Patient perspectives on AI-based decision support in surgery.

IF 1.6 Q2 SURGERY
BMJ Surgery Interventions Health Technologies Pub Date : 2025-04-02 eCollection Date: 2025-01-01 DOI:10.1136/bmjsit-2024-000365
Sara Ben Hmido, Houssam Abder Rahim, Corrette Ploem, Saskia Haitjema, Olga Damman, Geert Kazemier, Freek Daams
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Abstract

Background: Predictive machine learning in healthcare, especially in surgical decisions, is advancing swiftly. Yet, literature on patient views regarding predictive machine learning, specifically its use throughout the clinical course, is scarce. Views among patients who underwent colorectal surgery (CRS) on the use of intra-operative predictive machine learning (IPML) by surgeons, particularly those aiming to predict colorectal anastomotic leakage (CAL), were explored in this study.

Objective: This study investigated the views of patients who previously underwent CRS on the implementation of IPML models. Domains of interest were perceptions of IPML, perceived role in decision-making and information provided in the clinical encounter.

Methods: A qualitative research design was employed, using focus groups and semi-structured interviews with patients who had undergone CRS. Descriptive thematic analysis was used to analyse data and identify prevailing themes and attitudes. The associations in the code tree were established based on a co-occurrence table. The patient sample size was determined using a saturation analysis.

Results: A study with n=19 participants across four focus groups and seven interviews found a generally positive perception regarding the use of IPML models in CRS. Participants recognised their potential to enhance surgical decision-making but stressed the surgeon's role as the primary decision-maker, suggesting IPML models act as advisory tools, with surgeons able to override recommendations. Personalised communication and consideration of quality of life were emphasised, highlighting the need for a balanced integration of IPML models to support clinical judgement and the construction of patient preferences.

Conclusion: IPML in CRS is well-received by participants, provided that surgeons retain the ability to override model recommendations and document their decisions transparently. Trust in the surgeon remains a key factor in patient acceptance of IPML, reinforcing the need for clear explanations during consultation sessions. Regardless of the use of IPML, tailoring patient communication and addressing the quality-of-life impacts of anastomosis vs stoma are also critical.

基于人工智能的手术决策支持的患者观点。
背景:预测机器学习在医疗保健领域,特别是在手术决策方面进展迅速。然而,关于患者对预测机器学习的看法,特别是其在整个临床过程中的应用的文献很少。本研究探讨了接受结肠直肠手术(CRS)的患者对外科医生使用术中预测机器学习(IPML)的看法,特别是那些旨在预测结肠直肠吻合口漏(CAL)的人。目的:探讨曾行CRS的患者对IPML模型实施的看法。感兴趣的领域是对IPML的感知,在决策中的感知角色和在临床遭遇中提供的信息。方法:采用质性研究设计,对CRS患者进行焦点小组和半结构化访谈。描述性专题分析用于分析数据和确定普遍的主题和态度。代码树中的关联是基于共现表建立的。采用饱和度分析确定患者样本量。结果:一项包括4个焦点小组和7个访谈的n=19名参与者的研究发现,在CRS中使用IPML模型总体上是积极的。参与者认识到IPML模型在提高手术决策方面的潜力,但强调外科医生作为主要决策者的角色,建议IPML模型作为咨询工具,外科医生可以推翻建议。强调了个性化沟通和对生活质量的考虑,强调了平衡整合IPML模型以支持临床判断和构建患者偏好的必要性。结论:如果外科医生保留推翻模型建议并透明地记录其决定的能力,则CRS中的IPML受到参与者的欢迎。对外科医生的信任仍然是患者接受IPML的关键因素,在会诊期间需要明确的解释。无论使用IPML,定制患者沟通和解决吻合与造口对生活质量的影响也至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.80
自引率
0.00%
发文量
22
审稿时长
17 weeks
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