TIBBİ GÖRÜNTÜLEME ARAÇLARI İÇİN BULUT BİLİŞİM TABANLI ÖNGÖRÜCÜ BAKIM UYGULAMA ÇATISI

Abstract

Nesnelerin İnterneti ve Bulut Bilişim alanlarındaki son teknolojik gelişmeler, hastanelerde sunulan sağlık hizmetlerinin kalitesinin iyileştirilmesine olanak sağlamaktadır. Bu teknolojilerden biri olan, akıllı sensör ve aktüatör teknolojilerinin hastanelerde yaygın kullanımı ile çeşitli tıbbi cihazlardan toplanan veriler sayesinde, sunulan sağlık hizmetlerinin iyileştirilmesi sağlanmaktadır. Örneğin, cihazlarda oluşacak hataları önceden görerek, bu hataların düzeltilmesini kapsayan öngörücü bakım sistemleri için biyomedikal cihazlardan toplanan veriler önemli bir potansiyele sahiptirler. Ancak, öngörücü bakım sistemlerinden azami fayda elde etmek, bakım maliyetlerini düşürmek ve sağlık hizmetlerini iyileştirilmek için Bulut Bilişim ve Nesnelerin İnterneti teknolojilerinin

 tıbbi görüntüleme cihazları ile entegrasyonun etkin bir şekilde gerçekleştiği bir çözüme ihtiyaç duyulmaktadır. Literatürde bu sorunu çözmek için umut verici bazı çalışmalar olmasına rağmen, günümüz bilgi çağında kullanılması için henüz yeterli olgunlukta değillerdir. Bu nedenle, bu çalışma kapsamında, temel olarak, tıbbi görüntüleme cihazları için Bulut Bilişim ve Nesnelerin İnterneti teknolojilerine dayanan bir öngörücü bakım uygulama çatısı tanımlanmıştır. Ardından, önerilen bu uygulama çatısının faydaları ve zararları tartışılmıştır. 

Keywords

• Öngörücü Bakım,Bulut Bilişim,Nesnelerin İnterneti,Tıbbi Görüntüleme Cihazları

CLOUD COMPUTING BASED PREDICTIVE MAINTENANCE FRAMEWORK FOR MEDICAL IMAGING DEVICES

Abstract

Recent technological advancements in Internet of Things (IoT) and Cloud Computing domains, enable improving quality of health services in hospitals. The widespread use of smart sensor and actuator technologies in hospitals allow us to improve healthcare services by collecting data from various medical devices. Therefore, hospitals grasp noteworthy potential to convert these collected data into valuable information for predictive maintenance of biomedical devices. However, in order to obtain maximum benefit from the predictive maintenance system to reduce maintenance costs and improve healthcare services, a well-integrated solution is needed to combine cloud computing and IoT technologies with medical imaging devices. Despite some promising efforts in this area to solve this problem, they are not sufficient to be used in the information era. Thus, in this study, we primarily focus on the problem of how to define a predictive maintenance framework for medical imaging devices based on cloud computing and IoT technologies. Then, we identify the benefits and challenges of the proposed predictive maintenance framework.

Keywords

• Predictive Maintenance,Cloud Computing,Internet of Things,Medical Imaging Devices

References

1. Armbrust, M., Stoica, I., Zaharia, M., Fox, A., Griffith, R., Joseph, A. D., … Rabkin, A. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50. https://doi.org/10.1145/1721654.1721672
2. Aruba Network. (2017). IoT Heading for Mass Adoption by 2019 Driven by Better-Than-Expected Business Results | Aruba Networks Newsroom.
3. Bliznakov, Z., Mitalas, G., & Pallikarakis, N. (2006). Analysis and Classification of Medical Device Recalls. IFMBE Proceedings, 14(6), 3782–3785. https://doi.org/10.1007/978-3-540-36841-0_957
4. Cooke Jr, R. E., Gaeta, M. G., Kaufman, D. M., & Henrici, J. G. (2003, June). Picture archiving and communication system. Google Patents.
5. Derrico, P., Ritrovato, M., Nocchi, F., Faggiano, F., Capussotto, C., Franchin, T., & De Vivo, L. (2011). Clinical engineering. In Applied Biomedical Engineering. InTech. European Commission. (2007). Directive 2007/47/EEC.
6. García, I. E. M., Sánchez, A. S., & Barbati, S. (2016). Reliability and Preventive Maintenance. In MARE-WINT (pp. 235–272). Springer.
7. Guo, J., Zhou, X., Sun, Y., Ping, G., Zhao, G., & Li, Z. (2016). Smartphone-Based Patients’ Activity Recognition by Using a Self-Learning Scheme for Medical Monitoring. Journal of Medical Systems, 40(6), 140. https://doi.org/10.1007/s10916-016-0497-2 HIMSS Analytics. (2014). HIMSS Analytics Cloud Survey.
8. Honeyman, J. C., Huda, W., Ott, M., Frost, M. M., Loeffler, W., & Staab, E. V. (1994). Picture archiving and communications systems (PACS). Current Problems in Diagnostic Radiology, 23(4), 103–158. https://doi.org/10.1016/0363-0188(94)90004-3

9. Jiang, P., Winkley, J., Zhao, C., Munnoch, R., Min, G., & Yang, L. T. (2016). An Intelligent Information Forwarder for Healthcare Big Data Systems With Distributed Wearable Sensors. IEEE Systems Journal, 10(3), 1147–1159. https://doi.org/10.1109/JSYST.2014.2308324
10. King, K. R., Grazette, L. P., Paltoo, D. N., McDevitt, J. T., Sia, S. K., Barrett, P. M., … Leeds, H. (2016). Point-of-Care Technologies for Precision Cardiovascular Care and Clinical Research. JACC: Basic to Translational Science, 1(1–2), 73–86. Lee, J., Kao, H.-A., & Yang, S. (2014). Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment. Procedia CIRP, 16, 3–8. https://doi.org/10.1016/j.procir.2014.02.001
11. Lowe, N., & Scott, W. L. (1996). Medical device reporting for user facilities. FDA Center for Devices and Radiologic Health Website.
12. Malec, B. (2016). Healthcare Information and Management Systems Society 2016. The Journal of Health Administration Education, 33(4), 625.
13. MHRA. (2014). Managing Medical Devices: Guidance for healthcare and social services organizations, (April), 60.
14. Miniati, R., Dori, F., Iadanza, E., Fregonara, M. M., & Gentili, G. B. (2011). Health technology management: A database analysis as support of technology managers in hospitals. Technology and Health Care, 19(6), 445–454.
15. Mkalaf, K. A. (2015). A study of current maintenance strategies and the reliability of critical medical equipment in hospitals in relation to patient outcomes.
16. Ren, L., Zhang, L., Tao, F., Zhao, C., Chai, X., & Zhao, X. (2015). Cloud manufacturing: from concept to practice. Enterprise Information Systems, 9(2), 186–209.
17. Schmidt, B., & Wang, L. (2016). Cloud-enhanced predictive maintenance. The International Journal of Advanced Manufacturing Technology, 1–9. https://doi.org/10.1007/s00170-016-8983-8
18. Sezdi, M., & Ozdemir, E. (2014). BMED: a web based application to analyze the performance of medical devices. Biomedical Engineering: Applications, Basis and Communications, 26(3), 1450036.
19. Srovnal, V. (2005). Using of embedded systems in biomedical applications. In Proceeding 3rd European Medical and Biological Engineering Conference EMBEC’05 Prague.
20. Swanson, E. B. (1976). The dimensions of maintenance. In Proceedings of the 2nd international conference on Software engineering (pp. 492–497). IEEE Computer Society Press.
21. Wang, K. (2016). Intelligent Predictive Maintenance (IPdM) System--Industry 4.0 Scenario. WIT Transactions on Engineering Sciences, 113(1), 259–268.
22. Wang, K.-S., Li, Z., Braaten, J., & Yu, Q. (2015). Interpretation and compensation of backlash error data in machine centers for intelligent predictive maintenance using ANNs. Advances in Manufacturing, 3(2), 97–104.
23. World Health Organization. (2011). Medical equipment maintenance programme overview. Xu, X. (2012). From cloud computing to cloud manufacturing. Robotics and Computer-Integrated Manufacturing, 28(1), 75–86.
24. Zhang, L., Luo, Y., Tao, F., Li, B. H., Ren, L., Zhang, X., … Liu, Y. (2014). Cloud manufacturing: a new manufacturing paradigm. Enterprise Information Systems, 8(2), 167–187.
25. Zhou, G., Wang, Y., & Cui, L. (2015). Biomedical Sensor , Device and Measurement Systems. Advances in Bioengineering. https://doi.org/10.5772/59941