A technology/research-driven proposal for opioid treatment
A critical barrier to progress in SUD is furthering the understanding of MAT, specifically, the side effects, alternative therapies to reduce side effects, methods to increase access and adoption, means of expediting and improving research, and means for ensuring drug makers are acting in the patient's best interest while saving money to achieve sustainable profits. The need is significant in that MAT is proven to be successful but its adoption is very low.
Based on the extensive behavioral health/psychosocial data collected from Addicaid, we propose an innovative AI-driven platform that would improve MAT and help researchers, drug makers, and communities better deliver treatment to those who need it. This new product will be called Treatease, a clinical information platform that will be accessible to consumers, providers, researchers, government agencies and pharmaceutical companies. Treatise is fundamentally about connecting patients, providers, and drug makers, while allowing aggregation and normalization of treatment data, generating self-reported information from patient and provider communities, and providing tools for researchers to access this data and information. Treatise will also conduct its own analysis, and send alerts to patients, providers, and drug makers if it discovers actionable insights. We will collect information that was before siloed between these stakeholders to further research in-real time with readily available medications and achievable improvements in protocols and interventions.
The Treatease portal will help create prescription protocols for providers, increase patient education, aggregate relevant research, allow real time analysis of community reported side effects and potential treatment biases, and assess patient treatment history and needs. These capabilities will be supported by a new version of the community-reinforcement and behavioral therapy based platform Addicaid pioneered. This platform has proven track record of engaging patients, increasing retention and reducing relapses based on pilot studies and actual adoption. The Treatease platform will help unify currently siloed data and user interfaces for different stakeholders into one, streamlined ecosystem that leverages expertly monitored, human-guided AI to identify trends and outliers faster than previously possible.
The proposed Treatease platform will become a highly valuable tool for patients, providers, researchers, community leaders, and drug makers. It will increase patient understanding and access to recovery through community reinforcement and aggregated information. Providers can obtain waivers and automatically know when a community member is interested in being treated. Researchers will have access to unparalleled data to expedite research on polypharmacological treatment for SUD and co-occurring mental disorders.
2. The Treatease Platform
The prototype resulting from this grant will be a web and mobile application with call and texting capabilities for increased engagement. This MVP will be the foundation for our April 8th HEAL Initiative Fast-track application. There will be four distinct audience experiences (users, providers, researchers, and Pharmaceutical companies):
Users: An example user journey of high-risk, difficult population -- heroin users who have tried and failed with injectable Suboxone and believe that MAT constitutes “cheating.” First a patient, their entry point will be a community driven web and mobile portal similar to Addicaid, which takes them to Treatease to learn about pharmacological treatment. From there, we create a stigma and substance abuse assessment/inventory surveying their personal experience with medications and any preconceived notions they have (i.e true recovery does not include use of drugs, or that all OUD MAT results in anhedonia, based on current, common hearsay).
The assessment results are threefold: personalized, community reinforcement that is validated by Addicaid’s NYCEDC HITLab pilot, easy to understand clinical evidence of success of MAT in research and promising statistical indicators, and immediate options to seek out treatment through a managed provider/insurer database, including ER facilities.
Based on user data and machine learning, we can the assess the response to these interventions, and if further guidance is needed we enter the patient into a community of people who have had similar initial negative responses to MAT and have overcome them to lead happy, productive lives. These interventions will be analyzed and refined continuously.
At all times users have access to and are guided towards a provider database where they can get medication (covered by their insurance or subsidized by different programs), as well as protocols for side effects management with evidence of success, (for example, CBD for Suboxone side effects, guided sleep hypnosis and physical health challenges that the community joins).
Providers: Providers can log into the database portal but we remove the onus from registration and information input with preexisting input forms, which will be used by researchers and pharmaceutical companies. From this platform, they can get help with Suboxone waivers, access education materials for best practices, share with their patients, and connect with the community.
Researchers: Researchers will have unprecedented, easy access to quantitative data from available studies that they can factor into their projects that allow them to quickly and accurately strengthen their outcomes and bypass inconclusive dead ends. An additional benefit will be a template engine for research studies, collaboration tools with version control, readily available references and visualization tools, along with an interface for publication submissions.
Pharmaceutical companies: Based on their drug’s performance and feedback in different populations, pharmaceutical companies can access reports that show how they perform head to head with their competitors, and this data would then be shared with providers in areas that would benefit from further distribution. This can be through our own platform and through life sciences management software like Veeva.
Overall: With a contemporary, minimal UX and appearance, the platform’s design will adhere to all the major design principles from the latest in digital design research, ensuring minimal amount of friction and frustration in the user experience, a stark contrast to the current standard of healthcare software.
The interaction experience will take the onus off the end user because the heavy lifting will be done by data-driven interventions and protocols defined by the program that will then update the users accordingly. For example, a provider will be alerted of an influx of suboxone requests, and will be prompted to increase their patient limit or recruit more colleagues to obtain a waiver. A drug company would be alerted in the same instance, and preemptively contact providers to increase distribution. Researchers would be able to set alerts for certain parameters to confirm or deny their hypothesis.
3. Commercialization Plan
The methods for testing our product-market-fit begins with customer immersion. We will continue dialogue with pharmaceutical companies to learn what they think they know and want, and using data discover and create this and what they never knew they needed.
For initial data on drug interactions and experiences we have numerous ways of accessing SUD affected individuals, including the tens of thousands of Addicaid users, along with the populations of numerous partner institutions we have engaged with over the years. These will be crucial for beta testing during development. We will also tap into larger audiences with social media and digital campaigns upon the actual launch of Treatise.
While Treatise is a digital product, it is strengthened by the density of institutional partnerships which has a geographical component. For initial distribution and expansion of the Treatise platform we will tentatively focus on Brooklyn in New York City where over a quarter of a million people receive SNAP benefits and over thirteen thousand are admitted for opioid overdoses each year. We have begun discussion with local stakeholders and lawmakers eager to work with us for new solutions to these issues.
For our provider database, we will scrape SAMSAs provider database and send out automated phone calls to the numbers listed prompting them to sign up to treatease. Providers not on the database we will scrape through zocdoc, and hospitals through their systems. The reason for them to sign up is because they will then get an alert when a patient in their area has expressed interest in treatment, and we can provide them with a link to make an appointment.
We will work closely with pharmaceutical companies to go straight to the data source on certain issues of mutual relevance (if providers hit their Suboxone quota, run out of medications, are not taking on anymore patients). A large part of this MVP preparation will go towards collaborating with pharma companies that have expressed interest in this product to implement the software properly from the outset.
The target audience’s need and willingness to pay will be initially based on follow up conversations I have already had with major stakeholders in the healthcare landscape who have expressed interest in a data insights tool that removes the guesswork in best practices and minimizes time spent looking at dashboards and inputting data.
Further evidence comes from the burgeoning industry of data-driven healthcare provider tools, but they overlook the unmet needs of pharmaceutical companies and do not factor in the conflict of interests in stakeholders which sometimes distort their approach to medical treatment. We will offer a platform that allows them to see in real time the interests and applicable uses and pitfalls of their products, where they are depleted and can be further promoted, where adoption is low but success is high, which will streamline their distribution and hopefully lower their prices while increasing profits.
The pricing model would begin as paid pilots, where they can access the researcher platform. Their R&D divisions would be charged annual subscription fee for using the bespoke research platform. The more ambitious and uncharted model is to move to a hybrid shared-savings and profit-sharing model. The former would be based on savings from misallocated spending, the latter from increased sales and success rates, which aligns all our business interests while putting patient health first. The specific decision makers in the pharma companies would most likely be R&D managers, and we would need to win the approval of automation engineers.
The ultimate value lies in data-driven proof that their protocols work, and if they don’t, or see consistent inferiority to other options, to tailor their spending accordingly. As the challenge states, these platforms must be able to address other disorders, and I have factored this into my prototyping from the beginning to maximize flexibility in data collection.