Improving radiology reporting accuracy: use of GPT-4 to reduce errors in reports.

Purpose: Radiology reports are essential for communicating imaging findings to guide diagnosis and treatment. Although most radiology reports are accurate, errors can occur in the final reports due to high workloads, use of dictation software, and human error. Advanced artificial intelligence models, such as GPT-4, show potential as tools to improve report accuracy.

Effects of patient survey feedback on improving patient experience with outpatient magnetic resonance imaging: Patient Feedback Helps Improve MRI Experience.

Objective: We examined the feasibility of collecting timely patient feedback after outpatient magnetic resonance imaging (MRI) and the effect of radiology staff responses or actions on patient experience scores.

Methods: This study included 6043 patients who completed a feedback survey via email after undergoing outpatient MRI at a tertiary care medical center between April 2021 and September 2022. The survey consisted of the question "How was your radiology visit?" with a 5-point emoji-Likert scale, an open-text feedback box, and an option to request a response.

Patient Access of Their Radiology Reports Before and After Implementation of 21st Century Cures Act Information-Blocking Provisions at a Large Multicampus Health System.

To implement provisions of the 21st Century Cures Act that address information blocking, federal regulations mandated that health systems provide patients with immediate access to elements of their electronic health information, including imaging results. The purpose of this study was to compare patient access of radiology reports before and after implementation of the information-blocking provisions of the 21st Century Cures Act. This retrospective study included patients who underwent outpatient imaging examinations from January 1, 2021, through December 31, 2022, at three campuses within a large health system.

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

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.