Part of the book series:Lecture Notes in Computer Science ((LNISA,volume 9545))
Included in the following conference series:
2596Accesses
Abstract
An unsuitable patient flow as well as prolonged waiting lists in the emergency room of a maternity unit, regarding gynecology and obstetrics care, can affect the mother and child’s health, leading to adverse events and consequences regarding their safety and satisfaction. Predicting the patients’ waiting time in the emergency room is a means to avoid this problem. This study aims to predict the pre-triage waiting time in the emergency care of gynecology and obstetrics of Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto, situated in the north of Portugal. Data mining techniques were induced using information collected from the information systems and technologies available in CMIN. The models developed presented good results reaching accuracy and specificity values of approximately 74 % and 94 %, respectively. Additionally, the number of patients and triage professionals working in the emergency room, as well as some temporal variables were identified as direct enhancers to the pre-triage waiting time. The implementation of the attained knowledge in the decision support system and business intelligence platform, deployed in CMIN, leads to the optimization of the patient flow through the emergency room and improving the quality of services.
This is a preview of subscription content,log in via an institution to check access.
Access this chapter
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
- Chapter
- JPY 3498
- Price includes VAT (Japan)
- eBook
- JPY 5719
- Price includes VAT (Japan)
- Softcover Book
- JPY 7149
- Price includes VAT (Japan)
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bergs, J., Verelst, S., Gillet, J.-B., Deboutte, P., Vandoren, C., Vandijck, D.: The number of patients simultaneously present at the emergency department as an indicator of unsafe waiting times: a receiver operated curve-based evaluation. Int. Emerg. Nurs.22, 185–189 (2014)
Hoot, N., Aronsky, D.: Systematic review of emergency department crowding: causes, effects and solutions. Ann. Emerg. Med.52, 126–136 (2008)
Stover-Baker, B., Stahlman, B., Pollack, M.: Triage nurse prediction of hospital admission. J. Emerg. Nurs.38(3), 306–310 (2012)
Sun, Y., Teow, K., Heng, B., Ooi, C., Tay, S.: Real-time prediction of waiting time in the emergency department, using quantile regression. Ann. Emerg. Med.60(3), 299–308 (2012)
Boudreaux, E., O’Hea, E.: Patient satisfaction in the emergency department: a review of the literature and implications for practice. J. Emerg. Med.26(1), 13–26 (2004)
Shaikh, S., Witting, M., Winters, M., Brodeur, M., Jerrad, D.: Support for a waiting room time tracker: a survey of patients waiting in an urban ED. J. Emerg. Med.44(1), 225–229 (2013)
Taylor, C., Benger, J.: Patient satisfaction in emergency medicine. J. Emerg. Med.21, 528–532 (2004)
Khodambashi, S.: Business process re-engineering application in healthcare in a relation to health information systems. Procedia Technol.9, 949–957 (2013)
Pereira, E., Brandão, A., Salazar, M., Portela, F., Santos, M., Machado, J., Abelha, A., Braga, J.: Pre-triage decision support improvement in maternity care by means of data mining. In: Integration of Data Mining in Business Intelligence Systems, pp. 175–192 (2014)
Paul, J., Jordan, R., Duty, S., Engstrom, J.: Improving satisfaction with care and reducing length of stay in an obstetric triage unit using a nurse-midwife-managed model of care. J. Midwifery Women’s Health58(2), 1–7 (2013)
Zocco, J., Williams, M., Longobucco, D., Bernstein, B.: A systems analysis of obstetric triage. J. Perinat. Neonatal Nurs.21(4), 315–322 (2007)
Murray, M., Bullard, M., Grafstein, E.: Revisions to the Canadian emergency department triage and acuity scale implementation guidelines. Cjem6(6), 421–427 (2004)
Abelha, A., Pereira, E., Brandão, A., Portela, F., Santos, M., Machado, J., Braga, J.: Improving quality of services in maternity care triage system. Int. J. E-Health Med. Commun.6(2), 10–26 (2015)
Abelha, A., Quintas, C., Cabral, A., Salazar, M., Machado, H., Machado, J., Neves, J., Santos, M.F., Portela, C.F., Pina, C.: Data acquisition process for an intelligent decision support in gynecology and obstetrics emergency triage. In: Cruz-Cunha, M.M., Varajão, J., Powell, P., Martinho, R. (eds.) CENTERIS 2011, Part III. CCIS, vol. 221, pp. 223–232. Springer, Heidelberg (2011)
Abelha, A., Pereira, E., Brandão, A., Portela, F., Santos, M., Machado, J.: Simulating a multi-level priority triage system for Maternity Emergency. In: European Simulation and Modelling Conference (2014)
Portela, F., Cabral, A., Abelha, A., Salazar, M., Quintas, C., Machado, J., Santos, M.: Knowledge acquisition process for intelligent decision support in critical health care. In: Healthcare Administration: Concepts, Methodologies, Tools, and Applications, p. 270 (2014)
Abelha, A., Analide, C., Machado, J., Neves, J., Santos, M., Novais, P.: Ambient intelligence and simulation in health care virtual scenarios. In: Camarinha-Matos, L.M., Afsarmanesh, H., Novais, P., Analide, C. (eds.) EFCN 2007, Part I. IFIP, vol. 243, pp. 461–468. Springer, New York (2007)
Abelha, A., Machado, J., Santos, M., Allegro, S., Rua, F., Paiva, M., Neves, J.: Agency for integration, diffusion and archive of medical information. In: IASTED International Conference - Artificial Intelligence and Applications (2002)
Peixoto, H., Santos, M., Abelha, A., Machado, J.: Intelligence in Interoperability with AIDA. In: 20th International Symposium on Methodologies for Intelligent Systems (2012)
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag.17(3), 37 (1996)
Lin, J., Gan, W., Hong, T.-P., Tseng, V.: Efficient algorithms for mining up-to-date high-utility patterns. Adv. Eng. Inform.29(3), 648–661 (2015)
Maimon, O., Rokach, L.: Introduction to Knowledge Discovery and Data Mining. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 1–17. Springer, New York (2005)
Xia, J., Xie, F., Zhang, Y., Caulfield, C.: Artificial intelligence and data mining: algorithms and applications. In: Abstract and Applied Analysis (2013)
Srinivas, K., Rani, B.K., Govrdhan, A.: Applications of data mining techniques in healthcare and prediction of heart attacks. Int. J. Comput. Sci. Eng.2(2), 250–255 (2010)
Yoo, I., Alafaireet, P., Marinov, M., Pena-Hernandez, K., Gopidi, R., Chang, J.-F., Hua, L.: Data mining in healthcare and biomedicine: a survey of the literature. J. Med. Syst.36, 2431–2448 (2012)
Braga, P., Portela, F., Santos, M., Rua, F.: Data mining models to predict patient’s readmission in intensive care units. In: ICAART - International Conference on Agents and Artificial Intelligence, Angers, France (2014)
Portela, F., Santos, M.F., Machado, J., Abelha, A., Silva, Á.: Pervasive and intelligent decision support in critical health care using ensembles. In: Bursa, M., Khuri, S., Renda, M.E. (eds.) ITBAM 2013. LNCS, vol. 8060, pp. 1–16. Springer, Heidelberg (2013)
Veloso, R., Portela, F., Santos, M., Sila, Á., Rua, F., Abelha, A., Machado, J.: A clustering approach for predicting readmissions in intensive medicine. Procedia Technol.16, 1307–1316 (2014)
Beta, J., Akolekar, R., Ventura, W., Syngelaki, A., Nicolaides, K.: Prediction of spontaneous preterm delivery from maternal factors, obstetric history and placental perfusion and function at 11–13 weeks. Prenat. Diagn.31(1), 75–83 (2011)
Brandão, A., Pereira, E., Portela, F., Santos, M., Abelha, A., Machado, J.: Managing voluntary interruption of pregnancy using data mining. Procedia Technol.16, 1297–1306 (2014)
Pereira, S., Portela, F., Santos, M., Abelha, A., Machado, J.: Clustering-based approach for categorizing pregnant in obstetrics and maternity care. In: C3S2E, Yokohoma, Japan, pp. 1–5 (2015)
Shafique, U., Qaiser, H.: A comparative study of data mining process models (KDD, CRISP-DM and SEMMA). Int. J. Innov. Sci. Res.12(1), 217–222 (2014)
Shearer, C.: The CRISP-DM model: the new blueprint for data mining. J. Data Warehouse.5(4), 13–22 (2000)
Pereira, E., Brandão, A., Portela, F., Santos, M., Machado, J., Abelha, A.: Business intelligence in maternity care. In: IDEAS - International Database Engineering and Applications Symposium, Portugal (2014)
Portela, F., Santos, M.F., Machado, J., Abelha, A., Silva, Á., Rua, F.: Pervasive and Intelligent Decision Support in Intensive Medicine – The Complete Picture. In: Bursa, M., Khuri, S., Renda, M. (eds.) ITBAM 2014. LNCS, vol. 8649, pp. 87–102. Springer, Heidelberg (2014)
Acknowledgments
This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013.
Author information
Authors and Affiliations
Algoritmi Centre, University of Minho, Braga, Portugal
Sónia Pereira, Filipe Portela, Manuel F. Santos, José Machado & António Abelha
ESEIG, Porto Polytechnique, Porto, Portugal
Filipe Portela
- Sónia Pereira
You can also search for this author inPubMed Google Scholar
- Filipe Portela
You can also search for this author inPubMed Google Scholar
- Manuel F. Santos
You can also search for this author inPubMed Google Scholar
- José Machado
You can also search for this author inPubMed Google Scholar
- António Abelha
You can also search for this author inPubMed Google Scholar
Corresponding author
Correspondence toFilipe Portela.
Editor information
Editors and Affiliations
Institute of Automation,Bldg.1004, Chinese Academy of Sciences, Beijing, China
Xiaolong Zheng
University of Arizona, Tucson, Arizona, USA
Daniel Dajun Zeng
University of Arizona, Phoenix, USA
Hsinchun Chen
Mayo Clinic, Scottsdale, USA
Scott J. Leischow
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Pereira, S., Portela, F., Santos, M.F., Machado, J., Abelha, A. (2016). Predicting Pre-triage Waiting Time in a Maternity Emergency Room Through Data Mining. In: Zheng, X., Zeng, D., Chen, H., Leischow, S. (eds) Smart Health. ICSH 2015. Lecture Notes in Computer Science(), vol 9545. Springer, Cham. https://doi.org/10.1007/978-3-319-29175-8_10
Download citation
Published:
Publisher Name:Springer, Cham
Print ISBN:978-3-319-29174-1
Online ISBN:978-3-319-29175-8
eBook Packages:Computer ScienceComputer Science (R0)
Share this paper
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative