TY - JOUR
T1 - Sharing Patient Praises With Radiology Staff
T2 - Workflow Automation and Impact on Staff
AU - Deahl, Zoe
AU - Banerjee, Imon
AU - Nadella, Meghana
AU - Patel, Anika
AU - Dodoo, Christopher
AU - Jaramillo, Iridian
AU - Varner, Jacob
AU - Nguyen, Evie
AU - Tan, Nelly
N1 - Publisher Copyright:
© 2023 American College of Radiology
PY - 2024
Y1 - 2024
N2 - 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 times to generate Patient Praises e-mails were compared between manual and hybrid workflows. Impact of Patient Praises on radiology staff was measured using a four-question Likert scale survey and an open text feedback box. Kruskal-Wallis test and post hoc Dunn's test were 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% 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 min 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.
AB - 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 times to generate Patient Praises e-mails were compared between manual and hybrid workflows. Impact of Patient Praises on radiology staff was measured using a four-question Likert scale survey and an open text feedback box. Kruskal-Wallis test and post hoc Dunn's test were 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% 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 min 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.
KW - Administrative efficiency
KW - semi-automated workflow
KW - staff appreciation
KW - staff recognition
KW - workplace morale
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U2 - 10.1016/j.jacr.2023.12.024
DO - 10.1016/j.jacr.2023.12.024
M3 - Article
C2 - 38159832
AN - SCOPUS:85184774606
SN - 1546-1440
JO - Journal of the American College of Radiology
JF - Journal of the American College of Radiology
ER -