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Related Concept Videos

Ethical Standards I01:25

Ethical Standards I

744
The American Nurses Association (ANA) created and implemented the first nationally accepted Code of Ethics for Nurses with Interpretive Statements. The Code of Ethics is a living document regularly updated by the ANA and establishes an ethical standard that is non-negotiable for nurses in all roles and settings.
The Code of Ethics provisions outline the nurse's duty to the patient, the healthcare team, the profession, and society. The Code's fundamental principles include advocacy,...
744
Assessment of radial pulse01:11

Assessment of radial pulse

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Assessment of Radial Pulse
The radial pulse, located at the wrist, is often the preferred site for assessing peripheral pulse because of its accessibility and dependability. The process of determining the radial pulse involves several steps:
740
Assessment of apical radial pulse01:25

Assessment of apical radial pulse

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Apical-Radial (A-R) Pulse Assessment
The A-R pulse assessment involves simultaneous evaluation of the apical and radial pulses. When the apical and radial pulse rates vary, this assessment helps identify a pulse deficit.
Pre-Procedural Preparation
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Ethical Standards II01:23

Ethical Standards II

621
Ethical standards are the backbone of nursing practice, guiding nurses as they interact with patients, families, and colleagues. These standards are crucial for providing safe, empathetic care centered on the patient's needs.
Nurses are entrusted with upholding various ethical principles and standards. Nurses forge solid therapeutic relationships using trust, empathy, autonomy, confidentiality, and professional competence.
Confidentiality is crucial, embodying respect for individual privacy...
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Guidelines and Strategies for Safe Computer Charting01:18

Guidelines and Strategies for Safe Computer Charting

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The guidelines and strategies provided by the American Nurses Association (ANA) and the Canadian Nurses Association (CNA) offer essential principles for ensuring safe and secure computer charting systems in healthcare settings. Let's break down each recommendation:
Maintain Confidentiality and Security:
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Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
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Secure healthcare data sharing and attack detection framework using radial basis neural network.

Abhishek Kumar1, Priya Batta1, Pramod Singh Rathore2

  • 1Department of Computer Science and Engineering, Chandigarh University, Punjab, Mohali, India.

Scientific Reports
|May 2, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a secure access control mechanism (PA2C) and an attack detection model (IntVO-RBNN) to enhance electronic health record (EHR) data sharing security. The novel methods improve data integrity and network security in healthcare applications.

Keywords:
Access controlAttack detectionBlockchain technologyDeep learningElectronic healthcare records

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Area of Science:

  • Computer Science
  • Information Security
  • Healthcare Informatics

Background:

  • Secure medical data sharing is critical but current architectures lack comprehensive security for sensitive health information.
  • Existing access control methods are often application-specific, failing to meet the dynamic and complex security demands of healthcare.
  • Healthcare requires dynamic permission enforcement, context-aware access control, and flexible authentication for secure data management.

Purpose of the Study:

  • To propose a novel security architecture for safeguarding sensitive medical data during sharing and access.
  • To develop an effective attack detection model for identifying network threats in healthcare systems.
  • To enhance the integrity, security, and dependability of electronic health record (EHR) data sharing.

Main Methods:

  • A proposed authenticate access control mechanism (PA2C) utilizing smart contracts, encryption, and secure key management.
  • An intelligent voyage optimization algorithm-based Radial basis neural network (IntVO-RBNN) for network attack detection.
  • Intelligent Voyage Optimization algorithm for hyperparameter tuning and hybrid features for effective attack pattern recognition.

Main Results:

  • The PA2C mechanism demonstrated superior performance over existing methods with 100.18s minimal responsiveness and 4.49% information loss for 100 blocks.
  • The IntVO-RBNN model achieved high performance in attack detection, with 95.26% recall, 97.84% precision, and 94.02% accuracy.
  • Comparative analysis confirmed the effectiveness of the proposed access control strategy and attack detection model.

Conclusions:

  • The PA2C mechanism significantly improves the security and dependability of EHR data sharing, ensuring data integrity.
  • The IntVO-RBNN model provides an effective solution for detecting network attacks within healthcare systems.
  • The research offers a robust security framework addressing the complex and dynamic security needs of the healthcare sector.