Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAe, Centre for Research in Epidemiology and Statistics (CRESS), Paris, 75004, France.
Department of General Medicine, Claude Bernard Lyon 1 University, Lyon, 69003, France.
BMC Med. 2024 Sep 27;22(1):415. doi: 10.1186/s12916-024-03616-4.
The objective of the study was to identify the psychosocial and contextual markers considered by physicians to personalize care.
An online questionnaire with one open-ended question, asking physicians to describe clinical situations in which they personalized care, was used. Physicians were recruited from March 31, 2023, to August 10, 2023, from three hospitals, five university departments of general practice and six physician organizations in France. Recruitment was conducted through email invitations, with participants encouraged to invite their colleagues via a snowball sampling method. The participants were a diverse sample of French general practitioners and other medical specialists who see patients in consultations or in hospital wards. We extracted the psychosocial and contextual markers considered by physicians to personalize care in each clinical situation. The analysis involved both manual and AI-assisted content analysis using GPT3.5-Turbo (OpenAI). Mathematical models to assess data saturation were used to ensure that a comprehensive list of markers was identified.
In total, 1340 people connected to the survey platform and 1004 (75.0%) physicians were eligible for the study (median age 39 years old, IQR 34 to 50; 60.5% women; 67.0% working in outpatient settings), among whom 290 answered the open-ended question. The participants reported 317 clinical situations during which they personalized care. Personalization was based on the consideration of 40 markers: 27 were related to patients' psychosocial characteristics (e.g., patient capacity, psychological state, beliefs), and 13 were related to circumstances (e.g., competing activities, support network, living environment). The data saturation models showed that at least 97.0% of the potential markers were identified. Manual and AI-assisted content analysis using GPT3.5-Turbo were concordant for 89.9% of clinical situations.
Physicians personalize care to patients' contexts and lives using a broad range of psychosocial and contextual markers. The effect of these markers on treatment engagement and effectiveness needs to be evaluated in clinical studies and integrated as tailoring variables in personalized interventions to build evidence-based personalization.
本研究旨在确定医生认为能够实现个性化医疗的心理社会和环境因素。
我们使用了一份在线问卷,其中包含一个开放式问题,要求医生描述他们在哪些临床情况下实施了个性化医疗。研究对象于 2023 年 3 月 31 日至 8 月 10 日期间,从法国的三家医院、五家普通科医生大学系和六家医师组织中招募而来。通过电子邮件邀请的方式进行招募,并鼓励参与者通过滚雪球抽样法邀请他们的同事。参与者是一个多样化的法国全科医生和其他医疗专家样本,他们在门诊或病房中为患者提供服务。我们从每个临床情况下医生认为能够实现个性化医疗的心理社会和环境因素中提取出个性化医疗的标记。分析涉及使用 GPT3.5-Turbo(OpenAI)进行的手动和 AI 辅助内容分析。使用评估数据饱和度的数学模型来确保确定了全面的标记列表。
共有 1340 人连接到调查平台,其中 1004 人(75.0%)符合研究条件(中位年龄 39 岁,IQR 34 至 50;60.5%为女性;67.0%在门诊环境中工作),其中 290 人回答了开放式问题。参与者报告了 317 种个性化医疗的临床情况。个性化医疗是基于对 40 个标记的考虑:27 个与患者的心理社会特征有关(例如,患者能力、心理状态、信念),13 个与环境有关(例如,竞争活动、支持网络、生活环境)。数据饱和度模型表明,至少 97.0%的潜在标记已被识别。使用 GPT3.5-Turbo 进行的手动和 AI 辅助内容分析在 89.9%的临床情况下是一致的。
医生根据广泛的心理社会和环境因素为患者的情况和生活提供个性化医疗。需要在临床研究中评估这些标记对治疗参与和效果的影响,并将其作为个性化干预措施中的定制变量进行整合,以建立基于证据的个性化医疗。