Authors: Stephen D Rappaport, Howard R Moskowitz.
This paper presents a framework for personalized care in pre-diabetes management, with a particular emphasis on understanding the mind-sets of patients, guided by Mind Genomics and developed from the training set of a popular LLM (large language model). The framework has been crafted to empower healthcare professionals to structure, understand and address the needs and desires of patients. Through a comprehensive AI analysis of patient mindsets, journey stages, and communication strategies contained in LLM (large language models), this paper offers potentially valuable insights to enhance the provision of effective patient care. The methods section gives a detailed explanation of how mind-sets, journey stages, and assessment tools were developed. The findings present detailed tables that document various mind-sets, stages of the journey, and suggested language for offering patient support emerging from the AI. The discussion explores the implications and limitations of the framework. The paper finishes by suggesting the benefits emerging from this mind-set based personalized care for pre-diabetes management. Future research directions are suggested to validate the framework in clinical settings and explore its adaptability to other chronic diseases.
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