Predictive Cloud Intelligence for Precision Patient Care

Authors

  • Murali Malempati Author
  • Alexander Petrov Author

Keywords:

Clinical Intelligence Fabric, Intelligent Health Systems, Predictive Patient Intelligence, Clinical Decision Support, Precision Care Delivery, Electronic Health Records (EHR), Healthcare Interoperability, Fast Healthcare Interoperability Resources (FHIR), Health Level Seven (HL7), Cloud Healthcare Analytics.

Abstract

Achieving clinically effective Intelligent Health Systems requires more than managing patient-level information overload — it demands the right technology to power clinical intelligence and the means to embed that intelligence into clinical workflows. This study synthesizes the Clinical Intelligence literature and proposes an architecture for a cloud-enabled Clinical Intelligence fabric designed for integration into healthcare organizations’ clinical workflows, advancing predictive patient insights and decision support for precision care delivery.

We define Clinical Intelligence as the actionable set of information, knowledge, evidence, processes, and best practices required to navigate the complexities of modern clinical care. Cloud computing is well suited to this goal: it makes data readily accessible with minimal local deployment, offers virtually unlimited scalable data management resources that let data volumes be addressed from within the workflow itself, and supports governance frameworks for managing health data privacy and security across multiple jurisdictions.

The clinical foundation draws on data from Electronic Health Records, imaging, genomics, wearables, and clinical trials. Interoperability is supported by internationally recognized standards, including the FHIR (Fast Healthcare Interoperability Resources) schema for data representation and HL7 (Health Level 7) standards for exchanging clinical information. A formal risk-based methodology ensures both accurate responses to modeled clinical questions and control of unintended harm.

The risk of patient-level information overload without a corresponding clinical intelligence layer is clear: clinical intelligence in therapeutic care lies not merely in what we know about patients, but in the ability to predict future health states and events.

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Additional Files

Published

2026-08-28

How to Cite

Predictive Cloud Intelligence for Precision Patient Care. (2026). The American Online Journal of Science and Engineering (AOJSE), 4(03). https://aojse.org/index.php/aojse/article/view/81

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