Differential impact of cognitive computing augmented by real world evidence on novice and expert oncologists
Autor: | Eric V Schultz, John Hervey, Stanley E. Waintraub, Phillip M Francis, Lisa M. Latts, Cody Landstrom, Nicholas Jungbluth, Deena Graham, Ching-Kun Wang, Andrew L. Pecora, Stuart L. Goldberg, Jane L. Snowdon, Andrew D. Norden, Donna M. McNamara |
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Jazyk: | angličtina |
Rok vydání: | 2019 |
Předmět: |
0301 basic medicine
Cancer Research medicine.medical_specialty Decision support system Point-of-Care Systems Concordance Clinical Decision-Making Cognitive computing Breast Neoplasms Disease Real world evidence point‐of‐care systems real world evidence lcsh:RC254-282 Tertiary Care Centers 03 medical and health sciences 0302 clinical medicine Breast cancer medicine Humans Radiology Nuclear Medicine and imaging Aged Original Research Aged 80 and over Oncologists Watson business.industry Clinical Cancer Research Decision Support Systems Clinical medicine.disease artificial intelligence lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens United States 030104 developmental biology electronic health records Oncology 030220 oncology & carcinogenesis Family medicine Female Clinical Competence business Medical literature |
Zdroj: | Cancer Medicine, Vol 8, Iss 15, Pp 6578-6584 (2019) Cancer Medicine |
ISSN: | 2045-7634 |
Popis: | 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 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, might prove useful to expert and novice oncologists. We compared recommendations from one platform and noted concordance with disease expert opinions. The platform was also able to influence novice oncologists in test cases. Since nearly a fifth of similar real world cases received non‐recommended therapies. Our study demonstrates a need for decision support tools. |
Databáze: | OpenAIRE |
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