Author
Listed:
- Jaymin Harishkumar Sutarwala
Abstract
Integrating Artificial Intelligence (AI) in Healthcare Customer Relationship Management (CRM) systems represents a significant advancement in modern healthcare delivery. This comprehensive review examines emerging innovations at the intersection of AI and Healthcare CRM, focusing on machine learning algorithms, predictive analytics, and natural language processing applications. The research demonstrates how these technologies enhance patient care through improved risk prediction, personalized treatment planning, and automated communication systems. Further investigation explores the implementation of AI-powered solutions for sentiment analysis and patient feedback interpretation, leading to enhanced service delivery and patient retention. The study addresses critical challenges in data privacy, ethical considerations, and cybersecurity measures necessary for protecting sensitive patient information. The findings indicate that while AI integration in Healthcare CRM systems demonstrates substantial potential for improving operational efficiency and patient outcomes, successful implementation requires careful consideration of regulatory compliance and ethical guidelines. This research contributes to the growing body of knowledge on healthcare digitalization by providing insights into the transformative potential of AI in patient relationship management and identifying key areas for future research and development. The work concludes with recommendations for healthcare providers and technology developers to optimize the integration of AI in Healthcare CRM systems while maintaining high standards of patient care and data security.
Suggested Citation
Jaymin Harishkumar Sutarwala, 2025.
"Artificial Intelligence in Healthcare CRM: A Systematic Review of Emerging Technologies and Patient-Centered Applications,"
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(1), pages 2717-2727, February.
Handle:
RePEc:jbh:ijsrcs:v11:y2025:i1:id:941
DOI: 10.32628/CSEIT251112294
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT251112294
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jbh:ijsrcs:v11:y2025:i1:id:941. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Pankaj Sharma (USA) (email available below). General contact details of provider: https://ijsrcseit.com/home .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.