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Driving Patient Data With AI for Patient Care Path Optimization: A Theoretical Framework
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Author(s): Nouhaila Ben Khizzou (National School of Commerce and Management, Sidi Mohamed Ben Abdellah University, Fez, Morocco), Mourad Aarabe (National School of Business and Management, Fez, Morocco), Meryem Bouizgar (National School of Business and Management, Morocco), Lhoussaine Alla (Sidi Mohamed Ben Abdellah University, Fez, Morocco)and Ahmed Benjelloun (Sidi Mohamed Ben Abdellah University, Fez, Morocco)
Copyright: 2025
Pages: 30
Source title:
AI Innovations for Customer Experience Optimization in the Service Sector
Source Author(s)/Editor(s): Mourad Aarabe (Sidi Mohamed Ben Abdellah University, Morocco), Nouhaila Ben Khizzou (Sidi Mohamed Ben Abdellah University, Morocco), Lhoussaine Alla (Sidi Mohamed Ben Abdellah University, Morocco)and Ahmed Benjelloun (Sidi Mohamed Ben Abdellah University, Morocco)
DOI: 10.4018/979-8-3373-0918-7.ch003
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Abstract
Background: Patient data and artificial intelligence are widely explored concepts for improving healthcare services. However, little is known about the application of lean thinking to quantify these two elements. Objective: The aim of this study is to develop a theoretical framework combining patient data and artificial intelligence, aimed at optimizing the care pathway by identifying the theories to be mobilized to address this issue. Methods: A systematic review of the literature was carried out. Inclusion criteria were that the article should focus on optimizing the patient's care pathway. Twenty articles were included. Results: The proposed framework can be used to create an overview of the potential of driving patient-related data with AI for optimizing the patient care pathway. Further research is needed to investigate the use of patient-related quantifications of this data. Conclusions: This research paper analyzes the application of artificial intelligence in data science to optimize patient care pathways.
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