Data-Driven Decision-Making for Addiction

(D2A Oregon Trial)

Overseen By Erika Crable, PhD, MPH

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Chestnut Health Systems

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

341 Participants Needed

Trial Summary

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. ^{1 2 3 4 5}

Is 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. ^{6 7 8 9 10}

How 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. ^{11 12 13 14 15}

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.

Eligibility 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.

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

Treatment Details

Interventions

• Data2Action Oregon Project

Trial Overview 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.

Participant Groups

Group I: No CDS Experimental Treatment 1 Intervention

This group will not participate in CDS. They will receive standardized data products at T3 or T4, depending on wedge assignment.

Group II: CDS Experimental Treatment 2 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.

Who Is Running the Clinical Trial?

Chestnut Health Systems

Lead Sponsor

Trials

27

Recruited

24,500+

National Institute on Drug Abuse (NIDA)

Collaborator

Trials

2,658

Recruited

3,409,000+

University of California, San Diego

Collaborator

Trials

1,215

Recruited

1,593,000+

Findings from Research

Medication treatment for opioid use disorder (MOUD) is effective in reducing opioid-related issues, but about half of patients stop treatment within a year, highlighting the need for better retention strategies.

Utilizing data science techniques, such as predictive modeling and natural language processing, can help personalize care and improve retention in MOUD by integrating diverse data sources, including electronic health records and social media.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Using data science to improve outcomes for persons with opioid use disorder. Hayes, CJ., Cucciare, MA., Martin, BC., et al.[2023]

The paper emphasizes the importance of translating research findings from addiction studies into practical applications for treatment programs, which can enhance program outcomes.

It discusses the development of an outcomes management system that utilizes evidence-based decision support to predict patient responses and reduce dropout rates in addiction treatment, highlighting its potential impact on improving treatment effectiveness.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Translating addictions research into evidence-based practice: the Polaris CD outcomes management system. Toche-Manley, L., Grissom, G., Dietzen, L., et al.[2021]

An exploratory study of eight substance abuse treatment programs across four US states revealed that these programs often manage data inefficiently and lack clear protocols for information management.

Barriers to effective data management include inadequate IT systems, limited funding and staff resources, and varying staff skills and attitudes towards technology, highlighting the need for improved training and funding to enhance data management capabilities.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Substance abuse treatment programs' data management capacity: an exploratory study. Wisdom, JP., Ford, JH., Wise, M., et al.[2021]

In a study of 2,789 veterans with substance use disorders, those who met the HEDIS engagement criterion showed significantly greater improvements in addiction severity compared to those who did not engage, particularly in alcohol and legal outcomes.

While the improvements were statistically significant, they were described as clinically modest, suggesting that while engagement in treatment is beneficial, the extent of improvement may vary among individuals.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Does meeting the HEDIS substance abuse treatment engagement criterion predict patient outcomes? Harris, AH., Humphreys, K., Bowe, T., et al.[2021]

A study involving 23 addiction treatment agencies showed that teaching these agencies to use data-driven strategies significantly improved client access to and retention in care.

Barriers to implementing these strategies included a lack of a data-driven culture and resources, but successful adoption was linked to strong leadership support, staff training, and effective communication of results.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Addiction treatment agencies' use of data: a qualitative assessment. Wisdom, JP., Ford Ii, JH., Hayes, RA., et al.[2018]

A new standardized strategy for reporting adverse events (AEs) and serious adverse events (SAEs) in substance use disorder (SUD) clinical trials was developed, which aims to reduce the reporting burden while maintaining safety monitoring.

In a review of 17 SUD trials involving 6737 participants, the new strategy showed a significant reduction in irrelevant safety event reporting, leading to a more consistent safety assessment system tailored to the risks associated with specific trial interventions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Strategies for safety reporting in substance abuse trials. Lindblad, R., Campanella, M., Styers, D., et al.[2013]

Visualizing adverse events in clinical trials using methods like dot plots and volcano plots can enhance communication about the severity, timing, and recurrence of harms, rather than just counting occurrences.

The study analyzed individual participant data from a randomized trial of gabapentin for neuropathic pain, demonstrating that effective visualizations can help stakeholders better understand the multidimensional nature of harms in clinical research.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Comparing the Value of Data Visualization Methods for Communicating Harms in Clinical Trials. Qureshi, R., Chen, X., Goerg, C., et al.[2022]

The definition of an adverse drug event (ADE) should be tailored to the specific purpose of the evaluation, with stricter definitions needed for scientific studies and more flexible ones for clinical use.

Postmarketing safety data for drugs is limited at launch, but can be supplemented through systems like the FDA's Spontaneous Reporting System and targeted research projects, emphasizing the importance of robust epidemiological evidence for regulatory actions like drug recalls.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Adverse drug events: identification and attribution. Rogers, AS.[2022]

A systematic review of 108 adverse drug event (ADE) reporting systems revealed 1782 unique data fields, highlighting significant variability in how ADEs are reported, which complicates data comparison and aggregation across different systems.

Despite consistent reporting concepts, the lack of standardized terminology and the use of multiple drug and disease dictionaries hinder effective drug safety monitoring, suggesting a need for a common standardized dataset to improve ADE reporting and surveillance.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Adverse drug event reporting systems: a systematic review. Bailey, C., Peddie, D., Wickham, ME., et al.[2021]

The RADARx software program effectively identified 34 actual adverse drug events (ADEs) and 48 potential ADEs, significantly outperforming traditional methods that only detected 23 ADEs during the evaluation period.

Despite generating 8-20 alerts daily, which required 10 to 30 minutes of pharmacist review, only 11% of RADARx alerts were confirmed as true positives, highlighting the need for efficient screening tools to manage ADEs in clinical settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

RADARx: Recognizing, Assessing, and Documenting Adverse Rx events. Brown, S., Black, K., Mrochek, S., et al.[2019]

In 2022, the US faced over 80,000 opioid overdose deaths, highlighting the severe impact of opioid use disorder (OUD) on public health and society.

The article emphasizes the importance of using analytics and modeling to inform policies aimed at understanding and addressing the opioid epidemic, including prevention, treatment, and potential reforms in regulation and criminal justice.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Responding to the US opioid crisis: leveraging analytics to support decision making. Brandeau, ML.[2023]

Missing data in substance use disorder (SUD) research can significantly hinder the reliability of findings, especially for underserved populations who may have unique reasons for data gaps.

The paper emphasizes the need for better statistical tools to address various types of missing data, highlighting that these gaps represent not just statistical issues but also lost narratives that can influence important policy and system decisions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Addressing Missing Data in Substance Use Research: A Review and Data Justice-based Approach. King, C., Englander, H., Priest, KC., et al.[2022]

The National Drug Abuse Treatment Clinical Trials Network (CTN) has successfully integrated digital technologies into substance use disorder (SUD) research, leading to the first FDA-authorized 'prescription digital therapeutic' for SUD treatment, showcasing the efficacy of digital health tools.

CTN's research has also explored telehealth and digital screening methods to enhance SUD treatment accessibility, particularly in rural and underserved communities, indicating a promising direction for expanding care models.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

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. Marsch, LA., Campbell, A., Campbell, C., et al.[2021]

The Massachusetts Public Health Data Warehouse provides valuable insights into opioid use, allowing researchers to detect significant relationships and understand treatment outcomes across different healthcare settings, which is crucial for addressing the opioid epidemic.

However, challenges such as oversimplified data, access issues, and potential biases highlight the need for careful interpretation of findings to ensure that research using this big data is both valid and useful for public health policy.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The Massachusetts public health data warehouse and the opioid epidemic: A qualitative study of perceived strengths and limitations for advancing research. Evans, EA., Delorme, E., Cyr, KD., et al.[2022]

The open-source pipeline developed enhances public open data sets, specifically improving the Cook County medical examiner case archive, which is crucial for research on substance use disorders.

By linking key variables from this data to other publicly available datasets, the pipeline supports more effective location-based analyses of overdoses, particularly benefiting opioid research.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

geoPIPE: Geospatial Pipeline for Enhancing Open Data for Substance Use Disorders Research. Harris, DR., Anthony, N., Mir, M., et al.[2023]

References

1. United States pubmed.ncbi.nlm.nih.gov

Using data science to improve outcomes for persons with opioid use disorder. [2023]

2. England pubmed.ncbi.nlm.nih.gov

Translating addictions research into evidence-based practice: the Polaris CD outcomes management system. [2021]

3. United States pubmed.ncbi.nlm.nih.gov

Substance abuse treatment programs' data management capacity: an exploratory study. [2021]

4. United States pubmed.ncbi.nlm.nih.gov

Does meeting the HEDIS substance abuse treatment engagement criterion predict patient outcomes? [2021]

5. United States pubmed.ncbi.nlm.nih.gov

Addiction treatment agencies' use of data: a qualitative assessment. [2018]

6. England pubmed.ncbi.nlm.nih.gov

Strategies for safety reporting in substance abuse trials. [2013]

7. United States pubmed.ncbi.nlm.nih.gov

Comparing the Value of Data Visualization Methods for Communicating Harms in Clinical Trials. [2022]

8. United States pubmed.ncbi.nlm.nih.gov

Adverse drug events: identification and attribution. [2022]

9. England pubmed.ncbi.nlm.nih.gov

Adverse drug event reporting systems: a systematic review. [2021]

10. United States pubmed.ncbi.nlm.nih.gov

RADARx: Recognizing, Assessing, and Documenting Adverse Rx events. [2019]

11. Netherlands pubmed.ncbi.nlm.nih.gov

Responding to the US opioid crisis: leveraging analytics to support decision making. [2023]

12. Netherlands pubmed.ncbi.nlm.nih.gov

Addressing Missing Data in Substance Use Research: A Review and Data Justice-based Approach. [2022]

13. United States pubmed.ncbi.nlm.nih.gov

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]

14. United States pubmed.ncbi.nlm.nih.gov

The Massachusetts public health data warehouse and the opioid epidemic: A qualitative study of perceived strengths and limitations for advancing research. [2022]

15. United States pubmed.ncbi.nlm.nih.gov

geoPIPE: Geospatial Pipeline for Enhancing Open Data for Substance Use Disorders Research. [2023]