ArticleCureus2025
Closing the Loop: A Software-Based Middleware Framework for Automated Vital Sign Integration With Cloud-Based Electronic Medical Records (EMRs).
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite significant advances in real-time physiologic monitoring, most hospitals continue to rely on manual vital sign documentation, a process prone to delays, transcription errors, and increased clinician workload. This paper presents a modular software-based middleware solution that enables seamless real-time transmission of vital signs from FDA-cleared monitoring devices to cloud-based electronic medical records (EMR). By leveraging Health Level Seven (HL7) and Fast Healthcare Interoperability Resources (FHIR) standards for interoperability, the system connects existing devices with major EMR platforms using standardized application programming interfaces, reducing infrastructure burden and improving documentation accuracy. We examine current barriers to adoption, including cost, workflow disruption, and compliance challenges, and highlight real-world examples of successful integration. Our analysis suggests that an interoperable and nurse-friendly architecture with built-in audit trails can enhance early warning system performance, reduce documentation errors, and alleviate clinician burnout by automating routine charting. This approach supports the transition to predictive, real-time clinical decision support in modern healthcare.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.