How to Use Predictive Analytics in Chronic Disease Prevention

Using Medical History to Predict and Identify Risk

Understanding a patient’s family history is critical in evaluating potential risk factors of chronic diseases. If a patient indicates that certain chronic diseases run in their family, providers can use predictive analytics to determine the likelihood of disease development and watch for early signs.

According to an article from the University of Illinois Chicago, chronic disease treatment is one of the industry’s largest costs.

“On a population-wide level, predictive analytics can help greatly cut costs by predicting which patients are at higher risk for disease and arrange early intervention, before problems develop. This involves aggregating data that are related to a variety of factors. These include medical history, demographic or socioeconomic profile, and comorbidities,” the article stated.

Medical history can include age, blood pressure, blood glucose, family history of chronic conditions, and cholesterol levels.

According to the Center for Disease Control and Prevention (CDC), those with a family history of chronic diseases such as cancer, heart disease, and diabetes are more likely to become sick with the condition themselves. Individuals should be keeping track of their family history, recording any changes, and speaking with their physician about preventive care.

“Knowing about your family health history of a disease can motivate you to take steps to lower your chances of getting the disease. You can’t change your family health history, but you can change unhealthy behaviors, such as smoking, not exercising or being active, and poor eating habits,” the CDC stated.

“Talk with your doctor about steps that you can take, including whether you should consider early screening for the disease. If you have a family health history of disease, you may have the most to gain from lifestyle changes and screening tests.”

While predictive analytics has shown that family history can contribute to chronic disease risks, other factors such as socioeconomic and physical components as well as lifestyle choices can also impact the development of chronic diseases.

Predictive Analytics and Social Determinants of Health

While medical history plays a key role in understanding chronic disease prevention, providers should also examine social determinants of health to evaluate a patient’s full health identity.

In a study from New York University’s School of Global Public Health and Tandon School, researchers used machine learning and predictive analytics to determine the likelihood of cardiovascular disease and guide treatment options.

However, the study researchers noted that including social determinants of health in the predictive model increases the accuracy and can prove better medical assistance for those in diverse communities. The machine learning predictive model can give providers actionable information by quantifying a patient’s risk and guiding treatment recommendations.

“Cardiovascular disease is increasing, particularly in low- and middle-income countries and among communities of color in places like the United States," Rumi Chunara, the study’s senior author and associate professor of biostatistics at NYU School of Global Public Health and of computer science and engineering at NYU Tandon School of Engineering, said in a press release.

“Because these changes are happening over such a short period of time, it is well known that our changing social and environmental factors, such as increased processed foods, are driving this change, as opposed to genetic factors which would change over much longer time scales,” Chunara continued.

Once physicians recognize risk factors and early signs of chronic disease development, they can begin taking preventive steps to decrease the likelihood of the disease.

Predictive Analytics in Early Intervention

Physicians can take steps to prevent chronic disease by recognizing early signs and risks through big data collection. Proving early identification of risk factors is the most effective method to reduce mortality as well as improve a patient’s quality of life.

There are currently five chronic diseases that account for 75 percent of healthcare spending: cancer, cardiovascular disease, diabetes, obesity, and kidney disease. By identifying high-risk patients and risk factors, medical professionals can recommend preventative care strategies to lessen the likelihood of chronic disease development.

Preventive care approaches should promote healthy behavior, increase early detection and diagnosis, support all demographics, and eliminate health disparities. Exercise, nutrients, and medicine can all serve as effective preventive care methods.

“Diet acts as medical intervention, to maintain, prevent, and treat disease. It is a major lifestyle factor that contributes extensively for disease prevention such as diabetes, cancer, cardiovascular disease, metabolic syndrome and obesity etc. Poor diet and an inactive lifestyle are a lethal combination,” an article from the National Center for Biotechnology Information stated.

The article explains that making positive lifestyle choices can significantly lower an individual’s risk for developing chronic diseases such as diabetes, cancer, cardiovascular disease, metabolic syndrome, and obesity. With the use of predictive analytics, providers can detect early signs of chronic disease and encourage early invention to prevent disease development. 

Predictive analytics allows providers to assess both biological and socioeconomic risks and promote early intervention to improve chronic disease prevention.