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Issues And Trends In Healthcare Delivery System

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.
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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

Biomedical cloud computing with Amazon Web Services.

Vincent A Fusaro1, Prasad Patil, Erik Gafni

  • 1Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America. vfusaro@hms.harvard.edu

Plos Computational Biology
|September 9, 2011
PubMed
Summary
This summary is machine-generated.

Leveraging cloud computing for biomedical informatics requires significant upfront effort beyond simple instance selection. This study outlines best practices for scalable cloud deployment, demonstrated with next-generation sequencing (NGS) mapping.

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Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management
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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
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Published on: February 23, 2019

Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management
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Biobank for Translational Medicine: Standard Operating Procedures for Optimal Sample Management

Published on: November 30, 2022

Area of Science:

  • Cloud Computing in Bioinformatics
  • Biomedical Data Analysis

Background:

  • Cloud computing offers scalable resources for biomedical research.
  • New users often underestimate the initial setup complexity for cloud environments.

Purpose of the Study:

  • To provide best practices for cloud adoption in biomedical computing.
  • To illustrate scalable cloud project development using a next-generation sequencing (NGS) mapping example.
  • To guide users on efficient cloud resource utilization and cost management.

Main Methods:

  • Discussed two cloud deployment models: single instance and cluster.
  • Provided a detailed case study of next-generation sequencing (NGS) read mapping on a cloud platform.
  • Highlighted cost considerations for cloud-based computational tasks.

Main Results:

  • Substantial upfront effort is necessary for optimal cloud application performance.
  • Scalable project development is achievable with proper planning and execution.
  • Efficient genome mapping can be cost-effective, e.g., mapping the African genome for approximately $48.

Conclusions:

  • Successful cloud implementation in biomedical informatics requires strategic planning and best practices.
  • The presented methods are generalizable to various computational problems beyond NGS.
  • Data transfer limitations should be considered, with physical data shipment as a potential alternative.