Management of Surgical Workflow - an Observation-Based Assessment Study
Surgery is considered as one of the most demanding and challenging domains of medical activities. This is because of the continuous development of procedures and implementation of modern and innovative methods of surgical treatment. However, the application of new medical technologies makes surgical procedures a highly cost-intensive medical area. In this regard, improvement efficiency should be a strategic component of daily management in a hospital. The objective of this paper is to introduce an observation method for assessing surgical workflow, as a component of management process in a hospital. Basically, possible workflow disruptions were cited as factors for delays and deviations from the natural progression of a procedure. In this regard, the research approach assumed investigating intraoperative activities during live surgeries which is quite a new aspect of hospital workflow. The main method for data acquisition was a direct observation with photo/ or video registration of live surgeries. Also, a common checklist containing workflow assessment criteria for recognizing any deviation was developed. Both the sequences and workflow assessment criteria were implemented using CAPTIV software version L7000 enabled for cross-sectional analyzes including descriptive statistics. Also, interviews with surgeons and scrub nurses were conducted in order to recognize the additional factors contributing to intraoperative efficiency. As a result, 10 factors influencing surgical workflow were recognized that are directly connected with important aspects of management such as the communication aspects of surgical teamwork members or physical workload of the surgeon and scrub nurse. (original abstract)
- Allers, J.C., Hussein, A.A., Ahmad, N., Cavuoto, L., Wing, J.F., Hayes, R.M., Hinata, N., Bisantz, A.M., & Guru, K.A. (2016). Evaluation and impact of workflow interruptions during robot-assisted surgery. Urology, 92, 33-7.
- Anderson, K.T., Bartz-Kurycki, M.A., Masada, K.M., Abraham, J.E, Wang, J., Kawaguchi, A.L., Austin, M.T., Kao, L.S., Lally, K.P., & Tsao, K. (2018). Decreasing intraoperative delays with meaningful use of the surgical safety checklist. Surgery, 163(2), 259-263.
- Barbagallo, S., Corradi, L., de Ville de Goyet, J., Iannucci, M., Porro, I., Rosso, N., Tanfani, E., & Testi, A. (2015). Optimization and planning of operating theatre activities: An original definition of pathways and process modeling. BMC Medical Informatics Decison Making,17, 15-38.
- Cohen, T.N., Cabrera, J.S., Sisk, O.D., Welsh, K.L., Abernathy, J.H., Reeves, S.T., Wiegmann, D.A., Shappell, S.A., & Boquet, A.J. (2016). Identifying workflow disruptions in the cardiovascular operating room. Anaesthesia. 71(8), 948-954.
- Elfering, A., Nützi, M., Koch, P., & Baur, H. (2004). Workflow interruptions and failed action regulation in surgery personnel workflow interruptions during surgery may cause a threat to patient's safety. Safety and Health at Work. 5(1), 1-6.
- Fong, A.J., Smith, M., & Langerman, A. (2016). Efficiency improvement in the operating room. Journal of Surgical Research, 204(2), 371-383.
- Forestier, G., Petitjean, F., Riffaud, L., & Jannin, P. (2017). Automatic matching of surgeries to predict surgeons' next actions. Artificial Intelligence in Medicine, 8(1), 3-11.
- Franke, S., Meixensberger, J., & Neumuth, T. (2015). Multi-perspective workflow modeling for online surgical situation models. Journal of Biomedical Informatics. 54, 158-166.
- Hayes, G. R., Lee, Ch. P., & Dourish, P. (2011). Organizational routines, innovation, and flexibility: The application of narrative networks to dynamic workflow. International Journal of Medical Informatics, 80(8), 161-177.
- Jalote-Parmar, A., & Badke-Schaub, P. (2008). Workflow integration matrix: A framework to support the development of surgical information systems. Design Studies, 29(4), 338-368.
- Jalote-Parmar, A., Badke-Schaub, P., Ali, W., & Samset, E. (2010). Cognitive processes as integrative component for developing expert decisionmaking systems: A workflow centered framework. Journal of Biomedical Informatics, 43(1), 60-74.
- Lemke, H.U., Trantakis, C., Köchy, K., Müller, A., Strauss, G., & Meixensberger, J. (2004). Workflow analysis for mechatronic and imaging assistance in head surgery. International Congress Series, 1268, 830-835.
- Liu, Ch.C.H., Chang, Ch.-H., Su, M.-Ch., Chu, H.-T., Hung, S.-H., Wong, J.- M., & Wang, P.-Ch. (2011). RFID-initiated workflow control to facilitate patient safety and utilization efficiency in operation theater. Computer Methods and Programs in Biomedicine, 104(3), 435-442.
- Numasakia, H., Harauchi, H., Ohno, Y., Inamura, K., Kasahara, S., Monden, M., & Sakon, M. (2007). New classification of medical staff clinical services for optimal reconstruction of job workflow in a surgical ward: Application of spectrum analysis and sequence relational analysis. Computational Statistics & Data Analysis, 51(12), 5708 - 5717.
- Silver, D.S., Kaye, A.D., Cornett, E.M., Fox, Ch., & Slakey, D.P. (2017). Disruptions in surgical workflow: Perceptions and implications. Journal of American Collage Surgeons, 225(4), e108.
- Weigl, M., Stefan, P., Abhari, K., Wucherer, P., Fallavollita, P., Lazarovici, M., Weidert, S., Euler, E., & Catchpole, K. (2016). Intra-operative disruptions, surgeon's mental workload, and technical performance in a full-scale simulated procedure. Surgical Endoscopic, 30(2), 559-566.
- Weigl, M., Weber, J., Hallett, E., Pfandler, M., Schlenker, B., Becker, A., & Catchpole, K. (2018). Associations of intraoperative flow disruptions and operating room teamwork during robotic-assisted radical prostatectomy. Urology,114, 105-11.
- Wiegmann, D.A., ElBardissi, A.W., Dearani, J.A., Daly, R.C., & Sundt, T.M.I. (2007). Disruptions in surgical flow and their relationship to surgical errors: An exploratory investigation. Surgery, 142(5), 658-665.
- Zasada, S. J., & Coveney, P.V. (2012). Computational biomedicine: The role of workflow tools. Procedia Computer Science. 1(1), 2753-2761.