A software and hardware architecture for a high-availability PACS
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Updated: May 21, 2026

Workflow and Framework for Collecting and Implementing Point-of-Care Ultrasound Data in the Management of Heart Failure Patients
Published on: July 12, 2024
Josefina Gutiérrez-Martínez1, Marco Antonio Núñez-Gaona, Heriberto Aguirre-Meneses
1Subdirección de Investigación Tecnológica, Instituto Nacional de Rehabilitación, Av. México- Xochimilco No. 289, Col. Arenal de Guadalupe, Tlalpan, 14389, México, Federal District, Mexico. jgutierrez@inr.gob.mx
Abstract:
Increasing radiology studies has led to the emergence of new requirements for management medical information, mainly affecting the storage of digital images. Today, it is a necessary interaction between workflow management and legal rules that govern it, to allow an efficient control of medical technology and associated costs. Another important topic that is growing in importance within the healthcare sector is compliance, which includes the retention of studies, information security, and patient privacy. Previously, we conducted a series of extensive analysis and measurements of pre-existing operating conditions. These studies and projects have been described in other papers. The first phase: hardware and software installation and initial tests were completed in March 2006. The storage phase was built step by step until the PACS-INR was totally completed. Two important aspects were considered in the integration of components: (1) the reliability and performance of the system to transfer and display DICOM images, and (2) the availability of data backups for disaster recovery and downtime scenarios. This paper describes the high-availability model for a large-scale PACS to support the storage and retrieve of data using CAS and DAS technologies to provide an open storage platform. This solution offers a simple framework that integrates and automates the information at low cost and minimum risk. Likewise, the model allows an optimized use of the information infrastructure in the clinical environment. The tests of the model include massive data migration, openness, scalability, and standard compatibility to avoid locking data into a proprietary technology.
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