Predictive Modeling in Healthcare: What it Tells Us About Pharmaceutical Drug Use
In the course of one generation, Amazon has grown to become a global giant. In fact, much of Amazon’s success can be attributed to Predictive Analytics, as this technology has been a tremendous game changer. With this tool, retailers can predict exactly when a shopper will make a purchase with an amazing degree of certainty.
Now consider what would happen if medical and pharmaceutical predictive models could do the same. Companies could use the power of prediction to prevent prescription drug addiction and improve the diagnostic process. The potential is very real, especially when you consider how the pharmaceutical industry is sitting upon mountains of data that could be leveraged to alter pharmaceutical drug use. Predictive Modeling can revolutionize outcomes for patients, doctors, pharmaceutical companies and the entire healthcare industry as a whole.
Predictive Modeling in Healthcare Can Improve Treatment Outcomes
Physicians use predictive algorithms to improve the accuracy of diagnosis. They are applying Machine Learning to leverage massive volumes of data. For instance, past treatment outcomes can reveal surprising insights and subtle trends that may otherwise escape detection even by the most experienced data analyst. This is just as true when it comes to drug prescriptions. Here are two of the many predictive modeling benefits that we may soon see in the pharmaceutical sector.
1. Prescription adherence intervention: Patients with chronic conditions have a 50% likelihood of falling off their prescription drugs in the first year of treatment. Patients could benefit from education on the topic of proper prescription use, combined with adherence intervention. Otherwise, unintentional prescription misuse will impact healthcare costs, in addition to causing poor outcomes for patients.
Predictive modeling goes beyond the binary adherence/non-adherence scenarios by projecting six different trajectories. Patients that fall between these two extremes are better positioned for intervention when it’s time to refill their medication. Predictive models can even pinpoint specific periods in a patient’s medication history when they are most likely to respond to intervention.
2. Prevention of adverse drug-drug fallouts: According to the CDC, more than 20% of the U.S. population takes three or more medications per day. Drug-drug interactions (DDIs) can lead to serious complications. DDI involves a synergistic change in a drug’s effects when it’s taken alongside another drug. Doctors often leverage these medication interactions for combination drug therapy. But in many cases, the synergistic drug effect is unintended. This is particularly true for elderly patients. Studies show that 7% of hospitalizations involving geriatric patients are related to drug-related adversity.
In this scenario, Predictive Analytics depends upon a Machine Learning interface whereby each drug can be matched against other drugs to determine the probability of an interaction. The lower the probability of a drug interaction, the safer it is for a patient to take the medications simultaneously.
Predictive Healthcare Modeling Reduces Prescription Drug Abuse
A health care provider may write up a prescription for a potentially-addictive opioid painkiller following major surgery. Opioids can also be prescribed in an attempt to treat severe chronic pain and even discomfort from cancer. However, there has been a rise in emergency room visits and deaths due to prescription drug misuse. Celebrities like Michael Jackson and Prince captured headlines with their deaths, which were attributed to prescription drug misuse. But for each case that makes headlines, there are hundreds of additional drug-related deaths that the public never hears about.
Pharmaceutical drug abuse spreads wider than just the patients who have prescribed access to these drugs. According to the U.S. Department of Health and Human Services, approximately 16 million people in the United States are abusing prescription drugs. With around 1,600 teens adding to this figure daily, early intervention can be the difference between a healthy future or a lifelong struggle with opioid addiction.
To tackle this growing menace, Machine Learning models are being trained to identify high-risk patients who are the most likely to develop an opioid dependency or an addiction to another potentially dangerous medication. The models have been built using control patients with no history of addiction, along with data from known substance-dependent patients, according to electronic health records (EHR). They are then matched by age, gender, zip code and disease history to build up clinical profiles. This data can then be processed in a way that can benefit intervention programs as they strive to identify patients who are at risk of pharmaceutical dependence. Visualization dashboards can even be used to plot opioid incidences by zip code and county, enabling healthcare agencies to develop outreach programs for high-risk localities and age groups.
The FDA decided to expand its data analytics capabilities in 2019 in response to the opioid crisis. Due to a $20 million grant, the FDA plans to build an all-encompassing data warehouse. The data will be used to evaluate clinical and social trends that have led to this epidemic in the hopes of creating checkpoints to halt the spread of misuse.
It is important to note that medication does not have to be prescribed or even highly addictive in order for patients to develop an addiction. Over-the-counter (OTC) medication can be purchased at a local store or pharmacy in order to treat common illnesses or health problems. Since OTC medication is easily available and self-administered, this type of drug abuse can be more difficult to track. Common types of misuse include the following.
- taking more than the prescribed amount
- failing to read the label when taking medication
- taking multiple medications at a time
- self-administering without a necessary reason.
More research is necessary to find ways to combat OTC drug misuse. However, drug education, physician monitoring and peer accountability are all factors that can help to prevent and address pharmaceutical drug abuse.
Predictive modeling tells us a lot about pharmaceutical drug use. But this technology also offers insight into how pharmaceutical drugs should be used. From improving treatment outcomes to preventing drug misuse, there are many benefits of applying predictive modeling in the pharmaceutical industry.
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