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Assessing the decision quality of artificial intelligence and oncologists of different experience in different regions in breast cancer treatment.
Han, Chunguang; Pan, Yubo; Liu, Chang; Yang, Xiaowei; Li, Jianbin; Wang, Kun; Sun, Zhengkui; Liu, Hui; Jin, Gongsheng; Fang, Fang; Pan, Xiaofeng; Tang, Tong; Chen, Xiao; Pang, Shiyong; Ma, Li; Wang, Xiaodong; Ren, Yun; Liu, Mengyou; Liu, Feng; Jiang, Mengxue; Zhao, Jiqi; Lu, Chenyang; Lu, Zhengdong; Gao, Dongjing; Jiang, Zefei; Pei, Jing.
Affiliation
  • Han C; Department of Pediatric Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
  • Pan Y; Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
  • Liu C; Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
  • Yang X; Department of Breast Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
  • Li J; Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
  • Wang K; Department of Breast Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
  • Sun Z; Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
  • Liu H; Department of Breast Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
  • Jin G; Department of Breast Cancer, Fifth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, China.
  • Fang F; Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital and Guangdong Academy of Medical Sciences, Guangzhou, China.
  • Pan X; Department of Breast Oncology Surgery, Jiangxi Cancer Hospital (The Second People's Hospital of Jiangxi Province), Nanchang, China.
  • Tang T; Department of Breast Surgery, Henan Provincial People's Hospital, Zhengzhou, China.
  • Chen X; Department of Oncological Surgery, the First Affiliated Hospital of Bengbu Medical College, Bengbu, China.
  • Pang S; Department of Thyroid and Breast surgery, the First Affiliated Hospital of Wannan Medical College (Yijishan Hospital), Wuhhu, China.
  • Ma L; Department of Thyroid and Breast surgery, the First Affiliated Hospital of Wannan Medical College (Yijishan Hospital), Wuhhu, China.
  • Wang X; Department of General Surgury, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
  • Ren Y; Department of General Surgury, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
  • Liu M; Department of General Surgery, Lu'an People's Hospital of Anhui Province (Lu'an Hospital of Anhui Medical University), Lu'an, China.
  • Liu F; Department of Thyroid and Breast Surgery, Anqing Municipal Hospital (Anqing Hospital Affiliated to Anhui Medical University), Anqing, China.
  • Jiang M; Department of Thyroid and Breast Surgery, The people's hospital of Bozhou (Bozhou Hospital Affiliated to Anhui Medical University), Bozhou, China.
  • Zhao J; Department of Thyroid and Breast surgery, Department of Oncological Surgery, Taihe county people's hospital (The Taihe hospital of Wannan Medical College), Fuyang, China.
  • Lu C; Department of Thyroid and Breast surgery, Lixin County People's Hospital, Bozhou, China.
  • Lu Z; Department of Breast Surgery, Fuyang Cancer Hospital, Fuyang, China.
  • Gao D; Department of Breast Surgery, Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
  • Jiang Z; Department of Breast Surgery, Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
  • Pei J; Department of Breast Surgery, Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Front Oncol ; 13: 1152013, 2023.
Article de En | MEDLINE | ID: mdl-37361565
ABSTRACT

Background:

AI-based clinical decision support system (CDSS) has important prospects in overcoming the current informational challenges that cancer diseases faced, promoting the homogeneous development of standardized treatment among different geographical regions, and reforming the medical model. However, there are still a lack of relevant indicators to comprehensively assess its decision-making quality and clinical impact, which greatly limits the development of its clinical research and clinical application. This study aims to develop and application an assessment system that can comprehensively assess the decision-making quality and clinical impacts of physicians and CDSS.

Methods:

Enrolled adjuvant treatment decision stage early breast cancer cases were randomly assigned to different decision-making physician panels (each panel consisted of three different seniority physicians in different grades hospitals), each physician made an independent "Initial Decision" and then reviewed the CDSS report online and made a "Final Decision". In addition, the CDSS and guideline expert groups independently review all cases and generate "CDSS Recommendations" and "Guideline Recommendations" respectively. Based on the design framework, a multi-level multi-indicator system including "Decision Concordance", "Calibrated Concordance", " Decision Concordance with High-level Physician", "Consensus Rate", "Decision Stability", "Guideline Conformity", and "Calibrated Conformity" were constructed.

Results:

531 cases containing 2124 decision points were enrolled; 27 different seniority physicians from 10 different grades hospitals have generated 6372 decision opinions before and after referring to the "CDSS Recommendations" report respectively. Overall, the calibrated decision concordance was significantly higher for CDSS and provincial-senior physicians (80.9%) than other physicians. At the same time, CDSS has a higher " decision concordance with high-level physician" (76.3%-91.5%) than all physicians. The CDSS had significantly higher guideline conformity than all decision-making physicians and less internal variation, with an overall guideline conformity variance of 17.5% (97.5% vs. 80.0%), a standard deviation variance of 6.6% (1.3% vs. 7.9%), and a mean difference variance of 7.8% (1.5% vs. 9.3%). In addition, provincial-middle seniority physicians had the highest decision stability (54.5%). The overall consensus rate among physicians was 64.2%.

Conclusions:

There are significant internal variation in the standardization treatment level of different seniority physicians in different geographical regions in the adjuvant treatment of early breast cancer. CDSS has a higher standardization treatment level than all physicians and has the potential to provide immediate decision support to physicians and have a positive impact on standardizing physicians' treatment behaviors.
Mots clés

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Type d'étude: Guideline / Prognostic_studies Langue: En Journal: Front Oncol Année: 2023 Type de document: Article

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Type d'étude: Guideline / Prognostic_studies Langue: En Journal: Front Oncol Année: 2023 Type de document: Article