By now you’ve seen the continued drive to help hospitals and healthcare organizations tackle their biggest challenge: the diagnosis and treatment of sepsis.
When such efforts were started by Intermedix, we were consistently amazed by the numbers. What was even more surprising, however, was the finding that, in the last 42 years, there has not been a single technological or clinical advancement beyond the identification protocols that remain the “gold standard” for sepsis identification. The screening processes are still being deployed. One cannot help but ask why.
Related: Sepsis is now the leading cause of death, costliest condition and number one cause of readmission in US hospitals. Download our whitepaper to learn more about the leading methods to address sepsis.
The answer is actually quite simple: those processes don’t take into account the precious value of time in the sepsis treatment equation.
With sepsis, time is what matters. Every hour delayed means an increased mortality of 7.6 percent. If you factor in the 30 percent miss rate that leads to additional testing and clinical identification, the results are significant losses in hours and lives. Further, the human element, coupled with the cost and expenses of readmissions, translates to a $3.1 billion annual loss. Also add the lack of communication between acute and post-acute providers and you’ve got our current reality: a sepsis epidemic.
The critical nature of early sepsis intervention is supported by a study recently published in The New England Journal of Medicine. Patients who receive antibiotics within the first three hours of admission have a significantly lower risk-adjusted in-hospital mortality rate.
With protocols that haven’t changed in four decades, how do we start ensuring that treatment begins within those first three hours? The answer: by implementing a sepsis strategy that leverages big data and machine learning. The ability to identify patients most likely to acquire sepsis at the point of admission provides care teams with insights leading to earlier protocol-based treatment.
However, clinical indicators, beginning with a rise in blood pressure, don’t occur until roughly 15 hours in to the 36-hour decline from infection to death. Due to the delayed onset of clinical indicators, EHR-based alert systems and traditional scoring systems like qSOFA are not able to suspect sepsis until the condition has already deteriorated. Implementing methods that utilize non-traditional datasets will play a huge role in early identification and treatment.
Thankfully, novel new approaches to early sepsis identification grant every healthcare organization the ability to utilize data science, giving precious time back to the clinical staff so they can save lives and reduce current and future expenses associated with readmissions and ongoing treatment.
That all sounds great on paper, but what does implementing a sepsis solution mean to an already taxed and stressed healthcare system? It means collaboration—a meaningful partnership between clinicians and technology. It means hope—the hope and promise of saving more lives.
You can learn more about the leading methods to address sepsis with our latest whitepaper, of which provides an overview of new data-driven approaches that are at the forefront of the field.