McNamara DM, Goldberg SL, Latts L, Atieh Graham DM, Waintraub SE, Norden AD, Landstrom C, Pecora AL, Hervey J, Schultz EV, Wang CK, Jungbluth N, Francis PM, Snowdon JL. Differential impact of cognitive computing augmented by real world evidence on novice and expert oncologists.
Cancer Med 2019;
8:6578-6584. [PMID:
31509353 PMCID:
PMC6825991 DOI:
10.1002/cam4.2548]
[Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Revised: 08/01/2019] [Accepted: 08/21/2019] [Indexed: 01/10/2023] Open
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 < .01) without WfO/Cota RWE and changed decisions in 39% cases.
CONCLUSIONS
Watson for Oncology with Cota RWE options were largely concordant with disease expert judged best oncology practices, and was able to improve treatment decisions among breast cancer novices. The observation that nearly a fifth of patients with similar disease characteristics received non-recommended options in a real world database highlights a need for decision support.
Collapse