| 4 Min Read

Rules-Based Prioritization Versus Machine Learning with Lance Eason and Troy Wasilefsky


Key Takeaways:

  • Rules-based systems are fundamentally limited
  • Thresholds lead to patients with clinical evidence for a disease state being ignored because they don’t meet the cut-off
  • The mid-revenue cycle is inherently clinical in nature, and solutions lie in establishing a source of clinical truth for a patient
  • Machine learning allows for more nuanced, accurate predictions

Iodine Intelligence tackles a new challenge in healthcare’s mid-revenue cycle every month, shedding light on problems and solutions and sharing valuable insights from industry experts. Listen to Episode 2: Rules-Based Prioritization Versus Machine Learning with Lance Eason and Troy Wasilefsky to learn more.

Hospitals and healthcare providers are currently facing enormous staffing challenges, and Clinical Documentation Integrity departments are not exempt. These workforce difficulties are likely here to stay, and with limited human resources, CDI departments turn to technology solutions to help scale their teams and improve the process. (Listen to Episode 1 here to learn more about the staffing challenges CDI departments face, and how hiring more specialists isn’t the answer, but deploying technology with AI is). 

There are a wide variety of products on the market purporting to solve the issues CDI teams face. Many leverage AI to help, but not all AI is created equal. Some adopt a rules-based approach to help identify cases in need of review, but software based off rules, thresholds, and cut-offs is fundamentally limited. 

There are several core problems with a rules-based approach: 

  1. Arbitrary cutoffs. This type of technology is forced to create a confidence threshold, for example: how high do lactate levels need to be for me to be 80% confident this patient has sepsis? You will inevitably have patients who fall just below this threshold. The system ends up discarding patients who don’t have enough clinical evidence, despite the fact that there is some.
  2. A limited number of factors are examined. Typically rules-based systems only examine 4 or 5 criteria, providing a very narrow, limited view that doesn’t allow for the capture of the patient’s full clinical picture. 
  3. Factors are examined independently. Rather than examining patient symptoms in conjunction with one another, they are all examined independently (for example, lactic acid levels and heart rate are looked at separately). The combination of a small number of factors, and looking at these factors independently, means there is a lot of opportunity for patients who do have clinical evidence of a disease state, still not meeting the letter of the law in criteria. 
  4. Divorced from the clinical nature of the task. Rules-based systems approach the problem algorithmically, trying to diagnose a patient based on a set of rules, which is not how medicine works.

The failures of these systems lead to false positives, false negatives, and ultimately loss in confidence from the CDI team. CDI specialists stop using the product, go back to trying to review every case every day, and are essentially back to square one. 

“Because we are actually unlocking the ability to understand what’s clinically going on with each patient, that allows us to tell our customers: specifically for this patient here, here is the intervention that’s necessary, here’s what you need to look at for this patient. And we don’t speak in generalities.”

Machine Learning provides a solution/answer to each of the core failings of rules based systems. 

Machine learning doesn’t utilize thresholds or cut-offs, and is often examining dozens of factors, not just four or five. While these factors may not all be slam dunk criteria giving definitive answers, they all contribute to a much more nuanced understanding of the patient. Additionally, these factors are all examined in conjunction with each other. Ultimately, what machine learning excels at is recognizing patterns in large pools of data. Diseases have multiple ways they can present, and machine learning is capable of identifying disparate clusters of symptoms that nonetheless are all indicative of the same disease state. At Iodine, the goal is to establish a source of truth: a complete clinical picture of what ha[[ened to the patient. By unlocking the ability to truly and accurately understand what’s clinically going on with each patient, we’re able to guide our clients specifically, rather than speaking in generalities.     

While it’s critical for technology aimed at improving the CDI process to leverage AI, the type of AI powering the tools is just as important. Teams using software that relies solely on thresholds and cut-offs with a rules-based system are likely to find themselves sorely disappointed in their results – and that the tool purchased to streamline processes and introduce efficiency and accuracy into the department, does anything but.

Interested in Being on the Show?

Iodine Software’s mission has been to change healthcare by applying our deep experience in healthcare along with the latest technologies like machine learning to improve patient care. The Iodine Intelligence podcast is always looking for leaders in the healthcare technology space to further the conversation in how technology and clinicians can work together to empower intelligent care. if that sounds like you, we want to hear from you!