A.   Infrastructure/application interface for ethical/secure sharing of opioid-related data among key stakeholders 

 

Opioid-related data typically suffer from reporting lagtimes and lack of detailed information, making it difficult for researchers and public health departments to address the growing crisis. Real-time data can be provided by various stakeholders (e.g., patients, families, industry, public health, law enforcement, etc) through social media, internet search data results, medical records, wearable data, etc. Analysis of these data may help to address this problem by providing real-time novel, personalized information that help public health departments take actionable steps such as deploying interventions and resources. However, there are a number of infrastructural, ethical, and user experience-related questions around collecting and facilitating the sharing of these types of personal data. For example, how could a system or technology like this be created for data to be shared and used in a secure and ethical way so that stakeholders are aware of the risks/benefits of being engaged in this activity?

 

Winning solutions to this track should include a working prototype of a technology and/or mockup showing the interface for how each of the key stakeholders would use and understand how to use the technology.

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B.   Designing personalized behavior change apps that can be applied by patients, their families, and/or providers/health systems for long-term reductions in overdose-related risk behaviors.

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Behavior change programs attempting to reduce risk addiction and overdose have had limited long-term success. A number of barriers prevent success in sustainable behavior change, such as physical location distance between patients and recovery centers, especially in rural areas; continued prescribing of opioids and/or co-prescribing of sedatives among providers to patients better suited for different therapies or lower doses; and current technologies that are designed to reduce addiction and/or overdose do not effectively incorporate knowledge and lessons from behavior change science on how to create sustainable behavior change. 

Winning solutions to this track should include a working prototype and/or mockup showing the interface/work-flow designs with features for lasting behavior change.

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C.   Real-time/prediction models and visualization tools to prevent addiction and overdose

 

Little data are available on the changing trends in opioid-related medical outcomes needed for public health to intervene and deploy appropriate resources. Through gaining insights into these changing trends, public health official and health systems may be able to immediately act on this information by providing resources and/or interventions to affected regions and individuals. Our advisory board has helped to identify 3 primary areas of need. We are seeking data-based tools/visualizations/models that could help track trends on or help 1) Decrease opioid prescribing among providers, 2) Increase willingness to prescribe (among providers) or use (among patients) medication-assisted therapy, and/or 3) Increase harm-reduction (e.g., increasing availability of naloxone or related overdose prevention kits).

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D.   Real-time/prediction models and visualization tools showing new integrative therapeutic benefits and approaches to prevent addiction and overdose

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Many new types of approaches (either stand-alone and/or integrative with traditional medical/other) are available for combating the opioid crisis (e.g., cannabis, VR/AR, yoga, meditation), however, there have been limited studies looking at whether and how these non-traditional data sources might actually affect or improve opioid-related outcomes.

 

Winning solutions to this track should be models and/or visualizations that include data from non-traditional public health datasets. For example, instead of relying entirely on models from emergency department/hospital visits or prescription drug use (as expected in Track C above), solutions for this track should include non-traditional data sources such as cannabis data, social media data, internet search data, or yoga data).