Bryan A Wilbanks1, Eta S Berner2, Gregory L Alexander3, Andres Azuero4, Patricia A Patrician4, Jacqueline A Moss4. 1. School of Nursing, University of Alabama at Birmingham, Birmingham, AL, USA. Electronic address: Bryan004@uab.edu. 2. Department of Health Services Administration, The University of Alabama at Birmingham, Birmingham, AL, USA. 3. Sinclair School of Nursing, The University of Missouri, Columbia, MO, USA. 4. School of Nursing, University of Alabama at Birmingham, Birmingham, AL, USA.
Abstract
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.
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.
Authors: Darinda E Sutton; Jennifer R Fogel; April S Giard; Lisa A Gulker; Catherine H Ivory; Amy M Rosa Journal: Appl Clin Inform Date: 2020-07-08 Impact factor: 2.342