Vol. 9, Issue 1, Part A (2026)

The persistent gap between clinical evidence and routine practice remains a critical challenge in internal medicine, where multimorbidity, time constraints, and fragmented care pathways complicate the application of research findings to real-world patient care. Despite the proliferation of clinical guidelines, randomized controlled trials, and systematic reviews, evidence translation into everyday internal medicine practice is often inconsistent, delayed, or incomplete. This paper examines how emerging trends are reshaping the bridge between evidence generation and clinical application in internal medicine. Using a structured mixed-methods research framework, the study synthesizes contemporary literature and proposes an evaluative methodology to assess evidence-to-practice translation across internal medicine settings. Key emerging trends explored include real-world evidence derived from electronic health records, artificial intelligence-enabled clinical decision support systems, precision and risk-stratified medicine, telemedicine, and de-implementation of low-value care. Model findings demonstrate that evidence translation improves most effectively when supported by workflow-integrated decision tools, team-based care models, pragmatic data feedback loops, and equity-centered implementation strategies. The analysis highlights that technology alone is insufficient to close the evidence-practice gap. Instead, successful translation depends on human-centered design, organizational readiness, governance mechanisms, and continuous learning systems that adapt evidence to local contexts. The paper concludes that internal medicine must evolve toward learning health systems where evidence generation and application are dynamically linked. By aligning emerging technologies with implementation science principles, internal medicine can move beyond guideline dissemination toward sustainable, equitable, and outcome-driven evidence-based care.