This review examines the literature on the impact of different information technologies applied in ambulatory healthcare models on health outcomes (clinical, costs, satisfaction, and quality of life) in a high-risk pregnant population. Given the heterogeneity in study methods, technologies utilized, and outcome measures, the results were analyzed and presented in a narrative overview of the literature. It concludes that the implementation of these models is technically feasible and could potentially result in cost savings for the service provider and patients. However, further clarity on clinical outcomes, supported by high-quality evidence, is needed for policies enabling these services to keep pace with technology.

Introduction. Medical staff often face difficulties in consulting and applying clinical guidelines in practice. Large language models, especially when combined with retrieval-augmented generation, may help overcome these challenges by producing context-specific outputs with improved adherence to medical guidelines.Objectives. To assess the performance of commercial large language models in answering maternal health questions within retrieval-augmented generation systems, using both human and automated evaluation metrics.Material and methods. A controlled experiment was designed to obtain accurate, consistent answers from a retrieval-augmented generation system based on Colombian maternal care guidelines. A physician formulated ten questions and defined the groundtruth answers. Various large language models were tested with a standardized prompt and evaluated through binary answer–concept ranking and retrieval-augmented generation assessment, metrics, judged by two independent large language models.Results. Generative pre-trained transformer 3.5 (GPT-3.5) achieved the highest physicianassessed accuracy (0.90). Claude 3.5 obtained the top faithfulness score (0.78) under GPT-4.o evaluation, while Mistral ranked highest (0.84) under Claude 3.5 evaluation. Regarding answer relevance, GPT-3.5 scored highest across both judges (0.94 and 0.86). Conclusions. Integrating retrieval-augmented generation into obstetric care has the potential to enhance evidence-based practices and improve patient outcomes. However, rigorous validation of accuracy and context-specific reliability is essential before clinical deployment. The findings of this study indicate that large-scale models (e.g., GPT-3.5, Claude, Llama 70B) consistently outperform lighter models such as Llama 8B.

Early detection of morbidities that complicate pregnancy improves health outcomes in low- and middle-income countries. Automatic revision of electronic health records (EHRs) can help identify such morbidity risks. There is a lack of corpora to train models in Spanish in specific domains, and there are no models specialized in maternal EHRs. This study aims to develop a fine-tuned model that detects clinical concepts using a built database with text extracted from maternal EHRs in Spanish. We created a corpus with 13.998 annotations from 200 clinical notes in Spanish associated with EHRs obtained from a reference institution of high obstetric risk in Colombia. Using the Beginning–Inside–Outside tagging scheme, we fine-tuned five different transformer-based models to classify between 16 classes associated with eight entities. The best model achieved a macro F1 score of 0.55 ± 0.03. The entities with the best performance were signs, symptoms, and negations, with exact F1 scores of 0.714 and 0.726, respectively. The lower scores were associated with those classes with fewer observations. Even though our dataset is shorter in size and more diverse in entity types than other datasets in Spanish, our results are comparable to other state-of-the-art named entity recognition models fine-tuned in Spanish and the biomedical domain. This work introduces the first fine-tuning of a model for named entity recognition specifically designed for maternal EHRs. Our results can be used as a base to develop new models to extract concepts in the maternal–fetal domains and help healthcare providers detect morbidities that complicate pregnancy early.