What is Clinical Intelligence Software and is it Ready for Clinical Reality?

ICU environments generate high volumes of physiologic data and alerts. “Alarm fatigue” is well documented in the literature, and it is linked to desensitisation, missed deterioration signals, and worse patient outcomes¹.

Clinical intelligence software can help here. It processes patient data continuously and highlights the risk signals that clinicians have defined as important, so teams can act earlier.

Clinical intelligence acts as a support layer above the EHR. It pulls data from monitors, ventilators, labs and other medical devices into one patient view, so hospitals get more use out of the data they already collect.

Hospital adoption of this support layer is growing. In 2024, 71% of U.S. hospitals reported using predictive AI integrated with the EHR, up from 66% in 2023².

Published data shows that the impact can be significant³. One study reported a 36% reduction in ICU length of stay after congenital heart surgery when a real-time physiologic analytic index was used⁴. A separate study reported an 18% reduction in ICU length of stay and a 41% reduction in unplanned ICU readmissions⁵.

The Black Box Problem: Algorithmic Medicine and Clinician Trust

Although adoption of predictive AI is increasing, translating that into trusted, day-to-day clinical decision support remains a challenge. Clinicians say they find it hard to trust systems whose reasoning is hidden, especially when they remain associated with every patient decision⁶.

Transparent, Adjunctive AI: The Opposite of a Black Box

Trust in algorithmic medicine appears to be dependent on clinician understanding of how the platform works. A systematic review of explainable AI in healthcare found that when clinicians can question the reasoning behind a prediction, trust and use both improve, though the effect depends on how the explanation is presented⁷.

Transparent clinical intelligence shows clinicians which physiologic inputs are driving a risk signal. They can then question the output or spot a sensor anomaly. The clinician stays in charge.

FDA clearance forces a degree of transparency. Cleared algorithms must have documented indications for use, validated performance data, and a clearly stated scope.

Etiometry: FDA-Cleared Risk Indices and Transparent Clinical Intelligence Software

Etiometry’s clinical intelligence platform provides adjunctive insight to support clinician-directed, personalized escalation and de-escalation decisions within hospital-defined workflows.  Each risk index is built on established physiologic parameters and published clinical thresholds. The algorithms were developed from more than 150 million hours of de-identified patient data⁸.

The platform holds 11 FDA clearances, more than any other clinical intelligence software platform. Each clearance required documented evidence of safety and a defined clinical scope⁹.

The platform is explicitly adjunctive AI. Its outputs supplement clinician judgment, alarms and monitoring devices. They do not replace them¹⁰.

Published observational studies suggest that transparent, adjunctive workflows can be associated with meaningful operational and clinical metrics in selected settings. Use of Etiometry’s automated spontaneous breathing trial pathway was associated with a 30% reduction in ventilation time following pediatric cardiac surgery¹¹. Use of the risk analytics clinical decision support has been associated with a 29% reduction in vasoactive infusion duration in the same patient group¹².

Conclusion

Trust in clinical intelligence software comes down to whether clinicians can see what the algorithm is doing and why. When that visibility is there, backed by FDA clearance and real outcome data, the software becomes something ICU teams can lean on with confidence, while keeping every decision firmly in human hands.

Frequently Asked Questions

Is clinical intelligence software the same as an EHR?

No. The EHR is the record. Clinical intelligence software is the layer above the EHR that organizes live physiologic data into clinically relevant views and adjunctive signals for clinician review.

What does “FDA-cleared” mean for ICU AI?

An FDA-cleared algorithm has been reviewed for safety and its clinical scope is documented. It is not an experimental tool.

How does adjunctive AI differ from black-box algorithms?

Adjunctive AI supplements clinician judgment and shows its reasoning. A black-box score gives an output without explaining how it got there.

References

1. Agency for Healthcare Research and Quality. Alarm Fatigue. Making Healthcare Safer III. https://www.ncbi.nlm.nih.gov/books/NBK555522/
2. ONC. Hospital Trends in the Use, Evaluation, and Governance of Predictive AI, 2023 to 2024. NCBI Bookshelf. https://www.ncbi.nlm.nih.gov/books/NBK618497/
3.  In observational analyses at select sites, platform-supported workflows have been associated with changes in certain utilization and outcome metrics; results vary by site and do not establish causality.
4. Salvin JW, et al. The Impact of a Real-Time Physiologic Data Analytic Index on Length of Stay in Neonates Following Surgery for Congenital Heart Disease. Circulation. 2017. https://www.ahajournals.org/doi/10.1161/circ.136.suppl_1.20603
5. Gaies M. Methods to Enhance Causal Inference for Assessing Impact of Clinical Informatics Platform Implementation. Circ Cardiovasc Qual Outcomes. 2023. https://www.ahajournals.org/doi/10.1161/CIRCOUTCOMES.122.009277
6. Choudhury A, Elkefi S. Acceptance, initial trust formation, and human biases in artificial intelligence: Focus on clinicians. Front Digit Health. 2022. https://pmc.ncbi.nlm.nih.gov/articles/PMC9445304/
7. Rosenbacke R, Melhus Å, McKee M, Stuckler D. How Explainable Artificial Intelligence Can Increase or Decrease Clinicians’ Trust in AI Applications in Health Care: Systematic Review. JMIR AI. 2024. https://pmc.ncbi.nlm.nih.gov/articles/PMC11561425/
8. Etiometry Platform, Risk Analytics. https://www.etiometry.com/the-etiometry-platform/risk-analytics/
9. Etiometry, Cardiogenic Shock Classification Tool. https://www.etiometry.com/resources/cardiogenic-shock-classification-tool/
10. Etiometry FAQs. https://www.etiometry.com/faqs/
11. Clark MG, et al. Automated Spontaneous Breathing Trial Performance Tool is Associated with Improved Outcomes Following Pediatric Cardiac Surgery. J Pediatr Crit Care Med. 2025. https://journals.lww.com/jpcr/fulltext/2025/01000/automated_spontaneous_breathing_trial_performance.1.aspx
12. Gazit AZ, et al. Risk Analytics Clinical Decision Support Decreases Duration of Vasoactive Infusions Following Pediatric Cardiac Surgery. Crit Care Med. 2025. https://journals.lww.com/ccmjournal/abstract/2025/07000/risk_analytics_clinical_decision_support_decreases.1.aspx