Effect of an Artificial Intelligence Clinical Decision Support System on Treatment Decisions for Complex Breast Cancer
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. A cross-sectional observational study was conducted involving 1,977 patients at high risk for recurrent or metastatic breas...
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| Vydané v: | JCO clinical cancer informatics Ročník 4; s. 824 |
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| Hlavní autori: | , , , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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United States
24.09.2020
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| ISSN: | 2473-4276, 2473-4276 |
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| Abstract | 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.
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.
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;
< .05) and less likely in those with stage IIA (OR, 0.29;
< .05) or IIIA cancer (OR, 0.08;
< .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%;
= .003).
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 (
= .22). Additional research on decision impact, patient-physician communication, learning, and clinical outcomes is needed to establish the overall value of the technology. |
|---|---|
| AbstractList | 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.PURPOSETo examine the impact of a clinical decision support system (CDSS) on breast cancer treatment decisions and adherence to National Comprehensive Cancer Center (NCCN) guidelines.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.PATIENTS AND METHODSA 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.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).RESULTSTreatment 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).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.CONCLUSIONUse 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. 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. 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. 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; < .05) and less likely in those with stage IIA (OR, 0.29; < .05) or IIIA cancer (OR, 0.08; < .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%; = .003). 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 ( = .22). Additional research on decision impact, patient-physician communication, learning, and clinical outcomes is needed to establish the overall value of the technology. |
| Author | Purcell Jackson, Gretchen Song, Yuhua Geng, Cuizhi Preininger, Anita M Yin, Yongmei Yan, Min Rhee, Kyu Sepúlveda, Martín-J Liu, Zhenzhen Wang, Haibo Li, Jianbin Shortliffe, Edward H Jiang, Zefei Tang, Jinhai Xu, Fengrui Roebuck, M Christopher |
| Author_xml | – sequence: 1 givenname: Fengrui surname: Xu fullname: Xu, Fengrui organization: Department of Breast Cancer, Academy of Military Medical Sciences, Beijing, People's Republic of China – sequence: 2 givenname: Martín-J orcidid: 0000-0001-6233-6923 surname: Sepúlveda fullname: Sepúlveda, Martín-J organization: IBM Research, Yorktown Heights, NY – sequence: 3 givenname: Zefei orcidid: 0000-0002-4295-0173 surname: Jiang fullname: Jiang, Zefei organization: Department of Breast Cancer, Fifth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, People's Republic of China – sequence: 4 givenname: Haibo surname: Wang fullname: Wang, Haibo organization: Department of Breast Cancer Center, Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China – sequence: 5 givenname: Jianbin surname: Li fullname: Li, Jianbin organization: Department of Breast Cancer, Fifth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, People's Republic of China – sequence: 6 givenname: Zhenzhen surname: Liu fullname: Liu, Zhenzhen organization: Department of Breast Cancer Center, Henan Cancer Hospital, Zhengzhou, People's Republic of China – sequence: 7 givenname: Yongmei surname: Yin fullname: Yin, Yongmei organization: Department of Breast Cancer, Jiangsu Province Hospital, Nanjing, People's Republic of China – sequence: 8 givenname: M Christopher orcidid: 0000-0003-4102-4925 surname: Roebuck fullname: Roebuck, M Christopher organization: RxEconomics, Hunt Valley, MD – sequence: 9 givenname: Edward H orcidid: 0000-0001-5201-6176 surname: Shortliffe fullname: Shortliffe, Edward H organization: Department of Biomedical Informatics, Columbia University, New York, NY – sequence: 10 givenname: Min surname: Yan fullname: Yan, Min organization: Department of Breast Cancer Center, Henan Cancer Hospital, Zhengzhou, People's Republic of China – sequence: 11 givenname: Yuhua surname: Song fullname: Song, Yuhua organization: Department of Breast Cancer Center, Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China – sequence: 12 givenname: Cuizhi surname: Geng fullname: Geng, Cuizhi organization: Department of Breast Surgery, Fourth Hospital of Hebei Medical University, Shijiazhuang, People's Republic of China – sequence: 13 givenname: Jinhai surname: Tang fullname: Tang, Jinhai organization: Department of Breast Cancer, Jiangsu Province Hospital, Nanjing, People's Republic of China – sequence: 14 givenname: Gretchen orcidid: 0000-0002-3242-8058 surname: Purcell Jackson fullname: Purcell Jackson, Gretchen organization: IBM Watson Health, Cambridge, MA – sequence: 15 givenname: Anita M orcidid: 0000-0001-8011-9391 surname: Preininger fullname: Preininger, Anita M organization: IBM Watson Health, Cambridge, MA – sequence: 16 givenname: Kyu surname: Rhee fullname: Rhee, Kyu organization: IBM Watson Health, Cambridge, MA |
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| SubjectTerms | Artificial Intelligence Breast Neoplasms - therapy Cross-Sectional Studies Decision Support Systems, Clinical Female Humans Medical Oncology |
| Title | Effect of an Artificial Intelligence Clinical Decision Support System on Treatment Decisions for Complex Breast Cancer |
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