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
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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