Integrity of clinical information in radiology reports documenting pulmonary nodules

Quantifying and Reducing Errors in Vascular Imaging Examination Orders Through a Multistage Quality Improvement Intervention

PURPOSE

The aims of this study were to quantify order error rates for vascular imaging examinations and to assess the effects of a multistage quality improvement intervention on those rates.

METHODS

In this prospective, institutional review board-exempt project at a large academic quaternary care hospital, the authors aimed to quantify and reduce the order error rate by 50%. The authors analyzed 844 orders for all vascular imaging examinations placed before the intervention (July 19 to August 1, 2021, and September 13 to September 26, 2021), after an intervention in the cardiac surgery department consisting of a new customized order option in the electronic health record for routine preoperative patients (postintervention 1, February 28 to March 27, 2022); and after an educational and feedback campaign (postintervention 2, May 23 to June 5, 2022). Incorrect orders were identified by a radiology trainee during protocoling if the reasons for ordered examination and imaging examination were discordant and subsequently confirmed with the ordering provider. The primary outcome, order error rate, was compared across the project periods using the χ2 test and by ordering department using the χ2 and Fisher exact tests.

RESULTS

The preintervention order error rate of 16% (50 of 306) decreased by 83% to 3% (10 of 353) at postintervention 1 (P < .001) and was durable at 3% (6 of 185) by project end. Chest CT with or without contrast constituted the majority of incorrect orders (44%, 22 of 50); "Pre-Op" was the most common examination reason (32% [16 of 50]). Cardiac surgery orderers were responsible for the most incorrect orders (32% [16 of 50]). All four most common ordering departments, including cardiac surgery, reduced their order error rates after the intervention (P < .001).

CONCLUSIONS

Incorrect orders for imaging examinations can be reduced through targeted quality improvement interventions combining tailored electronic health record order options with education and feedback on practice habits.

Artificial Intelligence (AI) for Lung Nodules, From the AJR Special Series on AI Applications

Interest in artificial intelligence (AI) applications for lung nodules continues to grow among radiologists, particularly with the expanding eligibility criteria and clinical utilization of lung cancer screening CT. AI has been heavily investigated for detecting and characterizing lung nodules and for guiding prognostic assessment. AI tools have also been used for image postprocessing (e.g., rib suppression on radiography or vessel suppression on CT) and for noninterpretive aspects of reporting and workflow, including management of nodule follow-up. Despite growing interest in and rapid development of AI tools and FDA approval of AI tools for pulmonary nodule evaluation, integration into clinical practice has been limited. Challenges to clinical adoption have included concerns about generalizability, regulatory issues, technical hurdles in implementation, and human skepticism. Further validation of AI tools for clinical use and demonstration of benefit in terms of patient-oriented outcomes also are needed. This article provides an overview of potential applications of AI tools in the imaging evaluation of lung nodules and discusses the challenges faced by practices interested in clinical implementation of such tools.