Literature DB >> 28665903

Man Versus Machine: Comparing Double Data Entry and Optical Mark Recognition for Processing CAHPS Survey Data.

Matthew Fifolt1, Justin Blackburn, David J Rhodes, Shemeka Gillespie, Aleena Bennett, Paul Wolff, Andrew Rucks.   

Abstract

OBJECTIVE: Historically, double data entry (DDE) has been considered the criterion standard for minimizing data entry errors. However, previous studies considered data entry alternatives through the limited lens of data accuracy. This study supplies information regarding data accuracy, operational efficiency, and cost for DDE and Optical Mark Recognition (OMR) for processing the Consumer Assessment of Healthcare Providers and Systems 5.0 survey.
METHODS: To assess data accuracy, we compared error rates for DDE and OMR by dividing the number of surveys that were arbitrated by the total number of surveys processed for each method. To assess operational efficiency, we tallied the cost of data entry for DDE and OMR after survey receipt. Costs were calculated on the basis of personnel, depreciation for capital equipment, and costs of noncapital equipment.
RESULTS: The cost savings attributed to this method were negated by the operational efficiency of OMR. There was a statistical significance between rates of arbitration between DDE and OMR; however, this statistical significance did not create a practical significance.
CONCLUSIONS: The potential benefits of DDE in terms of data accuracy did not outweigh the operational efficiency and thereby financial savings of OMR.

Entities:  

Mesh:

Year:  2017        PMID: 28665903     DOI: 10.1097/QMH.0000000000000138

Source DB:  PubMed          Journal:  Qual Manag Health Care        ISSN: 1063-8628            Impact factor:   0.926


  4 in total

1.  Extracting Medical Information from Paper COVID-19 Assessment Forms.

Authors:  Jacob D Schultz; Colin G White-Dzuro; Cheng Ye; Joseph R Coco; Janet M Myers; Claude Shackelford; S Trent Rosenbloom; Daniel Fabbri
Journal:  Appl Clin Inform       Date:  2021-03-10       Impact factor: 2.342

2.  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.

Authors:  Aksel Paulsen; Knut Harboe; Ingvild Dalen
Journal:  Health Sci Rep       Date:  2020-11-29

3.  A Nationwide Evaluation of the Prevalence of Human Papillomavirus in Brazil (POP-Brazil Study): Protocol for Data Quality Assurance and Control.

Authors:  Jaqueline Driemeyer Correia Horvath; Marina Bessel; Natália Luiza Kops; Flávia Moreno Alves Souza; Gerson Mendes Pereira; Eliana Marcia Wendland
Journal:  JMIR Res Protoc       Date:  2022-01-05

4.  Rubber stamp templates for improving clinical documentation: A paper-based, m-Health approach for quality improvement in low-resource settings.

Authors:  Bernadette Kleczka; Anita Musiega; Grace Rabut; Phoebe Wekesa; Paul Mwaniki; Michael Marx; Pratap Kumar
Journal:  Int J Med Inform       Date:  2017-10-23       Impact factor: 4.046

  4 in total

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