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Issues And Trends In Healthcare Delivery System01:29

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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.
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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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Related Experiment Video

Updated: Nov 7, 2025

TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients
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Artificial Intelligence Decision Support for Medical Triage.

Chiara Marchiori1, Douglas Dykeman1, Ivan Girardi1

  • 1IBM Research, Rueschlikon, Switzerland.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|May 3, 2021
PubMed
Summary

A new AI-powered triage system uses machine learning and NLP on teleconsultation data to guide patients to appropriate care. This scalable telemedicine solution enhances efficiency and patient experience.

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Machine Learning for Medical Triage
  • Natural Language Processing in Telemedicine

Background:

  • Telemedicine adoption is increasing, necessitating efficient patient management systems.
  • Current triage methods can be resource-intensive and may not offer personalized guidance.
  • The integration of AI offers potential to optimize remote patient assessment.

Purpose of the Study:

  • To develop and implement an AI-driven triage system for a large European telemedicine provider.
  • To evaluate the system's ability to personalize patient interaction and recommend care pathways.
  • To assess the system's potential for improving healthcare efficiency and patient outcomes.

Main Methods:

  • Utilized machine learning and natural language processing on one million teleconsultation records.
  • Developed a mobile application for patient interaction and AI-powered questioning.
  • Integrated the system with a major European telemedicine provider for real-world application.

Main Results:

  • A certified AI triage system is now operational, guiding patient care.
  • The system personalizes questions based on initial symptoms to refine assessments.
  • Demonstrated potential for improved cost efficiency, patient experience, and outcomes.

Conclusions:

  • AI-based decision support systems are crucial for telemedicine adoption.
  • This remote, scalable triage service is vital during high-demand health situations like pandemics.
  • The developed system offers a robust model for AI-driven remote patient guidance.