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BackgroundDelivering prescription digital therapeutics (ie, evidence-based interventions designed to treat, manage, or prevent disorders via websites or smartphone apps) in primary care could increase patient access to substance use disorder (SUD) treatments. However, the optimal approach to implementing prescription digital therapeutics in primary care remains unknown. ObjectiveThis pilot study is a precursor to a larger trial designed to test whether implementation strategies (practice facilitation [PF] and health coaching [HC]) improve the delivery of prescription digital therapeutics for SUDs in primary care. This mixed methods study describes outcomes among patients in the 2 pilot clinics and presents qualitative findings on implementation. MethodsFrom February 10 to August 6, 2021, a total of 3 mental health specialists embedded in 2 primary care practices of the same integrated health system were tasked with offering app-based prescription digital therapeutics to patients with SUD. In the first half of the pilot, implementation activities included training and supportive tools. PF (at 1 clinic) and HC (at 2 clinics) were added in the second half. All study analyses relied on secondary data, including electronic health records and digital therapeutic vendor data. Primary outcomes were the proportion of patients reached by the prescription digital therapeutics and fidelity related to ideal use. We used qualitative methods to assess the adherence to planned activities and the barriers and facilitators to implementing prescription digital therapeutics. ResultsOf all 18 patients prescribed the apps, 10 (56%) downloaded the app and activated their prescription, and 8 (44%) completed at least 1 module of content. Patients who activated the app completed 1 module per week on average. Ideal use (fidelity) was defined as completing 4 modules per week and having a monthly SUD-related visit; 1 (6%) patient met these criteria for 10 weeks (of the 12-week prescription period). A total of 5 (28%) patients had prescriptions while HC was available, 2 (11%) were successfully contacted, and both declined coaching. Clinicians reported competing clinical priorities, technical challenges, and logistically complex workflows in part because the apps required a prescription. Some pilot activities were impacted by staff turnover that coincided with the COVID-19 pandemic. The facilitators to implementation were high engagement and the perception that the apps could meet patient needs. ConclusionsThe pilot study encountered the barriers to implementing prescription digital therapeutics in a real-world primary care setting, especially staffing shortages, turnover, and competing priorities for clinic teams. The larger randomized trial will clarify the extent to which PF and HC improve the implementation of digital therapeutics. Trial RegistrationClinicalTrials.gov NCT04907045; https://clinicaltrials.gov/study/NCT04907045

Abstract Background Central nervous system (CNS) active medications have been consistently linked to falls in older people. However, few randomized trials have evaluated whether CNS-active medication reduction reduces falls and fall-related injuries. The objective of the Reducing CNS-active Medications to Prevent Falls and Injuries in Older Adults (STOP-FALLS) trial is to test the effectiveness of a health-system-embedded deprescribing intervention focused on CNS-active medications on the incidence of medically treated falls among community-dwelling older adults. Methods We will conduct a pragmatic, cluster-randomized, parallel-group, controlled clinical trial within Kaiser Permanente Washington to test the effectiveness of a 12-month deprescribing intervention consisting of (1) an educational brochure and self-care handouts mailed to older adults prescribed one or more CNS-active medications (aged 60 + : opioids, benzodiazepines and Z-drugs; aged 65 + : skeletal muscle relaxants, tricyclic antidepressants, and antihistamines) and (2) decision support for their primary health care providers. Outcomes are examined over 18–26 months post-intervention. The primary outcome is first incident (post-baseline) medically treated fall as determined from health plan data. Our sample size calculations ensure at least 80% power to detect a 20% reduction in the rate of medically treated falls for participants receiving care within the intervention (n = 9) versus usual care clinics (n = 9) assuming 18 months of follow-up. Secondary outcomes include medication discontinuation or dose reduction of any target medications. Safety outcomes include serious adverse drug withdrawal events, unintentional overdose, and death. We will also examine medication signetur fields for attempts to decrease medications. We will report factors affecting implementation of the intervention. Discussion The STOP-FALLS trial will provide new information about whether a health-system-embedded deprescribing intervention that targets older participants and their primary care providers reduces medically treated falls and CNS-active medication use. Insights into factors affecting implementation will inform future research and healthcare organizations that may be interested in replicating the intervention. Trial registration ClinicalTrial.gov NCT05689554. Registered on 18 January 2023, retrospectively registered.

Abstract Background Experts recommend that treatment for substance use disorder (SUD) be integrated into primary care. The Digital Therapeutics for Opioids and Other SUD (DIGITS) Trial tests strategies for implementing reSET® and reSET-O®, which are prescription digital therapeutics for SUD and opioid use disorder, respectively, that include the community reinforcement approach, contingency management, and fluency training to reinforce concept mastery. This purpose of this trial is to test whether two implementation strategies improve implementation success (Aim 1) and achieve better population-level cost effectiveness (Aim 2) over a standard implementation approach. Methods/Design The DIGITS Trial is a hybrid type III cluster-randomized trial. It examines outcomes of implementation strategies, rather than studying clinical outcomes of a digital therapeutic. It includes 22 primary care clinics from a healthcare system in Washington State and patients with unhealthy substance use who visit clinics during an active implementation period (up to one year). Primary care clinics implemented reSET and reSET-O using a multifaceted implementation strategy previously used by clinical leaders to roll-out smartphone apps (“standard implementation” including discrete strategies such as clinician training, electronic health record tools). Clinics were randomized as 21 sites in a 2x2 factorial design to receive up to two added implementation strategies: (1) practice facilitation, and/or (2) health coaching. Outcome data are derived from electronic health records and logs of digital therapeutic usage. Aim 1’s primary outcomes include reach of the digital therapeutics to patients and fidelity of patients’ use of the digital therapeutics to clinical recommendations. Substance use and engagement in SUD care are additional outcomes. In Aim 2, population-level cost effectiveness analysis will inform the economic benefit of the implementation strategies compared to standard implementation. Implementation is monitored using formative evaluation, and sustainment will be studied for up to one year using qualitative and quantitative research methods. Discussion The DIGITS Trial uses an experimental design to test whether implementation strategies increase and improve the delivery of digital therapeutics for SUDs when embedded in a large healthcare system. It will provide data on the potential benefits and cost-effectiveness of alternative implementation strategies. ClinicalTrials.gov Identifier: NCT05160233 (Submitted 12/3/2021). https://clinicaltrials.gov/ct2/show/NCT05160233

The Gorini-Kossakowski-Sudarshan-Lindblad master equation (ME) governs the density matrix of open quantum systems (OQSs). When an OQS is subjected to weak continuous measurement, its state evolves as a stochastic quantum trajectory, whose statistical average solves the ME. The ensemble of such trajectories is termed an unraveling of the ME. We propose a method to operationally distinguish unravelings produced by the same ME in different measurement scenarios, using nonlinear averages of observables over trajectories. We apply the method to the paradigmatic quantum nonlinear system of resonance fluorescence in a two-level atom. We compare the Poisson-type unraveling, induced by direct detection of photons scattered from the two-level emitter, and the Wiener-type unraveling, induced by phase-sensitive detection of the emitted field. We show that a quantum-trajectory-averaged variance is able to distinguish these measurement scenarios. We evaluate the performance of the method, which can be readily extended to more complex OQSs, under a range of realistic experimental conditions.