Addressing Neighborhood-Level Opioid Data Gaps: Development of an Interactive Dashboard for Prescription Trends and Buprenorphine Access in California
Author(s)
Ryan Stofer, MS1, Peter T. Lim, PharmD1, Elena Wu, PharmD1, Qingyang Yu, PhD2, Timothy Lanthier, MS1, Ryan Jewik, BS3, Lu Shi, PhD4, Usha Sambamoorthi, MA, PhD5, Hao Wang, PhD2, Sherry Yun WANG, BSc, MPhil, PhD1;
1Chapman University, School of Pharmacy, Irvine, CA, USA, 2Stevens Institute of Technology, Hoboken, NJ, USA, 3Chapman University, Fowler School of Engineering, Orange, CA, USA, 4Pace University, New York, NY, USA, 5University of North Texas Health Science Center, Fort Worth, TX, USA
1Chapman University, School of Pharmacy, Irvine, CA, USA, 2Stevens Institute of Technology, Hoboken, NJ, USA, 3Chapman University, Fowler School of Engineering, Orange, CA, USA, 4Pace University, New York, NY, USA, 5University of North Texas Health Science Center, Fort Worth, TX, USA
Presentation Documents
OBJECTIVES: The opioid crisis is complex, with ongoing challenges in ensuring safe opioid prescribing, addressing unequal access to medications for opioid use disorder (MOUD), and adapting to new policies, such as the removal of special requirements for prescribing buprenorphine. While state and federal agencies track opioid overdose data at the county level, they lack detailed community-level data on prescription opioid use, MOUD distribution, and the availability of buprenorphine prescribers. To address this, our project created an interactive dashboard using 13 years of Prescription Drug Monitoring Program (PDMP) data. This tool helps stakeholders explore local patterns in opioid prescribing and treatment, enabling more targeted and effective responses.
METHODS: We utilized 13 years of data from California’s Prescription Drug Monitoring Program (PDMP) to develop an interactive dashboard. The data was collected by prescription encounters consisting of prescription opioids, medications for opioid use disorder (MOUD), and buprenorphine prescriptions. Data cleaning was performed to address missing values, standardized for consistency, and categorized into key metrics such as opioid prescription volumes, MOUD dispensing patterns, and buprenorphine prescriber activity. Tableau software was used to design the dashboard, enabling users to explore trends through interactive visualizations and geographic insights (URL: http://bit.ly/40qrJiY)
RESULTS: Our interactive dashboard allows users to explore prescription opioid distributions, MOUD patterns, and active buprenorphine prescribers by entering their 5-digit ZIP code, offering a clear view of patient demographics, including age, gender, and payment types for prescriptions. By combining prescribing data with demographics, the dashboard reveals trends and insights into healthcare patterns in local communities.
CONCLUSIONS: Our interactive dashboard offers a tool for tracking opioid-related data at the micro-geographic level, supporting evidence-based decision-making for healthcare professionals, policymakers, and community leaders. This real-time data platform could enhance the understanding of regional prescribing patterns and inform targeted interventions to address the opioid crisis.
METHODS: We utilized 13 years of data from California’s Prescription Drug Monitoring Program (PDMP) to develop an interactive dashboard. The data was collected by prescription encounters consisting of prescription opioids, medications for opioid use disorder (MOUD), and buprenorphine prescriptions. Data cleaning was performed to address missing values, standardized for consistency, and categorized into key metrics such as opioid prescription volumes, MOUD dispensing patterns, and buprenorphine prescriber activity. Tableau software was used to design the dashboard, enabling users to explore trends through interactive visualizations and geographic insights (URL: http://bit.ly/40qrJiY)
RESULTS: Our interactive dashboard allows users to explore prescription opioid distributions, MOUD patterns, and active buprenorphine prescribers by entering their 5-digit ZIP code, offering a clear view of patient demographics, including age, gender, and payment types for prescriptions. By combining prescribing data with demographics, the dashboard reveals trends and insights into healthcare patterns in local communities.
CONCLUSIONS: Our interactive dashboard offers a tool for tracking opioid-related data at the micro-geographic level, supporting evidence-based decision-making for healthcare professionals, policymakers, and community leaders. This real-time data platform could enhance the understanding of regional prescribing patterns and inform targeted interventions to address the opioid crisis.
Conference/Value in Health Info
2025-05, ISPOR 2025, Montréal, Quebec, CA
Value in Health, Volume 28, Issue S1
Code
HSD43
Topic
Health Service Delivery & Process of Care
Disease
No Additional Disease & Conditions/Specialized Treatment Areas