I’m thrilled to sit down with Ivan Kairatov, a renowned biopharma expert with extensive experience in technology and innovation within the industry. With a strong background in research and development, Ivan has been at the forefront of integrating cutting-edge solutions like machine learning into healthcare challenges. Today, we’re diving into his insights on a fascinating project that used machine learning to evaluate triage systems in emergency departments in Norway, shedding light on how technology can transform patient care and challenge long-held assumptions in medicine.
Can you begin by explaining what triage systems are in emergency departments and why they play such a crucial role in patient care?
Triage systems are essentially decision-making frameworks used in emergency departments to prioritize patient care based on the severity of their condition. When someone arrives at the ED, medical staff assess them to determine who needs immediate attention and who can safely wait. This process is vital because it ensures that critical cases, like a heart attack or severe trauma, get treated first, while less urgent cases are managed in due time. Without an effective triage system, resources could be misallocated, potentially leading to worse outcomes for patients in dire need.
What sparked the idea for using machine learning to analyze triage systems in this particular project in Norway?
The inspiration came from recognizing that triage, while essential, isn’t foolproof and varies widely across regions since there’s no universal standard. Our team, working through a collaboration at a medical innovation center in Bergen, saw an opportunity to use machine learning to dig deeper into how these systems perform. We wanted to identify patterns and risks that traditional methods might miss, especially for patients who are misclassified—either not getting urgent care when needed or being prioritized unnecessarily. The idea really took shape during a Datathon event in 2022, where diverse experts came together to brainstorm innovative solutions for healthcare challenges.
Can you walk us through how the concept for this study evolved during that Datathon event?
Absolutely. The Datathon was a dynamic environment where researchers, data scientists, and medical professionals from Norway and Germany collaborated intensively over a short period. We started with broad discussions about pain points in emergency care, and triage quickly emerged as a key area needing better evaluation tools. From there, we brainstormed how machine learning could analyze vast datasets from emergency departments in Bergen and Trondheim to uncover hidden trends. By the end of the event, we had a clear plan to develop models that could assess triage accuracy and identify factors contributing to errors, setting the stage for a full-fledged research project.
What were some of the biggest hurdles your team encountered when evaluating triage systems, especially given the lack of a standardized approach?
One major challenge was the inconsistency in how triage is applied across different hospitals and regions. Without a universal benchmark, it’s tough to define what ‘correct’ triage looks like, making evaluation tricky. We also had to grapple with the complexity of medical data—patients present with a wide range of symptoms, histories, and outcomes, which can be messy to analyze. Ensuring our machine learning models could handle this variability while still producing meaningful insights required a lot of fine-tuning and validation against real-world patient records.
Could you elaborate on the machine learning models your team developed for this study and what sets them apart from traditional analysis methods?
We used advanced machine learning algorithms that can process multiple variables at once to predict outcomes and identify patterns in triage data. Unlike traditional statistical methods, which often focus on a few predefined factors, our models could weigh the importance of numerous patient characteristics simultaneously—like age, symptoms, and referral sources. This allowed for a more nuanced understanding of what drives triage decisions. The ability to handle such complexity and uncover relationships that might not be obvious to human analysts is what really distinguishes this approach.
Your study utilized a concept called SHAP-values. Can you explain what that is and how it helped in understanding triage mistakes?
SHAP-values, which come from game theory, are a way to measure how much each factor in a dataset contributes to a specific prediction. In our case, we used them to see which patient characteristics—like their clinical department or diagnostic codes—most influenced whether someone was triaged incorrectly. This method helped us break down the ‘why’ behind errors, offering a clear picture of the key drivers of undertriage or overtriage. It was like having a magnifying glass on the decision-making process of our models, which was incredibly useful for interpreting results.
Your findings showed that incorrect triage was quite rare, occurring in less than one percent of cases. Did this surprise you, or was it in line with your expectations?
Honestly, it was a bit of both. We expected triage systems to be fairly accurate since they’re designed by experienced clinicians and refined over time. However, seeing such a low error rate—less than one percent—was still reassuring and a testament to the systems in place at these Norwegian hospitals. It also highlighted that while errors are rare, they’re still critical to address because even a small percentage can mean life-or-death consequences for individual patients.
One intriguing result was that age might not be as significant a factor in overtriage as previously thought. Can you share why this stood out and what other factors seemed more influential?
That finding was quite unexpected because prior studies using more conventional methods suggested that younger patients, especially those under 18, were often overtriaged—given higher priority than necessary, possibly due to caution. Our analysis, however, showed that age wasn’t the dominant factor. Instead, things like the department referring the patient and specific diagnostic codes played a bigger role in overtriage. This suggests that operational or clinical context might weigh more heavily in triage decisions than demographic traits like age.
How did your machine learning approach challenge assumptions based on expert opinion in the medical field?
Expert opinion, or what we call ‘domain knowledge,’ is invaluable, but it can sometimes be shaped by biases or untested assumptions. For instance, doctors might assume certain patient traits, like age or gender, heavily influence triage outcomes based on their experience. Our machine learning models, by contrast, let the data speak for itself. One example is how we found that clinical referral sources were more tied to overtriage than age, which contradicted some prior beliefs. This data-driven perspective helps refine our understanding and ensures we’re not just leaning on subjective views.
There’s growing concern about gender bias in healthcare. Your study suggested gender might not play a major role in triage errors. What did you uncover as more significant contributors instead?
That’s right, while gender equality is a critical issue in healthcare, our data didn’t show it as a primary factor in triage misclassification. Instead, we found that elements like the specific department referring the patient to the ED and the diagnostic codes assigned were far more influential in cases of overtriage. This points to systemic or procedural factors rather than personal characteristics driving these errors, which is an important distinction for designing interventions.
In what ways do you believe machine learning can bring fresh insights to medical research compared to traditional approaches?
Machine learning offers a unique ability to handle massive, complex datasets and uncover patterns that might escape human observation or traditional statistical tools. In medical research, this means we can explore a broader range of factors and their interactions—like how a patient’s symptoms, history, and even hospital logistics interplay in triage decisions. It’s not about replacing expert judgment but complementing it with objective, data-driven insights that can challenge outdated assumptions and point to new areas for improvement.
What are some limitations of applying machine learning in studies like this, and how did your team work to mitigate them?
Machine learning isn’t a magic bullet. One big limitation is the risk of overfitting—where a model learns the data too well, including noise, and doesn’t generalize to new scenarios. There’s also the ‘black box’ issue, where it’s hard to fully understand why a model makes certain predictions. We tackled these by using techniques like SHAP-values to interpret results and by rigorously validating our models against separate datasets to ensure they weren’t just memorizing patterns. Additionally, we collaborated closely with medical experts to ground our findings in real-world relevance.
Based on your research, what do you think are the next steps for studying triage systems and their effectiveness?
I think there’s a lot of potential in expanding this research to more diverse settings—different countries, hospital sizes, and patient demographics—to see how triage performance varies. We also need to explore real-time applications, like whether machine learning can assist in triage decisions as they happen, not just analyze them afterward. Another area is digging deeper into rare but severe errors to understand their root causes better. Building on this foundation, we can refine systems to be even more precise and equitable.
What is your forecast for the future of machine learning in emergency care and triage systems?
I’m optimistic that machine learning will become an integral part of emergency care, particularly in triage. In the coming years, I foresee these tools evolving into real-time decision support systems that help clinicians prioritize patients more accurately by instantly analyzing incoming data. We might also see automated triage classification systems that reduce human error while still allowing for clinical judgment. The key will be ensuring these technologies are transparent, ethical, and tailored to specific healthcare environments, ultimately improving patient outcomes on a global scale.
