A Data-Driven Industrial Engineering Framework for Hospital Performance Evaluation Using the Balanced Scorecard | ||
| Advances in Industrial Engineering | ||
| دوره 60، شماره 1، شهریور 2026، صفحه 125-141 اصل مقاله (696.08 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/aie.2026.409978.1967 | ||
| نویسندگان | ||
| Zahra Mojaradi1؛ Ali Bozorgi-Amiri* 2؛ Zeinab Sazvar2 | ||
| 1Ph.D. Candidate, Department of Industrial Engineering, Alborz Campus, University of Tehran, Tehran, Iran. | ||
| 2Associate Professor, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran. | ||
| چکیده | ||
| Performance evaluation in complex service systems is a central concern in industrial engineering, particularly in regulated environments such as healthcare. This study develops a data-driven industrial engineering framework for hospital performance evaluation structured through the Balanced Scorecard (BSC) and supported by exploratory predictive modelling. A structured evidence-based screening process was conducted to identify operationally measurable performance indicators, which were subsequently organised within the four BSC perspectives and aggregated into composite performance dimensions using standardised equal-weight scoring. Quarterly organisational data (2018–2024) from a tertiary hospital were used to examine structural relationships among indicators through a multilayer perceptron neural network. The predictive component is intended as exploratory validation rather than universal forecasting. Results indicate strong alignment between predicted and observed composite performance scores (R = 0.84), suggesting that the selected indicators collectively explain substantial variation within the studied organisational context. Importance and sensitivity analyses further identify cost-efficiency and operational-process variables as influential drivers, while safety, workforce, and patient-related indicators demonstrate meaningful associations. The proposed framework enhances transparency in indicator selection, clarifies multidimensional performance structuring, and provides analytically informed decision support for complex service systems. | ||
| کلیدواژهها | ||
| Balanced Scorecard؛ Evidence-Based Decision Making؛ Kpi Selection؛ Performance Prediction؛ Service Systems | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 170 تعداد دریافت فایل اصل مقاله: 63 |
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