Rédiger un PREreview

Background/Objectives: The aging global population has led to rising prevalence of type 2 diabetes mellitus (T2DM), in which biomedical outcomes alone fail to capture patients’ lived experiences. Patient-Reported Outcome Measures (PROMs) can provide insights into psychological, psychosocial, and quality-of-life (QoL) dimensions, yet their use in older adults remains inconsistent. This AI-assisted rapid review aimed to map the current use of PROMs in older adults with T2DM, focusing on their role in assessing well-being, dis-tress, depression, and treatment satisfaction, as well as their implementation in clinical and organizational practice. Methods: A rapid review was conducted using Elicit, an AI tool designed to support sys-tematic evidence synthesis. Studies published between 2015 and 2025 were identified from Semantic Scholar and additional manual searches. Eligibility criteria required inclu-sion of older adults with T2DM and use of validated PROMs in real-world settings. After screening 504 records, 167 studies were included. Data extraction covered study design, instruments used, populations, outcomes, and implementation details. Results: The most frequently assessed outcomes were diabetes distress, depression, QoL, treatment satisfaction, and self-efficacy. Common instruments included PAID, DDS, PHQ-9, WHO-5, EQ-5D, SF-36, DTSQ, and GDS. Evidence showed PROMs effectively identified high-risk patients and informed tailored interventions, but integration into rou-tine care remained limited. Barriers included workflow disruption, lack of provider train-ing, heterogeneity of tools, and insufficient cultural validation. Facilitators included brief instruments, digital administration, and linkage with care planning. Conclusions: PROMs are valuable in capturing psychosocial and psychological burdens in older adults with T2DM, but routine implementation is inconsistent. Broader adoption will require digital infrastructure, clinician training, and organizational integration. Methodologically, this study illustrates the feasibility of AI-assisted rapid reviews to gen-erate timely, evidence-informed syntheses.