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临床医生对肺癌临床决策支持系统的看法:对共同决策的影响。

Clinician perspectives on clinical decision support systems in lung cancer: Implications for shared decision-making.

机构信息

Department of Radiation Oncology (MAASTRO), GROW School for Oncology, Maastricht University Medical Center+, Maastricht, The Netherlands.

The D-Lab, GROW School for Oncology, Maastricht University Medical Center+, Maastricht University, Maastricht, The Netherlands.

出版信息

Health Expect. 2022 Aug;25(4):1342-1351. doi: 10.1111/hex.13457. Epub 2022 May 10.

Abstract

BACKGROUND

Lung cancer treatment decisions are typically made among clinical experts in a multidisciplinary tumour board (MTB) based on clinical data and guidelines. The rise of artificial intelligence and cultural shifts towards patient autonomy are changing the nature of clinical decision-making towards personalized treatments. This can be supported by clinical decision support systems (CDSSs) that generate personalized treatment information as a basis for shared decision-making (SDM). Little is known about lung cancer patients' treatment decisions and the potential for SDM supported by CDSSs. The aim of this study is to understand to what extent SDM is done in current practice and what clinicians need to improve it.

OBJECTIVE

To explore (1) the extent to which patient preferences are taken into consideration in non-small-cell lung cancer (NSCLC) treatment decisions; (2) clinician perspectives on using CDSSs to support SDM.

DESIGN

Mixed methods study consisting of a retrospective cohort study on patient deviation from MTB advice and reasons for deviation, qualitative interviews with lung cancer specialists and observations of MTB discussions and patient consultations.

SETTING AND PARTICIPANTS

NSCLC patients (N = 257) treated at a single radiotherapy clinic and nine lung cancer specialists from six Dutch clinics.

RESULTS

We found a 10.9% (n = 28) deviation rate from MTB advice; 50% (n = 14) were due to patient preference, of which 85.7% (n = 12) chose a less intensive treatment than MTB advice. Current MTB recommendations are based on clinician experience, guidelines and patients' performance status. Most specialists (n = 7) were receptive towards CDSSs but cited barriers, such as lack of trust, lack of validation studies and time. CDSSs were considered valuable during MTB discussions rather than in consultations.

CONCLUSION

Lung cancer decisions are heavily influenced by clinical guidelines and experience, yet many patients prefer less intensive treatments. CDSSs can support SDM by presenting the harms and benefits of different treatment options rather than giving single treatment advice. External validation of CDSSs should be prioritized.

PATIENT OR PUBLIC CONTRIBUTION

This study did not involve patients or the public explicitly; however, the study design was informed by prior interviews with volunteers of a cancer patient advocacy group. The study objectives and data collection were supported by Dutch health care insurer CZ for a project titled 'My Best Treatment' that improves patient-centeredness and the lung cancer patient pathway in the Netherlands.

摘要

背景

肺癌的治疗决策通常由多学科肿瘤委员会(MTB)中的临床专家根据临床数据和指南做出。人工智能的兴起和患者自主权的文化转变正在使临床决策朝着个性化治疗的方向转变。这可以通过生成个性化治疗信息作为共同决策(SDM)基础的临床决策支持系统(CDSS)来实现。目前尚不清楚肺癌患者的治疗决策以及由 CDSS 支持的 SDM 的潜力。本研究旨在了解 SDM 在当前实践中的实施程度以及临床医生需要改进的地方。

目的

探讨(1)在非小细胞肺癌(NSCLC)治疗决策中,患者偏好被考虑到何种程度;(2)临床医生对使用 CDSS 支持 SDM 的看法。

设计

混合方法研究,包括对 NSCLC 患者偏离 MTB 建议的原因和患者偏离 MTB 建议的程度进行回顾性队列研究,对 9 名肺癌专家进行定性访谈以及对 MTB 讨论和患者咨询进行观察。

地点和参与者

257 名在一家放射治疗诊所接受治疗的 NSCLC 患者和来自荷兰 6 家诊所的 9 名肺癌专家。

结果

我们发现,MTB 建议的偏离率为 10.9%(n=28);其中 50%(n=14)是由于患者的偏好,其中 85.7%(n=12)选择了比 MTB 建议强度更低的治疗方案。目前的 MTB 建议是基于临床医生的经验、指南和患者的表现状态制定的。大多数专家(n=7)对 CDSS 持开放态度,但也提出了一些障碍,例如缺乏信任、缺乏验证研究和时间。CDSS 在 MTB 讨论中被认为是有价值的,而在咨询中则不然。

结论

肺癌决策受临床指南和经验的影响很大,但许多患者更喜欢强度较低的治疗方案。CDSS 可以通过提供不同治疗方案的危害和益处来支持 SDM,而不是给出单一的治疗建议。应优先对 CDSS 进行外部验证。

患者或公众贡献

本研究没有明确涉及患者或公众;然而,研究设计是基于对一个癌症患者权益组织志愿者的先前访谈。该研究的目标和数据收集得到了荷兰医疗保险公司 CZ 的支持,该公司的一个名为“我的最佳治疗”的项目旨在提高荷兰的以患者为中心和肺癌患者的治疗途径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7755/9327823/58120edf4818/HEX-25--g001.jpg

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