IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0035087.html
   My bibliography  Save this article

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

Author

Listed:
  • Aksel Paulsen
  • Søren Overgaard
  • Jens Martin Lauritsen

Abstract

Background: The 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. Methods: 200 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. Results: Manual 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)). Conclusions: Automated 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.

Suggested Citation

  • Aksel Paulsen & Søren Overgaard & Jens Martin Lauritsen, 2012. "Quality of Data Entry Using Single Entry, Double Entry and Automated Forms Processing–An Example Based on a Study of Patient-Reported Outcomes," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-6, April.
  • Handle: RePEc:plo:pone00:0035087
    DOI: 10.1371/journal.pone.0035087
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0035087
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0035087&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0035087?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Haegemans, Tom & Snoeck, Monique & Lemahieu, Wilfried, 2018. "Entering data correctly: An empirical evaluation of the theory of planned behaviour in the context of manual data acquisition," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 12-30.
    2. Marcin Kozak & Wojtek Krzanowski & Izabela Cichocka & James Hartley, 2015. "The effects of data input errors on subsequent statistical inference," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(9), pages 2030-2037, September.
    3. Pierluigi Zerbino & Davide Aloini & Riccardo Dulmin & Valeria Mininno, 2019. "Towards Analytics-Enabled Efficiency Improvements in Maritime Transportation: A Case Study in a Mediterranean Port," Sustainability, MDPI, vol. 11(16), pages 1-20, August.
    4. Robles-Palazón, Francisco Javier & Puerta-Callejón, José M. & Gámez, José A. & De Ste Croix, Mark & Cejudo, Antonio & Santonja, Fernando & Sainz de Baranda, Pilar & Ayala, Francisco, 2023. "Predicting injury risk using machine learning in male youth soccer players," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0035087. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.