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Patton Andrew

Description

Opioid overdoses have emerged within the last five to ten years to be a major public health concern. The high potential for fatal events, disease transmission, and addiction all contribute to negative outcomes. However, what is currently known about opioid use and overdose is generally gathered from emergency room data, public surveys, and mortality data. In addition, opioid overdoses are a non-reportable condition. As a result, state/national standardized procedures for surveillance or reporting have not been developed, and local government monitoring is frequently not specific enough to capture and track all opioid overdoses. Lastly, traditional means of data collection for conditions such as heart disease through hospital networks or insurance companies are not necessarily applicable to opioid overdoses, due to the often short disease course of addiction and lack of consistent health care visits. Overdose patients are also reluctant to follow-up or provide contact information due to law enforcement or personal reasons. Furthermore, collected data related to overdoses several months or years after the fact are useless in terms of short-term outreach. Therefore, given the potentially brief timeline of addiction or use to negative outcome, the current project set to create a near real-time surveillance and treatment/outreach system for opioid overdoses using an already existing EMS data collection framework.

Objective: To develop and implement a classifcation algorithm to identify likely acute opioid overdoses from text fields in emergency medical services (EMS) records.

Submitted by elamb on

Presented November 16, 2018.

The current opioid overdose/addiction crisis in the United States presents a challenge to public health intervention due to a lack of data on current and past incidence. Very little information is known regarding what is happening when/where and in comparison to the past. Marin County, California is addressing the lack of clarity in opioid overdose data by designing a novel cloud-based system to identify opioid overdoses for both surveillance and outreach purposes using county owned Emergency Medical Services (EMS) data.