​​

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 (pcssnow.org, 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 https://wiki.hl7.org/Publicly_Available_FHIR_Servers_for_testing.

​​

• 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 https://discovery.cdph.ca.gov/CDIC/ODdash/​

​​

• 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?

​

​

​

​​