Blog Post

Natural language processing changes the way dangerous alcohol use is found before surgery.

Researchers published a groundbreaking study in January 2024 that showed how artificial intelligence, especially natural language processing (NLP), could make it much easier to find surgery patients who drink too much. This new method, created by experts led by V. G. Vinod Vydiswaran, could make surgery much safer and improve patient outcomes by a huge amount.

A rule-based NLP model was used to look at the clinical notes of 53,629 patients who were about to have surgery at a tertiary care center for the study. Researchers looked at both the NLP method and the old way of finding people who drink too much by using International Classification of Diseases (ICD) diagnosis codes.

Important Facts:

1. Accuracy: The NLP model correctly found 87% of patients who were thought to be drinking too much by human experts. ICD codes alone, on the other hand, could only identify 29% of these cases.

2. Specificity: 84% of patients who didn’t drink too much were correctly identified by NLP, while 90% of this group were correctly identified by ICD codes.

Overall, the NLP method found three times more patients with risky alcohol use than ICD codes (14.5% vs. 4.8% of patients) when the whole dataset was analyzed.

It’s impossible to overstate how important these results are. Risky drinking before surgery is one of the most common things that can go wrong during surgery. It can lead to more problems, longer hospital stays, and higher costs for healthcare. Each possible complication after surgery adds $20,000 to the cost of health care. Even though it’s important, risky drinking is often not noticed in places where people are getting surgery.

Useful Applications:

1. Better screening before surgery: The NLP model could be added to electronic health record (EHR) tools to automatically show patients who might be drinking too much and be at risk. This would make it possible for more focused and timely steps to be taken before surgery.

2. Better care for patients: Finding at-risk patients early on could allow for preoperative alcohol interventions, recommendations, and, if needed, alcohol withdrawal prevention before or after surgery.

3. Resource Optimization: If healthcare providers can more accurately spot high-risk patients, they can better use their resources, which could lead to fewer complications and shorter hospital stays.

4. Research Advancement: The NLP approach could make research on alcohol use in surgical cohorts a lot better by making it easier to find people who are qualified to take part.

5. Lowering costs: This method could save a lot of money on healthcare costs by avoiding the problems that come with drinking too much.

Personalized Care: Being able to get more thorough information from clinical notes lets us understand each patient’s alcohol use history in a more complex way, which lets us make more personalized care plans.

The study shows promising results, but the experts stress that the NLP method should not be used instead of other alcohol screening methods. By adding another layer of knowledge, it helps doctors make decisions.

There are some problems with the study, like the fact that it only looked at one health system’s recording methods. It also needs to be checked out with bigger, more varied groups of patients. Also, the current design of the model looks at the whole patient history instead of finding out how much alcohol a patient is using at any particular time.

Although there are some problems with this study, it is still a big step toward using AI to improve surgical care. As healthcare systems continue to organize and collect huge amounts of patient data, this NLP model and other tools like it will become more useful for finding useful information that can be used to improve care for patients.

This study could have effects on more than just surgical care. It would be possible to use similar NLP techniques to find other health problems or risk factors that aren’t always found or addressed properly with traditional screening methods. This study is a great example of how artificial intelligence can be used to make healthcare better, patients’ results better, and resources better used in the medical field.

It will be very important to keep researching and improving these AI tools as time goes on. But this study clearly shows that NLP has the ability to change how we find and deal with surgical patients who drink too much, which could save lives and cut down on healthcare costs.

https://doi.org/10.1111/acer.15222