Ben Hmido Sara, Abder Rahim Houssam, Ploem Corrette, Haitjema Saskia, Damman Olga, Kazemier Geert, Daams Freek
Department of Surgery, Amsterdam UMC Locatie VUmc, Amsterdam, Noord-Holland, The Netherlands.
Cancer Centre Amsterdam, Amsterdam, Noord-Holland, The Netherlands.
BMJ Surg Interv Health Technol. 2025 Apr 2;7(1):e000365. doi: 10.1136/bmjsit-2024-000365. eCollection 2025.
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.
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.
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.
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.
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.
医疗保健领域的预测性机器学习,尤其是在手术决策方面,正在迅速发展。然而,关于患者对预测性机器学习的看法,特别是其在整个临床过程中的应用的文献却很稀少。本研究探讨了接受结直肠手术(CRS)的患者对外科医生使用术中预测性机器学习(IPML)的看法,尤其是那些旨在预测结直肠吻合口漏(CAL)的患者。
本研究调查了先前接受CRS的患者对IPML模型实施的看法。感兴趣的领域包括对IPML的认知、在决策中的感知作用以及临床会诊中提供的信息。
采用定性研究设计,对接受过CRS的患者进行焦点小组和半结构化访谈。使用描述性主题分析来分析数据并确定主要主题和态度。代码树中的关联基于共现表建立。使用饱和度分析确定患者样本量。
一项对来自四个焦点小组和七次访谈的19名参与者的研究发现,患者对CRS中使用IPML模型总体持积极看法。参与者认识到其在增强手术决策方面的潜力,但强调外科医生作为主要决策者的作用,表明IPML模型充当咨询工具,外科医生能够推翻建议。强调了个性化沟通和对生活质量的考虑,突出了平衡整合IPML模型以支持临床判断和构建患者偏好的必要性。
CRS中的IPML受到参与者的欢迎,前提是外科医生保留推翻模型建议的能力并透明地记录其决策。对外科医生的信任仍然是患者接受IPML的关键因素,这加强了在会诊期间进行清晰解释的必要性。无论是否使用IPML,调整患者沟通以及解决吻合术与造口术对生活质量的影响也至关重要。