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Related Concept Videos

Nursing Clinical Information System01:27

Nursing Clinical Information System

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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.
Critical attributes of NCIS include:
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Flow Sheet01:17

Flow Sheet

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Flowsheets are valuable tools in nursing documentation. They enable healthcare professionals to efficiently record and monitor various patient assessments and measurements in a consolidated format.
Here's a closer look at the examples of flowsheets commonly used by nurses:
Graphic Sheet Documentation:
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Health Information Technology and Healthcare Information System01:30

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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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Guidelines and Strategies for Safe Computer Charting01:18

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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:
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Formats for Nursing Documentation01:28

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Nursing documentation encompasses various formats designed to capture precise patient data, facilitate communication among healthcare team members, and ensure comprehensive and accurate patient records. Let's explore each of these formats in detail:
Nursing Assessment Form:
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• It includes patient demographics, medical history,...
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Methods of Documentation IV: Focus Charting01:26

Methods of Documentation IV: Focus Charting

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Focus Charting, also known as the focus charting system or "focus documentation," is a systematic documentation approach used in healthcare to organize patient information in medical records.
It typically involves three columns for recording information:
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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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EHDViz: clinical dashboard development using open-source technologies.

Marcus A Badgeley1, Khader Shameer1, Benjamin S Glicksberg1

  • 1Harris Center for Precision Wellness, Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York City, New York, USA Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York City, New York, USA.

BMJ Open
|March 26, 2016
PubMed
Summary
This summary is machine-generated.

We developed the EHDViz toolkit to create real-time clinical dashboards for integrating diverse health data. This open-source framework supports precision medicine by enabling data-driven insights and scalable visualization solutions.

Keywords:
biomedical informaticsclinical dashboardclinical decision systemsdata visuzalizationearly warning systems

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Area of Science:

  • Biomedical Informatics
  • Health Data Visualization
  • Clinical Decision Support

Background:

  • Integrating high-frequency health and wellness data is crucial for modern healthcare.
  • Existing tools often lack the flexibility to visualize heterogeneous data streams in real-time.
  • Effective data visualization aids in understanding complex health patterns and improving patient care.

Purpose of the Study:

  • To design, develop, and prototype clinical dashboards for integrating high-frequency health and wellness data.
  • To create an interactive and real-time data visualization and analytics platform.
  • To establish a versatile framework for diverse clinical and wellness data applications.

Main Methods:

  • Developed the electronic healthcare data visualization (EHDViz) toolkit, an extensible R-based framework.
  • Utilized R/Shiny web server architecture for generating web-based, real-time clinical dashboards.
  • Demonstrated utility through use cases in outpatient, inpatient, and quantified-self settings.

Main Results:

  • Prototyped clinical dashboards showcasing EHDViz's contextual versatility.
  • Visualized population health management (n=14,221) and real-time acuity risk (n=445).
  • Developed an open-source toolkit with publicly available source code and prototypes.

Conclusions:

  • EHDViz facilitates collaborative data visualization, trend prediction, risk estimation, and acuity monitoring.
  • The framework supports the implementation of data-driven precision medicine.
  • EHDViz is a valuable, scalable, open-source toolkit for rapid development of clinical data visualization dashboards.