The value of PDCA management model in improving the quality of breast ultrasound diagnosis
10.3760/cma.j.cn431274-20240808-01223
- VernacularTitle:PDCA管理模式对乳腺超声诊断质量改进的价值
- Author:
Qiong WEN
1
;
Yu WU
;
Feixiang XIANG
;
Li ZHANG
;
Mingxing XIE
Author Information
1. 华中科技大学同济医学院附属协和医院超声医学科，分子影像湖北省重点实验室，武汉 430022
- Keywords:
Ultrasonography;
Breast neoplasms;
PDCA management;
Quality control
- From:
Journal of Chinese Physician
2024;26(10):1447-1450
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the value of Plan-Do-Check-Act (PDCA) management model in improving the quality of breast ultrasound diagnosis.Methods:From January 1 to December 31, 2021, patients undergoing breast nodule surgery in the Union Hospital, Tongji Medical College, Huazhong University of Science and Technology were selected for breast ultrasound Breast Imaging Reporting and Data System (BI-RADS) classification diagnosis and follow-up. Using pathological results as the gold standard, the malignant rate of different BI-RADS grade and the coincidence rate of ultrasound diagnosis of breast cancer before the first treatment were analyzed. PDCA cycle management mode was introduced to analyze, learn and sort out the quality of breast ultrasound diagnosis. The same method was used to follow up the breast ultrasound diagnosis in the ultrasound department of our hospital from January 1 to December 31, 2022. The results of two follow-up visits were compared.Results:Compared with 2021, the malignancy rates of category 3 and 4a BI-RADS diagnosed by ultrasonic diagnosis of breast nodules in hospitals in 2022 were closer to the malignancy rates recommended by the classification guidelines of BI-RADS (all P<0.05). There was no significant difference in the malignant rate of BI RADS 4b, 4c and 5 types of nodules (all P>0.05). In 2022, compared with 2021, the ultrasound diagnosis coincidence rate before the first treatment of breast disease increased from 90.4%(1 592/1 761) to 95.9%(1 806/1 884). Conclusions:PDCA management mode is helpful to improve the classification and diagnosis quality of breast ultrasound BI-RADS, and better assist clinical diagnosis and treatment.
