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The Opioid



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November 12-13, 2019
Beckman Center, Irvine, CA
100 Academy Way, Irvine, CA 92617






The Opioid Hackathon

Two Days Changing the Future of Opioid Addiction

Every day, more than 130 people in the United States die after overdosing on opioids, making the opioid crisis the top current public health problem in the nation.


45 states saw opioid overdoses increase by 30% from July 2016 through September 2017. In California, some regions have a greater number of opioid overdose deaths than the national average, creating a dire need to develop and implement solutions.

Building on insights from the U.S. Department of Health and Human Services Code-a-Thon in 2017 and the 2018 Opioid Hackathon, the University of California Institute for Prediction Technology (UCIPT) is hosting the 2019 Opioid Hackathon at UC Irvine's Beckman Center. The event will bring together interdisciplinary teams to create innovative solutions to help save lives.


In November, data scientists; visualization experts; public health, medical, law enforcement professionals; legal, ethical and psychological researchers; and patients and family members affected by the crisis are banding together to find new approaches to address the crisis.

Each team will work on one of four tracks representing four different facets of the opioid epidemic.


Depending on which track they are working on, teams will be provided with data relevant to the track they are working on. In 2018, this data included virtual reality and augmented reality data, wearable and social media data, cannabis data, and data on behavior change approaches. Teams will have 24-hours to use the data provided to develop innovative solutions, with a final panel of judges deciding on four winning teams.

The goal of the Opioid Hackathon is for participants to expand their knowledge in this field, learn from and with each other, and apply their skills to create real-world solutions. This year will focus on the needs of Orange County, including the UCI Health System. 


There is an opportunity for winning teams to further their work after the 2019 Hackathon, and have their solutions implemented within the public health system to help address the opioid crisis. Lessons learned from the 2019 Hackathon can be applied to other states and settings to help address the national crisis.


The 2019 Opioid Hackathon is part of a study on whether and how hackathons can be used to scale implementations of opioid-related solutions in public health settings. All hackathon participants will become a part of the study. Selected participants will be invited to complete surveys and interviews related to their experiences at the event.

For more information, see:

The Conference
Hackathon Tracks


Registered teams will compete across 4 different tracks. 


Fellows will be selected

from participants 

and mentored after the event.


Winning teams can implement solutions into

public health settings.

2019 Hackathon Tracks

Hackathon participants choose to compete in one of five tracks. The first four tracks are related to software application development.  The fifth track is a data science/artificial intelligence and visualization track. 



Track 1: Assist the reentry population as part of their treatment plan. 

Software application development track

Problem: Individuals recently released from criminal justice settings (ie, “re-entry populations” who have been sentenced by drug courts/recently released from jails, prisons) who are suffering from opioid use disorder need assistance getting appropriate treatments.


However, for a number of reasons (e.g., lack of knowledge; lack of coordination between criminal justice officials and public health, etc), these populations are unable to receive appropriate treatments to address their opioid misuse. Individuals re-entering mainstream public society have specific and known “release” dates. We, therefore, have the opportunity for a warm “hand-off” from correctional facilities to public health services. This track focuses on improving the ability for re-entry populations suffering from opioid addiction to gain access to the information, services, and ongoing treatments they need. 

  • Team Composition: We expect teams in this track to have experience with a) understanding the unmet needs of public health/opioid treatment medical providers, criminal justice/law enforcement, and re-entry populations, b) designing patient- and other user-centered health/medical software, c) ethical/privacy issues in providing health information and resources to sensitive populations. 

  • Related questions that might be answered within this track:

    • How can we improve communication and services between justice and public officials/workers beginning before prisoner release and extending after to keep people engaged in treatments?

Track 2: Software application that provides peer to peer support (physician to physician) to increase the number of providers prescribing MAT (i.e., treatment for opioid use disorder). 

Software application development track


Problem: Although patients suffering from opioid use disorder are in need of treatment, many providers are reluctant to treat these patients because they’re fearful of a number of things (e.g., stigma of prescribing MAT/being known as an addiction or drug abuse doctor and having their practice defined by that; DEA investigations/requests for documentation; (often irrational) fears that prescribing these medications will take a lot of time and not allow sufficient reimbursement for it).


This track focuses on getting providers who have the ability/waiver and are already experienced with treating opioid use disorder to prescribe MAT. NOTE: A waiver/training is needed for prescribing MAT. This track is focused on providers who already have received this training but still aren’t prescribing as much as they can. 



  • Example Winner: Winning solutions should be a software application able to get providers already experienced with treating opioid use disorder to prescribe MAT more frequently. 

    • A potential solution might be an online community, message board, or method of allowing providers to mentor and train each other. It could use various incentive methods (financial, if available; peer pressure; etc) to increase MAT prescribing. However, as online communities, message boards and approaches (, ECHO model, Doximity) are already used, it would need to be novel and/or compelling as to how and why providers would be able to give up their (minimal available) time to use this software. 

  • Team Composition: We expect teams in this track to have experience with a) understanding the needs of treatment providers for opioid use disorder, b) an understanding of how to apply behavioral psychology/economics to change (provider) behavior, c) software design and development for creating the app, and d) product development experience in understanding how to create software that people will use. 

  • Related questions that might be answered within this track

    • How can new technologies and behavior change/behavioral economics approaches be integrated to help change provider behavior?

    • How can behavior change risk reduction apps incorporate communities of providers to assist with behavior change and treatment? 

    • What are the barriers to prescribing MAT? 

Track 3: Application that provides a local directory for available treatments and providers for opioid use disorder. 

Software application development track


Problem: Individuals with opioid use disorder (as well as many health providers) need assistance knowing about and getting appropriate treatments. There are a number of treatments available, with different reasons for when and why patients should use these treatments, as well as patients' own preferences.


However, this information is not user-friendly or easily accessible to those who need it for their choices. How can we improve the process for patients (as well as referring providers) to make an ethical, appropriate, and informed decision about the best treatment(s) for a patient?


  • Winning solutions to this track would include software applications that can help link individuals to appropriate services and therapy appointments for medication treatments (it should improve upon the SAMHSA website of available providers). It should incorporate a user-centered design that could help patients, families, and providers understand the treatment options and the different considerations they need to have to choose the best program for themselves (e.g., cost, location, specific medication considerations, lifestyle (how much time is involved for the treatment?), health insurance coverage.) It would be ideal if the application can establish a simulated data interface based on the SMART on FHIR protocol to retrieve clinic appointment information from electronic health records to help individuals find available appointment slots in clinics nearby. A list of publicly available FHIR servers for testing can be found at

  • Team Composition: We expect teams in this track to have experience with a) understanding the treatment options for and considerations among patients with opioid use disorder, b) designing patient- and other user-centered health/medical software, c) ethical/privacy issues in providing health information and resources to sensitive populations.

Track 4: Patient-centered software to reduce stigma and increase treatment retention. 

Software application development track

Problem: 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; continued prescribing of opioids and/or co-prescribing of sedatives among providers to patients better suited for different therapies or lower doses; stigma in seeking treatment or thinking that the only treatment is not taking medicine; 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 in this track would include a software application (e.g., mobile, VR, AR, and/or other modalities) that can decrease the stigma associated with seeking treatment for opioid use disorder and/or increasing engagement in recovery) among patients.

  • The application might include wearable trackers, visualizations, virtual reality, or other features that could help facilitate behavior change. Winning solutions will not solely provide information or education on behavior (e.g., education about opioid abuse, or a list of local places to seek counseling) as education alone is not a sufficient method of sustainable behavior change. Solutions might also integrate some component of tracking whether patients are staying in treatment.

  • Team Composition: We expect teams in this track to have experience with a) understanding the unmet behavioral needs of the opioid crisis (eg, addiction, stigma/law enforcement fear preventing people from seeking help for heroin, buprenorphine prescribing needs, lack of naloxone availability, etc, b) an understanding of how to apply behavioral psychology/economics to change behavior, c) software design and development for creating the app, and d) product development experience in understanding how to create software that people will use. 

  • Related questions that might be answered within this track

    • How can new technologies (e.g., wearables, blockchain-based apps) and behavior change/behavioral economics approaches be integrated to help track, facilitate, and sustain risk reduction?

    • How can behavior change risk reduction apps incorporate communities of patients’ families and friends to assist with behavior change and/or recovery?  What are the significant stressors and friction points that provide opportunities for tech engagement?

Track 5: Real-time/prediction models and visualization tools to assist Orange County Public Health and UCI Health to prevent addiction and overdose. 

Data science/artificial intelligence and visualization track

Problem: Little data are available on the changing trends in opioid-related medical outcomes needed for public health to intervene and deploy appropriate resources. For example, public health officials need to understand growth trends and more precise geo-targeted information about 1) opioid use 2) naloxone availability and distribution, 3) heroin use, 4) fentanyl use and testing strip outcomes. Through gaining insights into these changing trends, public health officials and health systems may be able to immediately act on this information by providing resources and/or interventions to affected regions and individuals.

  • Winning solutions in this track would include high-quality visualizations, powered by real-time/predictive models, that can be applied by Orange County public health officials and/or the UC Irvine health system/emergency department for overdose prevention. For example, it should be similar to or able to integrate with

  • Team Composition: We expect teams in this track to have experience with a) understanding the unmet public health and/or medical needs of the opioid crisis in Orange County, b) data science/artificial intelligence experience designing and testing best-fitting and robust statistical/machine learning models such as, but not limited to, random forest, support vector machine, and convolutional neural (deep) networks, along with understanding how to self-access and use relevant libraries for these analyses, c) research experience developing and testing/validating theory-driven hypothesis. 

  • Related questions that might be answered within this track

    • Where (being as precise as possible in location) are people engaging in behaviors putting them at risk for overdose? 

    • How can trends in discussions around opioid use on social media inform overdose prevention efforts?

    • How might social media, internet search, and other near-real-time sources of data be integrated into models to provide more timely information and results?


Hackathon: Day 1


November 12th, 2019

12pm - 12am

11:00 am - 12:00 pm 

Participant Check-In (Free Parking at Beckman Center)

Complete Consent Form, Baseline Survey & Media Release


12:00 pm - 1:00 pm

Lunch Provided

1:00 pm – 1:15 pm

Transition to Auditorium for Opening Remarks

in Beckman Center Auditorium

1:15 pm – 1:30 pm

Opening Remarks from UCIPT Director Sean Young 



1:30 pm – 12:00 am

Team Coding Period in Assigned Work Rooms


6:00 pm – 7:30 pm

Sponsored Dinner: Taco Bar



Hackathon: Day 2

November 13th, 2019

12am - 5:30pm

12:00 am – 7:30 am 

Optional Team Coding Period


7:30 am – 9:00 am 

Sponsored Breakfast


9:00 am – 1:30 pm 

Team Coding Period in Work Rooms


11:00 am – 1:00 pm

Sponsored Lunch


11:00 am – 1:30 pm

Round 1 Judging: 5-minute presentations in Work Rooms

(Teams Continue Coding Throughout)

1:30 pm - 2:00 pm 

End of 24-hour Coding Period

Round 1 Winners Announced


2:00 pm - 2:30 pm

Round 1 Winners Finalize 7-minute Pitches/Demos

(Work Rooms then return to Auditorium for Final Pitches)

2:30 pm – 3:00 pm 

Track 1: Final Pitches and Judging 


3:00 pm – 3:30 pm

Track 2: Final Pitches and Judging


3:30 pm – 4:00 pm

Track 3: Final Pitches and Judging


4:00 pm – 4:30 pm

Track 4: Final Pitches and Judging


4:30 pm – 5:00 pm

Track 5: Final Pitches and Judging


5:00 pm – 5:30 pm

Final Judges Deliberation and Winners Announced

5:30 pm – 6:00 pm

Winners Interviewed at Beckman Center

2019 Agenda


2019 Sponsors

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Frequently Asked Questions


A hackathon (also known as a hack day, hackfest or codefest) is an event in which computer programmers, and others involved in software development and hardware development, including graphic designers, interface designers and project managers, collaborate intensively on software projects.



No, you will not give up any intellectual property (IP) to what you create at the Hackathon. In addition, we strongly encourage you to continue to develop and implement your ideas post-Hackathon. 



You will be provided with datasets that you can use to build data science models and visualizations. The goal will be to build the models and visualizations that have the best chance of being implemented in public health settings. 



We welcome teams to brainstorm and make tentative plans in advance, but all content, code, analysis, and visualizations must be created during the Hackathon. 



Yes, while we will provide you with datasets, some visualization tools, and software on Socrata (closer to the start date), you are welcome to collect your own data and use your own visualization tools. However, any additional tools or data used must be shared with the judges in order to verify your analyses. We encourage teams to incorporate many different types of datasets including social media data, internet search data, wearable data, or virtual reality data. 




You will be judged on your creativity, the potential impact of your solution, and feasibility in which the solutions can be implemented in public health settings in California. 



Teams must consist of 3 to 5 participants. We welcome industry-based, academic-based, and mixed industry/academic teams to apply.



Bring any technology you want to use during the Hackathon. Please bring your laptop, and feel free to bring any other devices or hardware you may want to incorporate into your solutions (ie VR or AR headsets, activity trackers, etc). All food and drinks are provided for free.  



This event is part of an NIH-sponsored research study. By participating in this event, you are agreeing to be contacted with surveys and interviews about your experience during and after the event. Winning teams will be expected to continue attempting to work on and implement their solutions after the event, and will be provided with mentorship and travel support to assist them. We will seek to study whether and how winning teams continue working together and the results of this event.


Contact Us


Case Study

Using Google search data to predict opioids

As an example of a type of analysis/visualization for 'Hacking the Opioid Crisis,' our research team collected publicly available data on Google internet searches for both prescription opioids and illicit opioids in 9 large U.S. cities, in addition to data on emergency department admissions for heroin overdoses in those same cities.
We found that, within any year, cities that had a greater number of internet searches for opioids, also had a greater number of emergency department visits for heroin the next year. In other words, internet searches for opioids might be used to predict future emergency department visits for heroin overdoses.
Overall the study creates a call for more work on this topic. We are hoping that participants in this Hack-A-Thon will similarly find new ways to use data to address the opioid crisis. 


University of California Institute of Prediction Technology

University of California, Irvine


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Planning Committee

Dr. Sean Young, PhD

Executive Director of the University of California Institute for Prediction Technology (UCIPT)

Professor, UCI Informatics and Computer Science 

Lidia Flores

Staff Research Assistant, UCI

Advisory Board

Dr. Bharath Chakravarthy

MD/MPH Program Director, UCI School of Medicine

Dr. Clayton Chau 

Executive Medical Director, Institute for Mental Health and Wellness, St Joseph Hoag Health System Regional

Jonathan Ciampi

CEO, Bright Heart Health 

Dr. Nichole Quick

Orange County Health Officer

Dr. Mario San Bartolome

Addiction Medicine Specialist

Medical Director, Substance Use Disorders, Molina Healthcare

Dr. Kai Zheng

Associate Professor Department of Informatics, UC Irvine 

Director of the Center for Biomedical Informatics, UC Irvine Institute for Clinical and Translational Science

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