Araştırma Makalesi
BibTex RIS Kaynak Göster

TIBBİ GÖRÜNTÜLEME ARAÇLARI İÇİN BULUT BİLİŞİM TABANLI ÖNGÖRÜCÜ BAKIM UYGULAMA ÇATISI

Yıl 2017, Cilt: 3 Sayı: 2, 76 - 90, 20.12.2017

Öz



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. 

Kaynakça

  • 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
  • Aruba Network. (2017). IoT Heading for Mass Adoption by 2019 Driven by Better-Than-Expected Business Results | Aruba Networks Newsroom.
  • 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
  • Cooke Jr, R. E., Gaeta, M. G., Kaufman, D. M., & Henrici, J. G. (2003, June). Picture archiving and communication system. Google Patents.
  • 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.
  • García, I. E. M., Sánchez, A. S., & Barbati, S. (2016). Reliability and Preventive Maintenance. In MARE-WINT (pp. 235–272). Springer.
  • 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.
  • 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
  • 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
  • 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
  • Lowe, N., & Scott, W. L. (1996). Medical device reporting for user facilities. FDA Center for Devices and Radiologic Health Website.
  • Malec, B. (2016). Healthcare Information and Management Systems Society 2016. The Journal of Health Administration Education, 33(4), 625.
  • MHRA. (2014). Managing Medical Devices: Guidance for healthcare and social services organizations, (April), 60.
  • 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.
  • Mkalaf, K. A. (2015). A study of current maintenance strategies and the reliability of critical medical equipment in hospitals in relation to patient outcomes.
  • 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.
  • 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
  • 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.
  • Srovnal, V. (2005). Using of embedded systems in biomedical applications. In Proceeding 3rd European Medical and Biological Engineering Conference EMBEC’05 Prague.
  • 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.
  • Wang, K. (2016). Intelligent Predictive Maintenance (IPdM) System--Industry 4.0 Scenario. WIT Transactions on Engineering Sciences, 113(1), 259–268.
  • 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.
  • 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.
  • 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.
  • Zhou, G., Wang, Y., & Cui, L. (2015). Biomedical Sensor , Device and Measurement Systems. Advances in Bioengineering. https://doi.org/10.5772/59941

CLOUD COMPUTING BASED PREDICTIVE MAINTENANCE FRAMEWORK FOR MEDICAL IMAGING DEVICES

Yıl 2017, Cilt: 3 Sayı: 2, 76 - 90, 20.12.2017

Öz

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.

Kaynakça

  • 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
  • Aruba Network. (2017). IoT Heading for Mass Adoption by 2019 Driven by Better-Than-Expected Business Results | Aruba Networks Newsroom.
  • 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
  • Cooke Jr, R. E., Gaeta, M. G., Kaufman, D. M., & Henrici, J. G. (2003, June). Picture archiving and communication system. Google Patents.
  • 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.
  • García, I. E. M., Sánchez, A. S., & Barbati, S. (2016). Reliability and Preventive Maintenance. In MARE-WINT (pp. 235–272). Springer.
  • 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.
  • 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
  • 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
  • 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
  • Lowe, N., & Scott, W. L. (1996). Medical device reporting for user facilities. FDA Center for Devices and Radiologic Health Website.
  • Malec, B. (2016). Healthcare Information and Management Systems Society 2016. The Journal of Health Administration Education, 33(4), 625.
  • MHRA. (2014). Managing Medical Devices: Guidance for healthcare and social services organizations, (April), 60.
  • 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.
  • Mkalaf, K. A. (2015). A study of current maintenance strategies and the reliability of critical medical equipment in hospitals in relation to patient outcomes.
  • 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.
  • 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
  • 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.
  • Srovnal, V. (2005). Using of embedded systems in biomedical applications. In Proceeding 3rd European Medical and Biological Engineering Conference EMBEC’05 Prague.
  • 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.
  • Wang, K. (2016). Intelligent Predictive Maintenance (IPdM) System--Industry 4.0 Scenario. WIT Transactions on Engineering Sciences, 113(1), 259–268.
  • 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.
  • 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.
  • 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.
  • Zhou, G., Wang, Y., & Cui, L. (2015). Biomedical Sensor , Device and Measurement Systems. Advances in Bioengineering. https://doi.org/10.5772/59941
Toplam 25 adet kaynakça vardır.

Ayrıntılar

Bölüm Makaleler
Yazarlar

Selin Çoban Bu kişi benim

Mert Onuralp Gökalp Bu kişi benim

Ebru Gökalp

P. Erhan Eren

Yayımlanma Tarihi 20 Aralık 2017
Yayımlandığı Sayı Yıl 2017 Cilt: 3 Sayı: 2

Kaynak Göster

APA Çoban, S., Gökalp, M. O., Gökalp, E., Eren, P. E. (2017). TIBBİ GÖRÜNTÜLEME ARAÇLARI İÇİN BULUT BİLİŞİM TABANLI ÖNGÖRÜCÜ BAKIM UYGULAMA ÇATISI. Yönetim Bilişim Sistemleri Dergisi, 3(2), 76-90.