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Despite several known idiosyncrasies separating the synchronous and the asynchronous models, asynchronous secure multi-party computation (MPC) protocols demonstrate high-level similarities to synchronous MPC, both in design philosophy and abstract structure. As such, a coveted, albeit elusive, desideratum is to devise automatic translators (e.g., protocol compilers) of feasibility and efficiency results from one model to the other. In this work, we demonstrate new challenges associated with this goal. Specifically, we study the case of parallel composition in the asynchronous setting. We provide formal definitions of this composition operation in the UC framework, which, somewhat surprisingly, have been missing from the literature. Using these definitions, we then turn to charting the feasibility landscape of asynchronous parallel composition. We first prove strong impossibility results for composition operators that do not assume knowledge of the functions and/or the protocols that are being composed. These results draw a grim feasibility picture, which is in sharp contrast with the synchronous model, and highlight the question: Is asynchronous parallel composition even a realistic goal? To answer the above (in the affirmative), we provide conditions on the composed protocols that enable a useful form of asynchronous parallel composition, as it turns out to be common in existing constructions. 

Sepsis is a dysregulated host response to infection with high mortality and morbidity. Early detection and intervention have been shown to improve patient outcomes, but existing computational models relying on structured electronic health record data often miss contextual information from unstructured clinical notes. This study introduces COMPOSER-LLM, an open-source large language model (LLM) integrated with the COMPOSER model to enhance early sepsis prediction. For high-uncertainty predictions, the LLM extracts additional context to assess sepsis-mimics, improving accuracy. Evaluated on 2500 patient encounters, COMPOSER-LLM achieved a sensitivity of 72.1%, positive predictive value of 52.9%, F-1 score of 61.0%, and 0.0087 false alarms per patient hour, outperforming the standalone COMPOSER model. Prospective validation yielded similar results. Manual chart review found 62% of false positives had bacterial infections, demonstrating potential clinical utility. Our findings suggest that integrating LLMs with traditional models can enhance predictive performance by leveraging unstructured data, representing a significant advance in healthcare analytics.