Edwin Suey Wong

Objective: Two quality measures, initiation and engagement in substance use disorder (SUD) treatment (IET), have been adopted by stakeholders. Further evidence is needed to inform optimal use of these measures to support quality improvement efforts. The objective of this study was to examine variability, reliability, and multilevel correlates of IET. Methods: Retrospective cohort study of members of a large integrated health system in the Pacific Northwest. Descriptive statistics were used to characterize variation in quality. Mixed-effects models with random intercepts were applied to quantify variability and reliability at different health system levels. We examined associations between quality measures and a wide set of patient-, provider-, clinic-, and area-level characteristics. We examined 32,960 care episodes subject to IET quality criteria between 2017 and 2022. Results: Overall, 30.0% and 7.8% of episodes met the criteria for initiation and engagement in SUD treatment, respectively. Little variation in initiation (2.8%) and engagement (3.9%) was collectively explained at the provider and clinic levels. No providers met the threshold for 70% reliability on an annual basis. Across varying assumptions of provider-level annual episode volume, clinics were unlikely to meet reliability thresholds. Factors consistently associated with IET included insurance type and history of behavioral health conditions. Conclusion: A low proportion of variation explained implies a larger number of episodes is needed to ensure reliable estimates. Stakeholders should apply IET measures cautiously, ensuring sufficient sample sizes to support robust measurement. Factors associated with a lower probability of meeting quality criteria can inform specific patients who may benefit from efforts to increase IET.

Background: Many substance use disorder (SUD) care pathways exist in health care systems. However, patients with SUD often report poor care experiences, particularly regarding timely follow-up, clinician and general satisfaction ratings, and care communication. As SUD care pathways involve transitions across clinicians and venues of care, adequate treatment requires coordination across clinicians and settings to ensure unmet needs are addressed, and appropriate SUD care is delivered. Surprisingly little is known about the real-world care pathways patients engage in once identified as having SUD. Objective: This study protocol describes research that will comprehensively characterize the pathways of care used by those who obtain care for SUD and compare the quality and outcomes associated with these care pathways. Specific aims are to (1) apply multistate models (MSM) to characterize the spectrum of care transitions among patients with SUD within a large integrated health system, (2) generate data-driven insights to improve care delivery for SUD using estimated MSMs, and (3) observe and explore patient and clinician experiences with care transitions across common care pathways using qualitative methods. Methods: Quantitative data sources will include electronic health care records, insurance claims, self-reported measures of substance use and SUD symptoms, and death data from an integrated health care system in Washington State. To identify care pathways, we will apply continuous time, multistate modeling methods to empirically observe the longitudinal course of SUD care transitions undertaken by patients over time; each “state” or occurrence of care will be characterized by the intervention received (eg, evaluation or assessment, behavioral treatment or counseling, and pharmacotherapy) and setting of care (eg, outpatient, intensive outpatient, or inpatient or residential). The estimated parameters from the fitted MSMs will be transformed or interpreted to characterize SUD care quality, such as wait times for SUD visits, and receiving an adequate psychotherapy dose. To compare outcomes associated with care pathways, we will examine terminal states, including death and loss to follow-up. Using a mixed methods design, we will sample and interview patients engaged in empirically derived pathways to understand their care experiences, observe and interview clinicians to elucidate health system factors that impact SUD care transitions, and integrate qualitative and quantitative findings using joint displays. Results: This study was funded in June 2025 and received institutional review board approval on July 17, 2025. We expect preliminary data collection for quantitative analyses to be complete by May 2026, and final data collection will be complete by November 2027. We expect qualitative data collection completion by November 2029. Conclusions: This exploratory study will identify and compare the quality and outcomes associated with common pathways that patients take when they obtain treatment for SUD and provide decision-makers with information on how to better organize SUD care delivery.

BACKGROUND Many effective substance use disorder (SUD) care pathways exist in healthcare systems. However, patients with SUD often report poor care experiences particularly regarding timely follow-up, clinician and general satisfaction ratings, and care communication. Because SUD care pathways involve transitions across clinicians and venues of care, adequate treatment requires coordination across clinicians and settings to ensure unmet needs are addressed, and appropriate SUD care is delivered. Surprisingly little is known about the real-world care pathways patients engage in once identified as having SUD. OBJECTIVE This study protocol describes research that will comprehensively characterize the pathways of care utilized by those who obtain care for SUD and compare the quality and effectiveness of these care pathways. Specific Aims are to (1) Apply multi-state models (MSM) to characterize the spectrum of care transitions among patients with SUD within a large integrated health system, (2) Generate data-driven insights to improve care delivery for SUD using estimated MSMs, and (3) Observe and explore patient and clinician experiences with care transitions across common care pathways using qualitative methods. METHODS Quantitative data sources will include electronic healthcare records, insurance claims, self-reported measures of substance use and SUD symptoms, and death data from an integrated healthcare system in Washington State. To identify care pathways, we will apply continuous time, multi-state modeling methods to empirically observe the longitudinal course of SUD care transitions undertaken by patients over time; each “state” or occurrence of care will be characterized by the intervention received (e.g., evaluation or assessment, behavioral treatment or counseling, pharmacotherapy) and setting of care (e.g., outpatient, intensive outpatient, inpatient or residential). The estimated parameters from the fitted MSMs will be transformed or interpreted to characterize SUD care quality, such as wait times for SUD visits and receiving an adequate psychotherapy dose. To compare effectiveness of identified pathways, we will examine terminal states including death, remission of SUD, and loss to follow-up. Finally, employing a mixed methods design, we will purposively sample and interview patients engaged in the empirically derived pathways to understand their care experiences; we will also observe and interview clinicians to elucidate health system factors that facilitate or hinder their ability to link patients to SUD care. RESULTS This study was funded in June 2025 and received IRB approval on July 17, 2025. We expect preliminary data collection for quantitative analyses will be complete by May 2026 and final data collection will be complete by November 2027. We expect qualitative data collection will be complete by November 2029. CONCLUSIONS This study will identify and compare the quality and effectiveness of common pathways that patients take when they obtain treatment for SUD and provide decision makers with information on how to better organize SUD care delivery. CLINICALTRIAL N/A

Evidence-based digital therapeutics are a promising approach for the scale-up of substance use disorder (SUD) treatments. Despite demonstrated efficacy, utilization of digital therapeutics is low. Strategic implementation approaches have potential for increasing digital therapeutic use. Applicability to health systems depends, in part, on the economic costs. The objective of this study was to describe implementation and intervention costs of implementation strategies to increase uptake of an evidence-based digital treatment for SUD. We conducted an economic evaluation alongside a hybrid type III cluster-randomized trial within a large integrated health system. All clinics implemented a standard implementation (SI) strategy, and clinics were assigned using 2x2 factorial randomization to additionally receive practice facilitation (PF) and/or health coaching (HC). Implementation costs included the cost of time devoted to implementation activities and direct operating costs. Time devoted to implementation activities was ascertained through structured meeting logs and time use surveys. Operating costs were captured using project budget reports. Intervention costs included expenses for prescriptions and healthcare encounters related to the digital therapeutic, measured using electronic health record data. Univariate statistics were calculated for cost estimates with comparisons presented by trial arm, implementation activity, staff role and study month. Analyses were conducted from a health system perspective. Twenty-one primary care sites participated in the trial. Over the 50-month study period, the total cost of all implementation activities was $748,088. Implementation costs per clinic were highest in the SI + PF + HC arm ($48,029), followed by SI + HC ($36,544), SI + PF ($30,665) and SI alone ($24,774). Intervention costs were highest in the SI + PF + HC arm ($18,051), followed by SI + PF ($11,492), SI + HC ($967) and SI alone ($1,879). Findings from this study can guide health systems by informing the economic investment required to employ implementation strategies demonstrated to increase uptake of evidence-based practices for behavioral health conditions. Trial Registration: NCT05160233.

Objective: To measure the impact of electronic health record (EHR) transition on burnout among Veterans Health Administration (VA) frontline clinicians using pseudorandom variation from staggered EHR implementations across VA sites. Study Setting and Design: Employing a quasi‐experimental design, we studied 140 VA medical center sites nationwide (including five sites that implemented the new EHR from 2019 to 2023). Explanatory measures included year, VA transition site (grouped into three cohorts by transition timing), and their interaction. Our outcome measure encapsulated two dimensions of burnout—emotional exhaustion and depersonalization (symptoms > once per week indicated burnout). Data Sources and Analytic Sample: Using secondary data from the 2019 to 2023 VA All Employee Survey, we aggregated survey responses on the medical‐site level by year and respondent characteristics. Our analytic sample included 12,155 aggregated observations. We used a difference‐in‐difference approach to compare pre–post changes in burnout between VA sites implementing and not implementing the new EHR. Where available, we reported post‐transition treatment effects in the short term, medium term, and long term, relative to EHR implementation. Principal Findings: Unadjusted burnout from 2019 to 2023 was 36.9% for Cohort 1, 33.0% for Cohort 2, 37.0% for Cohort 3, and 33.2% for non‐transition sites. In adjusted analyses, burnout for Cohort 1 increased 4.8 percentage points ( p < 0.001) in the medium term; differences in burnout dissipated in the long term. For Cohort 2, we detected a 1 percentage point increase in burnout ( p = 0.004) in the short term and a 1.5 percentage point decrease ( p = 0.013) in the medium term. For Cohort 3, burnout increased 3.3 percentage points ( p < 0.001) in the medium term. Conclusions: The impact of EHR transition on burnout differed across deployment sites and post‐transition periods but was mild overall. Future research is needed to understand contextual and implementation process differences between sites that may explain differential effects and offer learnings to ensure a high‐functioning health workforce during EHR transition.

Background: Since 2010, all non‐VA hospitals performing cardiac surgeries and percutaneous interventions in Washington State have participated in the Cardiac Care Outcomes Assessment Program (COAP), a data‐driven, physician‐led collaborative quality improvement (QI) collaborative. Prior literature has demonstrated QI programs such as COAP can avert avoidable utilization such as hospital readmissions. However, it is unknown whether such improvements translate into economic benefits.Hypothesis: This study compared downstream healthcare costs between patients undergoing cardiac interventions for coronary artery disease (CAD) at hospitals that were and were not participating in COAP.Methods: Post hoc analysis of Medicare administrative and claims data examined 2.5 million randomly selected deidentified beneficiaries receiving a percutaneous coronary intervention or coronary artery bypass grafting between 2013 and 2020. Total costs were defined as all reimbursements paid by Medicare for up to 5 years following cardiac intervention. Because all non‐VA hospitals in Washington State participated in COAP, we compared respective groups of patients receiving intervention in Washington State with all non‐Washington states, adjusting for patient demographics and comorbidity. To model costs, we applied a multipart estimator, which distinguishes the impact of QI program participation due to survival and utilization while accounting for censoring.Results: Total 5‐year downstream costs were $3861 lower (95% confidence interval [CI] = $1794 to $5741) among patients receiving cardiac intervention at COAP‐exposed hospitals. Lower costs were largely driven by lower utilization during calendar quarters where death was not observed.Conclusions: Participation in this state‐wide cardiac quality improvement program was associated with economic benefits in patients receiving intervention for CAD.