Yabancı Cisim Unutulmasının Önlenmesinde Bilişim Teknolojilerinin Kullanımı

Abstract

Hasta güvenliğini tehdit eden olaylardan biri yabancı cisim unutulmasıdır. Yabancı cisim unutulması cerrahi malzemelerin ameliyat esnasında hastanın vücudunda istemsiz olarak kalmasıdır. Her cerrahi operasyonda sayım sürecinin kurum prosedürlerine uygun olarak yürütülmesi ve prosedürler arasında manuel sayıma ek olarak bilişim teknolojilerinin kullanımının yer alması rehberlerde önerilmektedir. Yabancı cisim unutulmasının önlenmesinde makine öğrenme ve derin öğrenme yöntemiyle cerrahi alet ve spançların takibi yapılabilmektedir. Ayrıca yapay zekâ yardımıyla radyografik görüntülerin yorumlanması daha hızlı ve güvenilir olmaktadır. Ameliyathanede radyofrekans teknolojisinin kullanımı, cerrahi aletlerin tespit edilmesini sağlayarak sayım takibini hızlandırmakta ve kolaylaştırmaktadır. Cerrahi sayımda kamera yardımıyla çekilen görüntülerin yapay zekâ teknolojisi ile izlenerek cerrahi aletlerin sayımı daha hızlı ve kolay yapılabilmektedir. Bilişim teknolojileri, sayım doğruluğunun sağlanmasında ve yabancı cisim unutulmasının önlenmesinde alet yönetimi ve hasta güvenliği bakımından önemli bir potansiyele sahiptir. Bilişim teknolojilerinin verimini artırmada personel eğitimi ve kurum politikalarına uyum anahtar role sahiptir. Bu derlemede, yabancı cisim unutulmasının önlenmesinde bilişim teknolojilerinin kullanımı hakkında bilgi verilmektedir.

Keywords

• : Hasta güvenliği
• Yabancı cisimler
• Yapay zekâ
• Bilgi teknolojisi
• Ameliyathaneler

The Use of Information Technology in Preventing the Retention of Foreign Objects

Abstract

One of the events that threatens patient safety is the foreign body is a forgotten foreign body is the unintentional retention of surgical materials in the patient's body during surgery. It is advised that the counting process in each surgical procedure be conducted in accordance with institutional protocols, incorporating information technologies alongside manual counting. Machine learning and deep learning methods can be used to track surgical instruments and sponges to prevent foreign bodies from being forgotten. Artificial intelligence can also be used to interpret X-ray images more quickly and reliably. The use of radiofrequency technology in the operating room accelerates and facilitates count tracking by enabling the detection of surgical instruments during the counting process. By using artificial intelligence technology to monitor the images taken by the camera during surgical counting, surgical instruments can be counted more quickly and easily. Information technologies have an important potential in terms of instrument management and patient safety by ensuring counting accuracy and preventing foreign bodies from being missed. Staff training and adherence to institutional policies play a key role in increasing the effectiveness of information technology. This review provides information about the use of information technologies in the prevention of foreign object retention.

Keywords

• Patient Safety
• Foreign Bodies
• Artificial Intelligence
• Information Technology
• Operating Rooms

References

1. Abo-Zahhad, M., El-Malek, A. H. A., Sayed, M. S., & Gitau, S. N. (2024). Minimization of occurrence of retained surgical items using machine learning and deep learning techniques: a review. Biodata Mining, 17(1), 17.
2. Alotaibi, Y. K., & Federico, F. (2017). The impact of health information technology on patient safety. Saudi Medical Journal, 38(12), 1173-1180.
3. Bairwa, B. (2021). ‘Gossypiboma-an unusual cause of surgical abdomen and surgeon’s nightmare: A rare case report’. International Journal of Surgery Case Reports, 80, (105521), 1-4.
4. Carmack, A., Valleru, J., Randall, K., Baka, D., Angarano, J., & Fogel, R. (2023). A Multicenter Collaborative Effort to Reduce Preventable Patient Harm Due to Retained Surgical Items. Joint Commission Journal on Quality and Patient Safety, 49(1), 3-13.
5. Cima, R. R., Bearden, B. A., Kollengode, A., Nienow, J. M., Weisbrod, C. A., Dowdy, S. C., Amstutz, G. J., & Narr, B. J. (2022). Avoiding Retained Surgical Items at an Academic Medical Center: Sustainability of a Surgical Quality Improvement Project. American journal of medical quality. The Official Journal of The American College of Medical Quality, 37(3), 236–245.
6. Cochran, K. (2022). ‘Guidelines in practice: Prevention of unintentionally retained surgical ıtems’. AORN Journal, 116(5), 27-440.
7. Deol, E. S., Henning, G., Basourakos, S., Vasdev, R. M. S., Sharma, V., Kavoussi, N. L., Karnes, R. J., Leibovich, B. C., Boorjian, S. A., & Khanna, A. (2024). Artificial intelligence model for automated surgical instrument detection and counting: an experimental proof-of-concept study. Patient Safety in Surgery, 18(1), 24.
8. Eghbali, F., Bhahdoust, M., Madankan, A., Mosavari, H., Vaseghi, H., Khanafshar, E. (2022). ‘Hidden retained surgical sponge with intestinal migration: A rare case report’. International Journal of Surgery Case Reports, 95(107274), 1-4.

9. Er, Ö. S., & Erkan, H. N. (2024). Identified risk factors related to retained surgical items and inconsistencies in surgical counts: A systematic review. Journal of the Turkish Nurses Association, 5(1), 17–32.
10. Gunnar, W., Soncrant, C., Lynn, M. M., Neily, J., Tesema, Y., & Nylander, W. (2020). The Impact of Surgical Count Technology on Retained Surgical Items Rates in the Veterans Health Administration. Journal of Patient Safety, 16(4), 255–258.
11. Hadar, A., Levy, N. ve Wi̇nokur, M. (2022). Management and detection system for medical surgical equipment. Proc. Of The İnterdi̇sci̇pli̇nary Conference On Mechani̇cs, Computers And Electri̇cs. Haider, S. A., Ho, O. A., Borna, S., Gomez-Cabello, C. A., Pressman, S. M., Cole, D., Sehgal, A., Leibovich, B. C., & Forte, A. J. (2025). Use of Multimodal Artificial Intelligence in Surgical Instrument Recognition. Bioengineering (Basel, Switzerland), 12(1), 72. Hendricks, W., Mecca, J., Rahimi, M., Rojo, M. R., Von Ballmoos, M. C. W., McFall, R. G., Haddad, P., Berczeli, M. T., Sinha, K., Barnes, R. G., Peden, E. K., Lumsden, A. B., MacGillivray, T. E., & Corr, S. J. (2022). Evaluation of a Novel System for RFID Intraoperative Cardiovascular Analytics. IEEE Journal of Translational Engineering In Health And Medicine, 10, 1900309.
12. Hibbert, P. D., Thomas, M. J. W., Deakin, A., Runciman, W. B., Carson-Stevens, A., & Braithwaite, J. (2020). A qualitative content analysis of retained surgical items: Learning from root cause analysis investigations. International Journal for Quality in Health Care, 32(3), 184–189.
13. Hill, I., Olivere, L., Helmkamp, J., Le, E., Hill, W., Wahlstedt, J., Khoury, P., Gloria, J., Richard, M. J., Rosenberger, L. H., & Codd, P. J. (2022). Measuring intraoperative surgical instrument use with radio-frequency identification. JAMIA Open, 5(1), ooac003.
14. Judd, D., Hawes, J., Oldham, J., Stucki, B., & Boyack DO, B. (2024). Complications Beyond Borders: A Case Study on a Retained Surgical Towel Leading to an Intra-abdominal Abscess Post-hysterectomy. Cureus, 16(10), e72601.
15. Kang, H. S., Khoraki, J., Gie, J., Duval, D., Haynes, S., Siev, M., Shah, J., Kim, F., Mangino, M., Procter, L., Autorino, R., & Weprin, S. (2023). Multiphase preclinical assessment of a novel device to locate unintentionally retained surgical sharps: a proof-of-concept study. Patient Safety in Surgery, 17(1), 10.
16. Kawakubo, M., Waki, H., Shirasaka, T., Kojima, T., Mikayama, R., Hamasaki, H., Akamine, H., Kato, T., Baba, S., Ushiro, S., & Ishigami, K. (2023). A deep learning model based on fusion images of chest radiography and X-ray sponge images supports human visual characteristics of retained surgical items detection. International Journal of Computer Assisted Radiology and Surgery, 18(8), 1459–1467.
17. Keane, O. A., Chambers, C., Brady, C. M., Rehberg, J., Iyer, S., & Santore, M. T. (2023). Reducing Retained Foreign Objects in the Operating Room: A Quality Improvement Initiative. Journal of the American College of Surgeons, 237(6), 864–872.
18. Kusuda, K., Yamashita, K., Morishita, E., Ishibashi, N., Shiraishi, Y., & Yamaguchi, H. (2024). Comparison of reading times of RFID-tagged and barcode-engraved surgical instruments. Journal of Surgical Research, 304, 121–125.
19. Kusuda, K., Yamashita, K., Tanaka, S., Tanaka, K., & Ohta, Y. (2020). Development of a Surgical Sponge Counting System Using Radiographic Images. Surgical Innovation, 27(6), 647–652.
20. Kyle, E. (2023). ‘Preventing unintentionally retained surgical items’. AORN Journal, 117(3), 192-199.
21. Mushtaq, F., O'Driscoll, C., Smith, F., Wilkins, D., Kapur, N., & Lawton, R. (2018). Contributory factors in surgical incidents as delineated by a confidential reporting system. Annals of the Royal College of Surgeons of England, 100(5), 401–405.
22. Peng, J., Ang, S. Y., Zhou, H., Nair, A. (2022). ‘The effectiveness of radiofrequency scanning technology in preventing retained surgical items: An integrative review’. Journal of Clinical Nursing, 32(13-14), 3315-3327. Profetto, L., Gherardelli, M., & Iadanza, E. (2022). Radio Frequency Identification (RFID) in health care: where are we? A scoping review. Health and Technology, 12(5), 879–891.
23. Schwartz, A. L., Nourian, M. M., & Bucher, B. T. (2019). Retained foreign bodies and associated risk factors and outcomes in pediatric surgical patients. Journal of Pediatric Surgery, 54(4), 640-644.
24. Schnock, K. O., Biggs, B., Fladger, A., Bates, D. W., & Rozenblum, R. (2021). Evaluating the Impact of Radio Frequency Identification Retained Surgical Instruments Tracking on Patient Safety: Literature Review. Journal of Patient Safety, 17(5), e462–e468.
25. Sittig, D. F., Wright, A., Coiera, E., Magrabi, F., Bates, D. W., & Singh, H. (2020). Current challenges in health information technology–related patient safety. Health Informatics Journal, 26(1), 181–189.
26. Steelman, V. M., Schaapveld, A. G., Storm, H. E., Perkhounkova, Y., & Shane, D. M. (2019). The Effect of Radiofrequency Technology on Time Spent Searching for Surgical Sponges and Associated Costs. AORN Journal, 109(6), 718–727.
27. Takahashi, K., Fukatsu, T., Oki, S., Iizuka, Y., Otsuka, Y., Sanui, M., & Lefor, A. K. (2023). Characteristics of retained foreign bodies and near-miss events in the operating room: a ten-year experience at one institution. Journal of Anesthesia, 37(1), 49–55.
28. Taşdemir, N. (2022). Retained foreign objects. In M. Yavuz van Giersbergen & Ş. Kaymakçı (Eds.), Operating room nursing (2nd ed., pp. 195–199). İzmir: Meta Basım.
29. The Joint Commission. (2022, May). Quick Safety 20: Strategies to prevent URFOs (updated May 2022). The Joint Commission. https://www.jointcommission.org/resources/news-and-multimedia/newsletters/newsletters/quick-safety/quick-safety--issue-20-strategies-to-prevent-urfos/strategies-to-prevent-urfos/
30. The Joint Commission. (2024). Sentinel event data: 2023 annual review. Retrieved February 24, 2025, from http://www.jointcommission.org/Sentinel_Event_Policy_and_Procedures
31. Turkish Ministry of Health. (2011). Safe surgery checklist implementation guide. https://dosyamerkez.saglik.gov.tr/Eklenti/4333/0/guvenlicerrahikontrollistesiuygulamarehberipdf.pdf (Accessed February 24, 2025)
32. Weprin, S., Crocerossa, F., Meyer, D., Maddra, K., Valancy, D., Osardu, R. et al. (2021). ‘Risk factors and preventive strategies for unintentionally retained surgical sharps: a systematic review’. Patient Safety in Surgery, 15(1),1-10.
33. World Health Organization. (2008). The second global patient safety challenge: Safe surgery saves lives. Geneva: World Health Organization. Retrieved February 24, 2025, from https://iris.who.int/handle/10665/70080
34. World Health Organization. (2021). Global patient safety action plan 2021–2030: Towards eliminating avoidable harm in health care. Geneva: World Health Organization. Retrieved February 24, 2025, from https://www.who.int/publications
35. Yamaguchi, S., Soyama, A., Ono, S., Hamauzu, S., Yamada, M., Fukuda, T., Hidaka, M., Tsurumoto, T., Uetani, M., & Eguchi, S. (2021). Novel Computer-Aided Diagnosis Software for the Prevention of Retained Surgical Items. Journal of the American College of Surgeons, 233(6), 686–696.
36. Yılmaz, A., & Ölçer, İ. (2021). Integration of artificial intelligence into surgical applications. Journal of Science and Engineering Sciences of Beykent University, 13(2), 21–27.