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Adaptive Healthcare Policies: GPT-Powered Insights for Agile Decision-Making in Public Health

EasyChair Preprint no. 12969

9 pagesDate: April 9, 2024


In the ever-evolving landscape of public health, the ability to adapt swiftly to emerging challenges is paramount. This paper explores the utilization of GPT-powered insights for agile decision-making in the realm of healthcare policies. Leveraging the capabilities of Generative Pre-trained Transformers (GPT), this study demonstrates how AI-driven models can provide valuable insights and predictions to inform adaptive healthcare policies. Through a synthesis of real-time data, historical trends, and contextual understanding, GPT-powered systems offer the agility needed to respond effectively to dynamic public health scenarios. The paper discusses the potential applications of GPT in forecasting disease outbreaks, optimizing resource allocation, and designing targeted interventions. Furthermore, it highlights the importance of collaboration between policymakers, healthcare professionals, and AI developers in harnessing the full potential of GPT-powered insights. By embracing adaptive healthcare policies informed by AI- driven analytics, societies can better navigate the complexities of public health challenges and safeguard the well-being of populations.

Keyphrases: GPT, Healthcare, public health

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Shophia Lorriane},
  title = {Adaptive Healthcare Policies: GPT-Powered Insights for Agile Decision-Making in Public Health},
  howpublished = {EasyChair Preprint no. 12969},

  year = {EasyChair, 2024}}
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