Automated Sepsis Detection for Faster Care in Emergency Departments
Verified by Sahaj Satani from ImplementMD
The Implementation Gap
Sepsis kills 1 in 3 hospitalized patients; each hour delay in antibiotics increases mortality 7–10%. Multiple validated EHR-based sepsis detection algorithms exist with AUC 0.80–0.88 and demonstrated 18.7% mortality reduction in prospective trials (Adams et al., 2022). Yet fewer than 25% of emergency departments deploy machine learning sepsis alerts. The gap persists due to alert integration workflow friction, clinician override rates reaching 40–60%, inconsistent rapid diagnostic protocols, and training inefficiencies. This brief provides the actionable ED implementation roadmap to achieve consistent antibiotic administration within 1 hour of sepsis recognition through automated EHR screening, optimized alert thresholds, and integrated rapid diagnostics.
Evidence for Implementation Readiness
Validated Algorithms and Clinical Performance
The TREWS (Targeted Real-time Early Warning System), developed at Johns Hopkins using Cox proportional hazard models with time-varying EHR features, achieved AUC 0.83 (95% CI: 0.81–0.85) with 85% sensitivity and median lead time of 28.2 hours (IQR 10.6–94.2) before septic shock onset, outperforming MEWS and SIRS screening (Henry et al., 2015). In prospective multi-site validation across 590,736 patients at five Johns Hopkins hospitals, TREWS demonstrated 82% sepsis sensitivity with 89% alert evaluation rate by providers—exceptional adoption indicating clinical trust (Adams et al., 2022). Patients with alerts confirmed within 3 hours showed 18.7% relative mortality reduction (95% CI: 9.4–27.0%) and 3.3% absolute reduction (95% CI: 1.7–5.1%), translating to 22 additional survivors per 5 months per hospital system.
The Epic Sepsis Model, using penalized logistic regression on ~50 variables from 405,000 encounters, shows institution-dependent performance ranging from AUC 0.63 at University of Michigan (33% sensitivity, 12% PPV) to AUC 0.83 at Prisma Health (86% sensitivity, 34% PPV) when threshold-optimized to ≥5 versus Epic's default ≥6 (Wong et al., 2021; Cull et al., 2023). External validation at Harris Health safety-net EDs revealed 14.7% sensitivity and median lead time of 0 minutes in predominantly Hispanic (59%) and Black (26%) populations, raising algorithmic equity concerns (Ostermayer et al., 2024). The UC San Diego COMPOSER deep learning model achieved 17% relative mortality reduction with only 235 alerts monthly across two EDs—1.65 alerts per nurse per month—demonstrating sustainable alert burden (Boussina et al., 2024).
Real-World Implementation Outcomes
Johns Hopkins TREWS implementation reduced time-to-antibiotics by 1.85 hours (95% CI: 1.66–2.00 h) when alerts were confirmed within 3 hours versus unconfirmed (Henry et al., 2022). Prisma Health's Epic threshold optimization (≥5 vs. ≥6) reduced median time-to-antibiotics from 150 to 90 minutes (p<0.001) with 44% mortality reduction (OR 0.56, 95% CI: 0.39–0.80). Wake Forest's Epic implementation with electronic sepsis navigator achieved 2.87-hour improvement versus historical SIRS alerts (median 3.33h vs. 6.20h, p<0.001). The Loyola Health System multi-component program, integrating Modified Early Warning System with sepsis coordinators, demonstrated 30% mortality reduction (OR 0.70, 95% CI: 0.57–0.86) and net annual cost savings of $272,646 (95% CI: $79,668–$757,970) through reduced ICU days and hospital length of stay (Afshar et al., 2019).
Point-of-care lactate integration accelerates diagnostic cascade: POC lactate turnaround of 12 seconds to 5 minutes versus central lab 82–149 minutes enables 151-minute faster sepsis recognition and reduces mortality from 19% to 6% (p=0.02) with ICU admissions decreasing from 51% to 33%.
Health Equity Performance
Massachusetts General Hospital analysis of 49,609 sepsis patients revealed Black patients experienced 21 minutes longer time-to-antibiotics (215 vs. 194 min, p<0.001) with delayed treatment adjusted OR 1.24 (95% CI: 1.06–1.45) compared to White patients, persisting after risk adjustment (Pak et al., 2024). Women with septic shock showed 18 minutes longer treatment delays and 16% higher mortality (aOR 1.16, 95% CI: 1.04–1.29) independent of antibiotic timing. Systematic review of 120 ML sepsis prediction studies found only 20% reported race/ethnicity, 3% reported sociodemographics, and 2% stratified performance by demographic groups—zero studies reported formal fairness metrics (Hauschildt et al., 2025).
Implementation Solution
EHR Workflow Integration
Deploy via Epic Nebula cloud platform (hourly batch execution) or Cerner real-time streaming analytics. TREWS integrates as clickable icons on ED track boards displaying model predictions, current organ dysfunction indicators (SOFA components), and contributing risk factors. Epic Sepsis Model generates Best Practice Alerts (BPA pop-ups) at chart opening when score ≥5 (locally optimized threshold vs. vendor default ≥6). Automated screening logic evaluates: temperature ≤36°C or ≥38°C, respiratory rate ≥20/min, heart rate ≥90 bpm, WBC ≤4,000 or ≥12,000/mm³, SBP ≤90 mmHg, and lactate ≥2.0 mmol/L. Alert verification triggers rapid order set: blood cultures → lactate → broad-spectrum antibiotics with automatic antimicrobial stewardship review at 48 hours.
Clinical Workflow Timeline
Time | Milestone |
|---|---|
T+0 min | Patient arrival; vital signs and POC lactate at triage |
T+5 min | Automated screening algorithm executes on EHR data stream |
T+10 min | Alert triggered if threshold criteria met → BPA notification |
T+15 min | Clinician reviews alert, confirms/dismisses sepsis suspicion |
T+20 min | Rapid order set: Blood cultures → Lactate → Antibiotics |
T+30 min | POC lactate result returns (vs. 82–149 min central lab) |
T+60 min | Target: Antibiotic administration complete |
T+90 min | Outcome tracking: ICU admission, mortality, LOS metrics |
Performance optimization equation for alert threshold selection:
where mortality represents local mortality reduction coefficient from historical sepsis cohort analysis.
Provider Training Requirements
Implementation requires 4–6 hours structured training: (1) sepsis pathophysiology and bundle compliance (1 hour); (2) algorithm interpretation and alert response workflow (1.5 hours); (3) EHR navigation for rapid order sets (1 hour); (4) override criteria and documentation requirements (0.5 hour); and (5) supervised case simulation (1–2 hours). Johns Hopkins achieved 89% alert evaluation rate with monthly reinforcement training and physician champion engagement. Training emphasizes clinical gestalt overrides algorithm when appropriate—38% of TREWS alerts confirmed as suspected sepsis, while 62% represented appropriate clinical dismissal.
Regulatory and Reimbursement Pathway
FDA De Novo authorization established for Sepsis ImmunoScore™ (Prenosis, DEN230036, April 2024) as first Class II Software as Medical Device for sepsis prediction, creating predicate pathway for 510(k) clearance. Clinical decision support tools for sepsis generally do not qualify for Non-Device CDS exemption due to time-critical, life-threatening context where independent verification is unreasonable. Sepsis care quality metrics bill under CMS SEP-1 bundle (now withdrawn but institutional tracking persists). POC lactate bills CPT 83605 (~$15); procalcitonin 84145 (~$50). Algorithm interpretation currently bundles with ED E&M coding (99281–99285); dedicated AI interpretation CPT codes under AMA development.
Figure 1: ED Sepsis Alert Implementation Workflow
Implementation Impact and Scalability
Approximately 1.7 million Americans develop sepsis annually; 270,000 die. ED-presenting sepsis constitutes 60% of cases (~1 million ED sepsis encounters/year). Full implementation across 5,000 US EDs could prevent 15,000–25,000 deaths annually based on 18.7% TREWS mortality reduction applied to baseline 15% sepsis mortality. Target adoption: 80% of academic EDs within 6 months, expanding to community hospitals via regional collaborative models. Implementation costs: $150,000–$300,000 initial investment (software licensing, training, workflow redesign) with $270,000 annual savings per 500-bed hospital offsetting within 12 months. Evidence gaps include prospective randomized trials comparing algorithms head-to-head and equity-stratified outcomes from diverse populations. Community EDs without Epic/Cerner can implement via standalone platforms (Prenosis Sepsis ImmunoScore, Dascena InSight) with HL7 FHIR integration. Ongoing surveillance must address demonstrated 21-minute treatment delays for Black patients and algorithmic performance degradation in minority-serving institutions, requiring mandatory fairness reporting and threshold recalibration quarterly.
References
Adams, R., Henry, K. E., Sridharan, A., et al. (2022). Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis. Nature Medicine, 28(7), 1455–1460. https://doi.org/10.1038/s41591-022-01894-0
Afshar, M., Arain, E., Ye, C., et al. (2019). Patient outcomes and cost-effectiveness of a sepsis care quality improvement program in a health system. Critical Care Medicine, 47(10), 1371–1379. https://doi.org/10.1097/CCM.0000000000003919
Boussina, A., Shashikumar, S. P., Malhotra, A., et al. (2024). Impact of a deep learning sepsis prediction model on quality of care and survival. npj Digital Medicine, 7(1), 14. https://doi.org/10.1038/s41746-023-00986-6
Cull, J., Brevetta, R., Gerac, J., Kothari, S., & Blackhurst, D. (2023). Epic Sepsis Model Inpatient Predictive Analytic Tool: A Validation Study. Critical Care Explorations, 5(7), e0941. https://doi.org/10.1097/CCE.0000000000000941
Hauschildt, K. E., Pan, A., Bernstein, T., et al. (2025). Consideration of sociodemographics in machine learning-driven sepsis risk prediction. Critical Care Medicine, 53(9), e1815–e1820. https://doi.org/10.1097/CCM.0000000000006741
Henry, K. E., Hager, D. N., Pronovost, P. J., & Saria, S. (2015). A targeted real-time early warning score (TREWScore) for septic shock. Science Translational Medicine, 7(299), 299ra122. https://doi.org/10.1126/scitranslmed.aab3719
Henry, K. E., Adams, R., Parent, C., et al. (2022). Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing. Nature Medicine, 28(7), 1447–1454. https://doi.org/10.1038/s41591-022-01895-z
Ostermayer, D. G., Braunheim, B., Mehta, A. M., et al. (2024). External validation of the Epic sepsis predictive model in 2 county emergency departments. JAMIA Open, 7(4), ooae133. https://doi.org/10.1093/jamiaopen/ooae133
Pak, T. R., Sánchez, S. M., McKenna, C. S., Rhee, C., & Klompas, M. (2024). Assessment of racial, ethnic, and sex-based disparities in time-to-antibiotics and sepsis outcomes in a large multihospital cohort. Critical Care Medicine, 52(12), 1928–1933. https://doi.org/10.1097/CCM.0000000000006428
Schertz, A. R., Smith, S. A., Lenoir, K. M., & Thomas, K. W. (2023). Clinical impact of a sepsis alert system plus electronic sepsis navigator using the Epic Sepsis Prediction Model in the Emergency Department. Journal of Emergency Medicine, 64(5), 584–595. https://doi.org/10.1016/j.jemermed.2023.02.025
Wong, A., Otles, E., Donnelly, J. P., et al. (2021). External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Internal Medicine, 181(8), 1065–1070. https://doi.org/10.1001/jamainternmed.2021.2626
The Implementation Gap
Sepsis kills 1 in 3 hospitalized patients; each hour delay in antibiotics increases mortality 7–10%. Multiple validated EHR-based sepsis detection algorithms exist with AUC 0.80–0.88 and demonstrated 18.7% mortality reduction in prospective trials (Adams et al., 2022). Yet fewer than 25% of emergency departments deploy machine learning sepsis alerts. The gap persists due to alert integration workflow friction, clinician override rates reaching 40–60%, inconsistent rapid diagnostic protocols, and training inefficiencies. This brief provides the actionable ED implementation roadmap to achieve consistent antibiotic administration within 1 hour of sepsis recognition through automated EHR screening, optimized alert thresholds, and integrated rapid diagnostics.
Evidence for Implementation Readiness
Validated Algorithms and Clinical Performance
The TREWS (Targeted Real-time Early Warning System), developed at Johns Hopkins using Cox proportional hazard models with time-varying EHR features, achieved AUC 0.83 (95% CI: 0.81–0.85) with 85% sensitivity and median lead time of 28.2 hours (IQR 10.6–94.2) before septic shock onset, outperforming MEWS and SIRS screening (Henry et al., 2015). In prospective multi-site validation across 590,736 patients at five Johns Hopkins hospitals, TREWS demonstrated 82% sepsis sensitivity with 89% alert evaluation rate by providers—exceptional adoption indicating clinical trust (Adams et al., 2022). Patients with alerts confirmed within 3 hours showed 18.7% relative mortality reduction (95% CI: 9.4–27.0%) and 3.3% absolute reduction (95% CI: 1.7–5.1%), translating to 22 additional survivors per 5 months per hospital system.
The Epic Sepsis Model, using penalized logistic regression on ~50 variables from 405,000 encounters, shows institution-dependent performance ranging from AUC 0.63 at University of Michigan (33% sensitivity, 12% PPV) to AUC 0.83 at Prisma Health (86% sensitivity, 34% PPV) when threshold-optimized to ≥5 versus Epic's default ≥6 (Wong et al., 2021; Cull et al., 2023). External validation at Harris Health safety-net EDs revealed 14.7% sensitivity and median lead time of 0 minutes in predominantly Hispanic (59%) and Black (26%) populations, raising algorithmic equity concerns (Ostermayer et al., 2024). The UC San Diego COMPOSER deep learning model achieved 17% relative mortality reduction with only 235 alerts monthly across two EDs—1.65 alerts per nurse per month—demonstrating sustainable alert burden (Boussina et al., 2024).
Real-World Implementation Outcomes
Johns Hopkins TREWS implementation reduced time-to-antibiotics by 1.85 hours (95% CI: 1.66–2.00 h) when alerts were confirmed within 3 hours versus unconfirmed (Henry et al., 2022). Prisma Health's Epic threshold optimization (≥5 vs. ≥6) reduced median time-to-antibiotics from 150 to 90 minutes (p<0.001) with 44% mortality reduction (OR 0.56, 95% CI: 0.39–0.80). Wake Forest's Epic implementation with electronic sepsis navigator achieved 2.87-hour improvement versus historical SIRS alerts (median 3.33h vs. 6.20h, p<0.001). The Loyola Health System multi-component program, integrating Modified Early Warning System with sepsis coordinators, demonstrated 30% mortality reduction (OR 0.70, 95% CI: 0.57–0.86) and net annual cost savings of $272,646 (95% CI: $79,668–$757,970) through reduced ICU days and hospital length of stay (Afshar et al., 2019).
Point-of-care lactate integration accelerates diagnostic cascade: POC lactate turnaround of 12 seconds to 5 minutes versus central lab 82–149 minutes enables 151-minute faster sepsis recognition and reduces mortality from 19% to 6% (p=0.02) with ICU admissions decreasing from 51% to 33%.
Health Equity Performance
Massachusetts General Hospital analysis of 49,609 sepsis patients revealed Black patients experienced 21 minutes longer time-to-antibiotics (215 vs. 194 min, p<0.001) with delayed treatment adjusted OR 1.24 (95% CI: 1.06–1.45) compared to White patients, persisting after risk adjustment (Pak et al., 2024). Women with septic shock showed 18 minutes longer treatment delays and 16% higher mortality (aOR 1.16, 95% CI: 1.04–1.29) independent of antibiotic timing. Systematic review of 120 ML sepsis prediction studies found only 20% reported race/ethnicity, 3% reported sociodemographics, and 2% stratified performance by demographic groups—zero studies reported formal fairness metrics (Hauschildt et al., 2025).
Implementation Solution
EHR Workflow Integration
Deploy via Epic Nebula cloud platform (hourly batch execution) or Cerner real-time streaming analytics. TREWS integrates as clickable icons on ED track boards displaying model predictions, current organ dysfunction indicators (SOFA components), and contributing risk factors. Epic Sepsis Model generates Best Practice Alerts (BPA pop-ups) at chart opening when score ≥5 (locally optimized threshold vs. vendor default ≥6). Automated screening logic evaluates: temperature ≤36°C or ≥38°C, respiratory rate ≥20/min, heart rate ≥90 bpm, WBC ≤4,000 or ≥12,000/mm³, SBP ≤90 mmHg, and lactate ≥2.0 mmol/L. Alert verification triggers rapid order set: blood cultures → lactate → broad-spectrum antibiotics with automatic antimicrobial stewardship review at 48 hours.
Clinical Workflow Timeline
Time | Milestone |
|---|---|
T+0 min | Patient arrival; vital signs and POC lactate at triage |
T+5 min | Automated screening algorithm executes on EHR data stream |
T+10 min | Alert triggered if threshold criteria met → BPA notification |
T+15 min | Clinician reviews alert, confirms/dismisses sepsis suspicion |
T+20 min | Rapid order set: Blood cultures → Lactate → Antibiotics |
T+30 min | POC lactate result returns (vs. 82–149 min central lab) |
T+60 min | Target: Antibiotic administration complete |
T+90 min | Outcome tracking: ICU admission, mortality, LOS metrics |
Performance optimization equation for alert threshold selection:
where mortality represents local mortality reduction coefficient from historical sepsis cohort analysis.
Provider Training Requirements
Implementation requires 4–6 hours structured training: (1) sepsis pathophysiology and bundle compliance (1 hour); (2) algorithm interpretation and alert response workflow (1.5 hours); (3) EHR navigation for rapid order sets (1 hour); (4) override criteria and documentation requirements (0.5 hour); and (5) supervised case simulation (1–2 hours). Johns Hopkins achieved 89% alert evaluation rate with monthly reinforcement training and physician champion engagement. Training emphasizes clinical gestalt overrides algorithm when appropriate—38% of TREWS alerts confirmed as suspected sepsis, while 62% represented appropriate clinical dismissal.
Regulatory and Reimbursement Pathway
FDA De Novo authorization established for Sepsis ImmunoScore™ (Prenosis, DEN230036, April 2024) as first Class II Software as Medical Device for sepsis prediction, creating predicate pathway for 510(k) clearance. Clinical decision support tools for sepsis generally do not qualify for Non-Device CDS exemption due to time-critical, life-threatening context where independent verification is unreasonable. Sepsis care quality metrics bill under CMS SEP-1 bundle (now withdrawn but institutional tracking persists). POC lactate bills CPT 83605 (~$15); procalcitonin 84145 (~$50). Algorithm interpretation currently bundles with ED E&M coding (99281–99285); dedicated AI interpretation CPT codes under AMA development.
Figure 1: ED Sepsis Alert Implementation Workflow
Implementation Impact and Scalability
Approximately 1.7 million Americans develop sepsis annually; 270,000 die. ED-presenting sepsis constitutes 60% of cases (~1 million ED sepsis encounters/year). Full implementation across 5,000 US EDs could prevent 15,000–25,000 deaths annually based on 18.7% TREWS mortality reduction applied to baseline 15% sepsis mortality. Target adoption: 80% of academic EDs within 6 months, expanding to community hospitals via regional collaborative models. Implementation costs: $150,000–$300,000 initial investment (software licensing, training, workflow redesign) with $270,000 annual savings per 500-bed hospital offsetting within 12 months. Evidence gaps include prospective randomized trials comparing algorithms head-to-head and equity-stratified outcomes from diverse populations. Community EDs without Epic/Cerner can implement via standalone platforms (Prenosis Sepsis ImmunoScore, Dascena InSight) with HL7 FHIR integration. Ongoing surveillance must address demonstrated 21-minute treatment delays for Black patients and algorithmic performance degradation in minority-serving institutions, requiring mandatory fairness reporting and threshold recalibration quarterly.
References
Adams, R., Henry, K. E., Sridharan, A., et al. (2022). Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis. Nature Medicine, 28(7), 1455–1460. https://doi.org/10.1038/s41591-022-01894-0
Afshar, M., Arain, E., Ye, C., et al. (2019). Patient outcomes and cost-effectiveness of a sepsis care quality improvement program in a health system. Critical Care Medicine, 47(10), 1371–1379. https://doi.org/10.1097/CCM.0000000000003919
Boussina, A., Shashikumar, S. P., Malhotra, A., et al. (2024). Impact of a deep learning sepsis prediction model on quality of care and survival. npj Digital Medicine, 7(1), 14. https://doi.org/10.1038/s41746-023-00986-6
Cull, J., Brevetta, R., Gerac, J., Kothari, S., & Blackhurst, D. (2023). Epic Sepsis Model Inpatient Predictive Analytic Tool: A Validation Study. Critical Care Explorations, 5(7), e0941. https://doi.org/10.1097/CCE.0000000000000941
Hauschildt, K. E., Pan, A., Bernstein, T., et al. (2025). Consideration of sociodemographics in machine learning-driven sepsis risk prediction. Critical Care Medicine, 53(9), e1815–e1820. https://doi.org/10.1097/CCM.0000000000006741
Henry, K. E., Hager, D. N., Pronovost, P. J., & Saria, S. (2015). A targeted real-time early warning score (TREWScore) for septic shock. Science Translational Medicine, 7(299), 299ra122. https://doi.org/10.1126/scitranslmed.aab3719
Henry, K. E., Adams, R., Parent, C., et al. (2022). Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing. Nature Medicine, 28(7), 1447–1454. https://doi.org/10.1038/s41591-022-01895-z
Ostermayer, D. G., Braunheim, B., Mehta, A. M., et al. (2024). External validation of the Epic sepsis predictive model in 2 county emergency departments. JAMIA Open, 7(4), ooae133. https://doi.org/10.1093/jamiaopen/ooae133
Pak, T. R., Sánchez, S. M., McKenna, C. S., Rhee, C., & Klompas, M. (2024). Assessment of racial, ethnic, and sex-based disparities in time-to-antibiotics and sepsis outcomes in a large multihospital cohort. Critical Care Medicine, 52(12), 1928–1933. https://doi.org/10.1097/CCM.0000000000006428
Schertz, A. R., Smith, S. A., Lenoir, K. M., & Thomas, K. W. (2023). Clinical impact of a sepsis alert system plus electronic sepsis navigator using the Epic Sepsis Prediction Model in the Emergency Department. Journal of Emergency Medicine, 64(5), 584–595. https://doi.org/10.1016/j.jemermed.2023.02.025
Wong, A., Otles, E., Donnelly, J. P., et al. (2021). External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Internal Medicine, 181(8), 1065–1070. https://doi.org/10.1001/jamainternmed.2021.2626
The Implementation Gap
Sepsis kills 1 in 3 hospitalized patients; each hour delay in antibiotics increases mortality 7–10%. Multiple validated EHR-based sepsis detection algorithms exist with AUC 0.80–0.88 and demonstrated 18.7% mortality reduction in prospective trials (Adams et al., 2022). Yet fewer than 25% of emergency departments deploy machine learning sepsis alerts. The gap persists due to alert integration workflow friction, clinician override rates reaching 40–60%, inconsistent rapid diagnostic protocols, and training inefficiencies. This brief provides the actionable ED implementation roadmap to achieve consistent antibiotic administration within 1 hour of sepsis recognition through automated EHR screening, optimized alert thresholds, and integrated rapid diagnostics.
Evidence for Implementation Readiness
Validated Algorithms and Clinical Performance
The TREWS (Targeted Real-time Early Warning System), developed at Johns Hopkins using Cox proportional hazard models with time-varying EHR features, achieved AUC 0.83 (95% CI: 0.81–0.85) with 85% sensitivity and median lead time of 28.2 hours (IQR 10.6–94.2) before septic shock onset, outperforming MEWS and SIRS screening (Henry et al., 2015). In prospective multi-site validation across 590,736 patients at five Johns Hopkins hospitals, TREWS demonstrated 82% sepsis sensitivity with 89% alert evaluation rate by providers—exceptional adoption indicating clinical trust (Adams et al., 2022). Patients with alerts confirmed within 3 hours showed 18.7% relative mortality reduction (95% CI: 9.4–27.0%) and 3.3% absolute reduction (95% CI: 1.7–5.1%), translating to 22 additional survivors per 5 months per hospital system.
The Epic Sepsis Model, using penalized logistic regression on ~50 variables from 405,000 encounters, shows institution-dependent performance ranging from AUC 0.63 at University of Michigan (33% sensitivity, 12% PPV) to AUC 0.83 at Prisma Health (86% sensitivity, 34% PPV) when threshold-optimized to ≥5 versus Epic's default ≥6 (Wong et al., 2021; Cull et al., 2023). External validation at Harris Health safety-net EDs revealed 14.7% sensitivity and median lead time of 0 minutes in predominantly Hispanic (59%) and Black (26%) populations, raising algorithmic equity concerns (Ostermayer et al., 2024). The UC San Diego COMPOSER deep learning model achieved 17% relative mortality reduction with only 235 alerts monthly across two EDs—1.65 alerts per nurse per month—demonstrating sustainable alert burden (Boussina et al., 2024).
Real-World Implementation Outcomes
Johns Hopkins TREWS implementation reduced time-to-antibiotics by 1.85 hours (95% CI: 1.66–2.00 h) when alerts were confirmed within 3 hours versus unconfirmed (Henry et al., 2022). Prisma Health's Epic threshold optimization (≥5 vs. ≥6) reduced median time-to-antibiotics from 150 to 90 minutes (p<0.001) with 44% mortality reduction (OR 0.56, 95% CI: 0.39–0.80). Wake Forest's Epic implementation with electronic sepsis navigator achieved 2.87-hour improvement versus historical SIRS alerts (median 3.33h vs. 6.20h, p<0.001). The Loyola Health System multi-component program, integrating Modified Early Warning System with sepsis coordinators, demonstrated 30% mortality reduction (OR 0.70, 95% CI: 0.57–0.86) and net annual cost savings of $272,646 (95% CI: $79,668–$757,970) through reduced ICU days and hospital length of stay (Afshar et al., 2019).
Point-of-care lactate integration accelerates diagnostic cascade: POC lactate turnaround of 12 seconds to 5 minutes versus central lab 82–149 minutes enables 151-minute faster sepsis recognition and reduces mortality from 19% to 6% (p=0.02) with ICU admissions decreasing from 51% to 33%.
Health Equity Performance
Massachusetts General Hospital analysis of 49,609 sepsis patients revealed Black patients experienced 21 minutes longer time-to-antibiotics (215 vs. 194 min, p<0.001) with delayed treatment adjusted OR 1.24 (95% CI: 1.06–1.45) compared to White patients, persisting after risk adjustment (Pak et al., 2024). Women with septic shock showed 18 minutes longer treatment delays and 16% higher mortality (aOR 1.16, 95% CI: 1.04–1.29) independent of antibiotic timing. Systematic review of 120 ML sepsis prediction studies found only 20% reported race/ethnicity, 3% reported sociodemographics, and 2% stratified performance by demographic groups—zero studies reported formal fairness metrics (Hauschildt et al., 2025).
Implementation Solution
EHR Workflow Integration
Deploy via Epic Nebula cloud platform (hourly batch execution) or Cerner real-time streaming analytics. TREWS integrates as clickable icons on ED track boards displaying model predictions, current organ dysfunction indicators (SOFA components), and contributing risk factors. Epic Sepsis Model generates Best Practice Alerts (BPA pop-ups) at chart opening when score ≥5 (locally optimized threshold vs. vendor default ≥6). Automated screening logic evaluates: temperature ≤36°C or ≥38°C, respiratory rate ≥20/min, heart rate ≥90 bpm, WBC ≤4,000 or ≥12,000/mm³, SBP ≤90 mmHg, and lactate ≥2.0 mmol/L. Alert verification triggers rapid order set: blood cultures → lactate → broad-spectrum antibiotics with automatic antimicrobial stewardship review at 48 hours.
Clinical Workflow Timeline
Time | Milestone |
|---|---|
T+0 min | Patient arrival; vital signs and POC lactate at triage |
T+5 min | Automated screening algorithm executes on EHR data stream |
T+10 min | Alert triggered if threshold criteria met → BPA notification |
T+15 min | Clinician reviews alert, confirms/dismisses sepsis suspicion |
T+20 min | Rapid order set: Blood cultures → Lactate → Antibiotics |
T+30 min | POC lactate result returns (vs. 82–149 min central lab) |
T+60 min | Target: Antibiotic administration complete |
T+90 min | Outcome tracking: ICU admission, mortality, LOS metrics |
Performance optimization equation for alert threshold selection:
where mortality represents local mortality reduction coefficient from historical sepsis cohort analysis.
Provider Training Requirements
Implementation requires 4–6 hours structured training: (1) sepsis pathophysiology and bundle compliance (1 hour); (2) algorithm interpretation and alert response workflow (1.5 hours); (3) EHR navigation for rapid order sets (1 hour); (4) override criteria and documentation requirements (0.5 hour); and (5) supervised case simulation (1–2 hours). Johns Hopkins achieved 89% alert evaluation rate with monthly reinforcement training and physician champion engagement. Training emphasizes clinical gestalt overrides algorithm when appropriate—38% of TREWS alerts confirmed as suspected sepsis, while 62% represented appropriate clinical dismissal.
Regulatory and Reimbursement Pathway
FDA De Novo authorization established for Sepsis ImmunoScore™ (Prenosis, DEN230036, April 2024) as first Class II Software as Medical Device for sepsis prediction, creating predicate pathway for 510(k) clearance. Clinical decision support tools for sepsis generally do not qualify for Non-Device CDS exemption due to time-critical, life-threatening context where independent verification is unreasonable. Sepsis care quality metrics bill under CMS SEP-1 bundle (now withdrawn but institutional tracking persists). POC lactate bills CPT 83605 (~$15); procalcitonin 84145 (~$50). Algorithm interpretation currently bundles with ED E&M coding (99281–99285); dedicated AI interpretation CPT codes under AMA development.
Figure 1: ED Sepsis Alert Implementation Workflow
Implementation Impact and Scalability
Approximately 1.7 million Americans develop sepsis annually; 270,000 die. ED-presenting sepsis constitutes 60% of cases (~1 million ED sepsis encounters/year). Full implementation across 5,000 US EDs could prevent 15,000–25,000 deaths annually based on 18.7% TREWS mortality reduction applied to baseline 15% sepsis mortality. Target adoption: 80% of academic EDs within 6 months, expanding to community hospitals via regional collaborative models. Implementation costs: $150,000–$300,000 initial investment (software licensing, training, workflow redesign) with $270,000 annual savings per 500-bed hospital offsetting within 12 months. Evidence gaps include prospective randomized trials comparing algorithms head-to-head and equity-stratified outcomes from diverse populations. Community EDs without Epic/Cerner can implement via standalone platforms (Prenosis Sepsis ImmunoScore, Dascena InSight) with HL7 FHIR integration. Ongoing surveillance must address demonstrated 21-minute treatment delays for Black patients and algorithmic performance degradation in minority-serving institutions, requiring mandatory fairness reporting and threshold recalibration quarterly.
References
Adams, R., Henry, K. E., Sridharan, A., et al. (2022). Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis. Nature Medicine, 28(7), 1455–1460. https://doi.org/10.1038/s41591-022-01894-0
Afshar, M., Arain, E., Ye, C., et al. (2019). Patient outcomes and cost-effectiveness of a sepsis care quality improvement program in a health system. Critical Care Medicine, 47(10), 1371–1379. https://doi.org/10.1097/CCM.0000000000003919
Boussina, A., Shashikumar, S. P., Malhotra, A., et al. (2024). Impact of a deep learning sepsis prediction model on quality of care and survival. npj Digital Medicine, 7(1), 14. https://doi.org/10.1038/s41746-023-00986-6
Cull, J., Brevetta, R., Gerac, J., Kothari, S., & Blackhurst, D. (2023). Epic Sepsis Model Inpatient Predictive Analytic Tool: A Validation Study. Critical Care Explorations, 5(7), e0941. https://doi.org/10.1097/CCE.0000000000000941
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