Presentation Title

Information Visualization for Prescription Drug Monitoring Program Data

Faculty Mentor

Gregory Polston

Start Date

23-11-2019 10:45 AM

End Date

23-11-2019 11:30 AM

Location

184

Session

poster 4

Type of Presentation

Poster

Subject Area

health_nutrition_clinical_science

Abstract

The opioid epidemic has been a major medical issue in the United States for decades. The National Safety Council even stated in their 2019 report that Americans are more likely to die from opioid overdoses than from car crashes. First, it is important to identify that there are two types of opioids: prescription and illegally obtained. A patient, however, can still become dependent and addicted to prescription opioids. Individuals who become addicted may start to schedule their lives around these drugs, prioritizing them over other activities. When it comes to stabilizing overprescription, the prime tools utilized are prescription drug monitoring programs (PDMP). PDMPs are systems created to promote public health by identifying the misuse of prescription medications. In California, the PDMP is called the Controlled Substance Utilization Review and Evaluation System (CURES). PDMPs are effective programs, and there’s data proving that they are a significant factor in the overall decrease of opioid deaths involving prescription drugs. There is, however, a major problem associated with the majority of PDMPs. They are difficult to comprehend and digest. PDMPs are usually displayed through raw data, which can be challenging to understand. PDMPs are utilized by multiple groups of people: from healthcare providers to law enforcement. These groups have different sets of skills and come from different backgrounds. So, it is unreasonable to give them the same, raw data. My project centers on utilizing data visualization to make PDMP data better digestible and comprehensible. I used R programming and plot.ly to make visualizations that I believe PDMP groups would benefit from. I am currently conducting trials and surveys with these groups to see how effective these visualizations are. Overall, this project centers on using data visualization to alter PDMPs in an effort to decrease opioid deaths involving prescription drugs.

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Nov 23rd, 10:45 AM Nov 23rd, 11:30 AM

Information Visualization for Prescription Drug Monitoring Program Data

184

The opioid epidemic has been a major medical issue in the United States for decades. The National Safety Council even stated in their 2019 report that Americans are more likely to die from opioid overdoses than from car crashes. First, it is important to identify that there are two types of opioids: prescription and illegally obtained. A patient, however, can still become dependent and addicted to prescription opioids. Individuals who become addicted may start to schedule their lives around these drugs, prioritizing them over other activities. When it comes to stabilizing overprescription, the prime tools utilized are prescription drug monitoring programs (PDMP). PDMPs are systems created to promote public health by identifying the misuse of prescription medications. In California, the PDMP is called the Controlled Substance Utilization Review and Evaluation System (CURES). PDMPs are effective programs, and there’s data proving that they are a significant factor in the overall decrease of opioid deaths involving prescription drugs. There is, however, a major problem associated with the majority of PDMPs. They are difficult to comprehend and digest. PDMPs are usually displayed through raw data, which can be challenging to understand. PDMPs are utilized by multiple groups of people: from healthcare providers to law enforcement. These groups have different sets of skills and come from different backgrounds. So, it is unreasonable to give them the same, raw data. My project centers on utilizing data visualization to make PDMP data better digestible and comprehensible. I used R programming and plot.ly to make visualizations that I believe PDMP groups would benefit from. I am currently conducting trials and surveys with these groups to see how effective these visualizations are. Overall, this project centers on using data visualization to alter PDMPs in an effort to decrease opioid deaths involving prescription drugs.