Effect of an Artificial Intelligence Clinical Decision Support System on Treatment Decisions for Complex Breast Cancer.

Autor:	Xu F; Department of Breast Cancer, Academy of Military Medical Sciences, Beijing, People's Republic of China., Sepúlveda MJ; IBM Research, Yorktown Heights, NY., Jiang Z; Department of Breast Cancer, Fifth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, People's Republic of China., Wang H; Department of Breast Cancer Center, Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China., Li J; Department of Breast Cancer, Fifth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, People's Republic of China., Liu Z; Department of Breast Cancer Center, Henan Cancer Hospital, Zhengzhou, People's Republic of China., Yin Y; Department of Breast Cancer, Jiangsu Province Hospital, Nanjing, People's Republic of China., Roebuck MC; RxEconomics, Hunt Valley, MD., Shortliffe EH; Department of Biomedical Informatics, Columbia University, New York, NY., Yan M; Department of Breast Cancer Center, Henan Cancer Hospital, Zhengzhou, People's Republic of China., Song Y; Department of Breast Cancer Center, Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China., Geng C; Department of Breast Surgery, Fourth Hospital of Hebei Medical University, Shijiazhuang, People's Republic of China., Tang J; Department of Breast Cancer, Jiangsu Province Hospital, Nanjing, People's Republic of China., Purcell Jackson G; IBM Watson Health, Cambridge, MA., Preininger AM; IBM Watson Health, Cambridge, MA., Rhee K; IBM Watson Health, Cambridge, MA.
Jazyk:	angličtina
Zdroj:	JCO clinical cancer informatics [JCO Clin Cancer Inform] 2020 Sep; Vol. 4, pp. 824-838.
DOI:	10.1200/CCI.20.00018
Abstrakt:	Purpose: To examine the impact of a clinical decision support system (CDSS) on breast cancer treatment decisions and adherence to National Comprehensive Cancer Center (NCCN) guidelines. Patients and Methods: A cross-sectional observational study was conducted involving 1,977 patients at high risk for recurrent or metastatic breast cancer from the Chinese Society of Clinical Oncology. Ten oncologists provided blinded treatment recommendations for an average of 198 patients before and after viewing therapeutic options offered by the CDSS. Univariable and bivariable analyses of treatment changes were performed, and multivariable logistic regressions were estimated to examine the effects of physician experience (years), patient age, and receptor subtype/TNM stage. Results: Treatment decisions changed in 105 (5%) of 1,977 patients and were concentrated in those with hormone receptor (HR)-positive disease or stage IV disease in the first-line therapy setting (73% and 58%, respectively). Logistic regressions showed that decision changes were more likely in those with HR-positive cancer (odds ratio [OR], 1.58; P < .05) and less likely in those with stage IIA (OR, 0.29; P < .05) or IIIA cancer (OR, 0.08; P < .01). Reasons cited for changes included consideration of the CDSS therapeutic options (63% of patients), patient factors highlighted by the tool (23%), and the decision logic of the tool (13%). Patient age and oncologist experience were not associated with decision changes. Adherence to NCCN treatment guidelines increased slightly after using the CDSS (0.5%; P = .003). Conclusion: Use of an artificial intelligence-based CDSS had a significant impact on treatment decisions and NCCN guideline adherence in HR-positive breast cancers. Although cases of stage IV disease in the first-line therapy setting were also more likely to be changed, the effect was not statistically significant ( P = .22). Additional research on decision impact, patient-physician communication, learning, and clinical outcomes is needed to establish the overall value of the technology.
Databáze:	MEDLINE
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