| Autor: |
Donna M. McNamara, Stuart L. Goldberg, Lisa Latts, Deena M. Atieh Graham, Stanley E. Waintraub, Andrew D. Norden, Cody Landstrom, Andrew L. Pecora, John Hervey, Eric V. Schultz, Ching-Kun Wang, Nicholas Jungbluth, Phillip M. Francis, Jane L. Snowdon |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
Cancer Medicine, Vol 8, Iss 15, Pp 6578-6584 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
2045-7634 |
| DOI: |
10.1002/cam4.2548 |
| Popis: |
Abstract Introduction Cognitive computing point‐of‐care decision support tools which ingest patient attributes from electronic health records and display treatment options based on expert training and medical literature, supplemented by real world evidence (RWE), might prove useful to expert and novice oncologists. The concordance of augmented intelligence systems with best medical practices and potential influences on physician behavior remain unknown. Methods Electronic health records from 88 breast cancer patients evaluated at a USA tertiary care center were presented to subspecialist experts and oncologists focusing on other disease states with and without reviewing the IBM Watson for Oncology with Cota RWE platform. Results The cognitive computing “recommended” option was concordant with selection by breast cancer experts in 78.5% and “for consideration” option was selected in 9.4%, yielding agreements in 87.9%. Fifty‐nine percent of non‐concordant responses were generated from 8% of cases. In the Cota observational database 69.3% of matched controls were treated with “recommended,” 11.4% “for consideration”, and 19.3% “not recommended.” Without guidance from Watson for Oncology (WfO)/Cota RWE, novice oncologists chose 75.5% recommended/for consideration treatments which improved to 95.3% with WfO/Cota RWE. The novices were more likely than experts to choose a non‐recommended option (P |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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