Developing a Machine Learning Model to Shorten Emergency Department Length of Stay: Model Testing and Nurses Acceptance

Laksita Barbara; Neny Rosmawarni; Arief Wahyudi Jadmiko

doi:10.52958/iftk.v22i1.13031

Authors

Laksita Barbara Universitas Pembangunan Nasional Veteran Jakarta
Neny Rosmawarni Universitas Pembangunan Nasional "Veteran" Jakarta https://orcid.org/0009-0009-2988-2717
Arief Wahyudi Jadmiko Universitas Pembangunan Nasional "Veteran" Jakarta https://orcid.org/0000-0002-9691-0986

DOI:

https://doi.org/10.52958/iftk.v22i1.13031

Keywords:

emergency service, length of stay, machine learning, predictive analytics, Indonesia

Abstract

This study aimed to develop and evaluate a portfolio of ML models to predict ED LoS and examine nurses’ acceptance of AI-based clinical decision support. Secondary data from two public hospitals in Jakarta were analysed using three ML algorithms Random Forest, Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) to classify ED LoS into short, medium, and prolonged categories. Predictor variables included triage level, arrival time, referral source, disposition, number of diagnostic tests, and consultations. Model performance was assessed using precision, recall, and F1-scores across training, testing, and blind validation datasets. Additionally, nurses’ readiness to adopt ML tools was evaluated using a survey. Across 687 ED cases, XGBoost achieved the best overall performance (precision, recall, and F1-score = 1.00), indicating excellent discrimination and balance between sensitivity and specificity. SVM also demonstrated strong external validation (blind-test F1 = 1.00), confirming robust generalisation across hospital sites. High-performance metrics across all models indicate consistent accuracy and calibration. Most nurses (89.3%) expressed high performance expectancy, and 95.7% high effort expectancy toward technology adoption. The developed ML framework accurately predicts ED LoS in Jakarta’s hospital settings, providing a foundation for data-driven resource management.

References

J. Andersson, L. Nordgren, I. Cheng, U. Nilsson, and L. Kurland, “Long emergency department length of stay: A concept analysis,” Int Emerg Nurs, vol. 53, no. August, p. 100930, 2020, doi: 10.1016/j.ienj.2020.100930.

L. Burgess, G. Ray‐Barruel, and K. Kynoch, “Association between emergency department length of stay and patient outcomes: A systematic review,” Res Nurs Health, vol. 45, no. 1, pp. 59–93, Feb. 2022, doi: 10.1002/nur.22201.

H. R. Rasouli et al., “Outcomes of Crowding in Emergency Departments; a Sys-tematic Review,” 2019. [Online]. Available: http://journals.sbmu.ac.ir/aaem

J. Andersson, L. Kurland, L. Nordgren, A. K. Gusdal, and I. Cheng, “Length of stay in the emergency department and its associated input-, throughput-, and output factors at two hospitals in Sweden,” BMC Emerg Med, vol. 25, no. 1, p. 120, Jul. 2025, doi: 10.1186/s12873-025-01283-z.

“Data Kunjungan dan Diagnosa Rumah Sakit,” Jakarta, 2025.

B. E. L. Vrijsen, S. Haitjema, J. Westerink, C. A. R. Hulsbergen-Veelken, W. W. van Solinge, and M. J. ten Berg, “Shorter laboratory turnaround time is associated with shorter emergency department length of stay: a retrospective cohort study,” BMC Emerg Med, vol. 22, no. 1, p. 207, Dec. 2022, doi: 10.1186/s12873-022-00763-w.

M. Ba-Aoum, N. Hosseinichimeh, K. P. Triantis, K. Pasupathy, M. Sir, and D. Nestler, “Statistical analysis of factors influencing patient length of stay in emergency departments,” International Journal of Industrial Engineering and Operations Management, vol. 5, no. 3, pp. 220–239, Aug. 2023, doi: 10.1108/IJIEOM-10-2022-0056.

A. B. el Ariss et al., “Development and validation of a machine learning framework for improved resource allocation in the emergency department,” American Journal of Emergency Medicine, vol. 84, pp. 141–148, Oct. 2024, doi: 10.1016/j.ajem.2024.07.040.

B. Duceau et al., “Prehospital triage of acute aortic syndrome using a machine learning algorithm.,” Br J Surg, vol. 107, no. 8, pp. 995–1003, Jul. 2020, doi: 10.1002/bjs.11442.

J. Miles, J. Turner, R. Jacques, J. Williams, and S. Mason, “Using machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: a systematic review.,” Diagn Progn Res, vol. 4, p. 16, 2020, doi: 10.1186/s41512-020-00084-1.

R. Sánchez-Salmerón et al., “Machine learning methods applied to triage in emergency services: A systematic review.,” Int Emerg Nurs, vol. 60, p. 101109, Jan. 2022, doi: 10.1016/j.ienj.2021.101109.

F. Cotte et al., “Safety of Triage Self-assessment Using a Symptom Assessment App for Walk-in Patients in the Emergency Care Setting: Observational Prospective Cross-sectional Study.,” JMIR Mhealth Uhealth, vol. 10, no. 3, p. e32340, Mar. 2022, doi: 10.2196/32340.

Z. Bouzid et al., “Novel ECG features and machine learning to optimize culprit lesion detection in patients with suspected acute coronary syndrome.,” J Electrocardiol, vol. 69S, pp. 31–37, 2021, doi: 10.1016/j.jelectrocard.2021.07.012.

J. Stewart et al., “Applications of machine learning to undifferentiated chest pain in the emergency department: A systematic review.,” PLoS One, vol. 16, no. 8, p. e0252612, 2021, doi: 10.1371/journal.pone.0252612.

R. M. Farimani et al., “Models to predict length of stay in the emergency department: a systematic literature review and appraisal,” BMC Emerg Med, vol. 24, no. 1, pp. 1–39, 2024, doi: 10.1186/s12873-024-00965-4.

V. Venkatesh, J. Y. L. Thong, and X. Xu, “Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology,” MIS Quarterly, vol. 36, no. 1, pp. 157–178, Apr. 2012, doi: 10.2307/41410412.

R. Abdullah and B. Fakieh, “Health care employees’ perceptions of the use of artificial intelligence applications: Survey study,” J Med Internet Res, vol. 22, no. 5, pp. 1–8, 2020, doi: 10.2196/17620.

L. Breiman, “Random Forests,” Mach Learn, vol. 45, no. 1, pp. 5–32, Oct. 2001, doi: 10.1023/A:1010933404324.

K. Budholiya, S. K. Shrivastava, and V. Sharma, “An optimized XGBoost based diagnostic system for effective prediction of heart disease,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 7, pp. 4514–4523, Jul. 2022, doi: 10.1016/j.jksuci.2020.10.013.

C. Cortes and V. Vapnik, “Support-vector networks,” Mach Learn, vol. 20, no. 3, pp. 273–297, Sep. 1995, doi: 10.1007/BF00994018.

R. Guido, S. Ferrisi, D. Lofaro, and D. Conforti, “An Overview on the Advancements of Support Vector Machine Models in Healthcare Applications: A Review,” Information, vol. 15, no. 4, p. 235, Apr. 2024, doi: 10.3390/info15040235.

Z. Rahmatinejad et al., “A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department,” Sci Rep, vol. 14, no. 1, p. 3406, Feb. 2024, doi: 10.1038/s41598-024-54038-4.

G. S. Collins et al., “TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods,” BMJ, vol. 385, p. e078378, Apr. 2024, doi: 10.1136/bmj-2023-078378.

E. W. Faida and D. W. Sutha, “The Intentions of Health Personnel Using UTAUT-Based Electronic Medical Records (Unified Theory of Acceptance and Use of Technology),” International Journal of Health and Information System, vol. 2, no. 2, pp. 67–77, Aug. 2024, doi: 10.47134/ijhis.v2i2.43.

A. W. Demsash, M. H. Kalayou, and A. D. Walle, “Health professionals’ acceptance of mobile-based clinical guideline application in a resource-limited setting: using a modified UTAUT model,” BMC Med Educ, vol. 24, no. 1, p. 689, Jun. 2024, doi: 10.1186/s12909-024-05680-z.