5 Clinical Trials for Various Conditions
The purpose of this study is to determine whether, for individuals in inpatient opioid detoxification, linking to outpatient Suboxone treatment increases treatment adherence and reduces relapse to illicit opioid use.
The purpose of this research study is to: 1. assess how participants like the AWAITS e-health application as measured by their feedback on the intervention 2. test the impact of AWAITS on knowledge about opioid overdose and risk-reduction strategies. 3. assess the proportion of participants who accept a list of local treatment providers 4. test the impact of AWAITS on interest in being tested for HCV/HIV.
The DIGITS Trial addresses a critical knowledge gap: How to best implement digital treatments for opioids and other substance use disorders in primary care. The DIGITS Trial is a partnership between Kaiser Permanente Washington Health Research Institute (KPWHRI) in Seattle, and Kaiser Permanente Washington, a healthcare delivery system in Washington State. In this study, the FDA-authorized reSET and reSET-O digital therapeutics will be implemented in Kaiser Permanente Washington primary care clinics. The study will evaluate the extent to which two implementation strategy interventions, health coaching and practice coaching, improve the implementation. Primary care clinics are randomized to receive these implementation strategy interventions. Each clinic will have a 12-month active implementation period beginning on its date of randomization. To study the continued use of reSET and reSET-O after the active implementation period is completed, a sustainment period of up 12 months will follow the active implementation period.
The iSTART intervention is a 30-day substance prevention web-app whereby students complete five weekly interactive modules using a smart device or computer. Each module is approximately 15 minutes long, and focuses on a select substance: (i) alcohol, (ii) marijuana, (iii) nicotine, (iv) prescription drugs, and (v) illicit drugs. The modules are based on key theoretical constructs, behavior change strategies, and practical module components: attitudes (knowledge), perceived susceptibility (risk perceptions), subjective norms (normative re-education), and self-efficacy (refusal skills). This intervention will be evaluated via a time series design using a sample of 600 students randomly assigned to either the intervention, comparison, or control condition at a public institution in southern California.
The investigators propose to develop an open-source, publicly available machine learning model that health systems could download and apply to their electronic health record data marts to screen for substance misuse in their patients. The investigators hypothesize that the natural language processing algorithm can provide a standardized and interoperable approach for an automated daily screen on all hospitalized patients and provide better implementation fidelity for screening, brief intervention, and referral to treatment.