Department of Radiation Oncology, University of California San Francisco, San Francisco, California, USA
Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, USA.
BMJ Health Care Inform. 2023 Feb;30(1). doi: 10.1136/bmjhci-2022-100674.
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.
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.
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.
Randomised data and firsthand experience support positive reception of clinical ML. Providers highlighted future priorities, including patient counselling and workflow optimisation.
临床人工智能和机器学习（ML）面临与实施和信任相关的障碍。很少有机会对这些问题进行前瞻性评估。放射治疗中高强度评估系统（NCT03775265）是一项随机对照研究，表明 ML 可以准确指导临床评估，减少癌症放射治疗期间的急性护理。我们描述了随后对实施的看法和障碍。
向专注于工作流程、对 ML 的认同和患者体验的多学科工作人员进行了一项匿名的 7 个问题李克特量表调查，并提供了可选的自由文本。
71 人中 59 人（83%）做出了回应。81%的人不同意/强烈不同意他们的工作流程被打乱。67%的人同意/强烈同意接受干预的患者风险较高。75%的人同意/强烈同意如果研究结果为阳性，他们将常规采用 ML 方法。自由文本反馈集中在患者教育和 ML 预测上。
随机数据和第一手经验支持临床 ML 的积极接受。提供者强调了未来的优先事项，包括患者咨询和工作流程优化。
