Gestão & Produção
https://gestaoeproducao.com/article/doi/10.1590/0104-530x5416-20
Gestão & Produção
SEÇÃO TEMÁTICA

Application of Automation and Manufacture techniques oriented to a service-based business using the Internet of Things (IoT) and Industry 4.0 concepts. Case study: Smart Hospital

Camilo Andrés Cáceres Flórez; João Mauricio Rosário; Dario Amaya Hurtado

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Abstract

Abstract: The implementation of Manufacture and Automation techniques is mandatory in the current world. Mainly, the enhancement and progress of healthcare are fundamental in wellbeing improvement. This paper points to the utilization of the Internet of Things (IoT) and Industry 4.0 concepts oriented to the optimization of a Smart Hospital using the Hospital Emergency Department (HED) as a case study. This proposal focuses on the development of a smart Hospital-based of the IoT, Industry 4.0, Health 4.0, and other current technology. On the other hand, the use of a computational simulation tool like the Discrete Event Simulation Model (DES) will allow the test, recognition, and reduction of bottlenecks in the HED workflow. The issue given by the bottlenecks is automatically controlled using an improved dynamic shift management proposal based on control theory, forecasting methods, and telemedicine. The results show an improvement in the use of the resources and a reduction of the length of stay that directly reduces the HED mortality rate, improving the service quality. The objective of this paper is to propose a simulation tool-based on DES for a selected HED, using forecasting methods of the patients’ arrival in a HED using the Autoregressive integrated moving average (ARIMA) model. Following the forecasted entries, a proposal for bottleneck avoidance using a HED DES was realized. The forecasting data provided useful predictive information for the improvement of the HED workflow. As well as the analyzed data of a traditional HED system is helpful to solve the overcrowding problem. Finally, the use of simulation tools allows the test and validation of novel proposals for two smart HED optimization proposals following e-Health and Hospital 4.0 principles.

Keywords

Emergency Department, Smart Hospital, Discrete Event Simulation, Hospital 4.0, Health 4.0, Smart Decision Support Tool

Referências

Abe Tolu K., Beamon B. M., Storch R. L., Agus J. Operations research applications in hospital operations: Part I. Journal IIE Transactions on Healthcare Systems Engineering. 2016;6(1):42-54.

Abe Tolu K., Beamon B. M., Storch R. L., Agus J. Operations research applications in hospital operations: Part II. Journal IIE Transactions on Healthcare Systems Engineering. 2016;6(2):96-109.

Abe Tolu K., Beamon B. M., Storch R. L., Agus J. Operations research applications in hospital operations: Part III. Journal IIE Transactions on Healthcare Systems Engineering. 2016;6(3):175-91.

Aboagye-Sarfo P., Mai Q., Sanfilippo F. M., Preen D. B., Stewart L. M., Fatovich D. M. A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia. Journal of Biomedical Informatics. Academic Press. 2015;57:62-73.

Ahmed M. A., Alkhamis T. M. Simulation optimization for an emergency department healthcare unit in Kuwait. European Journal of Operational Research. Elsevier B.. 2009;198(3):936-42.

Bittencourt R. J., Hortale V. A. Intervenções para solucionar a superlotação nos serviços de emergência hospitalar: uma revisão sistemática. Cadernos de Saude Publica. 2009;25(7):1439-54.

Box G., Jenkins G. M., Reinsel G. C., Ljung G. M. Time series analysis: forecasting and control. 2015.

Bradley D., Russell D., Ferguson I., Isaacs J., MacLeod A., White R. The internet of things – the future or the end of mechatronics. Mechatronics. 2015;27:57-74.

Cáceres Flórez C. A., Rosário J. M., Amaya D. Control structure for a car-like robot using artificial neural networks and genetic algorithms. (Ed.), Neural computing and applications. 2018:1-14.

Cáceres C., Rosário J. M. J. M., Amaya D. Proposal of a smart hospital based on internet of things (IoT) concept. Processing and analysis of biomedical information. 2019;11379:93-104.

Caceres C., Rosário J., Amaya D. Design, simulation, and control of an omnidirectional mobile robot. International Review of Mechanical Engineering. 2018;12(4):382.

Calegari R., Fogliatto F. S., Lucini F. R., Neyeloff J., Kuchenbecker R. S., Schaan B. D. Forecasting daily volume and acuity of patients in the emergency department. Computational and Mathematical Methods in Medicine. 2016;2016.

Carvalho-Silva M., Monteiro M. T. T., Sá-Soares F., Dória-Nóbrega S. Assessment of forecasting models for patients arrival at Emergency Department. Operations Research for Health Care. 2018;18:112-8.

Chou C.-Y., Juang C.-F. Navigation of an autonomous wheeled robot in unknown environments based on evolutionary fuzzy control. Inventions, Multidisciplinary Digital Publishing Institute. 2018;3(1):3.

Demir E., Gunal M. M., Southern D. Demand and capacity modelling for acute services using discrete event simulation. Health Systems. 2017;6(1):33-40.

Djanatliev A., German R. Prospective healthcare decision-making by combined system dynamics, discrete-event and agent-based simulation. 2013:270-81.

Gilchrist A. Industry 4.0.. 2016.

Grigoriadis N., Bakirtzis C., Politis C., Danas K., Thuemmler C., Lim A. K. A health 4.0 based approach towards the management of multiple sclerosis. Health 4.0: how virtualization and big data are revolutionizing healthcare. 2017:205-18.

Gül M., Güneri A. F. Forecasting patient length of stay in an emergency department by artificial neural networks. Journal of Aeronautics and Space Technologies. 2015;8(2):43-8.

Handel D. A., Hilton J. A., Ward M. J., Rabin E., Zwemer Jr. F. L., Pines J. M. Emergency department throughput, crowding, and financial outcomes for hospitals. Academic Emergency Medicine. 2010;17(8):840-7.

Jalalpour M., Gel Y., Levin S. Forecasting demand for health services: development of a publicly available toolbox. Operations research for health care. 2015:1-9.

Jensen K. Emergency department crowding: the nature of the problem and why it matters. Patient flow: reducing delay in healthcare delivery. 2013:97-105.

Latifi R., Weinstein R. S., Porter J. M., Ziemba M., Judkins D., Ridings D., Nassi R., Valenzuela T., Holcomb M., Leyva F. Telemedicine and telepresence for trauma and emergency care management.. Scandinavian journal of Surgery. 2007;96(4):281-9.

Mapuwei T. W., Masamha B., Mukavhi L. Forecasting and simulation modelling, decision support tools for ambulance emergency preparedness. International Journal of Academic Research in Economics and Management Sciences. 2013;2(3).

Marconi G. P., Chang T., Pham P. K., Grajower D. N., Nager A. L. Traditional nurse triage vs physician telepresence in a pediatric ED. American Journal of Emergency Medicine. 2014;32(4):325-9.

McHugh M. The consequences of emergency department crowding and delays for patients. Patient flow: reducing delay in healthcare delivery. 2013:107-27.

Ordu M., Demir E., Tofallis C. A decision support system for demand and capacity modelling of an accident and emergency department. Health Systems. 2019:1-26.

Oueida S., Char P. A., Kadry S., Ionescu S. Simulation models for enhancing the health care systems. FAIMA Business & Management Journal. 2016;4(4):5-20.

Pines J. M., Hilton J. A., Weber E. J., Alkemade A. J., Al Shabanah H., Anderson P. D., Bernhard M., Bertini A., Gries A., Ferrandiz S., Kumar V. A., Harjola V. P., Hogan B., Madsen B., Mason S., Ohlén G., Rainer T., Rathlev N., Revue E., Richardson D., Sattarian M., Schull M. J. International perspectives on emergency department crowding. Academic Emergency Medicine. 2011;18(12):1358-70.

Riazul Islam S. M., Daehan Kwak , Humaun Kabir M., Hossain M., Kyung-Sup Kwak . The internet of things for health care: a comprehensive survey. IEEE Access: Practical Innovations, Open Solutions. 2015;3:678-708.

Siciliano B., Khatib O. Springer handbook of robotics. 2016.

Thorwarth M., Arisha A. Application of discrete-event simulation in health care: a review.. 2009.

Thuemmler C. The case for health 4.0. Health 4.0: how virtualization and big data are revolutionizing healthcare. 2017:1-22.

Thuemmler C., Bai C. Health 4.0: application of industry 4.0 design principles in future asthma management. Health 4.0: how virtualization and big data are revolutionizing healthcare. 2017:23-37.

Yucesan M., Gul M., Mete S., Celik E. A forecasting model for patient arrivals of an emergency department in healthcare management systems. Intelligent systems for healthcare management and delivery. 2019:266-84.

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