Hospital Management Leveraging AI

The use of artificial intelligence in the hospital management system is one revolutionizing development in modern health care, with great potential for improving administrative services, quality of patient care, and operational effectiveness in a hospital setting. The current study analyzed the interdependencies and composite structure of the AI applications used in patient interaction, administrative and financial services, and hospital operations. We systematize the review of the integration of AI in three broad areas: optimization of hospital operations and workflow, administrative and financial services, and patient engagement in experiences. The consideration of these features includes analysis of data collection and analysis layers, DSS, execution, and monitoring frameworks, and integration and communication infrastructures that make these functions possible. Resource allocation, supply chain management, facilities management, billing and claims processing, fraud detection, patient data management, personalized patient communication, appointment scheduling, and remote monitoring are some of the components we can consider and how they interlink. Our studies have shown that AI-enabled hospital management systems are premised on vast data collection and analysis, using machine learning models in predicting, optimizing, and automating procedures. Meanwhile, decision support systems make hospital supply chain optimization, facilities management, and resource allocation easier. An execution and monitoring layer ensures these choices are affected. To be truly effective and efficient, these components must be interdependent, and the feedback loops allow for continuous development. AI would help improve fraud detection, simplify patient data administration, and automate billing and claims processing in administrative and financial services. Personalized communication, efficient appointment scheduling, and cutting-edge remote monitoring tools all help to better engage the patients themselves, making healthcare more patient-centered. That is, the better the understanding of the structure and interdependencies of system components, the better healthcare providers can already implement and utilize AI technologies. Integration of AI in hospital management systems has a great promise for bringing improvement in patient outcomes and making healthcare delivery easier

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Publication Details

Journal
SciNexuses.
Published
2024-12-30
DOI
https://doi.org/10.61356/j.scin.2024.1519
Citations
1
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
0.35
Controls
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article

Hospital Management Leveraging AI

Ahmed Abdelhafeez, Mohamed T. Hekal, Mohamed A. Al-Kazaz, Mahmoud H.S. Elgarhy et al.
1 citations
SciNexuses.
Machine Learning in Healthcare
0.35
article

Hospital Management Leveraging AI

Ahmed Abdelhafeez, Mohamed T. Hekal, Mohamed A. Al-Kazaz, Mahmoud H.S. Elgarhy, Abdulhamed S. Elhasanin
article en
1 citations

Abstract

The use of artificial intelligence in the hospital management system is one revolutionizing development in modern health care, with great potential for improving administrative services, quality of patient care, and operational effectiveness in a hospital setting. The current study analyzed the interdependencies and composite structure of the AI applications used in patient interaction, administrative and financial services, and hospital operations. We systematize the review of the integration of AI in three broad areas: optimization of hospital operations and workflow, administrative and financial services, and patient engagement in experiences. The consideration of these features includes analysis of data collection and analysis layers, DSS, execution, and monitoring frameworks, and integration and communication infrastructures that make these functions possible. Resource allocation, supply chain management, facilities management, billing and claims processing, fraud detection, patient data management, personalized patient communication, appointment scheduling, and remote monitoring are some of the components we can consider and how they interlink. Our studies have shown that AI-enabled hospital management systems are premised on vast data collection and analysis, using machine learning models in predicting, optimizing, and automating procedures. Meanwhile, decision support systems make hospital supply chain optimization, facilities management, and resource allocation easier. An execution and monitoring layer ensures these choices are affected. To be truly effective and efficient, these components must be interdependent, and the feedback loops allow for continuous development. AI would help improve fraud detection, simplify patient data administration, and automate billing and claims processing in administrative and financial services. Personalized communication, efficient appointment scheduling, and cutting-edge remote monitoring tools all help to better engage the patients themselves, making healthcare more patient-centered. That is, the better the understanding of the structure and interdependencies of system components, the better healthcare providers can already implement and utilize AI technologies. Integration of AI in hospital management systems has a great promise for bringing improvement in patient outcomes and making healthcare delivery easier

SciNexuses.Vol. 1
October 6 University (EG)
Openalex Percentile: Top 29%
Machine Learning in Healthcare
0.35
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