Patient satisfaction is critical to a health organization’s sustainability, making patient feedback an important source of insight for improving healthcare delivery. This study analyzes 31,054 patient responses across seven healthcare units to identify factors that influence patient satisfaction and to examine how satisfaction is reflected in qualitative comments across diverse care settings. Deep learning methods were employed to extract features related to sentiment and topics from patient comments. Linear regressions were used to evaluate the explanatory power of both quantitative ratings of healthcare service attributes and features extracted from qualitative comments, accounting for unit-specific healthcare settings. Our results reveal that trust and communication factors are the strongest predictors of patient satisfaction. However, the explanatory power of quantitatively rated survey items shows clear variations across units, suggesting that standardized survey questions inadequately capture patient experience in specific contexts. Text analysis uncovered important unit-specific priorities, such as prescription management in Medical Practice and result efficiency in OP Lab, that were absent from the rated survey items. Patients who mentioned topics not covered in the rated survey items tended to report lower satisfaction scores, indicating potential service gaps. The integration of quantitative ratings and qualitative comments in our analysis significantly improved explanatory power in six of the seven units. Based on the identified key determinants of patient satisfaction and emerging concerns from patient comments, we propose a tiered approach for healthcare practitioners to leverage both forms of feedback for more targeted, emotionally responsive, and context-aware improvements in patient care. As healthcare delivery continues to evolve, our findings also underscore the value of flexible, multi-modal feedback strategies—supported by an adaptive analytical framework—that can inform future research and system-level responses to the diverse and shifting expectations of patients.

Background/Objectives: Emergency department (ED) overcrowding contributes to delayed care, prolonged length of stay (LOS), resource strain, and adverse patient outcomes. This systematic review aimed to examine how artificial intelligence (AI) and machine learning (ML) have been used to address ED crowding and patient flow, with emphasis on modeling approaches, validation practices, and real-world implementation. Methods: Following PRISMA 2020 guidelines, Scopus, Embase, Ovid MEDLINE, and CENTRAL were searched for relevant studies published from 2020 onward. After deduplication, 1888 records underwent title and abstract screening using two locally deployed LLaMA models with human adjudication. Screening performance was assessed against 150 manually annotated records. Full-text eligibility assessment and structured data extraction were conducted independently by multiple reviewers, with disagreements resolved by consensus. Results: Thirty-two studies were included. Most were retrospective, single-site investigations using electronic health record, administrative, or operational data. Common outcomes included ED LOS, waiting time, occupancy, boarding, disposition, and crowding indices. Tree-based and boosting models frequently performed well, although no approach was consistently superior across tasks and settings. Most studies relied on same-site validation, while external and temporal validation were uncommon. Prospective implementation, workflow integration, model maintenance, and direct operational, clinical, economic, or equity impacts were rarely evaluated. For LLM-assisted screening, LLaMA 4 Scout achieved 84.0% accuracy, 80.0% recall, 88.9% precision, and an F1 score of 84.2%, compared with 78.0%, 67.5%, 88.5%, and 76.6%, respectively, for LLaMA 3.3 on 150 randomly sampled papers. Conclusions: AI and ML show promise for addressing ED overcrowding, but the literature remains concentrated at the model-development stage. Future research should prioritize standardized outcomes, multicenter validation, prospective implementation, and direct evaluation of operational and patient-care outcomes.