Ai-Driven Predictive Analytics In Healthcare: Leveraging Salesforce For Scalable, Data-Driven Patient Management Systems

Abstract

Introduction: The study evaluates how Salesforce-based AI-driven predictive analytics might improve healthcare patient management systems. It aims to investigate the aforementioned technologies' influence on patient results, resource efficiency, and the adaptability of medical procedures.

 Literature Review: Healthcare information and cutting-edge algorithms are employed to improve governance in healthcare patient management systems through the implementation of AI-driven predictive analytics and Salesforce. The overarching concept concentrates on pinpointing the issues and fixes related to expanding AI-driven healthcare systems that depend on Salesforce.

 Methodology: The primary quantitative method for data collection makes certain that the outcomes are founded on actual, first-hand accounts from the people involved. A total count of 70 individuals was chosen as a representative sample to encompass a wide range of patients with disabilities and diverse complications.

 Findings and analysis: SPSS software has been used in this research to analyze the collected data statistically. Therefore, based on demographic and statistical tests data has been collected in this research.

 Conclusion: The study finally shows that a shift in perspective in the field of medicine has been brought about by the incorporation of Artificial Intelligence into predictive modeling for the identification and management of disease. It therefore, the use of AI in patient management structures encompasses tremendous potential for both bettering patient outcomes and allocating resources optimally.