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International Journal of Advanced Artificial Intelligence Research

Open Access Peer Review International
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A Longitudinal Patient Reasoning Layer for Intelligent Sepsis Surveillance in Real-Time Laboratory Networks

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Rahul Reddy Hanumanthgari 1 ORCID iD ,
4 Independent Researcher, AI Engineering, NC, USA

Abstract

Automated sepsis alert systems are now standard components of hospital clinical decision support infrastructure, yet their clinical value is undermined by false positive rates exceeding 75%, with independently validated systems reporting positive predictive values as low as 22.4%. This paper introduces the Longitudinal Patient Reasoning Layer (LPRL), a middleware architecture deployed at the reference laboratory layer that addresses this limitation by replacing population-derived reference thresholds with patient-specific longitudinal baselines. The LPRL maintains a rolling 36-month analyte history for each enrolled patient and applies four sequential reasoning modules to enrich each incoming laboratory result before alert generation. Central to this process is the Personal Deviation Ratio (PDR), which quantifies deviation from an individual patient's own historical norm rather than a population mean. The architecture is evaluated against published performance benchmarks for population-threshold and machine learning alert systems, using primary metrics of sensitivity, specificity, positive predictive value, and the Alert-to-Sepsis Positive Ratio (ASPR). Beyond sepsis surveillance, the LPRL's longitudinal profile store and PDR reasoning engine are substantively extensible to chronic disease surveillance, multi-site epidemiological monitoring, and real-world evidence generation.

Keywords

Sepsis Detection, Longitudinal Patient Baseline, Alert Fatigue, Clinical Decision Support, HL7 FHIR, Laboratory Information Systems, Real-World Evidence, EHR Interoperability

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