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Develop a machine learning framework to forecast multi-step ahead queue lengths during peak operational hours in an outpatient phlebotomy department.

August 15, 2026BMC Health Services ResearchOpen Access

Developing machine learning multi-step ahead forecasting scheme to predict outpatient queue length in phlebotomy department

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Develop a machine learning framework to forecast multi-step ahead queue lengths during peak operational hours in an outpatient phlebotomy department.
Evaluated three machine learning models—extreme gradient boosting (XGBoost), random forest (RF), and least absolute shrinkage and selection operator (Lasso) regression—against two traditional forecasting approaches using real medical center data.
Performed multi-step ahead forecasting across one-, two-, and three-step intervals while conducting feature selection to identify primary queue determinants.
All machine learning models demonstrated superior predictive performance compared to traditional baseline models, with the random forest algorithm achieving the highest accuracy.
Feature selection identified the number of patients without wheelchairs, the volume of adult services, and the number of completed phlebotomy procedures as the top three predictors of queue length.

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Implication

Modeling study reveals that random forest algorithms accurately predict outpatient phlebotomy queue lengths, suggesting machine learning aids proactive hospital capacity planning.

Cite This Study

Liu et al. (2026) studied this question.

synapsesocial.com/papers/6a80197375c2e31742c8573a https://doi.org/10.1186/s12913-026-15327-y

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Implication

Modeling study reveals that random forest algorithms accurately predict outpatient phlebotomy queue lengths, suggesting machine learning aids proactive hospital capacity planning.

Developing machine learning multi-step ahead forecasting scheme to predict outpatient queue length in phlebotomy department

Key Points

Discussion

Implication

Cite This Study

Developing machine learning multi-step ahead forecasting scheme to predict outpatient queue length in phlebotomy department

Key Points

Discussion

Implication

Cite This Study

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