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Issues And Trends In Healthcare Delivery System

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Related Experiment Videos

Real-time healthcare staffing optimization using fuzzy logic and genetic algorithm.

Balveer Saini1, Dinesh Kumar Saini2

  • 1Department of Mathematics, M. S. J. Govt. P. G. College, Bharatpur, affiliated to Maharaja Surajmal Brij University, Bharatpur, Rajasthan, India.

Health Economics Review
|June 17, 2026
PubMed
Summary

This study introduces a new healthcare staffing model, the Fuzzy-Based Time-Dependent Multi-Server, Multi-Queueing (FB-TDMS-MQ) System, which significantly reduces patient waiting times. The model optimizes staff allocation in real-time, improving healthcare system efficiency.

Keywords:
Dynamic healthcareFB-TDMS-MQ modelFuzzy logicGenetic algorithmHybrid queueing approach

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Area of Science:

  • Healthcare Management
  • Operations Research
  • Artificial Intelligence in Healthcare

Background:

  • Healthcare systems face challenges like rising costs, increased demand, and resource constraints.
  • Effective management and efficient resource utilization are crucial for healthcare delivery.
  • Optimized systems improve service delivery and reduce patient waiting times.

Purpose of the Study:

  • To introduce a novel model for optimizing healthcare staffing in real-time.
  • To address the limitations of traditional fixed-staffing models in dynamic healthcare environments.
  • To improve the efficiency and responsiveness of healthcare services.

Main Methods:

  • Development of the Fuzzy-Based Time-Dependent Multi-Server, Multi-Queueing (FB-TDMS-MQ) System.
  • Integration of fuzzy logic and a genetic algorithm (GA) for dynamic staff optimization.
  • Validation through a case study at Dhanwantri Hospital and Research Centre (DHRC) using simulation studies.

Main Results:

  • A 72.73% reduction in average peak-hour waiting time was achieved with fuzzy logic modifications.
  • The genetic algorithm optimization led to a 50.2% decrease in average waiting time.
  • The model demonstrated superior performance compared to static staffing models and proved useful for handling unexpected events.

Conclusions:

  • The FB-TDMS-MQ model offers an adaptable and effective solution for real-time healthcare staffing optimization.
  • Dynamic staff allocation using this model can significantly reduce patient waiting times and improve hospital management.
  • The model's ability to handle unexpected events highlights its potential for enhancing healthcare system resilience.