The effect of data-entry template design and anesthesia provider workload on documentation accuracy, documentation efficiency, and user-satisfaction

Academic Article

Abstract

  • © 2018 Introduction: Currently, there are few evidence-based guidelines to inform optimal clinical data-entry template design that maximizes usability while reducing unintended consequences. This study explored the impact of data-entry template design and anesthesia provider workload on documentation accuracy, documentation efficiency, and user-satisfaction to identify the most beneficial data-entry methods for use in future documentation interface design. Methodology: A study using observational data collection and psychometric instruments (for perceived workload and user-satisfaction) was conducted at three hospitals using different methods of data-entry for perioperative documentation (auto-filling with unstructured data, computer-assisted data selection with semi-structured documentation, and paper-based documentation). Nurse anesthetists at each hospital (N = 30) were observed completing documentation on routine abdominal surgical cases. Results: Auto-filling (61.2%) had the lowest documentation accuracy scores compared to computer-assisted (81.3%) and paper-based documentation (76.2%). Computer-assisted data-entry had the best documentation efficiency scores and required the least percentage of the nurse anesthetists’ time (9.65%) compared to auto-filling (11.43%) and paper-based documentation (15.23%). Paper-based documentation had the highest perceived workload scores (M = 288, SD = 88) compared to auto-filling (M = 160, SD = 93, U = 16.5, p < 0.01) and computer assisted data-entry (M = 93, SD = 50, U = 4.0, P < 0.001). Conclusions: Auto-filling with unstructured data needs to be used sparingly because of its low documentation accuracy. Computer-assisted data entry with semi-structured data needs to be further study because of its better documentation accuracy, documentation efficiency, and perceived workload.
  • Digital Object Identifier (doi)

    Pubmed Id

  • 24871859
  • Author List

  • Wilbanks BA; Berner ES; Alexander GL; Azuero A; Patrician PA; Moss JA
  • Start Page

  • 29
  • End Page

  • 35
  • Volume

  • 118