Over the last two decades, the U.S. has been plagued with high rates of prescription drug overdose. About 142 American citizens die every day from drug overdoses, many of which are caused by prescription medications obtained legally. Additionally, the number of opioids prescribed as of 2015 is three times as high as the number of prescriptions given out in 1999. Due to these staggering statistics, doctors and data scientists are working towards using predictive analytics to reduce the number of prescription drug overdoses.
Predictive analytics (PA) refers to software that is equipped with algorithms and uses data to identify patterns and provide valuable insights regarding these patterns. When it comes to prescription drugs, the technology can be used to battle overdoses by better assessing a patient’s risk of overdose based on their medical history.
Improving Upon Prescription Drug Monitoring Programs
Most states have prescription drug monitoring programs (PDMPs) that are designed to help reduce the number of prescriptions given to at-risk patients. In fact, PDMPs are mandatory in some states.
However, PDMP systems are inefficient, as it can take seven minutes to find a patient’s file—which could be too long if the doctor only has fifteen minutes with the patient. As such, physicians are sometimes reluctant to consult PDMP systems before prescribing medications.
If predictive analytics were implemented alongside PDMPs to monitor patients who may misuse medications, it could help reduce overdoses even more. PA can unearth the needed information in a quicker and more efficient manner, thereby providing valuable insights without slowing down the diagnosis process.
With PA, data from PDMPs could become available to a patient’s entire care team and government agencies. This would ensure that patients are not obtaining multiple prescriptions across various states or even in the same state. Having this data accessible across various healthcare entities and government organizations would make it easier to monitor a patient’s chances of misusing a drug. Software could look at their biographical information, the dosage they are being prescribed, and their medical history to better predict the likelihood of an overdose.
It is important to put PDMP data to good use, as collecting this data is not effective unless it is being analyzed in order to monitor suspicious behaviors. By making these files more accessible and integrating PDMP data across various medical and government entities, PA can identify at-risk patients and reduce the chances of an overdose.
Using Predictive Analytics to Identify Patients At-Risk for Prescription Drug Overdose
A predictive analytics engine can be used alongside descriptive analytics to make predictions about a patient. Descriptive analytics give you a big picture idea of what’s happening in society. In this case, the technology reveals that the rate of opioid use disorders (OUD) increase after patients receive high-dose prescriptions. Descriptive analytics can reveal broader patterns, such as what demographic groups are most likely to overdose on prescription drugs, in order to identify at-risk patients.
Another useful data analysis field is diagnostic analytics, which helps identify the causes of prescription drug abuse. For instance, gender, age, whether the medication is being given for an injury or chronic conditions, the dosage and the number of times a patient’s prescription is refilled are all factors that could lead to an overdose.
Combining these sciences is a key part of applying predictive analytics in the battle to reduce prescription drug overdoses. It’s necessary to define which patients are most at-risk based on their medical history and biographical information. Their personal profiles can then be compared with those of similar patients to predict whether it would be dangerous to prescribe a high dose of medication.
Sharing Data Across Healthcare Organizations
PA is transforming healthcare by helping physicians better understand how successful their treatments may be at helping a patient without risking a drug overdose. By leveraging predictive analytics software, physicians can compare their treatments with those of their peers while also recognizing the patterns that may warn of an addiction or overdose. The goal is to have an integrated healthcare system that shares every piece of a patient’s medical background in order to make the most informed decision.
Having these tools in place not only improves a patient’s outcome, but it also helps reduce a hospital’s chances of being charged with fraud or misuse of prescription medications. It also allows hospital systems, licensing boards and public health agencies to make decisions in a swifter and more informed manner. As such, healthcare organizations can use their resources more efficiently and can develop clearer treatment guidelines and educational tools. PA can also help pharmacies understand how their dispersal activity varies between geographical areas in order to identify any anomalies in the system.
Ultimately, having access to such data allows multiple healthcare and government entities to work together to reduce prescription drug overdoses. PA can better assess a patient’s chance of misusing a prescription drug while identifying the most at-risk patients and lowering their chances of abusing a prescription medication.
If you’re interested in integrating predictive analytics in your healthcare organization, the team at SevenTablets is ready to help. We specialize in predictive analytics and custom mobile app development and have experience in the healthcare industry. In addition, we’re well-versed in other innovative technologies, including augmented reality, virtual reality, artificial intelligence, blockchain and natural language processing.
Shane earned a B.S. at Texas A&M (whoop!) and studied mathematics as a graduate student at Southern Methodist University.
Latest posts by Shane Long (see all)
- How to Use Predictive Analytics Tools for HR - March 11, 2019
- 2019-2020 Retail Forecast: The Top 5 Tech Trends - February 20, 2019
- IoT Applications for Healthcare: How are Companies Using This Technology? - February 6, 2019