Patients and Physicians Fail to Predict Each Other’s Disease Severity Grading in Eczema and Psoriasis (Preprint)

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Authors: Ray Ee Tay, Erika Rusli, Xuan Hui Ang, Diego Pitta de Araujo, Ellie Choi
Cited by: 0
References: 15

Abstract

BACKGROUND Discordant severity grading (DSG) is a well-documented issue where patients and physicians grade disease severity differently. However, less is known about whether patients and physicians can accurately predict how the other perceives disease severity. This study examined prediction accuracy and factors influencing accuracy. OBJECTIVE This study examined prediction accuracy and factors influencing accuracy. METHODS We conducted a mixed methods study using 1. secondary analysis of 1053 patient physician pairs, and 2. semi-structured interviews with 16 patients and physicians. Quantitative analyses compared predicted and actual severity scores, with regression to identify significant variables. Qualitative data was analysed using grounded theory. RESULTS Physicians underestimated patients’ severity, while patients overestimated physicians’ grading, with over half of predictions inaccurate by ≥2 points. Female sex, having eczema over psoriasis and longer duration of disease increased discordance in patient predictions. Conversely, first visits and longer duration of disease reduced discordance in physician predictions. Qualitative analysis identified themes shaping perceptions, including preconceived beliefs, treatment centric consults, communication gaps, time constraints and re-evaluation of disease severity. CONCLUSIONS Patients and physicians frequently misjudge each other’s perceptions of disease severity. Prediction accuracy is shaped by demographic, clinical, and interpersonal factors, underscoring the importance of improved communication and mutual understanding to minimise DSG and optimise care. CLINICALTRIAL NIl

Fields of Study & Subjects

Dermatology and Skin Diseases Psoriasis: Treatment and Pathogenesis Patient-Provider Communication in Healthcare

Publication details

Publication date
1 February 2026
Document type
Preprint