A few weeks ago, our blog featured a post about k-means clustering, an unsupervised machine learning method. We use unsupervised methods when we don’t have an explicit idea of what patterns exist in a dataset. Clustering can help us surface insights about groups that exist in the data that we may not know about. To separate data into clusters, k-means first needs to calculate the distance between each data point. That distance is used to help define the “similarity” between two points and is normally calculated using some continuous technique…
Data Science Blog
The benefits of machine learning in healthcare
If you read much about technology, you have likely heard about machine learning, but may be wondering how it would work in healthcare. Where’s the low-hanging fruit? And how could it help my clinical team?
Throughout healthcare, and many other industries, there are heuristics and established best practices that help people make decisions. A popular example in healthcare is the LACE index, which provides the likelihood of patient 30-day readmission risk. You might have also heard of similar tools like the SOFA Score, Apgar Score, PRISM Score, and the PIM Score.
Like most of these scores, the LACE calculation is fairly simple. It’s based on length of stay, acuity of the admission, patient comorbities, and ED visits within the last six months. In each of these categories, points are assigned—a length of stay of three days equals three points, for example. Then the points from each categories are added up to form the LACE index.
It’s simple and indicative of how healthcare has worked for the last 20-30 years. First, there’s a national study, which eventually leads to guidelines and a simple calculation to help prioritize which patients are most at risk of something.
So what’s wrong with that? Well, the guidelines can only be narrowly applied and even then don’t give impressive results. Think of it—LACE was developed from patients seen in Ontario from 2004 to 2008. Do your patient demographics closely match those in Ontario? Or, do your patient demographics even match your same set from ten years ago? Perhaps not. Another issue is applicability—since LACE requires the patient’s length of stay, the score is only available upon discharge. What if you want a risk score early during their stay?
This is why machine learning is fantastic—it fills these gaps. First, it learns the important relationships in your data on past patients and their outcomes. This means that the model is customized on your data from the last few years–you don’t have to rely on scores made on other populations, 10-20 years ago. Second, machine learning allows you to create a model based on whatever data is available when you need a risk score (i.e., upon admission rather than discharge).
In summary, what does a machine learning model provide? Accurate, timely risk scores, enabling confident and precise resource allocation, leading to lower costs and improved outcomes. As an added bonus, healthcare.ai shows why a risk score was high, so the clinician not only knows which patients are most at risk, but also what can be done to lower that patient’s risk. We’ll detail this ability in a future post.
Thanks, and please reach out with any questions or comments!