A Comparative Evaluation of Assistance Systems for an Instrument Reprocessing Workbench
Authors: Heibeyn Jan , Son Ju-yun , Janß Armin , Radermacher Klaus 
Published: October 2023

IntroductionDigital assistant systems (DAS) can support tasks by mediating between complex data and users, promoting continuous learning and on-the-job training (Jwo et al., 2021; Longo et al., 2017; Prinz et al., 2017). Integrating a DAS into surgical instrument reprocessing, where instrument-specific and complex manual tasks need to be performed strictly according to the manufacturer's instructions, can be advantageous. In Germany, reprocessing by trained on-the-job personnel is common. In addition, human factors are often neglected in the instructions for instrument reprocessing (Choi et al., 2017). A cooperative robot can be a valuable addition to mitigate health risks associated with handling contaminated surgical instruments during reprocessing, resulting in a cyber-physical assistance system (Heibeyn et al., 2021). However, it is unclear how the transition from paper-based instructions to either a DAS or a DAS supplemented with a cooperative robot assistance (“cyber-physical assistance system” – CPAS) affects usability for complex and workpiece-specific tasks in instrument reprocessing. Therefore, this study aimed to investigate the differences in usability with different assistance systems for typical tasks in instrument reprocessing for untrained personnel.MethodsWe conducted an interaction-centered user study with 13 participants unfamiliar with the reprocessing tasks. The participants were asked to complete typical reprocessing tasks three times in a random order, using different assistance approaches each time. The reference process consisted of paper-based instructions that required stepping away from the workstation to retrieve information from the storage of guidelines, simulating common set-ups of current reprocessing processes. The first assistance approach presented digital instructions right at the workstation. The second assistant combined a cooperative robot with digital instructions. The robot performed simple processing steps, while the human operator focused on the complex tasks and verified the cleaning success. We measured the required time, counted user errors and rated the criticality, measured the perceived workload using the NASA-TLX questionnaire (NASA, 2020), and documented the remarks of the participants using the thinking-out-loud method for all assistance systems.Results The NASA-TLX did not reveal any significant differences among the three systems, however, the CPAS reduced the number of critical errors. The errors included omitted processing steps and deviations from required times, which could pose risks to patients. The DAS was perceived as a suitable way to check instructions, based on the participants’ comments. However, in the case of the CPAS, some participants missed or ignored messages provided by the user interface.Discussion and ConclusionIn summary, the CPAS improved usability the most, improving effectiveness (number of errors) while maintaining the same efficiency (total duration). Although our study found promising results for integrating a DAS or CPAS into on-the-job training assistance for novice personnel, future studies should compare the results obtained from inexperienced to those of experienced users to fully assess the usability of related approaches. This study contributes to the field of human factors by providing comparative data on usability across different levels of assistance for complex and workpiece-specific tasks in surgical instrument reprocessing.ReferencesChoi, J., Seraphina, S., & Knudsen, K. (2017). The Clean and Dirty of Redesigning Reprocessing Instructions for Use. Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care, 6(1), 150–153. https://doi.org/10.1177/2327857917061032Heibeyn, J., König, N., Domnik, N., Schweizer, M., Kinzius, M., Janß, A., & Radermacher, K. (2021). Design and Evaluation of a Novel Instrument Gripper for Handling of Surgical Instruments. Current Directions in Biomedical Engineering, 7(1), 1–5. https://doi.org/10.1515/cdbme-2021-1001Jwo, J.‑S., Lin, C.‑S., & Lee, C.‑H. (2021). An Interactive Dashboard Using a Virtual Assistant for Visualizing Smart Manufacturing. Mobile Information Systems, 2021, 1–9. https://doi.org/10.1155/2021/5578239Longo, F., Nicoletti, L., & Padovano, A. (2017). Smart operators in industry 4.0: A human-centered approach to enhance operators’ capabilities and competencies within the new smart factory context. Computers & Industrial Engineering, 113, 144–159. https://doi.org/10.1016/j.cie.2017.09.016NASA (Ed.). (2020, December 15). NASA TLX: Task Load Index. https://humansystems.arc.nasa.gov/groups/TLX/Prinz, C., Kreimeier, D., & Kuhlenkötter, B. (2017). Implementation of a Learning Environment for an Industrie 4.0 Assistance System to Improve the Overall Equipment Effectiveness. Procedia Manufacturing, 9, 159–166. https://doi.org/10.1016/j.promfg.2017.04.004