پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 127 |
| تعداد شمارهها | 7,196 |
| تعداد مقالات | 77,227 |
| تعداد مشاهده مقاله | 157,222,995 |
| تعداد دریافت فایل اصل مقاله | 118,406,270 |
A smart model for hospital performance evaluation based on balanced scorecard approach and machine learning | ||
| Advances in Industrial Engineering | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 24 اسفند 1404 | ||
| شناسه دیجیتال (DOI): 10.22059/aie.2026.409978.1967 | ||
| نویسندگان | ||
| Zahra Mojaradi1؛ Ali Bozorgi-Amiri* 2؛ zeinab sazvar3 | ||
| 1Department of Industrial Engineering, Alborz Campus, University of Tehran, Tehran, Iran | ||
| 2School of Industrial Engineering, College of Engineering, University of Tehran | ||
| 3UniversSchool of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iranity of Tehran | ||
| 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 | ||
|
آمار تعداد مشاهده مقاله: 64 |
||
سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب