study we chose to use TeleForm, a paper-based data capture software that uses recognition technology to create case report forms (CRFs) with similar functionality to EDC, including custom scripts to identify entry errors. We quantified the accuracy of the optimized system through a data audit of CRFs and the study database, examining selected critical variables for all subjects in the study, as well as an audit of all variables for 25 randomly selected subjects. Overall we found 6.7 errors per 10,000 fields, with similar estimates for critical (6.9/10,000) and non-critical (6.5/10,000) variables – values that fall below the acceptable quality threshold of 50 errors per 10,000 established by the Society for Clinical Data Management. However, error rates were found to widely vary by type of data field, with the highest rate observed with open text fields.

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Section: Methodsmentioning

confidence: 99%

Evaluation of a Teleform-based data collection system: A multi-center obesity research case study

Jenkins

Boyce

Akers

et al. 2014

Computers in Biology and Medicine

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“…Data from both the patient and surgeon questionnaires are scanned with TeleForm software (Cardiff Software, Inc, Vista, California) that uses optical character recognition to avoid manual data entry, and the returned data are verified and then exported to a database. 10,19 Both the patient and the surgeon questionnaire have a matched, barcoded identification number on each page to deidentify the data and to aid in database merging.…”

Section: Methodsmentioning

confidence: 99%

Descriptive Epidemiology of the Multicenter ACL Revision Study (MARS) Cohort

Wright¹,

Huston²,

Spindler³

et al. 2010

Am J Sports Med

384

181

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Background Revision anterior cruciate ligament (ACL) reconstruction has worse outcomes than primary reconstructions. Predictors for these worse outcomes are not known. The Multicenter ACL Revision Study (MARS) Group was developed to perform a multisurgeon, multicenter prospective longitudinal study to obtain sufficient subjects to allow multivariable analysis to determine predictors of clinical outcome. Purpose To describe the formation of MARS and provide descriptive analysis of patient demographics and clinical features for the initial 460 enrolled patients to date in this prospective cohort. Study Design Cross-sectional study; Level of evidence, 2. Methods After training and institutional review board approval, surgeons began enrolling patients undergoing revision ACL reconstruction, recording patient demographics, previous ACL reconstruction methods, intra-articular injuries, and current revision techniques. Enrolled subjects completed a questionnaire consisting of validated patient-based outcome measures. Results As of April 1, 2009, 87 surgeons have enrolled a total of 460 patients (57% men; median age, 26 years). For 89%, the reconstruction was the first revision. Mode of failure as deemed by the revising surgeon was traumatic (32%), technical (24%), biologic (7%), combination (37%), infection (<1%), and no response (<1%). Previous graft present at the time of injury was 70% autograft, 27% allograft, 2% combination, and 1% unknown. Sixty-two percent were more than 2 years removed from their last reconstruction. Graft choice for revision ACL reconstruction was 45% autograft, 54% allograft, and more than 1% both allograft and autograft. Meniscus and/or chondral damage was found in 90% of patients. Conclusion The MARS Group has been able to quickly accumulate the largest revision ACL reconstruction cohort reported to date. Traumatic reinjury is deemed by surgeons to be the most common single mode of failure, but a combination of factors represents the most common mode of failure. Allograft graft choice is more common in the revision setting than autograft. Concomitant knee injury is extremely common in this population.

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“…There are several potential benefits of using automated forms processing, including a low error level, an improved data verification process and, more importantly, (especially in big studies with many respondents) a significant reduction in time required for data entry [33]. In a registry setting, it is important to achieve an efficient data collection procedure.…”

Section: Discussionmentioning

confidence: 99%

Quality of Data Entry Using Single Entry, Double Entry and Automated Forms Processing–An Example Based on a Study of Patient-Reported Outcomes

2012

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BackgroundThe clinical and scientific usage of patient-reported outcome measures is increasing in the health services. Often paper forms are used. Manual double entry of data is defined as the definitive gold standard for transferring data to an electronic format, but the process is laborious. Automated forms processing may be an alternative, but further validation is warranted.Methods200 patients were randomly selected from a cohort of 5777 patients who had previously answered two different questionnaires. The questionnaires were scanned using an automated forms processing technique, as well as processed by single and double manual data entry, using the EpiData Entry data entry program. The main outcome measure was the proportion of correctly entered numbers at question, form and study level.ResultsManual double-key data entry (error proportion per 1000 fields = 0.046 (95% CI: 0.001–0.258)) performed better than single-key data entry (error proportion per 1000 fields = 0.370 (95% CI: 0.160–0.729), (p = 0.020)). There was no statistical difference between Optical Mark Recognition (error proportion per 1000 fields = 0.046 (95% CI: 0.001–0.258)) and double-key data entry (p = 1.000). With the Intelligent Character Recognition method, there was no statistical difference compared to single-key data entry (error proportion per 1000 fields = 6.734 (95% CI: 0.817–24.113), (p = 0.656)), as well as double-key data entry (error proportion per 1000 fields = 3.367 (95% CI: 0.085–18.616)), (p = 0.319)).ConclusionsAutomated forms processing is a valid alternative to double manual data entry for highly structured forms containing only check boxes, numerical codes and no dates. Automated forms processing can be superior to single manual data entry through a data entry program, depending on the method chosen.

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Data Collection in a Multisite Project: Teleform™

Cited by 16 publications

References 10 publications

Evaluation of a Teleform-based data collection system: A multi-center obesity research case study

Evaluation of a Teleform-based data collection system: A multi-center obesity research case study

Descriptive Epidemiology of the Multicenter ACL Revision Study (MARS) Cohort

Quality of Data Entry Using Single Entry, Double Entry and Automated Forms Processing–An Example Based on a Study of Patient-Reported Outcomes

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