Clinician perspectives on machine learning prognostic algorithms in the routine care of patients with cancer: a qualitative study.
Clinician perspectives on machine learning prognostic algorithms in the routine care of patients with cancer: a qualitative study.
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DOI：
10.1007/s00520-021-06774-w
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发表时间：
2022-05
期刊：
影响因子：
--
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肿瘤学家可能会高估癌症患者的预后，导致延迟或错过关于患者目标的对话以及随后的低质量临终关怀。机器学习算法可以准确地预测癌症的死亡风险，但目前尚不清楚肿瘤学临床医生如何在实践中使用这些算法。这项定性研究的目的是评估肿瘤临床医生对机器学习预后算法的效用和障碍的看法，以促进提前制定护理计划。参与者包括肿瘤内科医生和高级实践提供者（APP），他们在大型学术医疗保健系统中从事三级和社区实践。使用NVivo软件对转录本进行编码和归纳分析。该研究纳入了美国6个实践中心（1个三级，5个社区）的29名肿瘤学临床医生（19名医生，10名APP）。14名参与者之前曾接触过一种基于自动机器学习的预后算法，作为实用随机试验的一部分。临床医生认为，算法在验证他们自己对预后的直觉和促进关于患者目标和偏好的对话方面是有用的。然而，这种热情被算法准确性的担忧，对算法预测的过度依赖以及算法预测的道德影响所冲淡。假阳性预测与假阴性预测的容忍度存在显著差异。虽然肿瘤学家认为先进的预后算法在癌症患者的常规护理中有应用，但他们担心算法的准确性，确认和自动化偏差以及预后披露的伦理问题。
Oncologists may overestimate prognosis for patients with cancer, leading to delayed or missed conversations about patients’ goals and subsequent low-quality end-of-life care. Machine learning algorithms may accurately predict mortality risk in cancer, but it is unclear how oncology clinicians would use such algorithms in practice. The purpose of this qualitative study was to assess oncology clinicians’ perceptions on the utility and barriers of machine learning prognostic algorithms to prompt advance care planning. Participants included medical oncology physicians and advanced practice providers (APPs) practicing in tertiary and community practices within a large academic healthcare system. Transcripts were coded and analyzed inductively using NVivo software. The study included 29 oncology clinicians (19 physicians, 10 APPs) across 6 practice sites (1 tertiary, 5 community) in the USA. Fourteen participants had previously had exposure to an automated machine learning-based prognostic algorithm as part of a pragmatic randomized trial. Clinicians believed that there was utility for algorithms in validating their own intuition about prognosis and prompting conversations about patient goals and preferences. However, this enthusiasm was tempered by concerns about algorithm accuracy, over-reliance on algorithm predictions, and the ethical implications around disclosure of an algorithm prediction. There was significant variation in tolerance for false positive vs. false negative predictions. While oncologists believe there are applications for advanced prognostic algorithms in routine care of patients with cancer, they are concerned about algorithm accuracy, confirmation and automation biases, and ethical issues of prognostic disclosure.
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影响因子：
5.7
作者：
Sahni, Nishant;Simon, Gyorgy;Arora, Rashi
通讯作者：
Arora, Rashi
影响因子：
15.2
作者：
Rajkomar, Alvin;Oren, Eyal;Dean, Jeffrey
通讯作者：
Dean, Jeffrey
影响因子：
28.4
作者：
Manz CR;Parikh RB;Small DS;Evans CN;Chivers C;Regli SH;Hanson CW;Bekelman JE;Rareshide CAL;O'Connor N;Schuchter LM;Shulman LN;Patel MS
通讯作者：
Patel MS
影响因子：
13.8
作者：
Desai, Ravi J.;Good, Meghan M.;Good, Chester B.
通讯作者：
Good, Chester B.
DOI：
10.1200/edbk_238891
发表时间：
2019-01-01
期刊：
American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting
影响因子：
--
作者：
Parikh, Ravi B;Gdowski, Andrew;Bekelman, Justin E
通讯作者：
Bekelman, Justin E