What does this research mean for the field?

A hybrid PLS-SEM and machine learning framework reveals that age-based maintenance planning, immediate corrective maintenance, and continuous improvement are the most critical key performance indicators for hospital facilities maintenance management. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To develop a KPI-based decision-support framework for hospital facilities maintenance management and determine which key performance indicators most strongly predict maintenance performance under resource constraints.

September 6, 2026Engineering Construction & Architectural Management0 citations

Prioritizing Key Performance Indicators for Hospital Maintenance Management Using Machine Learning

Prioritizing key performance indicators for maintenance management in public hospitals: a hybrid SEM–machine learning approach

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Authors

XZXiaoyu Zhang YZYujie Zhang CACheong Peng Au-Yong

Discussion

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Overview

Mixed-methods study identifies top maintenance indicators across public hospitals, suggesting that prioritizing age-based planning and immediate repairs optimizes limited facility resources.

Key Points

To develop a KPI-based decision-support framework for hospital facilities maintenance management and determine which key performance indicators most strongly predict maintenance performance under resource constraints.
Conducted an explanatory mixed-methods study in Henan Province, China, incorporating a pilot survey (N=55 hospitals), a main survey (N=283 hospitals), and 11 expert interviews.
Used partial least squares structural equation modeling (PLS-SEM) to validate relationships between four KPI domains and maintenance performance.
Applied machine learning models paired with SHapley Additive exPlanations (SHAP) to evaluate indicator-level predictive importance.
All four evaluated KPI domains exhibited positive relationships with hospital facilities maintenance performance.
Machine learning models revealed that age-based maintenance planning, immediate corrective maintenance, and continuous improvement provide the greatest relative predictive contributions to maintenance outcomes.

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Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/6a9d1e3328139818eab20eba https://doi.org/10.1108/ecam-04-2026-0602

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September 6, 2026Engineering Construction & Architectural Management0 citations

Prioritizing Key Performance Indicators for Hospital Maintenance Management Using Machine Learning

Prioritizing key performance indicators for maintenance management in public hospitals: a hybrid SEM–machine learning approach

View Full Paper

Authors

XZXiaoyu Zhang YZYujie Zhang CACheong Peng Au-Yong

Discussion

Member takes

Overview

Mixed-methods study identifies top maintenance indicators across public hospitals, suggesting that prioritizing age-based planning and immediate repairs optimizes limited facility resources.

Key Points

To develop a KPI-based decision-support framework for hospital facilities maintenance management and determine which key performance indicators most strongly predict maintenance performance under resource constraints.
Conducted an explanatory mixed-methods study in Henan Province, China, incorporating a pilot survey (N=55 hospitals), a main survey (N=283 hospitals), and 11 expert interviews.
Used partial least squares structural equation modeling (PLS-SEM) to validate relationships between four KPI domains and maintenance performance.
Applied machine learning models paired with SHapley Additive exPlanations (SHAP) to evaluate indicator-level predictive importance.
All four evaluated KPI domains exhibited positive relationships with hospital facilities maintenance performance.
Machine learning models revealed that age-based maintenance planning, immediate corrective maintenance, and continuous improvement provide the greatest relative predictive contributions to maintenance outcomes.

Ask AI

Helpful

Bookmark

View Full Paper

Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/6a9d1e3328139818eab20eba https://doi.org/10.1108/ecam-04-2026-0602

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