Objective: This study aims to develop and evaluate a semi-automated workflow using Natural Language Processing (NLP) for sharing positive patient feedback with radiology staff, assessing its efficiency and impact on radiology staff morale. Methods: The HIPAA compliant, institutional review board-waived implementation study was conducted from April 2022 to June 2023 and introduced a Patient Praises program to distribute positive patient feedback to radiology staff collected from patient surveys. The study transitioned from an initial manual workflow to a hybrid process using an NLP model trained on 1,034 annotated comments and validated on 260 holdout reports. The time to generate Patient Praises e-mails were compared between manual and hybrid workflows. Impact of Patient Praises on radiology staff was measured using a 4 question Likert-scale survey and an open text feedback box. Kruskal-Wallis and post-hoc Dunn’s test was performed to evaluate differences in time for different workflows. Results: From April 2022 to June 2023, the radiology department received 10,643 patient surveys. Of those surveys, 95.6% of these surveys contained positive comments, with 9.6% (n = 978) shared as Patient Praises to staff. After implementation of the hybrid workflow in March 2023, 45.8% of Patient Praises were sent through the hybrid workflow and 54.2% were sent manually. Time efficiency analysis on 30-case subsets revealed that the hybrid workflow without edits was the most efficient, taking a median of 0.7 minutes per case. A high proportion of staff found the praises made them feel appreciated (94%) and valued (90%) responding with a 5/5 agreement on 5-point Likert scale responses. Conclusion: A hybrid workflow incorporating NLP significantly improves time efficiency for the Patient Praises program while increasing feelings of acknowledgment and value among staff.