Rationale And Objectives: The aim of this study was to describe the impact of a tailored Web-based educational program designed to reduce excessive screening mammography recall.

Materials And Methods: Radiologists enrolled in one of four mammography registries in the United States were invited to take part and were randomly assigned to receive the intervention or to serve as controls. The controls were offered the intervention at the end of the study, and data collection included an assessment of their clinical practice as well. The intervention provided each radiologist with individual audit data for his or her sensitivity, specificity, recall rate, positive predictive value, and cancer detection rate compared to national benchmarks and peer comparisons for the same measures; profiled breast cancer risk in each radiologist's respective patient populations to illustrate how low breast cancer risk is in population-based settings; and evaluated the possible impact of medical malpractice concerns on recall rates. Participants' recall rates from actual practice were evaluated for three time periods: the 9 months before the intervention was delivered to the intervention group (baseline period), the 9 months between the intervention and control groups (T1), and the 9 months after completion of the intervention by the controls (T2). Logistic regression models examining the probability that a mammogram was recalled included indication of intervention versus control and time period (baseline, T1, and T2). Interactions between the groups and time period were also included to determine if the association between time period and the probability of a positive result differed across groups.

Results: Thirty-one radiologists who completed the continuing medical education intervention were included in the adjusted model comparing radiologists in the intervention group (n = 22) to radiologists who completed the intervention in the control group (n = 9). At T1, the intervention group had 12% higher odds of positive mammographic results compared to the controls, after controlling for baseline (odds ratio, 1.12; 95% confidence interval, 1.00-1.27; P = .0569). At T2, a similar association was found, but it was not statistically significant (odds ratio, 1.10; 95% confidence interval, 0.96 to 1.25). No associations were found among radiologists in the control group when comparing those who completed the continuing medical education intervention (n = 9) to those who did not (n = 10). In addition, no associations were found between time period and recall rate among radiologists who set realistic goals.

Conclusions: This study resulted in a null effect, which may indicate that a single 1-hour intervention is not adequate to change excessive recall among radiologists who undertook the intervention being tested.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3638784PMC
http://dx.doi.org/10.1016/j.acra.2012.05.003DOI Listing

Publication Analysis

Top Keywords

intervention
16
time period
16
intervention group
12
designed reduce
8
screening mammography
8
recall rate
8
breast cancer
8
cancer risk
8
recall rates
8
months intervention
8

Similar Publications

Outcomes With Radiation Therapy as Primary Treatment for Unresectable Cutaneous Head and Neck Squamous Cell Carcinoma.

Clin Oncol (R Coll Radiol)

December 2024

Radiation Oncology Network, Westmead Hospital, Westmead, NSW, Australia; Sydney Medical School, The University of Sydney, Camperdown, NSW 2006, Australia. Electronic address:

Aims: Unresectable cutaneous squamous cell cancer of the head and neck (HNcSCC) poses treatment challenges in elderly and comorbid patients. Radiation therapy (RT) is often employed for locoregional control. This study aimed to determine progression-free survival (PFS) and overall survival (OS) outcomes achieved with upfront RT in unresectable HNcSCC.

View Article and Find Full Text PDF

Who is coming in? Evaluation of physician performance within multi-physician emergency departments.

Am J Emerg Med

January 2025

Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT, USA; Center for Outcomes Research and Evaluation, Yale University, New Haven, CT, USA.

Background: This study aimed to examine how physician performance metrics are affected by the speed of other attendings (co-attendings) concurrently staffing the ED.

Methods: A retrospective study was conducted using patient data from two EDs between January-2018 and February-2020. Machine learning was used to predict patient length of stay (LOS) conditional on being assigned a physician of average speed, using patient- and departmental-level variables.

View Article and Find Full Text PDF

National early warning score 2 plus non-invasive capnography and perfusion index to estimate poor outcomes in emergency departments.

Am J Emerg Med

January 2025

Faculty of Medicine, Universidad de Valladolid, Valladolid, Spain; Emergency Department, Hospital Clínico Universitario, Gerencia Regional de Salud de Castilla y León, Valladolid, Spain.

Background: The study of the inclusion of new variables in already existing early warning scores is a growing field. The aim of this work was to determine how capnometry measurements, in the form of end-tidal CO2 (ETCO2) and the perfusion index (PI), could improve the National Early Warning Score (NEWS2).

Methods: A secondary, prospective, multicenter, cohort study was undertaken in adult patients with unselected acute diseases who needed continuous monitoring in the emergency department (ED), involving two tertiary hospitals in Spain from October 1, 2022, to June 30, 2023.

View Article and Find Full Text PDF

This paper systematically evaluates saliency methods as explainability tools for convolutional neural networks trained to diagnose glaucoma using simplified eye fundus images that contain only disc and cup outlines. These simplified images, a methodological novelty, were used to relate features highlighted in the saliency maps to the geometrical clues that experts consider in glaucoma diagnosis. Despite their simplicity, these images retained sufficient information for accurate classification, with balanced accuracies ranging from 0.

View Article and Find Full Text PDF

Mild cognitive impairment (MCI) is a significant predictor of the early progression of Alzheimer's disease, and it can be used as an important indicator of disease progression. However, many existing methods focus mainly on the image itself when processing brain imaging data, ignoring other non-imaging data (e.g.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!