September is Sepsis Awareness Month, a time to spotlight one of the most urgent and complex challenges in healthcare. Sepsis remains a leading cause of hospital deaths worldwide, claiming millions of lives each year. Despite advances in medicine, the condition is still difficult to catch early, which is critical since outcomes worsen with every hour of delayed treatment. Increasing awareness among clinicians, patients, and caregivers is essential and many are now looking to artificial intelligence (AI) as a potential solution. But while AI holds promise, predicting sepsis has proven to be one of its hardest tasks.
Sepsis is one of the most formidable conditions for both clinicians and algorithms to recognize early, and the difficulty lies in its complexity. Unlike diseases with clear biomarkers, sepsis is a syndrome of the body’s overwhelming and dysregulated response to infection and presents very differently from patient to patient. A young trauma patient, an elderly nursing home resident, and an immunocompromised cancer patient may all develop sepsis, but the timing, symptoms, and physiological patterns can look entirely different. That heterogeneity makes it extremely difficult for AI models to find reliable, generalizable patterns across large populations.
There is no gold-standard definition for when sepsis begins, clinicians often disagree on whether a patient is septic or not, and hospital records capture labels inconsistently — sometimes long after interventions have started. AI models trained on such “noisy” data end up learning from delayed or imperfect labels, which limits their accuracy. On top of that, the signals that matter most vital signs, lab values, clinical notes are recorded at irregular intervals and with missing values, creating gaps that models struggle to fill without introducing bias.
Even when an AI model shows promise in retrospective testing, real-world deployment introduces another layer of complexity. Models must operate in real time, working with incomplete data and generating alerts early enough to impact outcomes. If they trigger too many false alarms, clinicians may lose trust; if they are too cautious, they risk missing cases altogether. Combined with variations in patient populations and workflows across hospitals, this makes it extremely difficult to build an AI system that generalizes effectively. Ultimately, predicting sepsis requires close collaboration among technical, clinical, and operational teams to develop tools that are accurate, trusted, and actionable.
As we recognize Sepsis Awareness Month, it’s clear that innovation in detection and prediction is urgently needed. At HCIS, we’ve partnered with a leading healthcare AI company (Cognome) to develop the leading sepsis prediction and surveillance model validated through published results that demonstrates significant improvements in early detection compared to existing approaches. Our team partners with health systems to move beyond theoretical models and deliver solutions that are clinically actionable, trusted by frontline staff, and proven to impact outcomes. If your organization is ready to strengthen its sepsis strategy, read our blog outlining the capabilities of HCIS’s Sepsis Management Solution or contact an HCIS specialist today to see how we can help you turn early recognition into lifesaving action.
Please join us at the upcoming NJDVHIMSS Annual Conference where HCIS’s Executive Director, Mary Aitken, and Marie Brownlee, Director of Performance Improvement.
Prime Healthcare – St. Michael’s Medical Center, will discuss how AI is being leveraged to drive performance and reimbursement benefits for quality improvement and population health programs. See the agenda here.