Abstract
Increasing healthcare costs have motivated researchers to seek ways to more efficiently use medical resources. The aim of our study was to adopt the explanatory data-mining approach to identify characteristics of emergency department (ED) visits for ED management. To that end, we adopted a behavior-based decision tree (DT) induction method that considers medical diagnoses and individual patients’ information, i.e., 11 input variables, in order to analyze characteristics of patients’ visits to EDs and predict the length of the stays. We interpreted the results based on the communicability and consistency of the DT, represented as a behavior-based DT profile in order to increase its explanatory power. Among the major preliminary findings, the DT with International Classification of Diseases diagnosis codes achieved better clinical values for explaining the characteristics of patients’ visits. Our results can serve as a reference for ED personnel to examine overcrowding conditions as part of medical management.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Uscher-Pines, L., Pines, J., Kellermann, A., Gillen, E., Mehrotra, A.: Deciding to visit the emergency department for non-urgent conditions: a systematic review of the literature. Am. J. Managed Care 19(1), 47–59 (2013)
Young, G.P., Wagner, M.B., Kellermann, A.L., Ellis, J., Bouley, D.: Ambulatory visits to hospital emergency departments: patterns and reasons for use. JAMA 276(6), 460–465 (1996)
Niska, R., Bhuiya, F., Xu, J.: National hospital ambulatory medical care survey: 2007 emergency department summary. Natl. Health Stat. Report 26, 1–31 (2010)
Milovic, B., Milovic, M.: Prediction and decision making in health care using data mining. Kuwait Chapter Arab. J. Bus. Manage. Rev. 1(12), 126 (2012)
Pendharkar, P.C., Khurana, H.: Machine learning techniques for predicting hospital length of stay in Pennsylvania federal and specialty hospitals. Int. J. Comput. Sci. Appl. 11(3), 45–56 (2014)
Graham, B., Bond, R., Quinn, M., Mulvenna, M.: Using data mining to predict hospital admissions from the emergency department. IEEE Access 6, 10458–10469 (2018)
Feng, Y.Y., Wu, I.C., Chen, T.L.: Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm. Health Care Manage. Sci. 20(1), 55–75 (2017). https://doi.org/10.1007/s10729-015-9335-1
Karhade, P.P., Shaw, M.J., Subramanyam, R.: Patterns in information systems portfolio prioritization: evidence from decision tree induction. MIS Q. 39(2), 413–433 (2015)
Feng, Y.-Y., Wu, I.-C., Chen, T.-L., Chang, W.-H.: A hybrid data mining approach for generalizing characteristics of emergency department visits causing overcrowding. J. Libr. Inf. Stud. 17(1), 1–35 (2019). https://doi.org/10.6182/jlis.201906_17(1).001
Acknowledgments
This research was supported by the Ministry of Science and Technology of Taiwan under Grant MOST 108-2410-H-003-132-MY2.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Feng, YY., Wu, IC., Ho, YP. (2020). Investigating Patients’ Visits to Emergency Departments: A Behavior-Based ICD-9-CM Codes Decision Tree Induction Approach. In: Nah, FH., Siau, K. (eds) HCI in Business, Government and Organizations. HCII 2020. Lecture Notes in Computer Science(), vol 12204. Springer, Cham. https://doi.org/10.1007/978-3-030-50341-3_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-50341-3_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-50340-6
Online ISBN: 978-3-030-50341-3
eBook Packages: Computer ScienceComputer Science (R0)