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Combination Therapies and Personalized Medicine

Combination Therapies and Personalized Medicine

Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...

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Improving resource allocation in the precision medicine Era: a simulation-based approach using R.

Mohamed Kamal¹, Mohamed Nagy^2,3, Omneya Hassanain¹

¹Research Department, Children's Cancer Hospital Egypt, 57357, Cairo, Egypt.

Personalized Medicine

|July 25, 2024

Summary

Optimizing hospital bed allocation is crucial for resource management in developing nations. Simulation results show that limited beds create unstable systems with long patient waiting times, highlighting the need for efficient resource allocation strategies.

Keywords:

Computer Simulation Decision Analytic models Healthcare Process Improvement Precision Medicine R Model

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Area of Science:

Healthcare Management
Operations Research
Health Economics

Background:

Personalized medicine application in developing countries faces significant economic challenges.
Efficient resource allocation is critical for improving healthcare delivery and patient outcomes.
Optimizing hospital bed allocation is paramount to balance resource utilization and patient care quality.

Purpose of the Study:

To evaluate the efficiency of hospital bed allocation using a simulation-based approach.
To identify system bottlenecks and analyze their impact on patient waiting times.
To provide insights for hospital management on optimizing resource allocation.

Main Methods:

Employed a simulation-based approach to model hospital bed allocation.
Utilized a patient arrival model with exponential distribution to simulate patient trajectories.
Examined system bottlenecks, focusing on waiting times and queue lengths.

Main Results:

Initial simulations indicated an unstable healthcare system with limited beds.
Simulations revealed significant increases in patient waiting times and queue lengths.
The study identified bed scarcity as a primary driver of system inefficiency.

Conclusions:

Efficient resource allocation, specifically bed management, is essential for stable healthcare systems.
Simulation modeling can effectively identify bottlenecks and inform optimization strategies.
Findings offer practical insights for hospital management to improve patient care through better resource optimization.