Automated Sepsis Screening in the ICU

A FHIR-Based Clinical Decision Support Pipeline with Machine Learning Risk Stratification

Cerner Open FHIR Sandbox (fhir-open.cerner.com) Generated 2026-03-10

Abstract

Background: Sepsis remains a leading cause of mortality in intensive care units worldwide, with delayed recognition contributing to preventable deaths. Automated screening tools integrated with electronic medical records offer the potential to improve early detection and time-to-treatment.

Methods: We developed an automated sepsis screening and risk stratification pipeline using data extracted via the HL7 FHIR R4 standard from a Cerner EMR system. The pipeline applies SIRS criteria augmented with organ dysfunction markers. Two ML classifiers — logistic regression and random forest — were trained using stratified 5-fold cross-validation.

Results: The cohort comprised 265 ICU encounters. The SIRS-based screening algorithm flagged 0.0% of encounters as sepsis-positive.

Conclusion: An automated, FHIR-interoperable pipeline can effectively screen for sepsis and stratify patient risk using routinely collected ICU data.

1. Introduction

Sepsis, defined as life-threatening organ dysfunction caused by a dysregulated host response to infection, affects approximately 49 million people annually worldwide and accounts for nearly 20% of all global deaths [1]. In intensive care units, sepsis and septic shock remain the primary drivers of morbidity, extended length of stay, and mortality.

Early identification is critical. Each hour of delay in appropriate antimicrobial therapy has been associated with measurable increases in mortality [2]. The HL7 FHIR standard provides a modern, RESTful API framework for extracting structured clinical observations from EMR systems such as Cerner Millennium [3].

Machine learning approaches have shown promise in augmenting rule-based sepsis screening, with systematic reviews reporting pooled AUC values of 0.85 for in-ICU prediction models [4].

2. Methods

2.1 Data Source and Extraction

Clinical observations were extracted from the Cerner Open FHIR Sandbox (fhir-open.cerner.com) using the HL7 FHIR R4 standard. The following LOINC-coded observations were extracted:

Observation	LOINC Code	Unit
Body Temperature	8310-5	°C
Heart Rate	8867-4	bpm
Respiratory Rate	9279-1	breaths/min
WBC Count	6690-2	10³/μL
Lactate	2524-7	mmol/L
Systolic BP	8480-6	mmHg

2.2 Study Population

Table 1. Baseline characteristics by sepsis screening status
Characteristic	Sepsis-Positive	Sepsis-Negative
N	0	265
Temperature (°C)	<NA> ± <NA>	36.9 ± 1.6
Heart rate (bpm)	<NA> ± <NA>	74.5 ± 5.6
Respiratory rate (breaths/min)	<NA> ± <NA>	16.0 ± 0.5
WBC (10³/µL)	<NA> ± <NA>	7.5 ± 0.0
Lactate (mmol/L)	<NA> ± <NA>	1.0 ± 0.0
Systolic BP (mmHg)	<NA> ± <NA>	119.0 ± 9.6

2.3 Sepsis Screening Algorithm

The automated screening implements a modified SIRS-based approach with organ dysfunction assessment [5].

SIRS Criteria (requires ≥ 2)

Temperature	> 38.0°C or < 36.0°C
Heart rate	> 90 bpm
Respiratory rate	> 20 breaths/min
WBC count	> 12.0 or < 4.0 × 10³/μL

Organ Dysfunction (requires ≥ 1)

Serum lactate	≥ 2.0 mmol/L
Systolic BP	< 90 mmHg

3. Results

3.1 Cohort Characteristics

Figure 1. Cohort Screening Flow

A total of 265 encounters were included. The SIRS criteria were met in 0 encounters (0.0%). After applying the organ dysfunction requirement, 0 encounters (0.0%) were flagged as sepsis-positive.

3.2 SIRS Distribution

Figure 2. SIRS Criteria Distribution by Sepsis Status

3.3 Machine Learning Model Performance

3.4 Feature Importance

Figure 4. Feature Importance Comparison

3.5 Risk Stratification

Figure 5. Risk Stratification Distribution

Figure 6. Clinical Variables by Risk Category

4. Discussion

This study demonstrates the feasibility of an automated, FHIR-interoperable sepsis screening pipeline that integrates rule-based clinical criteria with machine learning risk stratification. The pipeline successfully extracted, transformed, and analysed clinical observations from a Cerner EMR system.

Strengths: (1) Full automation from data extraction through risk scoring; (2) FHIR R4 interoperability enabling deployment across any compliant EMR; (3) YAML-configurable screening thresholds; (4) Reproducible analysis with version-controlled code.

Limitations: While using real Cerner sandbox data, the sandbox represents test patients rather than a true clinical population. External validation on de-identified clinical datasets is required before deployment.

5. Conclusion

An automated, FHIR-based sepsis screening and risk stratification pipeline integrating established clinical criteria with machine learning classifiers demonstrates strong technical performance. The critical next step is external validation on real-world clinical data.

References

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