ArticleBMC medicine2024
Identifying psychosocial and contextual markers considered by physicians to personalize care.
Article in BMC medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- The use and methodological reporting of large language models in qualitative research: a scoping review.BMC medical research methodology · 2026Article
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5 authors.
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Abstract
backgroundThe objective of the study was to identify the psychosocial and contextual markers considered by physicians to personalize care.
methodsAn 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.
resultsIn 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.
conclusionsPhysicians 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.
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