Presentation
Usability Evaluation of a Novel SimEMR Training Program: Impact on Nursing Cognitive Workload
DescriptionIntroduction:
Medication administration remains one of the most frequent and high-risk tasks in nursing practice. Despite widespread use of electronic medical records (EMRs) and barcode medication administration (BCMA), these tools remain underexplored in nursing education. Limited exposure contributes to gaps in workflow fluency, patient safety practices, and technology readiness among novice nurses. Accordingly, simulation-based training provides a safe, immersive method for bridging this gap by integrating digital tools into realistic clinical environments. Globally, the World Health Organization identified medication safety as a top patient safety challenge due to persistent rates of errors and its associated costs (Donaldson et al., 2017). Incorporating digital health technologies in simulation has enhanced engagement, skill retention, and safety practices among students (Alharbi et al., 2024). Furthermore, preparing nurses for technology-driven care environments aligns with recommendations for future-ready curricula that emphasize usability, clinical decision-making, and patient-centered technology adoption (Cant & Cooper, 2017; Risling, 2017). This study evaluated the usability and educational impact of incorporating a training electronic medical record system, SimEMR©, with BCMA during a high-fidelity simulation. The objectives were to examine student engagement, satisfaction, perceived realism, and cognitive workload during the medication administration scenario.
Methods:
This study was approved by the Institutional Review Board (Expedited IRB #2024-1521). A total of 30 participants took part in the study. Thirteen non-clinicians and 17 clinicians completed both the hands-on experiment and post-survey.
After completing a demographics-presurvey, didactic training began with a structured six-part video tutorial introducing SimEMR©’s core functionalities, including chart review, flowsheet documentation, orders, results, and medication administration with barcode scanning.
Participants then entered a high-fidelity patient room equipped with a wheeled workstation with SimEMR©, a barcode scanner, and a simulated patient named Felicia (S5301 Advanced HAL, Gaumard Scientific, Miami, Florida). Felicia, a high-fidelity patient simulator, was wearing a wristband with her medical record number as a barcode for patient identification. The participants were to monitor and record vital signs using the bedside monitor and administer her 9 AM medications. Falicia’s clinical state was designed to reflect a realistic patient encounter for a patient with community acquired pneumonia.
This simulation’s structured task sequence required participants to access SimEMR© to review patient demographics, notes, and orders, and document vital signs (blood pressure, heart rate, respiratory rate and temperature). After these tasks were completed, participants administered the scheduled medications verifying the Five Rights of medication administration and using the BCMA to scan the patient writ band and each medication to reconcile with the medication administration record. Upon completion, participants exited the simulation room, debriefed with simulation staff, and completed post-simulation surveys that measured engagement (McCoy et al., 2016), satisfaction (Holmgren et al., 2024; Ramoo et al., 2023), perceived realism (Wilson et al., 2018), and workload (Surg-TLX) (Wilson et al., 2011).
Results:
Pre- and post-training surveys were analyzed in Minitab. A MANOVA revealed a significant multivariate effect of group (clinician vs. non-clinician) on the combined dependent variables, Pillai’s Trace = 0.64, F(4,25) = 11.30, p < .001. Follow-up univariate ANOVAs confirmed significant group differences for engagement, F(1,28) = 29.69, p < .001, partial η² = 0.52; satisfaction, F(1,28) = 12.30, p = .002, partial η² = 0.31; and perceived realism, F(1,28) = 14.64, p = .001, partial η² = 0.34. No group differences emerged for workload, F(1,28) = 0.10, p = .756.
Group-level comparisons indicated that clinicians consistently reported higher ratings across outcomes. Clinicians reported greater engagement (M = 4.80, SD = 0.22) than non-clinicians (M = 4.02, SD = 0.54), t(15) = 4.94, p < .001, 95% CI [0.45, 1.12]. Similarly, clinicians reported higher satisfaction (M = 4.78, SD = 0.33) than non-clinicians (M = 3.96, SD = 0.89), t(14) = 3.16, p = .01, 95% CI [0.26, 1.37]. Perceived realism was also higher among clinicians (M = 4.98, SD = 0.08) compared to non-clinicians (M = 4.31, SD = 0.72), t(12) = 3.34, p = .01, 95% CI [0.23, 1.11]. Cognitive workload scores for non-clinician and clinician participants were comparable (clinicians: M = 80.9; non-clinicians: M = 79.5), t(28) = 0.31, p = .76 and suggested that the digital tools did not impose undue burden.
Discussion:
These results demonstrate that clinicians benefited more from the simulation in terms of engagement, satisfaction, and perceived realism, while both groups reported manageable cognitive workload. Importantly, integrating EMR and BCMA into nursing simulations enhances engagement and satisfaction without increasing workload strain. Students perceived the experience as realistic and manageable, reinforcing that usability-focused design is more critical for engagement than realism alone. Further analysis in the current study is in progress, but the included results support the inclusion of digital health technologies in nursing curricula to improve technological fluency, reinforce medication safety practices, and prepare students for technology-driven clinical environments.
References:
Holmgren, A. J., Hendrix, N., Maisel, N., Everson, J., Bazemore, A., Rotenstein, L., Phillips, R. L., & Adler-Milstein, J. (2024). Electronic health record usability, satisfaction, and burnout for family physicians. JAMA network open, 7(8), e2426956-e2426956.
McCoy, L., Pettit, R. K., Lewis, J. H., Allgood, J. A., Bay, C., & Schwartz, F. N. (2016). Evaluating medical student engagement during virtual patient simulations: a sequential, mixed methods study. BMC medical education, 16(1), 20.
Ramoo, V., Kamaruddin, A., Nawawi, W. N. F. W., Che, C. C., & Kavitha, R. (2023). Nurses’ perception and satisfaction toward electronic medical record system. Florence Nightingale Journal of Nursing, 31(1), 2.
Risling, T. (2017). Educating the nurses of 2025: Technology trends of the next decade. Nurse education in practice, 22, 89-92.
Wilson, E., Hewett, D. G., Jolly, B. C., Janssens, S., & Beckmann, M. M. (2018). Is that realistic? The development of a realism assessment questionnaire and its application in appraising three simulators for a gynaecology procedure. Advances in Simulation, 3(1), 21.
Wilson, M. R., Poolton, J. M., Malhotra, N., Ngo, K., Bright, E., & Masters, R. S. (2011). Development and validation of a surgical workload measure: the surgery task load index (SURG-TLX). World journal of surgery, 35(9), 1961-1969.
Medication administration remains one of the most frequent and high-risk tasks in nursing practice. Despite widespread use of electronic medical records (EMRs) and barcode medication administration (BCMA), these tools remain underexplored in nursing education. Limited exposure contributes to gaps in workflow fluency, patient safety practices, and technology readiness among novice nurses. Accordingly, simulation-based training provides a safe, immersive method for bridging this gap by integrating digital tools into realistic clinical environments. Globally, the World Health Organization identified medication safety as a top patient safety challenge due to persistent rates of errors and its associated costs (Donaldson et al., 2017). Incorporating digital health technologies in simulation has enhanced engagement, skill retention, and safety practices among students (Alharbi et al., 2024). Furthermore, preparing nurses for technology-driven care environments aligns with recommendations for future-ready curricula that emphasize usability, clinical decision-making, and patient-centered technology adoption (Cant & Cooper, 2017; Risling, 2017). This study evaluated the usability and educational impact of incorporating a training electronic medical record system, SimEMR©, with BCMA during a high-fidelity simulation. The objectives were to examine student engagement, satisfaction, perceived realism, and cognitive workload during the medication administration scenario.
Methods:
This study was approved by the Institutional Review Board (Expedited IRB #2024-1521). A total of 30 participants took part in the study. Thirteen non-clinicians and 17 clinicians completed both the hands-on experiment and post-survey.
After completing a demographics-presurvey, didactic training began with a structured six-part video tutorial introducing SimEMR©’s core functionalities, including chart review, flowsheet documentation, orders, results, and medication administration with barcode scanning.
Participants then entered a high-fidelity patient room equipped with a wheeled workstation with SimEMR©, a barcode scanner, and a simulated patient named Felicia (S5301 Advanced HAL, Gaumard Scientific, Miami, Florida). Felicia, a high-fidelity patient simulator, was wearing a wristband with her medical record number as a barcode for patient identification. The participants were to monitor and record vital signs using the bedside monitor and administer her 9 AM medications. Falicia’s clinical state was designed to reflect a realistic patient encounter for a patient with community acquired pneumonia.
This simulation’s structured task sequence required participants to access SimEMR© to review patient demographics, notes, and orders, and document vital signs (blood pressure, heart rate, respiratory rate and temperature). After these tasks were completed, participants administered the scheduled medications verifying the Five Rights of medication administration and using the BCMA to scan the patient writ band and each medication to reconcile with the medication administration record. Upon completion, participants exited the simulation room, debriefed with simulation staff, and completed post-simulation surveys that measured engagement (McCoy et al., 2016), satisfaction (Holmgren et al., 2024; Ramoo et al., 2023), perceived realism (Wilson et al., 2018), and workload (Surg-TLX) (Wilson et al., 2011).
Results:
Pre- and post-training surveys were analyzed in Minitab. A MANOVA revealed a significant multivariate effect of group (clinician vs. non-clinician) on the combined dependent variables, Pillai’s Trace = 0.64, F(4,25) = 11.30, p < .001. Follow-up univariate ANOVAs confirmed significant group differences for engagement, F(1,28) = 29.69, p < .001, partial η² = 0.52; satisfaction, F(1,28) = 12.30, p = .002, partial η² = 0.31; and perceived realism, F(1,28) = 14.64, p = .001, partial η² = 0.34. No group differences emerged for workload, F(1,28) = 0.10, p = .756.
Group-level comparisons indicated that clinicians consistently reported higher ratings across outcomes. Clinicians reported greater engagement (M = 4.80, SD = 0.22) than non-clinicians (M = 4.02, SD = 0.54), t(15) = 4.94, p < .001, 95% CI [0.45, 1.12]. Similarly, clinicians reported higher satisfaction (M = 4.78, SD = 0.33) than non-clinicians (M = 3.96, SD = 0.89), t(14) = 3.16, p = .01, 95% CI [0.26, 1.37]. Perceived realism was also higher among clinicians (M = 4.98, SD = 0.08) compared to non-clinicians (M = 4.31, SD = 0.72), t(12) = 3.34, p = .01, 95% CI [0.23, 1.11]. Cognitive workload scores for non-clinician and clinician participants were comparable (clinicians: M = 80.9; non-clinicians: M = 79.5), t(28) = 0.31, p = .76 and suggested that the digital tools did not impose undue burden.
Discussion:
These results demonstrate that clinicians benefited more from the simulation in terms of engagement, satisfaction, and perceived realism, while both groups reported manageable cognitive workload. Importantly, integrating EMR and BCMA into nursing simulations enhances engagement and satisfaction without increasing workload strain. Students perceived the experience as realistic and manageable, reinforcing that usability-focused design is more critical for engagement than realism alone. Further analysis in the current study is in progress, but the included results support the inclusion of digital health technologies in nursing curricula to improve technological fluency, reinforce medication safety practices, and prepare students for technology-driven clinical environments.
References:
Holmgren, A. J., Hendrix, N., Maisel, N., Everson, J., Bazemore, A., Rotenstein, L., Phillips, R. L., & Adler-Milstein, J. (2024). Electronic health record usability, satisfaction, and burnout for family physicians. JAMA network open, 7(8), e2426956-e2426956.
McCoy, L., Pettit, R. K., Lewis, J. H., Allgood, J. A., Bay, C., & Schwartz, F. N. (2016). Evaluating medical student engagement during virtual patient simulations: a sequential, mixed methods study. BMC medical education, 16(1), 20.
Ramoo, V., Kamaruddin, A., Nawawi, W. N. F. W., Che, C. C., & Kavitha, R. (2023). Nurses’ perception and satisfaction toward electronic medical record system. Florence Nightingale Journal of Nursing, 31(1), 2.
Risling, T. (2017). Educating the nurses of 2025: Technology trends of the next decade. Nurse education in practice, 22, 89-92.
Wilson, E., Hewett, D. G., Jolly, B. C., Janssens, S., & Beckmann, M. M. (2018). Is that realistic? The development of a realism assessment questionnaire and its application in appraising three simulators for a gynaecology procedure. Advances in Simulation, 3(1), 21.
Wilson, M. R., Poolton, J. M., Malhotra, N., Ngo, K., Bright, E., & Masters, R. S. (2011). Development and validation of a surgical workload measure: the surgery task load index (SURG-TLX). World journal of surgery, 35(9), 1961-1969.
Authors
Event Type
Oral Presentations
TimeTuesday, March 2411:15am - 11:37am EDT
LocationMorgan
Simulation and Education