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

Autor:	Hong JC; Department of Radiation Oncology, University of California San Francisco, San Francisco, California, USA julian.hong@ucsf.edu.; Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, USA.; Joint Program in Computational Precision Health, UCSF-UC Berkeley, San Francisco, California, USA., Patel P; Department of Radiation Oncology, Duke University, Durham, North Carolina, USA., Eclov NCW; Department of Radiation Oncology, Duke University, Durham, North Carolina, USA., Stephens SJ; Department of Radiation Oncology, Duke University, Durham, North Carolina, USA., Mowery YM; Department of Radiation Oncology, Duke University, Durham, North Carolina, USA.; Department of Head and Neck Surgery & Communication Sciences, Duke University, Durham, North Carolina, USA., Tenenbaum JD; Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA., Palta M; Department of Radiation Oncology, Duke University, Durham, North Carolina, USA.
Jazyk:	angličtina
Zdroj:	BMJ health & care informatics [BMJ Health Care Inform] 2023 Feb; Vol. 30 (1).
DOI:	10.1136/bmjhci-2022-100674
Abstrakt:	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. Methods: An anonymous 7-question Likert-type scale survey with optional free text was administered to multidisciplinary staff focused on workflow, agreement with ML and patient experience. Results: 59/71 (83%) responded. 81% disagreed/strongly disagreed their workflow was disrupted. 67% agreed/strongly agreed patients undergoing intervention were high risk. 75% agreed/strongly agreed they would implement the ML approach routinely if the study was positive. Free-text feedback focused on patient education and ML predictions. Conclusions: Randomised data and firsthand experience support positive reception of clinical ML. Providers highlighted future priorities, including patient counselling and workflow optimisation. Competing Interests: Competing interests: JDT, MP and JH are coinventors on a pending patent, 'Systems and methods for predicting acute care visits during outpatient cancer therapy,' related to the current work. (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)
Databáze:	MEDLINE
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