IoT EWS: Uma Abordagem Explorando Monitoramento Remoto de Pacientes na Internet das Coisas
Resumo
A mobilidade vem constituindo uma prática cotidiana de médicos, assim, é possível que estes fiquem períodos de tempo sem contato com as equipes que lhe dão suporte no tratamento dos pacientes. Períodos mais longos entre comunicações podem provocar retardos na realização de procedimentos, na prescrição de drogas, etc. Considerando este cenário, este trabalho tem por objetivo a concepção de uma abordagem, denominada IoT EWS, que integra: (i) uma plataforma para aquisição de sinais vitais; (ii) um ambiente para processamento contextual, que através de regras personalizáveis construa a Ciência de Situação dos pacientes; e (iii) uma interface de visualização textual e gráfica destes sinais, que possa ser acessada pela IoT. Como fonte de sinais vitais está sendo empregado o banco de dados MIMIC-III. Por sua vez, para avaliação da IoT EWSjunto aos profissionais de saúde foi utilizado o Technology Acceptance Model (TAM), tendo sido obtidos resultados promissores.
Palavras-chave:
Sinais Vitais, IoT, EWS
Referências
Bleyer, A. J., Vidya, S., Russell, G. B., Jones, C. M., Sujata, L., Daeihagh, P., and Hire, D. (2011). Longitudinal analysis of one million vital signs in patients in an academic medical center. Resuscitation, 82(11):1387–1392.
Brunker, C. and Harris, R. (2015). How accurate is the avpu scale in detecting neurological impairment when used by general ward nurses? an evaluation study using simulation and a questionnaire. Intensive and Critical Care Nursing, 31(2):69–75.
Commission, A. et al. (1999). Critical to success: the place of efficient and effective critical care services within the acute hospital. Audit Commission London.
Davis, F. D., Bagozzi, R. P., and Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management science, 35(8):982– 1003.
Dridi, A., Sassi, S., and Faiz, S. (2017). A smart iot platform for personalized healthcare monitoring using semantic technologies. In Tools with Artificial Intelligence (ICTAI), 2017 IEEE 29th International Conference on, pages 1198–1203. IEEE.
Edmondson, A. (1999). Psychological safety and learning behavior in work teams. Administrative science quarterly, 44(2):350–383.
Jaiswal, K., Sobhanayak, S., Turuk, A. K., Bibhudatta, S. L., Mohanta, B. K., and Jena, D. (2018). An iot-cloud based smart healthcare monitoring system using container based virtual environment in edge device. In 2018 International Conference on Emerging Trends and Innovations In Engineering And Technological Research (ICETIETR), pages 1–7. IEEE.
Jevon, P. (2010). How to ensure patient observations lead to prompt identification of tachypnoea. Nursing times, 106(2):12–14.
Johnson, A. E., Pollard, T. J., Shen, L., Li-wei, H. L., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., and Mark, R. G. (2016). Mimic-iii, a freely accessible critical care database. Scientific data, 3:160035.
Johnston, S. and Cox, S. (2017). The raspberry pi: a technology disrupter, and the enabler of dreams.
Karthikeyan, S., Devi, K. V., and Valarmathi, K. (2015). Internet of things: Hospice appliances monitoring and control system. In Green Engineering and Technologies (IC-GET), 2015 Online International Conference on, pages 1–6. IEEE.
Kelly, C. A., Upex, A., and Bateman, D. N. (2004). Comparison of consciousness level assessment in the poisoned patient using the alert/verbal/painful/unresponsive scale and the glasgow coma scale. Annals of emergency medicine, 44(2):108–113.
Likert, R. (1932). A technique for the measurement of attitudes. Archives of psychology.
Lopes, J. L., de Souza, R. S., Geyer, C., da Costa, C., Barbosa, J., Pernas, A. M., and Yamin, A. (2014). A middleware architecture for dynamic adaptation in ubiquitous computing. Journal of Universal Compuerter Science, 20(9):1327–1351.
Millikan, G. A. (1942). The oximeter, an instrument for measuring continuously the oxygen saturation of arterial blood in man. Review of scientific Instruments, 13(10):434–444.
Odell, M., Victor, C., and Oliver, D. (2009). Nurses’ role in detecting deterioration in ward patients: systematic literature review. Journal of advanced nursing, 65(10):1992– 2006.
Okoli, C. (2015). A guide to conducting a standalone systematic literature review. Communications of the Association for Information Systems, 37(1):43.
Perera, C., Zaslavsky, A., Christen, P., and Georgakopoulos, D. (2014). Context aware computing for the internet of things: A survey. Communications Surveys Tutorials, IEEE, 16(1):414–454.
Perry, A. and Potter, P. (2002). Clinical Nursing Skills & Techniques. Mosby.
Rufino, G. P., Gurgel, M. G., Pontes, T. d. C., and Freire, E. (2012). Avaliação de fatores determinantes do tempo de internação em clínica médica. Revista Brasileira Clínica Médica, 10(4):291–297.
Sezer, O. B., Dogdu, E., and Ozbayoglu, A. M. (2018). Context-aware computing, learning, and big data in internet of things: a survey. IEEE Internet of Things Journal, 5(1):1–27.
Souza, R., Lopes, J., Geyer, C., Cardozo, A., Yamin, A., and Barbosa, J. (2018). An architecture for iot management targeted to context awareness of ubiquitous applications. Journal of Universal Computer Science, 24(10):1452–1471.
Stedman, T. L. (2005). Stedman’s medical dictionary for the health professions and nursing. Lippincott Williams & Wilkins.
Teixeira, C., Teixeira, T. M. L., Brodt, S. F. M., Oliveira, R. P., Neto, F. L. D., Roehrig, C., and Oliveira, E. S. (2010). A adequada comunicação entre os profissionais médicos reduz a mortalidade no centro de tratamento intensivo. Revista Brasileira de Terapia Intensiva, 22(2):112–117.
Brunker, C. and Harris, R. (2015). How accurate is the avpu scale in detecting neurological impairment when used by general ward nurses? an evaluation study using simulation and a questionnaire. Intensive and Critical Care Nursing, 31(2):69–75.
Commission, A. et al. (1999). Critical to success: the place of efficient and effective critical care services within the acute hospital. Audit Commission London.
Davis, F. D., Bagozzi, R. P., and Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management science, 35(8):982– 1003.
Dridi, A., Sassi, S., and Faiz, S. (2017). A smart iot platform for personalized healthcare monitoring using semantic technologies. In Tools with Artificial Intelligence (ICTAI), 2017 IEEE 29th International Conference on, pages 1198–1203. IEEE.
Edmondson, A. (1999). Psychological safety and learning behavior in work teams. Administrative science quarterly, 44(2):350–383.
Jaiswal, K., Sobhanayak, S., Turuk, A. K., Bibhudatta, S. L., Mohanta, B. K., and Jena, D. (2018). An iot-cloud based smart healthcare monitoring system using container based virtual environment in edge device. In 2018 International Conference on Emerging Trends and Innovations In Engineering And Technological Research (ICETIETR), pages 1–7. IEEE.
Jevon, P. (2010). How to ensure patient observations lead to prompt identification of tachypnoea. Nursing times, 106(2):12–14.
Johnson, A. E., Pollard, T. J., Shen, L., Li-wei, H. L., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., and Mark, R. G. (2016). Mimic-iii, a freely accessible critical care database. Scientific data, 3:160035.
Johnston, S. and Cox, S. (2017). The raspberry pi: a technology disrupter, and the enabler of dreams.
Karthikeyan, S., Devi, K. V., and Valarmathi, K. (2015). Internet of things: Hospice appliances monitoring and control system. In Green Engineering and Technologies (IC-GET), 2015 Online International Conference on, pages 1–6. IEEE.
Kelly, C. A., Upex, A., and Bateman, D. N. (2004). Comparison of consciousness level assessment in the poisoned patient using the alert/verbal/painful/unresponsive scale and the glasgow coma scale. Annals of emergency medicine, 44(2):108–113.
Likert, R. (1932). A technique for the measurement of attitudes. Archives of psychology.
Lopes, J. L., de Souza, R. S., Geyer, C., da Costa, C., Barbosa, J., Pernas, A. M., and Yamin, A. (2014). A middleware architecture for dynamic adaptation in ubiquitous computing. Journal of Universal Compuerter Science, 20(9):1327–1351.
Millikan, G. A. (1942). The oximeter, an instrument for measuring continuously the oxygen saturation of arterial blood in man. Review of scientific Instruments, 13(10):434–444.
Odell, M., Victor, C., and Oliver, D. (2009). Nurses’ role in detecting deterioration in ward patients: systematic literature review. Journal of advanced nursing, 65(10):1992– 2006.
Okoli, C. (2015). A guide to conducting a standalone systematic literature review. Communications of the Association for Information Systems, 37(1):43.
Perera, C., Zaslavsky, A., Christen, P., and Georgakopoulos, D. (2014). Context aware computing for the internet of things: A survey. Communications Surveys Tutorials, IEEE, 16(1):414–454.
Perry, A. and Potter, P. (2002). Clinical Nursing Skills & Techniques. Mosby.
Rufino, G. P., Gurgel, M. G., Pontes, T. d. C., and Freire, E. (2012). Avaliação de fatores determinantes do tempo de internação em clínica médica. Revista Brasileira Clínica Médica, 10(4):291–297.
Sezer, O. B., Dogdu, E., and Ozbayoglu, A. M. (2018). Context-aware computing, learning, and big data in internet of things: a survey. IEEE Internet of Things Journal, 5(1):1–27.
Souza, R., Lopes, J., Geyer, C., Cardozo, A., Yamin, A., and Barbosa, J. (2018). An architecture for iot management targeted to context awareness of ubiquitous applications. Journal of Universal Computer Science, 24(10):1452–1471.
Stedman, T. L. (2005). Stedman’s medical dictionary for the health professions and nursing. Lippincott Williams & Wilkins.
Teixeira, C., Teixeira, T. M. L., Brodt, S. F. M., Oliveira, R. P., Neto, F. L. D., Roehrig, C., and Oliveira, E. S. (2010). A adequada comunicação entre os profissionais médicos reduz a mortalidade no centro de tratamento intensivo. Revista Brasileira de Terapia Intensiva, 22(2):112–117.
Publicado
31/07/2022
Como Citar
ALBANDES, Rogério; SOUZA, Alexandre; LAMBRECHT, Rodrigo; BARCELLOS, Franklin; YAMIN, Adenauer.
IoT EWS: Uma Abordagem Explorando Monitoramento Remoto de Pacientes na Internet das Coisas. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 14. , 2022, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 81-90.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2022.223092.
