Sepsis early warning systems in context

Sepsis is a leading cause of in-hospital mortality and cost, and earlier recognition and treatment consistently reduce deaths and complications. This has led hospitals to adopt automated early warning tools that continuously scan EHR data for early signs of deterioration. Over the past decade, these systems have evolved from simple rules-based triggers to machine learning models that use dozens of variables such as vital signs, lab values, comorbidities, and treatments to estimate sepsis risk in real time.^[1]

By the late 2010s, many large health systems in the United States were using sepsis prediction or alerting tools tightly integrated into their enterprise EHRs. These models were typically proprietary and treated as part of the EHR feature set rather than as standalone AI products, which meant that validation, monitoring, and governance practices often followed traditional IT patterns rather than lifecycle management approaches tailored for AI.^[2]

A surgeon's experience with a sepsis alert AI system

In 2024, I met a surgeon at a large academic hospital who described her experience with a sepsis early warning system built into the hospital's EHR. She mentioned the system generated automated alerts that her post-operative patients were septic, often at all hours of day and night, even when those patients were clinically stable. Over time, she found that the alerts were so often wrong that she came to treat them as "usually wrong" and largely ignored them in her daily practice. She mentioned that her colleagues reacted similarly.

Importantly, she was not aware that a machine learning model was driving the alerts. To her, the notifications appeared as generic EHR system messages about sepsis risk rather than as outputs of an AI model. This combination of degraded perceived usefulness and lack of transparency is a textbook example of model drift and alert fatigue in clinical AI: the model's performance deteriorates relative to the current patient population and care patterns, while frontline clinicians lose trust and mentally "decommission" the tool.^[3][4]

The anecdote is used as an illustrative case of an EHR-embedded sepsis early warning system whose behavior mirrors patterns documented in published evaluations of widely deployed sepsis prediction models.

What the evidence shows about sepsis prediction models

Research on sepsis prediction tools provides a quantitative backdrop that closely aligns with the surgeon's experience. A 2021 JAMA Internal Medicine study evaluated a widely implemented proprietary sepsis prediction model integrated into a commercial EHR across a large academic health system. The study found that the model:^[2]

A companion JAMA Network Open analysis quantified sepsis alerts from a sepsis prediction tool across 24 U.S. hospitals before and during the COVID-19 pandemic. That study showed that the onset of COVID-19 was associated with about a 43 percent increase in sepsis alert volume, without a commensurate improvement in detection of sepsis cases, which raised concerns about increased alert burden and potential alert fatigue among clinicians. FierceHealthcare's coverage of this work summarized the findings by noting that the sepsis algorithm used within a major EHR system generated substantially more alerts during the pandemic, which may have contributed to alert fatigue and underscored the need for stronger oversight of clinical AI tools.^[5][6][7]

Taken together, these studies and reports describe a pattern that aligns with the surgeon's story: sepsis early warning systems embedded in EHRs can produce large volumes of low-yield alerts, miss many true sepsis cases, and become less reliable over time as data and practice shift.^[6][5][2]

Model drift and dataset shift in hospital AI

The STAT article "AI gone astray: How subtle shifts in patient data send popular algorithms reeling, undermining patient safety" provides a broader lens on model drift in hospital algorithms. The investigation shows how changes in patient populations, coding practices, and care patterns can cause the performance of widely used prediction models to degrade after deployment, even if they appeared acceptable in their original validation.^[3]

In the context of sepsis AI, several types of dataset shift are particularly relevant.

Without continuous monitoring, recalibration, and structured drift analysis, a sepsis prediction model that initially provides some value can gradually become miscalibrated. It may overpredict risk in certain subgroups, underpredict in others, and generate alerts in contexts where clinicians perceive little clinical relevance, thereby eroding trust. The surgeon's impression that the system had become "worse than random" captures the endpoint of this trajectory from the frontline clinician's perspective.^[10]

Alert fatigue and the human side of AI failure

Alert fatigue has long been recognized as a patient safety concern in hospitals, and it is particularly acute for sepsis alerts. When clinicians are exposed to repeated alarms that rarely change management or outcomes, they naturally begin to discount or ignore them, regardless of whether a specific alert is accurate.^[7][11]

The JAMA Network Open and multi-hospital analyses cited above show that sepsis alert volumes can be very high and can spike further when external conditions change, such as during the COVID-19 pandemic. FierceHealthcare's reporting notes that this pattern likely contributed to alert fatigue, as clinicians were forced to sift through more alerts, many of which did not correspond to true sepsis or actionable deterioration.^[5][6][7]

In the surgeon's case, repeated false or low-value alerts for clinically stable post-operative patients shifted her behavior from cautious attention to habitual dismissal. She no longer perceived the system as a helpful safety net; instead, it became another source of noise that competed with her clinical judgment and time. This represents not just a technical performance issue, but a failure in the design of the human-AI interaction and in the governance structures that should align the tool with clinician workflows and expectations.^[9][12]

Why this happened: Readiness, governance, and design gaps

The sepsis alert system in this case did not fail solely because of its algorithm; it failed in the broader socio-technical system in which it was deployed. Several gaps stand out.

How sepsis alert technologies are evolving

Despite these challenges, sepsis prediction technology has continued to evolve, with several recent efforts focusing on more rigorous deployment and monitoring. Prospective studies of advanced machine learning systems, such as targeted real-time early warning systems for sepsis, have shown that when alerts are well integrated into clinical workflows and clinicians engage with them promptly, they can be associated with lower mortality and improved processes of care.^[14]

Newer approaches also emphasize explainability and adaptive behavior. For example, some sepsis prediction algorithms have been designed to "learn to say I don't know," deferring judgment when model confidence is low to reduce the risk of overconfident false positives. Others use interpretable machine learning techniques to highlight which variables are driving a given risk estimate, making it easier for clinicians to understand and critique model outputs.^[15][8]

On the governance side, there is increasing recognition that AI deployment requires formal frameworks for continuous monitoring, drift detection, and post-deployment evaluation. Recent reviews of data-driven early warning systems for sepsis recommend that health systems treat predictive models as living assets with defined performance metrics, retraining triggers, and retirement criteria. This aligns with broader trends in AI regulation and professional guidance, which emphasize transparency, accountability, and lifecycle management for high-risk clinical AI systems.^[16][12][10]

Implications for life science and health technology leaders

For biopharma, medtech, diagnostics, and digital health organizations, this case offers several strategic lessons that extend beyond sepsis or hospital IT.

Lessons learned from a drifting sepsis alert AI

The Surgeon's experience with an EHR-embedded sepsis early warning system captures the human reality of model drift and alert fatigue in clinical AI. A system that was designed to enhance safety instead became a source of noise that clinicians learned to ignore, in part because its performance degraded over time and in part because the deployment did not adequately consider transparency, governance, and workflow fit.

For senior leaders across life sciences and healthcare, the core lesson is that AI readiness, governance, and user-centered design are as important as algorithm selection. High-impact AI systems should be treated as evolving, high-stakes assets with clear accountability, continuous monitoring, and deliberate integration into human workflows. When these elements are in place, sepsis prediction tools and other clinical AI systems are far more likely to deliver sustained value and earn the trust of the clinicians and patients they are meant to serve.^[2][5][10]

References