Through the course of hospital treatment, a large number of electronic health records (EHRs) are created for a patient, detailingaspects of care history such as lab results, physician notes, and treatments administered.At the conclusion of treatment, this collection of EHRs must be summarized into a discharge summary,describing the course or care clearly and cohesively.In this paper, we present the design and development of a clinical summarization system integrated into a live German hospital workflowto help with the generation of discharge summaries.We first describe the system, its components, and its context of use within a hospital,before performing a number of experiments to gain insights into how best to use and evaluate our system.We investigate summarization performance across multiple input encoding strategies, compare expert judgments against automatic evaluation of summaries,and analyze the consistency of model summaries across multiple text generations.This work not only acts as a case study to demonstrate the feasibility of LLM integration into healthcare infrastructure,but also provides actionable insights into the use and evaluation of such systems.

In this paper, we present an effort to generate a joint Urdu, Roman Urdu and English trilingual lexicon using automated methods. We make a case for using statistical machine translation approaches and parallel corpora for dictionary creation. To this purpose, we use word alignment tools on the corpus and evaluate translations using human evaluators. Despite different writing script and considerable noise in the corpus our results show promise with over 85% accuracy of Roman Urdu–Urdu and 45% English–Urdu pairs.