Sharing Patient Praises With Radiology Staff: Workflow Automation and Impact on Staff

Zoe Deahl; Imon Banerjee; Meghana Nadella; Anika Patel; Christopher Dodoo; Iridian Jaramillo; Jacob Varner; Evie Nguyen; Nelly Tan

doi:10.1016/j.jacr.2023.12.024

Sharing Patient Praises With Radiology Staff: Workflow Automation and Impact on Staff

J Am Coll Radiol. 2023 Dec 28:S1546-1440(23)01043-8. doi: 10.1016/j.jacr.2023.12.024. Online ahead of print.

Authors

Zoe Deahl¹, Imon Banerjee², Meghana Nadella³, Anika Patel¹, Christopher Dodoo⁴, Iridian Jaramillo⁵, Jacob Varner⁶, Evie Nguyen¹, Nelly Tan⁷

Affiliations

¹ Research Intern, Department of Radiology, Mayo Clinic, Phoenix, Arizona.
² Researcher, Associate Professor, Department of Radiology, Mayo Clinic, Phoenix, Arizona; and School of Computing and Augmented Intelligence, Arizona State University, Tempe, Arizona.
³ Research Assistant and Master's Student, Department of Radiology, Mayo Clinic, Phoenix, Arizona; and School of Computing and Augmented Intelligence, Arizona State University, Tempe, Arizona.
⁴ Statistician, Quantitative Health Sciences, Mayo Clinic, Phoenix, Arizona.
⁵ Patient Navigator, Department of Radiology, Mayo Clinic, Phoenix, Arizona.
⁶ Department of Radiology, Mayo Clinic, Phoenix, Arizona.
⁷ Associate Professor, Department of Radiology, Mayo Clinic, Phoenix, Arizona. Electronic address: tan.nelly@mayo.edu.

PMID: 38159832
DOI: 10.1016/j.jacr.2023.12.024

Abstract

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.

Keywords: Administrative efficiency; semi-automated workflow; staff appreciation; staff recognition; workplace morale.