Prioritizing interrupt causes in minimally-invasive surgeries based on identifying causal relations between interrupt causes

Document Type: Research Paper

Authors

1 Department of Industrial Engineering, Tarbiat Modares University, Tehran, I.R. Iran

2 Hospital Management Research Center, Tehran University of Medical Sciences, Tehran, I.R. Iran

Abstract

Laparoscopy or minimally-invasive surgery is a surgical technique in which the surgical operations are performed via a few small incisions. This kind of surgery has fewer complications over open surgery. Finding methods for shortening the time of laparoscopic surgeries can improve operating room efficiency. An approach to shortening the time of laparoscopic surgeries is identifying the interruptions in these surgeries and preventing from their occurrence or reducing the potential of occurrence of the identified interrupt causes. In this paper, the interrupt causes of laparoscopic surgeries are prioritized based on the identified causal relations between interrupt causes.
Research population is the laparoscopic surgeries performed in Hasheminezhad kidney center in May-June 2013. For this purpose, 25 laparoscopic surgeries are observed in this hospital in this time interval. Causal relations among interrupt causes are identified from the gathered data. The main causes are identified and ranked based on Fuzzy TOPSIS method. For prioritizing the interrupt causes, frequency of occurrence, average length, severity degree, the potential of reducing interrupt occurrence and the potential of preventing interrupt occurrence are considered.
Experimental results show that the most important interrupt causes in laparoscopic surgeries are staff shortage or multi-tasking staff, foggy lens, unavailable surgical instruments, dirty lens and finally low-experienced staff. Moreover, sensitivity analysis on criteria weighting show that the mentioned interrupt causes are the five most-important interrupt causes in more than 80% of the evaluated scenarios.
Preventing the occurrence of the most-important interrupt causes can improve the surgical time. If it is not possible, reducing the average length of interrupts caused by the identified main causes can be considered for improvement of the operating room efficiency.

Keywords


1. Leblanc, K. A. (Ed.) (2004). “Management of laparoscopic surgical complications.”, New york, Marcel Dekker.

2. Nakada, S. Y. (Ed.) (2003). Essential urologic laparoscopy. 1st. Ed. Humana Press., Totowa, New jersey.

3. Al-HAkim, L. (2008). “Surgical disruption: information quality perspective.” International Journal of Information Quality, 2, 192-204.

4. Khasha, R., Sepehri, M. M., and Khatibi, T. (2012). “Design a comprehensive framework for evaluationg the readiness of starting surgical operation based on surgical cancellation reduction.” The first international conference on Electronic health. Tehran, Iran.

5. Robinson, J. A. (1993). “OR time delays: A time management plan that works.” AORN Journal, Vol. 58, 331-335.

6. Sevdalis, N., Forrest, D., Undre, S., Darzi, A., and Vincent, C. (2008). “Annoyances, disruptions, and interruptions in surgery: the Disruptions in Surgery Index (DiSI).” World  Journal of Surgery, Vol. 32, 1643-50.

7. Zheng, B., Martinec, D. V., Cassera, M. A., and Swanstrom, L. L. (2008). “A quantitative study of disruption in the operating room during laparoscopic antireflux surgery.” Surg. Endosc., Vol. 22, 2171-2177.

8. Gillepsie, B. M., Chaboyer, W., and  Fairweather, N. (2012). “Interruptions and miscommunications in surgery: An observational study.” AORN Journal, Vol. 95, 576-590.

9. Kniemeyer, H. W., Sandmann, W., Bach, D., Torsello, G., Jungblut, R. M., and Grabensee, B. (1994). “Complications following caval interruption.” European Journal of Vascular Surgery, Vol. 8, 617-621.

10. Rousou, J. A., Engleman, R. M., Flack, J. E., Deaton, D. W., Rifkin, R., and Elmansoury, A. (1995). “Does interruption of normothermic cardioplegia have adverse effects on myocardium? A retrospective and prospective clinical evaluation.” Cardiovascular Surgery, Vol. 3, 587-593.

11. Undre, S., Sevdalis, N., Mcdermott, J., Giddie, J., Dinner, L., and Smith, G. (2011). “Interruptions, teamwork, and safety in the operating room: A prospective quantitative study in urological surgery.” European Urology Supplements, Vol. 10, 60.

12. Hercules, P. A. (2010). “Instrument readiness: A patient safety issue.” Perioperative Nursing Clinics, Vol. 5, 15-25.

13. Ohsaki, M., Abe, H., Tsumoto, S., Yokoi, H., and  Yamaguchi, T. (2007). “Evaluation of rule interestingness measures in medical knowledge discovery in databases.” Artificial Intelligence in Medicine, Vol. 41, 177-196.

14. Buyukozkan, G., Feyzioglu, O., and Nebol, E. (2008). “Selection of the strategic alliance partner in logistics value chain.” International Journal of Production Economics, Vol. 113, 148-158.

15. Yao, S., Jiang, Z., Li, N., Zhang, H., and Geng, N. (2011). “A multi-objective dynamic scheduling approach using multiple attribute decision making in semi-conductor manufacturing.” International Journal of Production Economics, Vol. 130, 125-133.

16. Xiao, Z., Xia, S., Gong, K., and Li, D. (2012). “The trapezoidal fuzzy soft set and its application in MCDM.” Applied Mathematical Modelling, Vol. 36, 5844-5855.

17. Xu, Z. and Xia, M. (2012). “Identifying and eliminating dominated alternatives in multi-attribute decision making with intuitionistic fuzzy information.” Applied Soft Computing, Vol. 12, 1451-1456.

18. Wang, Y. M. (1998). “Using the method of maximizing deviations to make decision for multi-indices.” System Engineering and Electronic, Vol. 7, 24-26, 31.

19. Hwang, C. L. and Yoon, K. (1981). “Multiple attribute decision making.” Lecture Notes in Economics and Mathematical Systems, Vol. 186.

20. Benayoun, R., Roy, B., and Sussman, B. (1966). “ELECTRE: Une méthode pour guider lechoix en présence de points de vue multiples.” Note de travail 49, SEMA-METRA International, direction scientifique.

21. Opricovic, S. and  Tzeng, G. H. (2002). “Multicriteria planning of post-earthquake sustainable reconstruction.” Computer-Aided Civil and Infrastructure Engineering, Vol. 17, 211-220.

22. Saaty, T. L. (Ed.) (1977). “The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation” McGraw-Hill, New York.

23. Kang, H. Y. and Lee, A. H. I. (2007). “Priority mix planning for semiconductor fabrication by fuzzy AHP ranking.” Expert Systems with Applications, Vol. 32, 560-570.

24. Rostamzadeh, R. and Sofian, S. (2011). “Prioritizing effective 7Ms to improve production systems performance using fuzzy AHP and fuzzy TOPSIS (case study).” Expert Systems with Applications, Vol. 38, 5166-5177.

25. Han, J., Kamber, M., and Pei, J. (Eds.) (2012). “Data mining: Concepts and techniques.” Morgan Kauffman.

26. Bralis, E., Chiusano, S., and Dutto, R. (2008). “Applying sequential rules to protein localization prediction.” Computers and Mathematics with Applications, Vol. 55, 867-878.

27. Han, H. K., Kim, H. S., and Sohn, S. Y. (2009). “Sequential association rules for forecasting failure patterns of aircrafts in Korean airforce.” Expert Systems with Applications, Vol. 36, 1129-1133.

28. Chang, J. (2011). “Mining weighted sequential patterns in a sequence database with a time-interval weight.” Knowledge-based Systems, Vol. 24, 1-9.

29. Railean, I., Lenca, P., Moga, S., and Borda, M. (2013). “Closeness preference- A new interestingness measure for sequential rules mining.” Knowledge-based Systems, Vol. 44, 48-56.

30. Zhang, Z. and Chu, X. (2011). “Risk prioritization in failure mode and effects analysis under uncertainty.” Expert Systems with Applications, Vol. 38, 206-214.

31. Yazdani, M. (2012). “Risk analysis of critical infrastructures using fuzzy TOPSIS.” Journal of Management Research, Vol. 4, No. 1, 1-19.

32. Secme, N. Y., Bayrakdaroglu, A., and Kahraman, C. (2009). “Fuzzy performance evaluation in turkish banking sector using analytic hierarchy process and TOPSIS.” Expert Systems with Applications, Vol. 36, 11699-11709.

33. Yue, Z. (2011). “An extended TOPSIS for determining weights of decision makers with interval numbers.” Knowledge-based Systems, Vol. 24, 146-153.

34. Torfi, F., Farahani, R. Z., and  Rezapour, S. (2010). “Fuzzy AHP to determine the relative weights of evaluation criteria and fuzzy TOPSIS to rank alternatives.” Applied Soft Computing, Vol. 10, 520-528.

35. Awasthi, A., Chauhan, S. S., and Omrani, H. (2011). “Application of fuzzy TOPSIS in evaluating sustainable transportation systems.” Expert Systems with Applications, Vol. 38, 12270-12280.

36. Khasha, R., Sepehri, M. M., Khatibi, T., and Sorush, A. (2013). “Fuzzy FMEA application to improve workflow in operating rooms.” JIENG, Vol. 47, No. 2, 135-147.