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Related Concept Videos

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

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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
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
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Methods Of Healthcare Delivery System01:26

Methods Of Healthcare Delivery System

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At the different levels of the healthcare system, we see varying methods of healthcare used. These methods include managed care systems, case management, and primary healthcare.
Managed Care System:
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Trends in nursing are multifactorial and associated with changes in society, within the nursing profession, and in other professions. Notably, telehealth and remote nursing contribute to successful healthcare delivery for numerous patients and help reduce stress for nurses due to nursing shortages. Nurses can reach patients, monitor their conditions, and interact with them using computers, audio, visual accessories, and telephones—for example, remote patient monitoring systems. Likewise,...
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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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Introduction To Health Care Delivery System01:18

Introduction To Health Care Delivery System

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The healthcare system is constantly changing and complex. Various services are available from different healthcare providers, but gaining access to these services has become challenging for people with limited healthcare insurance. Uninsured people present a challenge to healthcare because they frequently postpone or forego treatment.
The Institute of Medicine (IOM) advocates for a patient-centered, effective, safe, timely, equitable, and effective healthcare system. The National Priorities...
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Secondary Healthcare System01:11

Secondary Healthcare System

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Secondary healthcare is offered by a specialist, generally in hospitals or clinics for patients referred by primary healthcare providers. It occurs when a person has an illness or injury that requires specific medical care. Secondary care is often referred to as acute care. Secondary care can range from uncomplicated care to repair a minor laceration or treat a strep throat infection to more complicated emergent care, such as treating a head injury sustained in an automobile accident. Whatever...
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Related Experiment Video

Updated: Nov 5, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Deep Learning and its Application for Healthcare Delivery in Low and Middle Income Countries.

Douglas Williams1, Heiko Hornung2, Adi Nadimpalli3

  • 1Harvard, MA, United States.

Frontiers in Artificial Intelligence
|May 17, 2021
PubMed
Summary

Deep learning image analysis advances reduce technical barriers for healthcare innovation in low-resource settings. This empowers domain experts to address unaddressed health concerns in low- and middle-income countries (LMICs).

Keywords:
NGOsartificial intelligencedeep learningdigital healthglobal healthmachine learningpoint of care diagnosis

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Area of Science:

  • Medical Artificial Intelligence
  • Digital Health Equity
  • Low-Resource Healthcare Technologies

Background:

  • Healthcare delivery in low-resource settings presents unique challenges compared to affluent regions.
  • Artificial Intelligence (AI), particularly deep learning (DL), has seen significant advancements, yet risks exacerbating the digital divide.
  • General DL applications often require specialized expertise, limiting adoption in underserved areas.

Purpose of the Study:

  • To explore advances in deep learning image analysis that lower adoption barriers.
  • To enable individuals with less specialized software skills to utilize advanced AI techniques.
  • To foster innovation in low- and middle-income countries (LMICs) by leveraging domain expertise.

Main Methods:

  • Focus on advancements within deep learning image analysis.
  • Emphasis on techniques that reduce the need for deep technical software expertise.
  • Exploration of how problem domain experts can apply these techniques.

Main Results:

  • Deep learning image analysis tools are becoming more accessible.
  • Reduced technical barriers facilitate the application of AI by healthcare professionals in LMICs.
  • Potential for significant innovation driven by local expertise and AI application.

Conclusions:

  • Accessible deep learning image analysis can democratize AI adoption in global health.
  • Empowering local experts is key to addressing specific healthcare challenges in LMICs.
  • Non-governmental organizations (NGOs) play a crucial role in problem identification, data management, and technology integration.