Outcomes of Implementation of a Same-Day Breast Biopsy Program in a Safety-Net Public Hospital.
Journal of the American College of Radiology JACR(2025)
Department of Radiology | The Warren Alpert Medical School | Department of Radiology. Denver Health. 777 Bannock St. Denver CO
Abstract
PURPOSE:Safety-net hospitals play an important role in providing care to patients from historically underserved groups, representing a natural target for initiatives to reduce health disparities. This study evaluates the implementation of a same-day biopsy (SDB) program on time to breast biopsy in the safety-net setting. MATERIALS AND METHODS:This study used an interrupted time series design with pre- and postanalysis. After institutional review board approval, all diagnostic and ultrasound (US) examinations leading to US-guided biopsy during the phase-in period (May 2021 to February 2022), official implementation period (March 2022 to April 2023), and follow-up period (May 2023 to December 2023) were identified. Demographic characteristics of the groups using Wilcoxon rank-sum tests for continuous variables in χ2 tests of independence for categorical variables were evaluated. Spline modeling with generalized linear models was used to assess differences in days from biopsy recommendation to biopsy and rates of having a SDB in pre- and postimplementation groups. RESULTS:A total of 677 patients received recommended US-guided breast biopsies during the study period, with 233 patients in the phase-in group, 306 patients in the official implementation group, and 138 patients in the follow-up group. For all patients, the SDB program reduced the median time from biopsy recommendation to biopsy from 13.0 to 3.5 (interquartile range: 6.0-12.0) days (P < .0001). There was no statistically significant decrease in time from biopsy recommendation to initial surgical (19.0-15.0 days; P = .29) or oncologic (26.0-21.0 days; P = .19) appointment. CONCLUSION:Implementation of a SDB program is effective in reducing overall diagnostic delays after a breast biopsy recommendation for patients seen in safety-net institutions. Additional administrative and ancillary support may be required, however, to aid surgical and oncologic services to further improve overall time to treatment in these settings.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest