AI-Driven Patient Transfer Summarization Using Salesforce Health Cloud as a Clinical CRM Intelligence Platform

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Authors: Sai Saketh Sunkara
Cited by: 0

Abstract

The increasing complexity of inter-hospital patient transfers, combined with the widespread use of heterogeneous and unstructured medical documentation, has intensified challenges related to data fragmentation, delayed clinical decision-making, and inefficient care coordination. Existing healthcare information systems and CRM platforms often operate in silos, limiting their ability to provide timely, standardized, and actionable patient insights. To address these limitations, this paper proposes a Salesforce Health Cloud–centric AI framework, where Salesforce Health Cloud acts as the primary clinical CRM platform orchestrating patient transfer workflows, while AI modules provide embedded document intelligence and predictive decision support. The proposed architecture combines an Adaptive Document Parsing and Structuring (ADPS) Framework for layout-aware document understanding, a Context-Aware Clinical Summarization (CACS) Engine for ontology-guided extraction of medically significant information, a FHIR-Compliant Interoperability Integration (FCI) Layer for standardized data exchange, and a Predictive Admission Intelligence Module (PAIM) for proactive triage and risk assessment. By unifying document intelligence, interoperability, CRM workflow automation, and machine learning–based prediction into a single end-to-end pipeline, the framework converts patient transfer documentation from a manual, error-prone process into an automated, decision-support-driven workflow. Experimental evaluation on real-world inter-hospital transfer datasets demonstrates high document structuring accuracy (94.2%), clinically relevant summarization performance (96.8%), substantial reductions in admission processing time (from 38 to 11 minutes), and strong predictive accuracy (97.1%) with reliable risk discrimination. The results confirm that the proposed approach enhances clinical efficiency, reduces administrative burden, and enables CRM platforms to function as active participants in clinical decision-making, establishing a scalable and intelligent foundation for digital transformation in healthcare transfer management.

Fields of Study & Subjects

Artificial Intelligence in Healthcare and Education Electronic Health Records Systems Machine Learning in Healthcare

Publication details

Journal / source
Journal of Advances in Developmental Research
Volume
17
Issue
1
Publication date
20 January 2026
Language
EN