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Hospital discharge summaries are critical for care transitions, directly impacting readmission prevention and medication reconciliation, yet physicians spend 15-30 minutes per patient drafting these documents, contributing substantially to documentation burden and professional burnout. Manual summarization of daily progress notes and laboratory results is repetitive, time-consuming, and error-prone, as clinicians must sift through lengthy unstructured notes across multiple hospital days while identifying salient events and trends. We propose a large language model with parameter-efficient fine-tuning for automated discharge summary generation that processes chronologically ordered daily progress notes alongside time-series laboratory results to produce structured discharge documentation. The framework consists of a base LLM augmented with LoRA adapters, a progress note encoder for section segmentation, a laboratory result integrator that computes trend indicators, and a summary generator that produces sectioned discharge output. Parameter-efficient fine-tuning enables domain adaptation to clinical text with minimal computational resources, preserving patient-specific information while reducing hallucination through retrieval of key factual details from the input notes. This framework offers a practical pathway to reduced documentation burden and improved discharge quality, with potential for widespread deployment across health systems given the modest computational requirements of PEFT approaches.

Large language models (LLMs) have rapidly advanced since the transformer architecture was introduced in 2017, with systems such as GPT-3, GPT-4, Med-PaLM, and Claude increasingly explored for applications in medical education, clinical documentation, decision support, and patient communication, raising both optimism and concerns regarding safety and reliability. This systematic review synthesizes evidence across studies retrieved from PubMed, arXiv, ACL Anthology, IEEE Xplore, and Google Scholar that empirically evaluated LLMs in clinical settings using quantitative performance metrics, with risk of bias assessed using an adapted PROBAST framework for machine learning research. Findings show that LLMs achieve 60–90% accuracy on USMLE-style examinations, with leading models such as GPT-4 and Med-PaLM 2 reaching or surpassing passing thresholds, while in clinical documentation tasks they can reduce physician workload by approximately 30–50% in generating outputs such as discharge summaries, though human review remains consistently required. Performance in clinical decision support is more variable and specialty-dependent, and hallucination rates ranging from 5–30% have been reported, alongside persistent issues of bias and overconfidence in incorrect outputs. Overall, while LLMs demonstrate strong capabilities in structured medical knowledge tasks and documentation support, current limitations including hallucinations, bias, and lack of prospective clinical validation prevent safe autonomous deployment, making clinician oversight and robust safety safeguards essential for any clinical use.

Clinical documentation is central to continuity of care, coding, billing, compliance, and quality measurement, yet it remains a major source of administrative burden for physicians. Artificial intelligence, especially natural language processing, has been proposed as a way to improve documentation quality, coding accuracy, billing support, and clinical workflow efficiency. This systematic review synthesised evidence from 2017 to 2023 on natural language processing methods applied to clinical documentation improvement. The review focused on automated clinical coding, note quality, billing support, computer-assisted physician documentation, and physician workflow efficiency. A PRISMA 2020-compliant search strategy was applied to PubMed, Scopus, IEEE Xplore, and Web of Science for publications from 1 January 2017 to 31 December 2023. Screening was performed in duplicate, and eligible studies were narratively synthesised by documentation domain, model type, deployment maturity, and evaluation approach. The evidence base expanded rapidly during the review period, especially in automated coding and clinical note generation. Several studies reported technically promising systems for ICD coding, documentation summarisation, and speech-derived notes, whereas billing outcomes and sustained workflow effects were less commonly evaluated. Natural language processing for clinical documentation improvement appears most mature for automated coding and note analysis. Evidence for billing support and physician workflow transformation remains less developed, particularly in prospective clinical environments.

Healthcare administration generates large volumes of textual data, including clinical notes, billing documentation, incident narratives, portal messages, and operational records. These data create administrative burden but also provide opportunities for natural language processing to support documentation, coding, communication, safety review, and workflow automation. This systematic review examined natural language processing models applied to healthcare administration from 2017 to 2024. The review focused on five domains: clinical documentation, coding support, incident reporting, patient communication, and workflow automation. A PRISMA 2020-compliant review was conducted using structured searches of PubMed, Scopus, IEEE Xplore, and Web of Science for studies published between 2017 and 2024. Eligible studies were screened by two reviewers, extracted using a structured template, and synthesized narratively by administrative domain, model type, evaluation approach, and implementation maturity. Transformer-based models were increasingly prominent across the reviewed literature, particularly in documentation support, automated coding, and clinical text classification. Clinical documentation and coding support had the strongest evidence base, while incident reporting, patient communication, and workflow automation remained less mature and less frequently evaluated in real-world settings. Natural language processing for healthcare administration is technically promising, but most applications remain at the retrospective or proof-of-concept stage. Prospective validation, workflow integration, governance, and human oversight are needed before widespread operational adoption.

Prior authorization delays impede timely patient care and contribute to administrative pressure across clinical and revenue cycle workflows. These delays can affect scheduling, medication access, procedural planning, and patient confidence in the care process. Current authorization management tools are largely reactive and often focus on tracking request status after submission. They rarely predict which requests are likely to experience approval delays or explain the operational, clinical, or payer-specific reasons behind those delays. This article proposes an interpretable machine learning model for predicting the likelihood of prior authorization approval delays. The model is designed to provide transparent, request-level explanations that can guide pre-submission correction and authorization preparation. The proposed framework uses a gradient-boosted tree model trained conceptually on historical authorization requests. Inputs include payer-specific rules, clinical documentation features, procedure type, medical necessity indicators, and historical approval timelines, with SHAP used to attribute predicted delay risk to individual features. Conceptually, the model would output a delay probability and an explanation of the dominant drivers of that prediction. These drivers could include incomplete documentation, mismatch with payer medical necessity criteria, procedure categories associated with additional review, or payer-procedure combinations with historically slow turnaround. An interpretable prior authorization delay model could support earlier correction of incomplete requests, reduce administrative waste, and improve patient access. By aligning predictive analytics with transparent explanations, the framework could make authorization preparation more proactive and accountable.

Healthcare administration is document-intensive, communication-heavy, and increasingly dependent on digital systems that require timely synthesis of clinical and operational information. Generative artificial intelligence has been proposed as a potential means of reducing administrative workload across documentation, reporting, patient communication, governance, and workflow coordination. This systematic review examined applications of generative artificial intelligence across five healthcare administrative domains: documentation support, operational reporting, patient communication, governance dashboards, and workflow automation. The review aimed to characterize reported use cases, model types, evaluation approaches, implementation barriers, and evidence maturity from 2017 to 2025. A PRISMA 2020-compliant search strategy was applied to PubMed, Scopus, IEEE Xplore, and Web of Science for studies published between January 1, 2017, and December 31, 2025. Screening was conducted in duplicate, and eligible studies were synthesized narratively by administrative domain. Documentation support was the most mature domain, particularly for clinical summarization, discharge summaries, patient-message drafting, and document classification. Operational reporting and governance dashboards were emerging areas, while patient communication and workflow automation showed diverse prototypes but limited prospective validation. Common challenges included hallucination, privacy, bias, regulatory uncertainty, integration burden, and limited evidence of real-world administrative impact. Generative artificial intelligence appears technically capable of supporting multiple healthcare administrative tasks, but evidence remains uneven across domains. Real-world impact, safety, scalability, and governance require stronger evaluation before these systems can be relied upon for high-stakes administrative decision-making.