Predicting future diagnostic codes from Electronic Health Records (EHRs) is a complex task due to the unstructured nature of medical texts. This study introduces RiskProg, a system designed to predict future diagnoses from Spanish EHRs. It aims to structure EHRs through generative LLMs and then predict future diagnoses by highlighting the most influential words that contributed to the prediction. RiskProg utilizes a three-step approach: (1) a summarizer that structures patient visit information into comprehensive summaries, (2) a future potential diagnoses predictor, and (3) a salient word identifier based on integrated gradients to highlight the textual elements influencing predictions. The system was evaluated using the Osa dataset, which includes 26,731 annotated Spanish discharge summaries. The incorporation of summarized EHR documents enhanced classification accuracy, with expanded summaries outperforming the baseline classification with raw documents. Explainability, achieved through word-level attributions, increased clinician trust by providing insights into the model’s decision-making process. RiskProg showcases the potential of structuring unstructured clinical data for next diagnosis prediction. The approach offers both an explanation of the predicted codes and structured summaries that make it easier to assess patient admission information.