The Advancement of Design: The Rise of AI
For ages, computer scientists have been pushing the limits of programming to create AI systems with human-level intelligence, drawing from ever-expanding knowledge pools to enhance decision-making abilities. However, results were limited, as the system’s advancement was contingent on the knowledge added to it.
The new approach aims to create systems capable of learning by extracting rules and evaluating cause-and-effect relationships from the data fed to them, along with direct observation of choices made. With a foundation provided by traditional AI protocols and big data, augmented by deep learning and predictive analytics, an evolution in healthcare methodology is underway.
A faster and more reliable trend line or risk score can be achieved through predictive analytics driven by enhanced AI systems, resulting in a heightened personalized healthcare program that could prevent health decline through Predictive Healthcare.
What is Personalized Healthcare, and How is it Different from Personalized Medicine?
Before delving into where AI fits into this scenario, it’s essential to distinguish between two distinct categories: Healthcare and Medicine.
Isn’t healthcare and medicine the same thing?
No, these two may seem similar, but they are, in fact, quite different.
Personalized Medicine utilizes descriptive data to determine the right drug, at the right time, with the right dose to treat a specific ailment. It focuses on using descriptive analytics, akin to turning raw data into a report. However, it falls short in terms of preventative healthcare, merely preventing further decline rather than preventing decline entirely. This is where Personalized Healthcare comes in, using Predictive Analytics to assess an accurately detailed, precise trend line of potential health risks, often referred to as a Risk Score.
The key difference lies in the ability to extrapolate the course of future events from descriptive data, achieved through incorporating Big Data and AI Deep Learning technology to derive Predictive Analytics.
Predictive analytics is revolutionizing the healthcare industry, allowing us to treat to prevent decline rather than treating to prevent further decline.
Personalized Healthcare Through AI Predictive Analytics
Let’s use Personalized Healthcare Programs to treat illnesses before they fully occur. However, there’s no guarantee that they will even acquire this illness, so how can you treat it?
Simple, we cheat, and by cheat, I mean we use Predictive Analytics derived from Enhanced AI functions.
Using a solution based on predictive machine learning will enable the analysis of huge amounts of data over large time periods from multiple projects. It utilizes correlations and statistics to provide more precise estimates. With experience comes knowledge and expertise, which the AI can leverage more effectively.
By using this new technology, we can address potential health issues by referencing the individual’s descriptive data against vast scores of other descriptive data. This allows us to assess the likelihood of progression towards different health issues based on trends from others with similar profiles.
The objective of using Predictive Analytics is to improve long-term engagement and reduce the risks associated with chronic diseases, leading to more preventative care and less post-manifestation treatment—an end goal for healthcare.
While everyone is different, there are common risk factors for every illness that, when addressed and mitigated, significantly lower the chance of occurrence or minimize the effects of manifestation when it does occur. At this point, Predictive Healthcare takes on a new form called Prescriptive Healthcare, which, using prescriptive analytics, derives optimal treatment methods.
The Goal: Prescriptive Healthcare
Prescriptive Healthcare can be thought of as an approach similar to Predictive Healthcare but goes one step ahead, replacing Predictive Analysis with Prescriptive Analysis. This is where Personalized Medicine meets Personalized Healthcare through Prescriptive analytics. It can tell a user what course of action would produce the highest likelihood of maximum benefit when a predicted event does occur, seamlessly tying into the approach of Personalized Medicine.
This proactive approach allows for more efficient addressing of client needs, reducing the costs of time and energy. Since AI has access to Big Data and Deep Learning capabilities, it can identify patterns and trends too complex for humans or other automated techniques. Being aware of the current standings of each patient, a change in one area allows the AI to react and prescribe advisable treatment options based on these markers that we may miss.
As a result, the client gains peace of mind as they no longer need to worry. We ‘understand their needs,’ accurately assess growing risk factors, and address them before they become major health concerns.
The outlook of such AI advancements would change how healthcare is approached entirely. Although AI hasn’t reached the point of prediction to prescription, it has reached this point in Personalized Medicine. Thus, it is only a matter of time until Personalized Healthcare becomes commonplace.