Clinical communication is central to patient outcomes, yet large-scale human annotation of patient-provider conversation remains labor-intensive, inconsistent, and difficult to scale. Existing approaches based on large language models typically rely on single-task models that lack adaptability, interpretability, and reliability, especially when applied across various communication frameworks and clinical domains. In this study, we developed a Multi-framework Structured Agentic AI system for Clinical Communication (MOSAIC), built on a LangGraph-based architecture that orchestrates four core agents, including a Plan Agent for codebook selection and workflow planning, an Update Agent for maintaining up-to-date retrieval databases, a set of Annotation Agents that applies codebook-guided retrieval-augmented generation (RAG) with dynamic few-shot prompting, and a Verification Agent that provides consistency checks and feedback. To evaluate performance, we compared MOSAIC outputs against gold-standard annotations created by trained human coders. We developed and evaluated MOSAIC using 26 gold standard annotated transcripts for training and 50 transcripts for testing, spanning rheumatology and OB/GYN domains. On the test set, MOSAIC achieved an overall F1 score of 0.928. Performance was highest in the Rheumatology subset (F1 = 0.962) and strongest for Patient Behavior (e.g., patients asking questions, expressing preferences, or showing assertiveness). Ablations revealed that MOSAIC outperforms baseline benchmarking.

This study introduces an AI-driven platform for continuous and passive patient monitoring in hospital settings, developed by LookDeep Health. Leveraging advanced computer vision, the platform provides real-time insights into patient behavior and interactions through video analysis, securely storing inference results in the cloud for retrospective evaluation. The dataset, compiled in collaboration with 11 hospital partners, encompasses over 300 high-risk fall patients and over 1,000 days of inference, enabling applications such as fall detection and safety monitoring for vulnerable patient populations. To foster innovation and reproducibility, an anonymized subset of this dataset is publicly available. The AI system detects key components in hospital rooms, including individual presence and role, furniture location, motion magnitude, and boundary crossings. Performance evaluation demonstrates strong accuracy in object detection (macro F1-score = 0.92) and patient-role classification (F1-score = 0.98), as well as reliable trend analysis for the "patient alone" metric (mean logistic regression accuracy = 0.82 \pm 0.15). These capabilities enable automated detection of patient isolation, wandering, or unsupervised movement-key indicators for fall risk and other adverse events. This work establishes benchmarks for validating AI-driven patient monitoring systems, highlighting the platform's potential to enhance patient safety and care by providing continuous, data-driven insights into patient behavior and interactions.

Automatic coding patient behaviors is essential to support decision making for psychotherapists during the motivational interviewing (MI), a collaborative communication intervention approach to address psychiatric issues, such as alcohol and drug addiction. While the behavior coding task has rapidly adapted machine learning to predict patient states during the MI sessions, lacking of domain-specific knowledge and overlooking patient-therapist interactions are major challenges in developing and deploying those models in real practice. To encounter those challenges, we introduce the Chain-of-Interaction (CoI) prompting method aiming to contextualize large language models (LLMs) for psychiatric decision support by the dyadic interactions. The CoI prompting approach systematically breaks down the coding task into three key reasoning steps, extract patient engagement, learn therapist question strategies, and integrates dyadic interactions between patients and therapists. This approach enables large language models to leverage the coding scheme, patient state, and domain knowledge for patient behavioral coding. Experiments on real-world datasets can prove the effectiveness and flexibility of our prompting method with multiple state-of-the-art LLMs over existing prompting baselines. We have conducted extensive ablation analysis and demonstrate the critical role of dyadic interactions in applying LLMs for psychotherapy behavior understanding.

Having served on the board of directors of a university hospital for eight years while serving as mayor of a US community of nearly ninety-thousand, I have a unique perspective on the importance of making healthcare more accessible, efficient, reliable, and affordable. As an educator, I have also had the privilege of teaching on the topic of emerging technologies. Now, as a technologist and "futurist" (titled so by IBM), I work with leading technology companies to share my insights. This post is sponsored by AT&T Business but the opinions are my own and don't necessarily represent AT&T Business's positions or strategies. The challenge for public and private healthcare is making medical care more accessible, efficient, reliable, and affordable.

Artificial intelligence is increasingly being harnessed in various aspects of drug development, from discovering compounds with life-saving potential, to identifying potential patients, and more. Outsourcing-Pharma recently spoke with Rich Christie, chief medical officer of AI-focused solutions provider AiCure, about how AI can be used to analyze and predict patient behavior, develop precision medicine solutions, and improve both care and quality of life. OSP: Could you please share the'elevator presentation' description of AiCure--who you are, what you do, and what sets you apart from other companies operating in the same space? RC: AiCure is a patient-focused technology company that empowers life science and healthcare organizations with actionable insights to accelerate drug development and improve patient care. With more than a decade of experience managing complex protected health information (PHI) in regulated settings, AiCure helps organizations to optimize care at each step of the clinical continuum by delivering objective, predictive behavioral, and interventional insights, uniquely built on unbiased patient-level audio and visual data capture.

The Medicaid Innovations Forum is a highly anticipated annual event that offers a unique combination of forward-thinking perspectives, first-hand case studies and examples of true innovation from both Medicaid managed care plans and state government agencies. Visit the AllazoHealth Booth at the Medicaid Innovations Forum to learn how we are leveraging artificial intelligence to identify adherence risk for individual high-risk Medicaid patients with chronic disease and multiple medications. Join us in Orlando to discover how AllazoHealth can provide specific and effective interventions to help Medicaid managed care plans improve patient behaviors and meet quality measures to achieve HEDIS score goals. To make an appointment to meet with an AllazoHealth representative at the Medicaid Innovations Forum, contact us today. AllazoHealth is a healthcare AI solution that uplifts adherence by predicting individual patient behavior and specifying personalized interventions.

In the year we celebrate America's moon landing from 50 years ago, we are reminded that aiming high and thinking big have led to feats that once seemed impossible. But there were many exploded rockets on the launchpad before our astronauts set foot on the moon. That's why both the hype and the disappointment around artificial intelligence (AI) in health care don't bother me. Although IBM Watson Health did not meet expectations for its "moonshot" goal to tame cancer with AI, there's reason to be optimistic. Incremental progress for health care AI may not astonish, but it is nonetheless very promising.