Could AI-powered Predictive Medicine be the next step in healthcare?

Imagine if you could predict a health issue before it became a problem?

Predicting before it becomes a problem 

Imagine if you could predict a health issue before it became a problem?  

The health system is currently set up to treat individuals who develop a health issue and are then treated for their symptoms. This reactive approach costs the health system and the patient, time, money and resources.  

But what if the health system had already predicted that a certain individual was at risk of developing that condition? We could then incorporate preventative measures and guide them on a health journey, so the impact of the disease was significantly reduced, and they would not require costly treatment. 

What is Predictive Medicine? 

Predictive Medicine is a relatively new sub-speciality in healthcare, but the concept is not novel. Predictive Medicine evaluates the probability and the risk of an individual developing a disease in the future. Predictive medicine utilises specific laboratory tests, genetic tests while analysing an individual’s health and social data and reviewing it against research and outcomes to determine the probability of an individual developing a disease.  

Biomarkers were originally used in the field of Oncology to predict the recurrence of cancer. Now, similar use of biomarkers can predict the more common clinical disorders in everyday life. For instance, blood cholesterol is a well-known biomarker of risk for coronary heart disease, and Prostrate-specific antigen (PSA) which is associated with prostate cancer.  

Can algorithms revolutionise healthcare? 

Predictive Medicine enables clinicians and caregivers to tailor intervention treatments that sustain health in a more precise way than ever before.  

This field of medicine does not just use genetic tests but also accounts for past treatment outcomes, latest research findings, hospital admissions and re-admission rates. Predictive Medicine uses AI and many data types to create a prediction profile (algorithm) for individuals. 

With all the data analyses Predictive Medicine requires, there is an incredible opportunity for the healthcare industry to improve the overall accuracy of diagnoses, aid in Preventative Medicine, and reduce rising healthcare costs.  

Examples of Predictive Medicine  

There are many facets of Predictive Medicine already in practice today.  

For instance, shortly after birth, blood samples are taken from a new-born to identify potential genetic disorders as early as possible. This is one of the most widespread forms of predictive medicine. 

Another common approach assesses a patient’s risk factors that could exacerbate the likelihood of disease. For instance, a heavy smoker is more likely to be susceptible to lung cancer, emphysema and other diseases compared to a non-smoker. 

Diagnostic testing is when a doctor has made a tentative diagnosis; it is used to confirm or refute the diagnoses. For instance, a gluten sensitivity test can assess if a patient has a gluten intolerance.  

And lastly, pre-conception testing assesses parents before they start trying to conceive to identify if the parents carry a gene mutation that could cause genetic disorders.  

In recent years, services like AncestryDNA and 23andMe have become a phenomenon. They analyse variations called Single Nucleotide Polymorphisms (SNPs) at specific positions in your genome, which have the potential to tell you about your traits and certain health conditions. Although not as comprehensive as other types of predictive medicine, they do have the advantage of increased accessibility and greater privacy. 

Can machine learning predict and curb disease before it’s too late? 

Machine learning solutions such as Orion Health Intelligence can analyse large data sets to predict long-term conditions, enabling interventions when they are needed the most.  

The ability to predict an individual’s risk of developing a disease provides an opportunity for clinicians to diagnose patients in an early stage of their condition and to intervene sooner, resulting in improved health outcomes for patients.  

With machine learning tools in place, healthcare organisations have the potential not only to benefit patients with precision care delivery but to reduce treatment costs and save billions of dollars each year.  

Interested in learning more about Orion Health Intelligence? 

This is an introduction to Predictive Medicine. The next blog in this series will focus on an example of Predictive Medicine in action.