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Related Concept Videos

Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
For example, a patient with a chronic illness...
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GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
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Levels of Use of a GIS

Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and family,...

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Updated: May 9, 2026

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

Strategies for data management engagement.

Deborah H Charbonneau1

  • 1School of Library and Information Science, Wayne State University, Detroit, MI 48202, USA. dcharbon@wayne.edu

Medical Reference Services Quarterly
|July 23, 2013
PubMed
Summary
This summary is machine-generated.

Academic librarians are adapting to support data-intensive research by enhancing their data management skills. This involves exploring new roles and collaborations within library and information science education.

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Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

Area of Science:

  • Library and Information Science
  • Data Science
  • Health Sciences Librarianship

Background:

  • The research landscape is rapidly expanding, necessitating new support structures.
  • Librarians are exploring evolving roles to meet the demands of data-intensive research.
  • Health sciences librarians are key stakeholders in managing research data.

Purpose of the Study:

  • To describe curricular enhancements in data management for librarians.
  • To identify essential data management skills for health sciences librarians.
  • To present strategic roles and opportunities for librarians in data management.

Main Methods:

  • Curricular review and enhancement at a School of Library and Information Science.
  • Identification of key data management areas relevant to health sciences.
  • Analysis of potential librarian roles in data initiatives.

Main Results:

  • Specific data management skills are outlined for librarian development.
  • New roles and collaborative opportunities for librarians are presented.
  • Curricular changes aim to equip librarians for data-intensive research environments.

Conclusions:

  • Health sciences librarians require enhanced data management expertise.
  • Strategic engagement in data initiatives is crucial for librarians.
  • Educational programs must evolve to support librarians in the data science era.