Data-Driven Decision-Making for Addiction
(D2A Oregon Trial)
Recruiting in Palo Alto (17 mi)
Age: 18+
Sex: Any
Travel: May Be Covered
Time Reimbursement: Varies
Trial Phase: Academic
Recruiting
Sponsor: Chestnut Health Systems
No Placebo Group
Trial Summary
What is the purpose of this trial?Oregon's decision makers (e.g., community service providers, public health, justice, advocacy groups, payers) are calling for comprehensive, current, and trusted data to inform how they allocate resources to improve substance use services and mitigate the growing opioid and methamphetamine epidemics in their state. Consistent with the HEAL Data2Action call for Innovation projects that drive action with data in real-world settings, this study will refine and test the impact of a novel implementation strategy to engage cross- sector decision makers and make data that they identify as relevant to their decisions available to them in easy- to-use products. The proposed study aims to not only address critical knowledge gaps regarding how and when data can inform impactful, transparent decision-making, but to provide decision makers with the data that they need to achieve community-wide substance use prevention and treatment goals, including the increased delivery of high-quality, evidence-informed, services and the prevention of overdoses.
Will I have to stop taking my current medications?
The trial information does not specify whether participants need to stop taking their current medications.
What data supports the effectiveness of the Data2Action Oregon Project treatment for addiction?
The use of data science, such as predictive modeling and big data, has shown promise in improving retention in medication treatment for opioid use disorder, which is crucial for better outcomes. Additionally, addiction treatment agencies have successfully used data-driven strategies to enhance client access and retention in care.
12345Is the Data-Driven Decision-Making for Addiction treatment generally safe for humans?
The research articles do not provide specific safety data for the Data-Driven Decision-Making for Addiction treatment or its related projects. They discuss general issues with reporting adverse events in clinical trials, but no specific safety information for this treatment is available.
678910How does this treatment differ from other treatments for addiction?
This treatment is unique because it uses digital technologies to assess and treat substance use disorders, offering tools like digital screeners and telehealth services, which can reach underserved communities and provide remote intervention delivery.
1112131415Eligibility Criteria
This trial is for decision-makers in Oregon involved with community services, public health, justice, advocacy groups, and payers. They need comprehensive data to tackle substance use issues and the opioid/methamphetamine epidemics. Participants should be those seeking to improve resource allocation for substance use services and overdose prevention.Inclusion Criteria
I am over 18 and make decisions in substance use services or policy.
I am over 18 and make decisions in substance use services or policy.
Exclusion Criteria
Not applicable.
Trial Timeline
Screening
Participants are screened for eligibility to participate in the trial
2-4 weeks
Co-Design Sessions (CDS)
Counties participate in Co-Design Sessions to co-design and tailor data products with the study team.
Varies by condition
Data Product Release
Counties receive tailored or standardized data products depending on their group assignment.
Annually for up to two years
Follow-up
Participants are monitored for the impact of data products on substance use service gaps and service-recipient outcomes.
Up to 3 years
Participant Groups
The study tests a new strategy that helps these decision-makers by providing relevant data in easy-to-use formats (Data products). It involves Co-Design Sessions (CDS) where participants work together to identify what data they need and how it can best support their decisions.
2Treatment groups
Experimental Treatment
Group I: No CDSExperimental Treatment1 Intervention
This group will not participate in CDS. They will receive standardized data products at T3 or T4, depending on wedge assignment.
Group II: CDSExperimental Treatment2 Interventions
This group will participate in 4 CDS to co-design and tailor data products with the study team. They will receive fully tailored Data Products at T3 or T4, depending on wedge assignment.
Find a Clinic Near You
Research Locations NearbySelect from list below to view details:
Chestnut Health SystemsEugene, OR
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Who Is Running the Clinical Trial?
Chestnut Health SystemsLead Sponsor
National Institute on Drug Abuse (NIDA)Collaborator
University of California, San DiegoCollaborator
References
Using data science to improve outcomes for persons with opioid use disorder. [2023]Medication treatment for opioid use disorder (MOUD) is an effective evidence-based therapy for decreasing opioid-related adverse outcomes. Effective strategies for retaining persons on MOUD, an essential step to improving outcomes, are needed as roughly half of all persons initiating MOUD discontinue within a year. Data science may be valuable and promising for improving MOUD retention by using "big data" (e.g., electronic health record data, claims data mobile/sensor data, social media data) and specific machine learning techniques (e.g., predictive modeling, natural language processing, reinforcement learning) to individualize patient care. Maximizing the utility of data science to improve MOUD retention requires a three-pronged approach: (1) increasing funding for data science research for OUD, (2) integrating data from multiple sources including treatment for OUD and general medical care as well as data not specific to medical care (e.g., mobile, sensor, and social media data), and (3) applying multiple data science approaches with integrated big data to provide insights and optimize advances in the OUD and overall addiction fields.
Translating addictions research into evidence-based practice: the Polaris CD outcomes management system. [2021]Converting the findings from addictions studies into information actionable by (non-research) treatment programs is important to improving program outcomes. This paper describes the translation of the findings of studies on Patient-Services matching, prediction of patient response to treatment (Expected Treatment Response) and prediction of dropout to provide evidence-based decision support in routine treatment. The findings of the studies and their application to the development of an outcomes management system are described. Implementation issues in a network of addictions treatment programs are discussed. The work illustrates how outcomes management systems can play an important role in translating research into practice.
Substance abuse treatment programs' data management capacity: an exploratory study. [2021]Despite treatment improvement and performance management imperatives, little research describes the data management capacity of substance abuse treatment programs, and useful metrics are not available to gauge capacity. This exploratory study evaluates clinical and administrative data management at eight substance abuse treatment programs in four US states to identify factors for developing an appropriate metric. Findings indicate that programs tend to manage data inefficiently and have few protocols guiding information management. Barriers to better data management included lack of integrated information technology (IT) systems; limited funding, time, and staff for developing and implementing IT-related changes; and divergent staff skills in and attitudes toward IT. This snapshot of substance abuse treatment programs' data management capabilities suggests a need for a metric to examine data management capability in these settings. Infusion of expertise, training, and funding are needed to improve substance abuse treatment programs' IT-related systems and data management processes.
Does meeting the HEDIS substance abuse treatment engagement criterion predict patient outcomes? [2021]This study examines the patient-level associations between the Health Plan Employer Data and Information Set (HEDIS) substance use disorder (SUD) treatment engagement quality indicator and improvements in clinical outcomes. Administrative and survey data from 2,789 US Department of Veterans Affairs SUD patients were used to estimate the effects of meeting the HEDIS engagement criterion on improvements in Addiction Severity Index Alcohol, Drug, and Legal composite scores. Patients meeting the engagement indicator improved significantly more in all domains than patients who did not engage, and the relationship was stronger for alcohol and legal outcomes for patients seen in outpatient settings. The benefit accrued by those who engaged was statistically significant but clinically modest. These results add to the literature documenting the clinical benefits of treatment entry and engagement. Although these findings only indirectly support the use of the HEDIS engagement measure for its intended purpose-discriminating quality at the facility or system level-they confirm that the processes of care captured by the measure are associated with important patient outcomes.
Addiction treatment agencies' use of data: a qualitative assessment. [2018]Addiction treatment agencies typically do not prioritize data collection, management, and analysis, and these agencies may have barriers to integrating data in agency quality improvement. This article describes qualitative findings from an intervention designed to teach 23 addiction treatment agencies how to make data-driven decisions to improve client access to and retention in care. Agencies demonstrated success adopting process improvement and data-driven strategies to make improvements in care. Barriers to adding a process improvement and data-driven focus to care included a lack of a data-based decision making culture, lack of expertise and other resources, treatment system complexity, and resistance. Factors related to the successful adoption of process-focused data include agency leadership valuing data and providing resources, staff training on data collection and use, sharing of change results, and success in making data-driven decisions.
Strategies for safety reporting in substance abuse trials. [2013]Reporting all adverse events (AEs) and serious adverse events (SAEs) in substance use disorder (SUD) clinical trials has yielded limited relevant safety information and has been burdensome to research sites.
Comparing the Value of Data Visualization Methods for Communicating Harms in Clinical Trials. [2022]In clinical trials, harms (i.e., adverse events) are often reported by simply counting the number of people who experienced each event. Reporting only frequencies ignores other dimensions of the data that are important for stakeholders, including severity, seriousness, rate (recurrence), timing, and groups of related harms. Additionally, application of selection criteria to harms prevents most from being reported. Visualization of data could improve communication of multidimensional data. We replicated and compared the characteristics of 6 different approaches for visualizing harms: dot plot, stacked bar chart, volcano plot, heat map, treemap, and tendril plot. We considered binary events using individual participant data from a randomized trial of gabapentin for neuropathic pain. We assessed their value using a heuristic approach and a group of content experts. We produced all figures using R and share the open-source code on GitHub. Most original visualizations propose presenting individual harms (e.g., dizziness, somnolence) alone or alongside higher level (e.g., by body systems) summaries of harms, although they could be applied at either level. Visualizations can present different dimensions of all harms observed in trials. Except for the tendril plot, all other plots do not require individual participant data. The dot plot and volcano plot are favored as visualization approaches to present an overall summary of harms data. Our value assessment found the dot plot and volcano plot were favored by content experts. Using visualizations to report harms could improve communication. Trialists can use our provided code to easily implement these approaches.
Adverse drug events: identification and attribution. [2022]The definition of an adverse drug event should be tailored to one's purpose in examining the incident. Although the more specific of these definitions is required for scientific evaluation of the link between drug and event, other less stringent definitions are usually adequate for clinical purposes. Knowledge about the safety profile of a drug in humans is limited at the time of marketing. The mechanisms for supplementing safety data during postmarketing include (1) the Spontaneous Reporting System maintained by the Food and Drug Administration, (2) formal projects to assemble safety data on larger or more complex populations, and (3) formal projects designed to answer specific research questions. Judgments about attribution can be no better than the data that support them. The criteria applied by the clinician to the individual adverse drug experience to determine association differ from those required to establish causation based on epidemiologic evidence. In most situations, regulatory action on drug recall should be based on epidemiologic evidence. This article will discuss the choice of a definition for an adverse drug event, examine the extent and nature of the safety data assembled on a drug at the time it is marketed, propose the best methods for collecting additional information after marketing, and designate factors to be considered in judging a drug to be causally related to an adverse event.
Adverse drug event reporting systems: a systematic review. [2021]Adverse drug events (ADEs) are harmful and unintended consequences of medications. Their reporting is essential for drug safety monitoring and research, but it has not been standardized internationally. Our aim was to synthesize information about the type and variety of data collected within ADE reporting systems.
RADARx: Recognizing, Assessing, and Documenting Adverse Rx events. [2019]Adverse events are a leading cause of morbidity and mortality. Adverse Drug Events (ADEs) are frequent, under-reported, costly, and largely preventable. Computerized tools expose effectively ADEs and can reduce their impact.
Responding to the US opioid crisis: leveraging analytics to support decision making. [2023]The US is experiencing a severe opioid epidemic with more than 80,000 opioid overdose deaths occurring in 2022. Beyond the tragic loss of life, opioid use disorder (OUD) has emerged as a major contributor to morbidity, lost productivity, mounting criminal justice system costs, and significant social disruption. This Current Opinion article highlights opportunities for analytics in supporting policy making for effective response to this crisis. We describe modeling opportunities in the following areas: understanding the opioid epidemic (e.g., the prevalence and incidence of OUD in different geographic regions, demographics of individuals with OUD, rates of overdose and overdose death, patterns of drug use and associated disease outbreaks, and access to and use of treatment for OUD); assessing policies for preventing and treating OUD, including mitigation of social conditions that increase the risk of OUD; and evaluating potential regulatory and criminal justice system reforms.
Addressing Missing Data in Substance Use Research: A Review and Data Justice-based Approach. [2022]: Missing data in substance use disorder (SUD) research can pose a challenge as researchers attempt to publish reliable findings based on the limited available information. Tools to address missing data exist, but are underused and may not address all types of missingness. Missing data are more than a statistical problem: for underserved populations and people with SUDs who may have missing data for a myriad of reasons, missing data represents missing stories and information that can have real-world impacts on system and policy-level decision making. This paper reviews types of missing data and, through a data justice lens, asserts the importance of the increased use and development of statistical tools to handle missing data in SUD research.
The application of digital health to the assessment and treatment of substance use disorders: The past, current, and future role of the National Drug Abuse Treatment Clinical Trials Network. [2021]The application of digital technologies to better assess, understand, and treat substance use disorders (SUDs) is a particularly promising and vibrant area of scientific research. The National Drug Abuse Treatment Clinical Trials Network (CTN), launched in 1999 by the U.S. National Institute on Drug Abuse, has supported a growing line of research that leverages digital technologies to glean new insights into SUDs and provide science-based therapeutic tools to a diverse array of persons with SUDs. This manuscript provides an overview of the breadth and impact of research conducted in the realm of digital health within the CTN. This work has included the CTN's efforts to systematically embed digital screeners for SUDs into general medical settings to impact care models across the nation. This work has also included a pivotal multi-site clinical trial conducted on the CTN platform, whose data led to the very first "prescription digital therapeutic" authorized by the U.S. Food and Drug Administration (FDA) for the treatment of SUDs. Further CTN research includes the study of telehealth to increase capacity for science-based SUD treatment in rural and under-resourced communities. In addition, the CTN has supported an assessment of the feasibility of detecting cocaine-taking behavior via smartwatch sensing. And, the CTN has supported the conduct of clinical trials entirely online (including the recruitment of national and hard-to-reach/under-served participant samples online, with remote intervention delivery and data collection). Further, the CTN is supporting innovative work focused on the use of digital health technologies and data analytics to identify digital biomarkers and understand the clinical trajectories of individuals receiving medications for opioid use disorder (OUD). This manuscript concludes by outlining the many potential future opportunities to leverage the unique national CTN research network to scale-up the science on digital health to examine optimal strategies to increase the reach of science-based SUD service delivery models both within and outside of healthcare.
The Massachusetts public health data warehouse and the opioid epidemic: A qualitative study of perceived strengths and limitations for advancing research. [2022]Due to the opioid overdose epidemic, Massachusetts created a Public Health Data Warehouse, encompassing individually-linked administrative data on most of the population as provided by more than 20 systems. As others seek to assemble and mine big data on opioid use, there is a need to consider its research utility. To identify perceived strengths and limitations of administrative big data, we collected qualitative data in 2019 from 39 stakeholders with knowledge of the Massachusetts Public Health Data Warehouse. Perceived strengths included the ability to: (1) detect new and clinically significant relationships; (2) observe treatments and services across institutional boundaries, broadening understanding of risk and protective factors, treatment outcomes, and intervention effectiveness; (3) use geographic-specific lenses for community-level health; (4) conduct rigorous "real-world" research; and (5) generate impactful findings that legitimize the scope and impacts of the opioid epidemic and answer urgent questions. Limitations included: (1) oversimplified information and imprecise measures; (2) data access and analysis challenges; (3) static records and substantial lag times; and (4) blind spots that bias or confound results, mask upstream or root causes, and contribute to incomplete understanding. Using administrative big data to conduct research on the opioid epidemic offers advantages but also has limitations which, if unrecognized, may undermine its utility. Findings can help researchers to capitalize on the advantages of big data, and avoid inappropriate uses, and aid states that are assembling big data to guide public health practice and policy.
geoPIPE: Geospatial Pipeline for Enhancing Open Data for Substance Use Disorders Research. [2023]We present our open-source pipeline for quickly enhancing open data sets with research-focused expansions and show its effectiveness on a cornerstone open data set released by the Cook County government in Illinois. The City of Chicago and Cook County were both early adopters of open data portals and have made a wide variety of data available to the public; we focus on the medical examiner case archive which provides information about deaths recorded by Cook County's Office of the Medical Examiner, including overdoses invaluable to substance use disorder research. Our pipeline derives key variables from open data and links to other publicly available data sets in support of accelerating translational research on substance use disorders. Our methods apply to location-based analyses of overdoses in general and, as an example, we highlight their impact on opioid research. We provide our pipeline as open-source software to act as open infrastructure for open data to help fill the gap between data release and data use.