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Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

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Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
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Issues And Trends In Healthcare Delivery System01:29

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
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Essential infection prevention measures are based on the knowledge of the infection chain, the modes of transmission in healthcare settings, and the use of the best practices in all healthcare settings. Compulsory public reporting of healthcare-associated infection rates is needed to allow individuals and the community to make informed choices regarding selecting a healthcare facility.
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Integrated Healthcare System01:20

Integrated Healthcare System

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An integrated healthcare system (IHS) is a set of organizations that provides for or arranges to provide coordinated and continuous service to a defined population. The IHS takes responsibility for that particular population's health status and outcome, both clinically and fiscally. An integrated healthcare system is a well-organized, well-coordinated, and collaborative network. The integrated delivery system is a network that connects different healthcare providers to deliver organized,...
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Nursing Clinical Information System (NCIS)
A Nursing Clinical Information System (NCIS) is a specialized type of healthcare information system tailored to meet the unique needs of nursing practice. It incorporates the principles of nursing informatics to streamline information management and improve the quality of care delivery.
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Principles of Disease Surveillance01:26

Principles of Disease Surveillance

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Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
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A New Approach for Detecting Fundus Lesions Using Image Processing and Deep Neural Network Architecture Based on YOLO Model.

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A Comprehensive Survey on Intrusion Detection Systems for Healthcare 5.0: Concepts, Challenges, and Practical

Lucas P Siqueira1, Cassio L Batista2, Pedro H Lui1

  • 1PPGCC, Universidade Federal de Santa Maria, Santa Maria 97105-900, Brazil.

Sensors (Basel, Switzerland)
|October 29, 2025
PubMed
Summary

Healthcare 5.0 systems require robust security. This study reviews Intrusion Detection Systems (IDSs), highlighting AI, datasets, and Explainable AI (XAI) for enhanced threat detection in connected healthcare environments.

Keywords:
Explainable Artificial IntelligenceHealthcare 5.0Internet of Medical Thingsdatasetintrusion detections systemspractical applicationsecuritysurvey

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Area of Science:

  • Cybersecurity
  • Healthcare Technology
  • Artificial Intelligence

Background:

  • Healthcare 5.0 integrates AI and IoMT for advanced patient care.
  • Intrusion Detection Systems (IDSs) are vital for securing these complex systems.
  • Existing research lacks a unified evaluation of AI models, datasets, and XAI for Healthcare 5.0 IDSs.

Purpose of the Study:

  • To systematically review Intrusion Detection Systems (IDSs) in the context of Healthcare 5.0.
  • To analyze state-of-the-art AI models, suitable datasets, and the role of Explainable AI (XAI).
  • To bridge the gap between cybersecurity and clinical practice for secure healthcare systems.

Main Methods:

  • Comprehensive literature review of IDSs for Healthcare 5.0.
  • Analysis of current AI approaches and available datasets.
  • Presentation of a case study fusing network and biomedical features for threat detection.

Main Results:

  • The fusion of network and biomedical features significantly enhances threat detection capabilities.
  • Physiological signals are critical for identifying sophisticated attacks such as spoofing.
  • AI models and appropriate datasets are key to effective IDS in Healthcare 5.0.

Conclusions:

  • An integrated analysis of IDSs for Healthcare 5.0 is crucial.
  • Future systems require secure, transparent, and patient-centric cybersecurity solutions.
  • This survey guides researchers and practitioners in developing advanced Healthcare 5.0 security.