IoT Integration Protocol for Enhanced Hospital Care

• URL: http://arxiv.org/abs/2503.03334v1
• Date: Wed, 05 Mar 2025 10:07:48 GMT
• Title: IoT Integration Protocol for Enhanced Hospital Care
• Authors: Ellie Zontou, Antonia Kyprioti,
• Abstract summary: This paper introduces the "IoT Integration Protocol for Enhanced Hospital Care"<n>The protocol aims to harness the potential of IoT devices to optimize patient monitoring, enable remote care, and support clinical decision-making.<n>By integrating IoT seamlessly into nursing and patient care plans, hospitals can achieve higher levels of patient-centric care and real-time data insights.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper introduces the "IoT Integration Protocol for Enhanced Hospital Care", a comprehensive framework designed to leverage Internet of Things (IoT) technology to enhance patient care, improve operational efficiency, and ensure data security in hospital settings. With the growing emphasis on utilizing advanced technologies in healthcare, this protocol aims to harness the potential of IoT devices to optimize patient monitoring, enable remote care, and support clinical decision-making. By integrating IoT seamlessly into nursing workflows and patient care plans, hospitals can achieve higher levels of patient-centric care and real-time data insights, leading to better treatment outcomes and resource allocation. This paper outlines the protocol's objectives, key components, and expected benefits, while emphasizing the importance of ethical considerations and ongoing evaluation to ensure successful implementation.

Related papers

• New care pathways for supporting transitional care from hospitals to home using AI and personalized digital assistance [0.12728819383164874]
  We present an overview of the integration of Internet of Things, artificial intelligence, and digital assistance technologies with traditional care pathways in Europe. We identify the current gaps in transitional care and define the technology mapping to enhance the care pathways.
  arXiv  Detail & Related papers  (2025-04-01T11:37:36Z)
• A Hybrid Data-Driven Approach For Analyzing And Predicting Inpatient Length Of Stay In Health Centre [0.0]
  The study proposes an all-encompassing framework for the optimization of patient flow.<n>Using a comprehensive dataset of 2.3 million de-identified patient records, we analyzed demographics, diagnoses, treatments, services, costs, and charges.<n>Our model predicts patient length of stay (LoS) upon admission using supervised learning algorithms.
  arXiv  Detail & Related papers  (2025-01-30T18:01:48Z)
• Block MedCare: Advancing healthcare through blockchain integration with AI and IoT [0.0]
  We propose a novel-based system that empowers patients with secure control over their medical data.<n>The system incorporates digital signatures, Role-Based Access Control, and a multi-layered architecture to ensure secure, controlled access.
  arXiv  Detail & Related papers  (2024-12-03T21:31:46Z)
• Beyond One-Time Validation: A Framework for Adaptive Validation of Prognostic and Diagnostic AI-based Medical Devices [55.319842359034546]
  Existing approaches often fall short in addressing the complexity of practically deploying these devices. The presented framework emphasizes the importance of repeating validation and fine-tuning during deployment. It is positioned within the current US and EU regulatory landscapes.
  arXiv  Detail & Related papers  (2024-09-07T11:13:52Z)
• Intelligent Clinical Documentation: Harnessing Generative AI for Patient-Centric Clinical Note Generation [0.0]
  This paper explores the potential of generative AI (Artificial Intelligence) to streamline the clinical documentation process. We present a case study demonstrating the application of natural language processing (NLP) and automatic speech recognition (ASR) technologies to transcribe patient-clinician interactions. The study highlights the benefits of this approach, including time savings, improved documentation quality, and enhanced patient-centered care.
  arXiv  Detail & Related papers  (2024-05-28T16:43:41Z)
• RAISE -- Radiology AI Safety, an End-to-end lifecycle approach [5.829180249228172]
  The integration of AI into radiology introduces opportunities for improved clinical care provision and efficiency. The focus should be on ensuring models meet the highest standards of safety, effectiveness and efficacy. The roadmap presented herein aims to expedite the achievement of deployable, reliable, and safe AI in radiology.
  arXiv  Detail & Related papers  (2023-11-24T15:59:14Z)
• Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning Approach to Critical Care [46.2482873419289]
  We introduce a deep Q-learning approach to obtain more reliable critical care policies. We evaluate our method in off-policy and offline settings using simulated environments and real health records from intensive care units.
  arXiv  Detail & Related papers  (2023-06-13T18:02:57Z)
• Robust and Efficient Medical Imaging with Self-Supervision [80.62711706785834]
  We present REMEDIS, a unified representation learning strategy to improve robustness and data-efficiency of medical imaging AI. We study a diverse range of medical imaging tasks and simulate three realistic application scenarios using retrospective data.
  arXiv  Detail & Related papers  (2022-05-19T17:34:18Z)
• Optimal discharge of patients from intensive care via a data-driven policy learning framework [58.720142291102135]
  It is important that the patient discharge task addresses the nuanced trade-off between decreasing a patient's length of stay and the risk of readmission or even death following the discharge decision. This work introduces an end-to-end general framework for capturing this trade-off to recommend optimal discharge timing decisions. A data-driven approach is used to derive a parsimonious, discrete state space representation that captures a patient's physiological condition.
  arXiv  Detail & Related papers  (2021-12-17T04:39:33Z)
• MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation [110.31526448744096]
  We argue that unlocking this potential requires a systematic way to measure the performance of medical AI models on large-scale heterogeneous data. We are building MedPerf, an open framework for benchmarking machine learning in the medical domain.
  arXiv  Detail & Related papers  (2021-09-29T18:09:41Z)
• COVID-Net Clinical ICU: Enhanced Prediction of ICU Admission for COVID-19 Patients via Explainability and Trust Quantification [71.80459780697956]
  We introduce COVID-Net Clinical ICU, a neural network for ICU admission prediction based on patient clinical data. The proposed COVID-Net Clinical ICU was built using a clinical dataset from Hospital Sirio-Libanes comprising of 1,925 COVID-19 patients. We conducted system-level insight discovery using a quantitative explainability strategy to study the decision-making impact of different clinical features.
  arXiv  Detail & Related papers  (2021-09-14T14:16:32Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)
• I-Health: Leveraging Edge Computing and Blockchain for Epidemic Management [36.55809341110476]
  Epidemic situations demand intensive data collection and management from different locations/entities within a strict time constraint. This paper proposes an Intelligent-Health (I-Health) system that aims to aggregate diverse e-health entities in a unique national healthcare system. In particular, we design an automated patients monitoring scheme, at the edge, which enables the prompt discovery, remote monitoring, and fast emergency response.
  arXiv  Detail & Related papers  (2020-12-18T23:41:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.