Dataset Information

Data entry quality of double data entry vs automated form processing technologies: A cohort study validation of optical mark recognition and intelligent character recognition in a clinical setting.

ABSTRACT:

Background and aims

Patient-reported outcome measures (PROMs) are increasingly used in health services. Paper forms are still often used to register such data. Manual double data entry (DDE) has been defined as the gold standard for transferring data to an electronic format but is laborious and costly. Automated form processing (AFP) is an alternative, but validation in a clinical context is warranted. The study objective was to examine and validate a local hospital AFP setup.

Methods

Patients over 18?years of age who were scheduled for knee or hip replacement at Stavanger University Hospital from 2014 to 2017 who answered PROMs were included in the study and contributed PROM data. All paper PROMs were scanned using the AFP techniques of optical mark recognition (OMR) and intelligent character recognition (ICR) and were processed by DDE by health secretaries using a data entry program. OMR and ICR were used to capture different types of data. The main outcome was the proportion of correctly entered numbers, defined as the same response recorded in AFP and DDE or by consulting the original paper questionnaire at the data field, item, and PROM level.

Results

A total of 448 questionnaires from 255 patients were analyzed. There was no statistically significant difference in error proportions per 10?000 data fields between OMR and DDE for data from check boxes (3.52 95% confidence interval (CI) 2.17 to 5.72 and 4.18 (95% CI 2.68-6.53), respectively P =?.61). The error proportion for ICR (nine errors) was statistically significantly higher than that for DDE (two errors), that is, 3.53 (95% CI 1.87-6.57) vs 0.78 (95% CI 0.22-2.81) per 100 data fields/items/questionnaires; P =?.033. OMR (0.04% errors) outperformed ICR (3.51% errors; P ConclusionsOMR can produce an error rate that is comparable to that of DDE. In our setup, ICR is still problematic and is highly dependent on manual validation. When AFP is used, data quality should be tested and documented.

SUBMITTER: Paulsen A

PROVIDER: S-EPMC7700101 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Data entry quality of double data entry vs automated form processing technologies: A cohort study validation of optical mark recognition and intelligent character recognition in a clinical setting.

Paulsen Aksel A Harboe Knut K Dalen Ingvild I

Health science reports 20201129 4

<h4>Background and aims</h4>Patient-reported outcome measures (PROMs) are increasingly used in health services. Paper forms are still often used to register such data. Manual double data entry (DDE) has been defined as the gold standard for transferring data to an electronic format but is laborious and costly. Automated form processing (AFP) is an alternative, but validation in a clinical context is warranted. The study objective was to examine and validate a local hospital AFP setup.<h4>Methods ...[more]

PMID: 33283058

Similar Datasets

Project description:BackgroundHistone modifications play important roles in gene regulation, heredity, imprinting, and many human diseases. The histone code is complex and consists of more than 100 marks. Therefore, biologists need computational tools to characterize general signatures representing the distributions of tens of chromatin marks around thousands of regions.ResultsTo this end, we developed a software tool, HebbPlot, which utilizes a Hebbian neural network in learning a general chromatin signature from regions with a common function. Hebbian networks can learn the associations between tens of marks and thousands of regions. HebbPlot presents a signature as a digital image, which can be easily interpreted. Moreover, signatures produced by HebbPlot can be compared quantitatively. We validated HebbPlot in six case studies. The results of these case studies are novel or validating results already reported in the literature, indicating the accuracy of HebbPlot. Our results indicate that promoters have a directional chromatin signature; several marks tend to stretch downstream or upstream. H3K4me3 and H3K79me2 have clear directional distributions around active promoters. In addition, the signatures of high- and low-CpG promoters are different; H3K4me3, H3K9ac, and H3K27ac are the most different marks. When we studied the signatures of enhancers active in eight tissues, we observed that these signatures are similar, but not identical. Further, we identified some histone modifications - H3K36me3, H3K79me1, H3K79me2, and H4K8ac - that are associated with coding regions of active genes. Other marks - H4K12ac, H3K14ac, H3K27me3, and H2AK5ac - were found to be weakly associated with coding regions of inactive genes.ConclusionsThis study resulted in a novel software tool, HebbPlot, for learning and visualizing the chromatin signature of a genetic element. Using HebbPlot, we produced a visual catalog of the signatures of multiple genetic elements in 57 cell types available through the Roadmap Epigenomics Project. Furthermore, we made a progress toward a functional catalog consisting of 22 histone marks. In sum, HebbPlot is applicable to a wide array of studies, facilitating the deciphering of the histone code.

Dataset Information

Data entry quality of double data entry vs automated form processing technologies: A cohort study validation of optical mark recognition and intelligent character recognition in a clinical setting.

Background and aims

Methods

Results

Publications

Data entry quality of double data entry vs automated form processing technologies: A cohort study validation of optical mark recognition and intelligent character recognition in a clinical setting.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets