Purpose <p>To develop a Bayesian method for dynamic borrowing of information from historical clinical trials and real-world data that has the potential to improve the efficiency of clinical trials.</p> Methods <p>We propose a novel statistical metric to quantify heterogeneity among data sources. Based on this metric, a Multi-Source Dynamic Borrowing (MSDB) Bayesian prior framework was proposed, which can address baseline imbalance and dynamically discount information from each data source separately without assuming exchangeability. In a simulation study, we compared the proposed framework and metrics with historical methods.</p> Results <p>We evaluated performance by comparing power, type I error, bias, and mean squared error (MSE) across methods. Our approach outperformed existing methods by enhancing power and reducing MSE, while effectively controlling type I error and bias in the presence of heterogeneity and baseline imbalances. Moreover, an application involving isatuximab in relapsed and refractory multiple myeloma further validated the performance of the MSDB prior.</p> Conclusion <p>The MSDB prior method dynamically adjusts borrowing information, allowing for a more robust and accurate incorporation of historical data. Its flexible framework allows it to play a crucial role in various applications, thereby accelerating drug development and supporting regulatory decision making.</p>