Healthcare provider evaluation of machine learning-directed care: reactions to deployment on a randomised controlled study.

Journal: BMJ health & care informatics
Published Date: Feb 1, 2023

Abstract

OBJECTIVES: Clinical artificial intelligence and machine learning (ML) face barriers related to implementation and trust. There have been few prospective opportunities to evaluate these concerns. System for High Intensity EvaLuation During Radiotherapy (NCT03775265) was a randomised controlled study demonstrating that ML accurately directed clinical evaluations to reduce acute care during cancer radiotherapy. We characterised subsequent perceptions and barriers to implementation.

Authors

  • Julian C Hong
    All Authors: Duke University, Durham, NC.
  • Pranalee Patel
    Department of Radiation Oncology, Duke University, Durham, North Carolina, USA.
  • Neville C W Eclov
    Department of Radiation Oncology, Duke University, Durham, NC.
  • Sarah J Stephens
    Department of Radiation Oncology, Duke University, Durham, NC.
  • Yvonne M Mowery
    Department of Radiation Oncology, Duke University, Durham, NC.
  • Jessica D Tenenbaum
    All Authors: Duke University, Durham, NC.
  • Manisha Palta
    Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States.

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