Automation Factors Influencing the Operation of IoT in Health Institutions: A Decision Support Methodology

  • Astrid M. Oddershede University of Santiago of Chile, 8320000 Santiago, Chile *Corresponding Author:
  • Claudio J. Macuada University of Santiago of Chile, 8320000 Santiago, Chile
  • Luis E. Quezada University of Santiago of Chile, 8320000 Santiago, Chile
  • Cecilia A. Montt School of Transport Engineering Pontifical Catholic University of Valparaiso, 2340000 Valparaiso, Chile


Health institutions are adopting new technologies for their processes through automation by means of the concept of "Internet of Things" (IoT). Hence, offering innovative tools, applications and technology for the collection of key data and information, which is then integrated and consolidated, covering the different systems and their collaborators. The necessity of receiving quality medical services is essential in the public Policy of any country. The increasing demand for having an adequate number of medical specialists, pharmacies and medications stock, dental and mental health coverage and other, together with the minimization of the waiting list and patient care time have been a crucial concern. Under this context, it is valuable to redesign the processes planning and its coordination through the use of Information & Communications Technology (ICT) and IoT that unifies the systems. Based on previous research, the general purpose is to generate a system model to examine healthcare quality of service and corroborate its effectiveness in a real environment. The aim of this paper focus on the development of a decision support model to define key areas where the inclusion of IoT would sustain the efficiency in health care service. The research methodology is based on case study, integrating planning processes, data analysis, scoring method that interacts with multicriteria approach. A pilot case study is pursued in health institutions in Chile, determining critical factors and the current automation level system appraisal to generate actions of improvement in processes that show poor service quality. The results give rise to the development of an investment plan that can be converted into action plans for a health institution.

Author Biography

Astrid M. Oddershede, University of Santiago of Chile, 8320000 Santiago, Chile *Corresponding Author:


[1] Ajami, S.; Ketabi, S. (2012). Performance Evaluation of Medical Records Departments by Analytical Hierarchy Process (AHP) Approach in the Selected Hospitals in Isfahan, Journal of Medical Systems, 36 (3), 1165-1171.

[2] Akdag, H.; Kalayci, T.; Karagoz, S. et al. (2014). The evaluation of hospital service quality by fuzzy MCDM, Applied Soft Computing, 23, 239-248.

[3] Ashouri, M.; Davidsson, P.; Spalazzese, R. (2018). Cloud, Edge, or Both? Towards Decision Support for Designing IoT Applications. In 2018 Fifth International Conference on Internet of Things: Systems, Management and Security, IEEE, 155-162, 2018.

[4] Bi, Z.; Da Xu, L.; Wang, C. (2014). Internet of things for enterprise systems of modern manufacturing. IEEE Transactions on industrial informatics, 10(2), 1537-1546, 2014.

[5] Candea, C.; Candea, G.; Constantin, Z.B. (2019). ArdoCare - a collaborative medical decision support system. In 7th International Conference on Information Technology and Quantitative Management (ITQM2019), Procedia Computer Science, 162, 762-769, 2019.

[6] Casado Mansilla, D.; Moschos, I.; Kamara-Esteban, O. et al. (2018). A human-centric & contextaware IoT framework for enhancing energy efficiency in buildings of public use. IEEE Access, 6, 31444-31456, 2018.

[7] Dewantara, B. S. B.; Ardilla, F. (2018, July). Self-Monitoring, Failure-Detection and Deci-sion- Making System to Support E-TrashBot (EEPIS Trash Bin Robot) Operations: Preliminary Report. In 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE), IEEE, 1-6, 2018..

[8] Fanti, M. P.; Mangini, A. M.; Dotoli, M.; Ukovich, W. (2013). A Three-Level Strategy for the Design and Performance Evaluation of Hospital Departments, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43 (4), 742-756, 2013.

[9] Filip F.G. (2012). A Decision-Making Perspective for Designing and Building Information Systems. International Journal of Computers Communications & Control, 7(2), 264-272, 2012.

[10] Giménez, V.; Prieto, W.; Prior, D.; Tortosa-Ausina, E. (2019). Evaluation of efficiency in Colombian hospitals: An analysis for the post-reform period, Socio-Economic Planning Sciences, 65, 20-35, 2019.

[11] Jung, J. J.; Kim, K.; Park, J. (2019). Framework of Big data Analysis about IoT-Home-device for supporting a decision making an effective strategy about new product design. In 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), IEEE, 582-584, 2019.

[12] Kashef, M. M.; Yoon, H.; Keshavarz, M.; Hwang, J. (2016). Decision support tool for IoT service providers for utilization of multi clouds. In 2016 18th International Conference on Advanced Communication Technology (ICACT), IEEE, 91-96, 2016

[13] Kovács, L.; Csizmás, E. (2018). Lightweight ontology in IoT architecture. In 2018 IEEE International Conference on Future IoT Technologies (Future IoT), IEEE, 1-6, 2018.

[14] Kumar, R. P.; Smys, S. (2018). A novel report on architecture, protocols and appli-cations in Internet of Things (IoT). In 2018 2nd International Conference on Inventive Systems and Control (ICISC), IEEE, 1156-1161, 2018.

[15] Lai, V.S.; Wong, B.K.; Cheung, W. (2002). Group decision making in a multiple criteria environment: A case using the AHP in software solution. European Journal of Operational Research, 137(1), 134-144, 2002.

[16] Liu, X.; Tamminen, S.; Su, X. et al. (2018). Enhancing Veracity of IoT Generated Big Data in Decision Making. In 2018 IEEE In-ternational Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), IEEE, 149-154, 2018.

[17] Lunardi, W. T.; Amaral, L.; Marczak, S. et al. (2016). Automated decision support IoT framework. In 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), IEEE, 1-8, 2016.

[18] Nabeeh, N. A.; Abdel-Basset, M.; El-Ghareeb, H. A.; Aboelfetouh, A. (2019). Neutrosophic multi-criteria decision making approach for IoT-based enterprises. IEEE Access, 7, 59559-59574, 2019.

[19] Nikjoo, R. G., Beyrami, H. J., Jannati, A., Jaafarabadi, M. A. (2013). Selecting hospital's key performance indicators, using analytic hierarchy process technique, Journal of Community Health Research, 2 (1), 30-38, 2013.

[20] Noje, D.; Tarca, R.; Dzitac, I.; Pop, N. (2019). IoT Devices Signals Processing based on Multidimensional Shepard Local Approximation Operators in Riesz MV-algebras, International Journal of Computers Communications & Control, 14(1), 56-62, 2019.

[21] Noje, D.; Dzitac, I.; Pop, N.; Tarca, R.(2020). IoT Devices Signals Processing Based on Shepard Local Approximation Operators Defined in Riesz MV-Algebras, Informatica, 31(1), 131-142, 2020.

[22] Pinnarelli, L.; Nuti, S.; Sorge, C. et al. (2012). What drives hospital performance? The impact of comparative outcome evaluation of patients admitted for hip fracture in two Italian regions, BMJ Quality & Safety, 21(2), 127-134, 2012.

[23] Sahin, T.; Ocak, S.; Top, M. (2019). Analytic hierarchy process for hospital site selection, Health Policy and Technology, 8(2), 42-50, 2019.

[24] Shekhar, S.; Gokhale, A. (2017). Enabling IoT Applications via Dynamic Cloud-Edge Resource Management. In 2017 IEEE/ACM Second International Conference on Internet-of-Things Design and Implementation (IoTDI), IEEE, 331-332, 2017.

[25] Sloane, E.; Liberatore, M.; Nydick, R.; Luo, W.; Chung, Q. (2003). Using the analytic hierarchy process as a clinical engineering tool to facilitate an iterative, multidisciplinary, microeconomic health technology assessment, Computers & Operations Research, 3(10), 1447-1465, 2003.

[26] Torkzad, A.; Beheshtinia. M. A. (2019). Evaluating and prioritizing hospital service quality, International Journal of Health Care Quality Assurance, 32(2), 332-346, 2019.

[27] Utekar, R. G.; Umale, J. S. (2018). Automated IoT Based Healthcare System for Monitoring of Remotely Located Patients. In 2018 Fourth International Conference on Compu-ting Communication Control and Automation (ICCUBEA), IEEE, 1-5, 2018.

[28] Wang, X.; Luo, H.; Qin, X. et al. (2016). Evaluation of performance and impacts of maternal and child health hospital services using data envelopment analysis in Guangxi Zhuang Autonomous Region, China: a comparison study among poverty and non-poverty county level hospitals, International Journal Equity in Health, 15(1), 131, 2016.

[29] Zimmermann, A.; Schmidt, R.; Sandkuhl, K. et al. (2017). Decision management for microgranular digital architecture. In 2017 IEEE 21st In-ternational Enterprise Distributed Object Computing Workshop (EDOCW), IEEE, 29-38, 2017.

[30] Zois, D. S. (2016). Sequential decision-making in healthcare IoT: Real-time health monitoring, treatments and interventions. In 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), IEEE, 24-29, 2016.
How to Cite
ODDERSHEDE, Astrid M. et al. Automation Factors Influencing the Operation of IoT in Health Institutions: A Decision Support Methodology. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 15, n. 4, june 2020. ISSN 1841-9844. Available at: <>. Date accessed: 12 july 2020. doi:


automation, IoT, healthcare system