Background: Benchmarking medical decision support algorithms often struggles due to limited access to datasets, narrow prediction tasks, and restricted input modalities. These limitations affect their clinical relevance and performance in high-stakes areas like emergency care, complicating replication, validation, and improvement of benchmarks. Methods: We introduce a dataset based on MIMIC-IV, benchmarking protocol, and initial results for evaluating multimodal decision support in the emergency department (ED). We use diverse data modalities from the first 1.5 hours of patient arrival, including demographics, biometrics, vital signs, lab values, and electrocardiogram waveforms. We analyze 1443 clinical labels across two contexts: predicting diagnoses with ICD-10 codes and forecasting patient deterioration. Results: Our multimodal diagnostic model achieves an AUROC score over 0.8 in a statistically significant manner for 357 out of 1428 conditions, including cardiac issues like myocardial infarction and non-cardiac conditions such as renal disease and diabetes. The deterioration model scores above 0.8 in a statistically significant manner for 13 out of 15 targets, including critical events like cardiac arrest and mechanical ventilation, ICU admission as well as short- and long-term mortality. Incorporating raw waveform data significantly improves model performance, which represents one of the first robust demonstrations of this effect. Conclusions: This study highlights the uniqueness of our dataset, which encompasses a wide range of clinical tasks and utilizes a comprehensive set of features collected early during the emergency after arriving at the ED. The strong performance, as evidenced by high AUROC scores across diagnostic and deterioration targets, underscores the potential of our approach to revolutionize decision-making in acute and emergency medicine.

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