In the area of cardiosenal metabolic diseases, a patient can either simplify his circumstances too much or not. In many cases, the severity of the disease falls on a spectrum and requires more careful care approaches.
The implementation of artificial intelligence (AI) in clinical environments has improved the examinations and reviews of the patient, newly defined ideas of “diseases”, highlighted the prediction channels and helped the management of clinical decisions. In this interview with The American Journal of Managed Care®Murillo spoke more with these topics and the value of the integration of AI into health care.
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Transcript
What role can the integration of artificial intelligence play when improving care delivery and the results?
Artificial intelligence will change the world [of] Health care. Period. It changes in many ways, but I would present that we have not even seen the actual skills and the extent of what AI can do in the healthcare system. At the moment we see efforts like Predictive Analytics to identify patients with high risk. We see works on what we call CDs, what clinical decision -making support is, where AI can come in and tell the doctors: “Hey, this patient will benefit from a mammogram or colon cancer screening” and so on. We also see efforts to use AI to reduce the administrative burden. For example, the payers use it for claims, not for rejections. It is important to clarify that AI does not deny that it approved those who are rationalized.
We are now also seeing AI in imaging. We can recognize diseases more precisely by reading the EKGS [electrocardiograms]CT scans, echocardiograms, mammograms, etc. There are many options, but there are two areas in which I see an opportunity for AI. Number 1 that bring in personalized care for people, since you can now collect tons of data in the patient's information and give the provider information about the patient who enable you to be more personalized in terms of your care.
The second area in which you see a great opportunity is the redefinition of diseases by the AI. Let me explain that at the moment we have an approach that is very binary for health care: someone has diabetes or has no diabetes. The reality is, everything is in the spectrum. The disease goes from as normal to a very complicated condition. This spectrum has the opportunity to define by patterns. When we say today when you have a hemoglobin A1c Let's say you are not diabetic. If it is 6.5, you are diabetic. The reality is that 6.3 due to a variety of other conditions that we are still not familiar with can have a higher risk than 6.5.