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Hospital length-of-stay is a central operational metric for inpatient capacity planning, discharge coordination, and resource allocation. Accurate prediction remains difficult because patient trajectories are heterogeneous, nonlinear, and shaped by evolving clinical events during admission. Traditional statistical models often have limited flexibility for high-dimensional and sequential electronic health record data. Across the literature, there is no settled consensus regarding optimal model architecture, feature representation, validation design, or clinical implementation strategy. This systematic review synthesizes machine learning approaches for hospital length-of-stay prediction published from 2017 to 2022. It focuses on EHR feature types, model architectures, validation methods, interpretability strategies, and reported operational outcomes. A structured review of peer-reviewed literature was conducted using targeted search strings related to machine learning, deep learning, electronic health records, discharge prediction, and hospital length-of-stay. The review included studies across emergency, inpatient, surgical, pediatric, cardiovascular, and intensive care settings. The literature suggests that gradient boosting, random forest, ensemble learning, and recurrent neural networks are common approaches for LOS prediction. However, external validation remains uncommon, prediction horizons vary widely, and operational implementation outcomes are reported less consistently than model development results. Future research should prioritize external validation, prospective implementation studies, standardized outcome definitions, and transparent reporting of workflow barriers. Shared benchmarking datasets and multi-center validation consortia would strengthen comparability across LOS prediction studies.

Interdisciplinary rounds, discharge planning meetings, and tumor boards contain high-value clinical reasoning that is often only partially reflected in the medical record. These discussions shape treatment priorities, medication decisions, consult plans, and discharge readiness, yet their verbal and collaborative nature makes them difficult to document comprehensively. Manual summarization of care team discussions requires time, attention, and clinical synthesis that busy clinicians may not have during or immediately after meetings. Existing documentation practices often capture final decisions but omit uncertainty, rationale, task ownership, and evolving care coordination needs. This article proposes a large language model pipeline that could summarize interdisciplinary care discussions using secure meeting transcripts combined with active problem lists, medication lists, and discharge planning notes. The objective is to describe a conceptual architecture for generating accurate, structured, and clinically reviewable summaries of team communication. The proposed approach uses retrieval-augmented generation to ground the language model in structured clinical context while processing a diarized transcript of the care discussion. The model would focus on identifying decisions, medication changes, unresolved issues, discharge barriers, and action items requiring follow-up. Conceptually, the pipeline would generate a note-ready summary with lower hallucination risk because the model is constrained by structured clinical anchors and transcript evidence. It could help distinguish new decisions from repeated background information and convert a documentation-light meeting into a structured clinical artifact. A secure, grounded large language model system could support safer and more complete documentation of interdisciplinary care discussions. By combining transcript evidence with patient-specific structured data, such a system could reduce cognitive burden and improve continuity across clinical teams.

Hospital patient flow depends on timely transfer decisions, accurate discharge planning, and reliable post-discharge coordination. Delays in placement, premature discharge, missed follow-up, and unrecognized post-discharge risk can increase avoidable utilization and compromise continuity of care. This systematic review examined machine learning models for predicting placement delay, discharge readiness, follow-up completion, and post-discharge risk from 2017 to 2024. The objective was to synthesize model purposes, data sources, validation practices, implementation maturity, and practical relevance for care transitions. A PRISMA 2020-compliant systematic review was conducted using PubMed, Scopus, IEEE Xplore, and Web of Science. Dual screening, structured data extraction, and narrative synthesis were used because heterogeneity in model objectives, predictors, settings, and outcomes prevented meta-analysis. The evidence base was largest for post-discharge readmission risk and discharge readiness prediction, while placement delay and follow-up completion received less focused attention. Prospective validation, workflow integration, and outcome-based implementation studies were uncommon across all four domains. Machine learning offers promising tools to support proactive discharge planning and transitional care, but the evidence remains dominated by retrospective model development. Translation into routine practice requires prospective testing, interpretability, equity assessment, and integration into multidisciplinary workflows.