Pragmatic Clinical Trials in Internal Medicine: Design Principles and Context‐Specific Implementation

Vollständiger Abstract

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ABSTRACT Background Pragmatic clinical trials (PCTs) evaluate interventions under routine care conditions, prioritizing external validity and decision relevance over tightly controlled efficacy. Internal medicine, characterized by multimorbidity, polypharmacy, and heterogeneous care pathways, is a prime field for PCTs embedded in real‐world practice. Methods This narrative review synthesizes methodological developments in pragmatic trial design with a focus on internal medicine. Key domains include eligibility criteria, recruitment and randomization strategies, intervention and comparator definition, outcome measurement using routinely collected data, and integration within learning health systems, alongside ethical and regulatory considerations. Results PCTs may use broad eligibility criteria and recruitment processes aligned with local care pathways to enrol patients who resemble those for whom the treatment decision arises in practice. EHR‐supported screening can facilitate this process in some health systems but is not a prerequisiteRandomization often occurs at the cluster level or at the point of care, minimizing disruption of workflows. Interventions typically compare accepted care strategies delivered via usual clinicians with flexible implementation. Outcomes emphasize patient‐ and system‐relevant endpoints (e.g., hospitalizations, major events, quality of life) ascertained primarily from EHRs, claims, registries, and patient‐reported systems. Embedding trials within health systems enables automated data capture, scalable implementation, and continuous learning but raises challenges in data quality, consent models, privacy, and equitable inclusion. Conclusions Pragmatic trials offer a powerful approach to generate decision‐relevant evidence for internal medicine by aligning design features with real‐world practice and existing data infrastructures. Their successful conduct requires close collaboration among clinicians, methodologists, data scientists, and health system leaders, with careful attention to ethics, governance, and health equity.