Vol. 14 No. 3 (2026): Business & Management Studies: An International Journal
Articles

Forecasting the consumption of medical consumables used in the emergency department using machine learning methods: A private hospital case

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  • Ercan Çulha,
  • Abdullah Mısırlıoğlu,
  • Refika Sultan Doğan
Ercan Çulha
PhD. Student, Sivas Cumhuriyet University, Sivas, Türkiye
Abdullah Mısırlıoğlu
Assist. Prof. Dr., Sivas Cumhuriyet University, Sivas, Türkiye
Refika Sultan Doğan
Dr., Abdullah Gül University, Kayseri, Türkiye

Published 2026-09-25

Keywords

  • Tıbbi Sarf Malzeme; Makine Öğrenmesi, Tüketim Tahmini
  • Medical Consumables; Machine Learning, Consumption Forecasting

How to Cite

Forecasting the consumption of medical consumables used in the emergency department using machine learning methods: A private hospital case. (2026). Business & Management Studies: An International Journal, 14(3), 1572-1586. https://doi.org/10.15295/bmij.v14i3.2788

Copyright (c) 2026 Ercan Çulha- Abdullah Mısırlıoğlu- Refika Sultan Doğan

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

Forecasting the consumption of medical consumables used in the emergency department using machine learning methods: A private hospital case. (2026). Business & Management Studies: An International Journal, 14(3), 1572-1586. https://doi.org/10.15295/bmij.v14i3.2788

Abstract

Objective: This study examines whether monthly consumption of high-volume medical consumables in an emergency department can be forecast using machine learning. Method: Six algorithms were compared using 480 monthly observations for 10 items from a private hospital in Ankara (2022–2025). Hyperparameters were tuned through chronological, month-grouped cross-validation. The best-performing model was selected solely from cross-validation results, while the held-out test set was used only for independent validation. Emergency-department visit volume was included in lagged form. Findings: Random Forest was selected as the best-performing model (cross-validation R²=0.792; SD=0.008) and achieved an independent test R² of 0.887. Ablation analysis showed no measurable incremental contribution of visit volume to Random Forest, although positive contributions were observed in linear models. Conclusion: The relationship between visit volume and consumption was limited at the product level. The model may support aggregate budgeting and human-supervised decision-making, but it is not sufficient as a standalone tool for automated product-level ordering.

PDF (Turkish)

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