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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.
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Related Experiment Video

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MedScrubCrew: A Medical Multi-Agent Framework for Automating Appointment Scheduling Based on Patient-Provider Profile

Jose M Ruiz Mejia1, Danda B Rawat1

  • 1Department of Electrical Engineering and Computer Science, Howard University, Washington, DC 20059, USA.

Healthcare (Basel, Switzerland)
|July 29, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces MedScrubCrew, a multi-agent system using AI for healthcare automation. It enhances appointment scheduling and triage, improving efficiency and patient care in medical settings.

Keywords:
Gale-Shapley Matchingagentic workflowsclinical decision support systemlarge language modelmulti-agent systempatient-provider profiles

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Medical Informatics
  • Computational Health

Background:

  • Generative Artificial Intelligence (AI) is increasingly adopted across industries to boost efficiency.
  • Contextual understanding is vital for AI in high-stakes fields like healthcare.
  • Current healthcare systems face inefficiencies in operational processes.

Purpose of the Study:

  • To develop a practical medical multi-agent system framework.
  • To automate appointment scheduling and triage classification in healthcare.
  • To improve the operational efficiency of healthcare settings.

Main Methods:

  • The MedScrubCrew framework integrates the Gale-Shapley algorithm for patient-provider matching.
  • Knowledge graphs are utilized for semantic compatibility profiling.
  • Specialized large language model-based agents emulate collaborative medical team decision-making.

Main Results:

  • The multi-agent architecture significantly enhances operational efficiency in healthcare scheduling.
  • Task completeness and contextual relevance were substantially improved.
  • The system demonstrated effectiveness in real-world healthcare automation scenarios.

Conclusions:

  • MedScrubCrew offers a practical blueprint for healthcare automation.
  • The framework addresses significant inefficiencies in appointment scheduling and patient triage.
  • This AI-driven approach has the potential to transform healthcare operations.