Artificial intelligence has come a long way, with new uses being discovered each day. One area where fascinating advances are being made is in healthcare. When it comes to improving predictive accuracy in patient care, AI is making significant inroads.
Today, we will discuss several of the ways AI can improve predictive accuracy in patient care, as well as the challenges to predictive accuracy in patient care that healthcare organizations face to begin with. If you would like to learn even more about the use of AI in healthcare, we encourage you to consider registering for artificial intelligence in healthcare courses where such topics are studied at length.
What is Predictive Accuracy in Patient Care?
First, let’s first define predictive accuracy in patient care. In short, predictive accuracy in patient care refers to the ability to correctly identify which patients will respond favorably to which treatments. This is important because, obviously, the goal of healthcare is to help patients get better.
The problem is that not all patients are the same. Therefore, what works for one might not work for another. So, correctly predicting which patients will respond favorably to which treatments is a major breakthrough that can save lives and improve the quality of life for countless people around the world.
It also has the potential to save healthcare organizations a lot of money. After all, if treatments can be better targeted to those who will respond favorably to them, that means less money wasted on treatments that won’t work. And, given the high cost of healthcare today, anything that can help reduce costs is very much welcome.
Prior to the use of AI, predictive accuracy in patient care was determined through the use of traditional methods. This typically involved looking at a patient’s medical history and symptoms and then making a determination based on that information. This will continue to be an important part of the process, but AI can supplement it and, in some cases, even improve upon it.
After all, while traditional methods can be effective, they leave room for error.
Challenges to Traditional Predictive Accuracy in Patient Care Methods
There are a number of challenges that can arise when using traditional methods to determine predictive accuracy in patient care.
Medical histories can be incomplete
Traditional methods place a lot of weight on a patient’s medical history. However, medical histories can be incomplete for a variety of reasons. For example, the patient may not remember all of their past treatments. Maybe they’ve never been to see a doctor before. Whatever the reason, an incomplete medical history can make it difficult to get an accurate prediction.
Symptoms can be deceiving
Another challenge with using traditional methods is that symptoms can be deceiving. For example, let’s say a patient comes in with what appears to be the flu. However, upon further examination, it turns out that the patient actually has pneumonia. The initial symptoms were deceiving and led to an inaccurate prediction.
A doctor’s experience and expertise can play a role in their ability to correctly interpret a patient’s symptoms and medical history. However, even the best doctors are not perfect and can make mistakes. In addition, their diagnoses are often based on their own experience, which is limited to the patients they have seen and what they’ve read, rather than all of the available data.
Time and money
It can also be time-consuming and expensive to gather all of the necessary information. This is especially true if a patient has to be seen by multiple doctors in order to get a complete picture of their medical history.
How AI Can Improve Predictive Accuracy in Patient Care
Now that we’ve looked at some of the challenges associated with traditional methods, let’s look at how AI can help improve predictive accuracy in patient care.
A more complete picture
One of the advantages of using AI is that it can provide a more complete picture. This is because, unlike humans, AI can gather and process large amounts of data quickly and efficiently. So, rather than relying on a patient’s memory or doctor’s notes, AI can access the relevant data and use it to make a prediction.
For example, let’s again say a patient comes in with the flu. Using AI, the system can not only look at the patient’s medical history but also examine data from other patients with the flu. This could include things like age, weight, and underlying health conditions. Looking at this data, AI can make a more informed prediction about how this particular patient will respond to treatment.
Inaccurate predictions can be corrected
Another advantage of using AI is that inaccurate predictions can be easily corrected. This is because the system constantly learns and updates itself as new data becomes available. So, if a prediction turns out to be wrong, the AI can learn from its mistake and adjust its predictions accordingly.
This is in contrast to traditional methods, where a doctor might make an incorrect prediction based on their experience and expertise. Furthermore, it can be difficult to change once that prediction is made.
AI can save time and money
Finally, using AI can save time and money. This is because the system can do much of the work that humans normally do. For example, rather than having a patient see multiple doctors to get a complete picture of their medical history, AI can gather all of the relevant data in one place.
In addition, because AI can make predictions more accurately than humans, it can help reduce unnecessary tests and procedures. This not only saves time and money, but it also helps improve the quality of care for patients.
While artificial intelligence is certainly not perfect, it boasts many advantages over traditional methods. AI can provide a more complete picture, make more accurate predictions, and save time and money. So when it comes to improving predictive accuracy in patient care, it’s one tool that simply cannot be overlooked.