Keyword: Electronic Health Records

ABSTRACT: Background: Autonomous medical documentation has become a central challenge in modern healthcare, as clinicians increasingly spend substantial portions of their workday on electronic health record (EHR) tasks rather than direct patient care, contributing to dissatisfaction, burnout, and workflow inefficiencies. Large language models (LLMs) and cloud-based speech services offer a potential solution by automating aspects of note generation, structured documentation, and coding support while preserving clinician oversight.
Objectives: To map and characterize the emerging evidence on autonomous or semi-autonomous documentation pipelines that integrate LLMs, cloud speech services, and EHR workflows, focusing on technical architecture, workflow integration, safety, governance, and clinician experience.
Methodology: A systematic search of PubMed, ACM Digital Library, and Dimensions AI was conducted for studies published from 2019 to March 2026, supplemented by reports and policy documents. Eligible studies included empirical research, reviews, and implementation reports addressing documentation efficiency, accuracy, and clinician outcomes.
Findings: Evidence indicates that layered pipelines combining speech processing, LLM-driven note generation, retrieval of structured EHR data, and FHIR-based integration can reduce documentation time, decrease after-hours charting, improve note quality, and enhance clinician satisfaction. Human oversight remains essential to mitigate risks from hallucination, transcription errors, and workflow misalignment. Governance, consent, and data security are critical for safe adoption.
Conclusion: Overall, autonomous documentation pipelines are most effective when implemented as assisted automation tools embedded within context-sensitive clinical workflows, with iterative evaluation and robust governance. These insights provide a foundation for future research, clinical validation, and scalable deployment strategies. 

ABSTRACT: Adverse drug reactions (ADRs) remain a major global challenge, contributing substantially to patient morbidity, mortality, and healthcare costs. Traditional pharmacovigilance approaches—spontaneous reporting and post-marketing surveillance—are hampered by underreporting, delays, and limited contextual data. The growing availability of electronic health records (EHRs), which capture longitudinal structured and unstructured patient information, presents an unprecedented opportunity to advance ADR prediction. This narrative review synthesizes recent progress in developing and validating predictive models that leverage EHRs, highlighting methodological approaches, challenges, and future directions. Predictive strategies range from traditional regression models to advanced machine learning and deep learning architectures, with multimodal frameworks increasingly integrating structured fields (demographics, labs, prescriptions) and unstructured clinical text through natural language processing. While ensemble and deep learning methods demonstrate superior performance, issues of data quality, missingness, bias, and interpretability persist. Robust validation frameworks—spanning internal cross-validation to multi-center external testing—are critical to ensure generalizability and clinical trustworthiness. Ethical considerations, including fairness, privacy, and transparency, remain central to safe deployment. Looking forward, promising avenues include federated learning across institutions, integration of multi-omics and pharmacogenomic data, explainable AI tailored for clinical use, and real-time monitoring through digital twin frameworks. These trajectories, combined with robust governance and clinician–data scientist collaboration, have the potential to transform ADR detection from a reactive process to proactive, personalized prevention. By synthesizing the existing evidence, this review provides insights into the development of more effective predictive models for ADRs and informs strategies for improving pharmacovigilance. This study will contribute to the ongoing efforts to leverage EHRs and predictive models for improving patient outcomes and reducing the burden of ADRs.