Adolescent Residential Addiction Treatment In The US: Uneven Access, Waitlists, And High Costs.

Drug overdose deaths among adolescents are increasing in the United States. Residential treatment facilities are one treatment option for adolescents with substance use disorders, yet little is known about their accessibility or cost. Using the Substance Abuse and Mental Health Services Administration's treatment locator and search engine advertising data, we identified 160 residential addiction treatment facilities that treated adolescents with opioid use disorder as of December 2022. We called facilities while role-playing as the aunt or uncle of a sixteen-year-old child with a recent nonfatal overdose, to inquire about policies and costs. Eighty-seven facilities (54.4 percent) had a bed immediately available. Among sites with a waitlist, the mean wait time for a bed was 28.4 days. Of facilities providing cost information, the mean cost of treatment per day was $878. Daily costs among for-profit facilities were triple those of nonprofit facilities. Half of facilities required up-front payment by self-pay patients. The mean up-front cost was $28,731. We were unable to identify any facilities for adolescents in ten states or Washington, D.C. Access to adolescent residential addiction treatment centers in the United States is limited and costly.

Expanding Inpatient Addiction Consult Services Through Accountable Care Organizations for Medicaid Enrollees: A Modeling Study.

INTRODUCTION: Addiction consult services (ACS) care for hospitalized patients with substance use disorder, including opioid use disorder (OUD). Medicaid Accountable Care Organizations (ACOs) could enhance access to ACS. This study extends data from Oregon's only ACS to Oregon's 15 regional Medicaid Coordinated Care Organizations (CCOs) to illustrate the potential value of enhanced in- and out-patient care for hospitalized patients with OUD. The study objectives were to estimate the effects of (1) expanding ACS care through CCOs in Oregon, and (2) increasing community treatment access within CCOs, on post-discharge OUD treatment engagement. METHODS: We used a validated Markov model, populated with Oregon Medicaid data from April 2015 to December 2017, to estimate study objectives. RESULTS: Oregon Medicaid patients hospitalized with OUD with care billed to a CCO (n = 5878) included 1298 (22.1%) patients engaged in post-discharge OUD treatment. Simulation of referral to an ACS increased post-discharge OUD treatment engagement to 47.0% (95% confidence interval [CI] 45.7%, 48.3%), or 2684 patients (95% CI 2610, 2758). Ten of fifteen (66.7%) CCOs had fewer than 20% of patients engage in post-discharge OUD care. Without ACS, increasing outpatient treatment such that 20% of patients engage increased the patients engaging in post-discharge OUD care from 12.9% or 296 patients in care at baseline to 20% (95% CI 18.1%, 21.4%) or 453 (95% CI 416, 491). DISCUSSION: ACOs can improve care for patients hospitalized with OUD. Implementing ACS in ACO networks can potentially improve post-discharge OUD treatment engagement, but community treatment systems must be prepared to accept more patients as inpatient addiction care improves.

Perioperative Management of Buprenorphine/Naloxone in a Large, National Health Care System: a Retrospective Cohort Study.

BACKGROUND: Medication for opioid use disorder, including buprenorphine and methadone, is considered the gold standard treatment for opioid use disorder (OUD). As the number of patients receiving buprenorphine has grown, clinicians increasingly care for patients prescribed buprenorphine who present for surgery and require management of perioperative pain. OBJECTIVE: To describe practice patterns of perioperative and post-surgical use of buprenorphine among patients prescribed buprenorphine for OUD who experience major surgery. DESIGN: Retrospective cohort study utilizing data from the VA Corporate Data Warehouse (CDW), a national repository of patient-level data. Data not accessible in CDW, including clinical instructions to patients to modify buprenorphine dose, were accessed via chart review. PARTICIPANTS: National sample of patients receiving care through the Veterans Health Administration. MAIN MEASURES: We report descriptive statistics on the incidence of buprenorphine dose hold prior to, during, and immediately following surgery, as well as post-surgical outcomes. Multivariable logistic regression identified socio-demographic and clinical characteristics associated with perioperative hold. KEY RESULTS: Our final sample comprised 183 patients, the majority of whom were white and male. Most patients (66%) experienced a perioperative buprenorphine dose hold: during the pre-operative, day of surgery, and post-operative periods, 40%, 62%, and 55% of patients had buprenorphine held. Buprenorphine dose hold was less likely for patients who had experienced homelessness/housing insecurity in the year prior to surgery (aOR = 0.25; 95% CI 0.10-0.61) as well as patients residing in rural areas (aOR=0.29; 0.12-0.68). Within the 12-month period following surgery, 122 patients (67%) were retained on buprenorphine, 10 patients (5.5%) had experienced an overdose, and 15 (8.2%) had died. CONCLUSIONS: We identified high rates of perioperative buprenorphine dose holds. As holding buprenorphine perioperatively does not align with emerging clinical recommendations and carries significant risks, educational campaigns or other provider-targeted interventions may be needed to ensure patients with OUD receive recommended care.

Designing and validating a Markov model for hospital-based addiction consult service impact on 12-month drug and non-drug related mortality.

INTRODUCTION: Addiction consult services (ACS) engage hospitalized patients with opioid use disorder (OUD) in care and help meet their goals for substance use treatment. Little is known about how ACS affect mortality for patients with OUD. The objective of this study was to design and validate a model that estimates the impact of ACS care on 12-month mortality among hospitalized patients with OUD. METHODS: We developed a Markov model of referral to an ACS, post-discharge engagement in SUD care, and 12-month drug-related and non-drug related mortality among hospitalized patients with OUD. We populated our model using Oregon Medicaid data and validated it using international modeling standards. RESULTS: There were 6,654 patients with OUD hospitalized from April 2015 through December 2017. There were 114 (1.7%) drug-related deaths and 408 (6.1%) non-drug related deaths at 12 months. Bayesian logistic regression models estimated four percent (4%, 95% CI = 2%, 6%) of patients were referred to an ACS. Of those, 47% (95% CI = 37%, 57%) engaged in post-discharge OUD care, versus 20% not referred to an ACS (95% CI = 16%, 24%). The risk of drug-related death at 12 months among patients in post-discharge OUD care was 3% (95% CI = 0%, 7%) versus 6% not in care (95% CI = 2%, 10%). The risk of non-drug related death was 7% (95% CI = 1%, 13%) among patients in post-discharge OUD treatment, versus 9% not in care (95% CI = 5%, 13%). We validated our model by evaluating its predictive, external, internal, face and cross validity. DISCUSSION: Our novel Markov model reflects trajectories of care and survival for patients hospitalized with OUD. This model can be used to evaluate the impact of other clinical and policy changes to improve patient survival.

Addressing a rapidly changing service landscape during the COVID-19 pandemic: Creation of the Oregon substance use disorder resource collaborative.

Following the rising crisis of COVID-19 and the Oregon governor's stay-at-home orders, members of the Oregon Health and Science University (OHSU) inpatient addiction consult service recognized that local addiction treatment and recovery organizations were operating at limited capacity. As a result, discharge planning, patient access to local community-based treatment, and safety-net programming were affected. Given structural and intersectional risk vulnerabilities of people with substance use disorders (SUDs), the OHSU members felt that COVID-19 would disproportionately impact chronically marginalized members of our community. These inequities inspired the formation of the Oregon substance use disorder resources collaborative (ORSUD) led by four medical students. ORSUD's mission is to support the efforts of local safety-net organizations that and front-line providers who serve chronically marginalized community members in the midst of the global pandemic. We operationalized our mission through: 1) collecting and disseminating operational and capacity changes in local addiction and harm reduction services to the broader treatment community, and 2) identifying and addressing immediate resource needs for local safety-net programs. Our program uses a real-time public-facing document to collate local programmatic updates and general community resources. COVID-19 disproportionately burdens people with SUDs; thus, ORSUD exists to support programs serving people with SUDs and will continue to evolve to meet their needs and the needs of those who serve them.

Population-based trends in hospitalizations due to injection drug use-related serious bacterial infections, Oregon, 2008 to 2018.

BACKGROUND: Injection drug use has far-reaching social, economic, and health consequences. Serious bacterial infections, including skin/soft tissue infections, osteomyelitis, bacteremia, and endocarditis, are particularly morbid and mortal consequences of injection drug use. METHODS: We conducted a population-based retrospective cohort analysis of hospitalizations among patients with a diagnosis code for substance use and a serious bacterial infection during the same hospital admission using Oregon Hospital Discharge Data. We examined trends in hospitalizations and costs of hospitalizations attributable to injection drug use-related serious bacterial infections from January 1, 2008 through December 31, 2018. RESULTS: From 2008 to 2018, Oregon hospital discharge data included 4,084,743 hospitalizations among 2,090,359 patients. During the study period, hospitalizations for injection drug use-related serious bacterial infection increased from 980 to 6,265 per year, or from 0.26% to 1.68% of all hospitalizations (P<0.001). The number of unique patients with an injection drug use-related serious bacterial infection increased from 839 to 5,055, or from 2.52% to 8.46% of all patients (P<0.001). While hospitalizations for all injection drug use-related serious bacterial infections increased over the study period, bacteremia/sepsis hospitalizations rose most rapidly with an 18-fold increase. Opioid use diagnoses accounted for the largest percentage of hospitalizations for injection drug use-related serious bacterial infections, but hospitalizations for amphetamine-type stimulant-related serious bacterial infections rose most rapidly with a 15-fold increase. People living with HIV and HCV experienced increases in hospitalizations for injection drug use-related serious bacterial infection during the study period. Overall, the total cost of hospitalizations for injection drug use-related serious bacterial infections increased from $16,305,129 in 2008 to $150,879,237 in 2018 (P<0.001). CONCLUSIONS: In Oregon, hospitalizations for injection drug use-related serious bacterial infections increased dramatically and exacted a substantial cost on the health care system from 2008 to 2018. This increase in hospitalizations represents an opportunity to initiate substance use disorder treatment and harm reduction services to improve outcomes for people who inject drugs.

Inpatient Addiction Medicine Consultation and Post-Hospital Substance Use Disorder Treatment Engagement: a Propensity-Matched Analysis.

BACKGROUND: Hospitalizations due to medical and surgical complications of substance use disorder (SUD) are rising. Most hospitals lack systems to treat SUD, and most people with SUD do not engage in treatment after discharge. OBJECTIVE: Determine the effect of a hospital-based addiction medicine consult service, the Improving Addiction Care Team (IMPACT), on post-hospital SUD treatment engagement. DESIGN: Cohort study using multivariable analysis of Oregon Medicaid claims comparing IMPACT patients with propensity-matched controls. PARTICIPANTS: 18-64-year-old Oregon Medicaid beneficiaries with SUD, hospitalized at an Oregon hospital between July 1, 2015, and September 30, 2016. IMPACT patients (n = 208) were matched to controls (n = 416) using a propensity score that accounted for SUD, gender, age, race, residence region, and diagnoses. INTERVENTIONS: IMPACT included hospital-based consultation care from an interdisciplinary team of addiction medicine physicians, social workers, and peers with lived experience in recovery. IMPACT met patients during hospitalization; offered pharmacotherapy, behavioral treatments, and harm reduction services; and supported linkages to SUD treatment after discharge. OUTCOMES: Healthcare Effectiveness Data and Information Set (HEDIS) measure of SUD treatment engagement, defined as two or more claims on two separate days for SUD care within 34 days of discharge. RESULTS: Only 17.2% of all patients were engaged in SUD treatment before hospitalization. IMPACT patients engaged in SUD treatment following discharge more frequently than controls (38.9% vs. 23.3%, p < 0.01; aOR 2.15, 95% confidence interval [CI] 1.29-3.58). IMPACT participation remained associated with SUD treatment engagement when limiting the sample to people who were not engaged in treatment prior to hospitalization (aOR 2.63; 95% CI 1.46-4.72). CONCLUSIONS: Hospital-based addiction medicine consultation can improve SUD treatment engagement, which is associated with reduced substance use, mortality, and other important clinical outcomes. National expansion of such models represents an opportunity to address an enduring gap in the SUD treatment continuum.

Tools to Support Hospital-Based Addiction Care: Core Components, Values, and Activities of the Improving Addiction Care Team.

: Hospitals are increasingly filled with people admitted for medical and surgical complications of substance use disorder (SUD). Hospitalization can be a reachable moment to engage and initiate SUD care. Yet most hospitals do not have systems in place to adequately address addiction, and most providers have little to no addiction training. There is widespread need for protocols and tools to implement hospital-based SUD care. We share best practices from our hospital-based Improving Addiction Care Team (IMPACT). We include a description of interprofessional roles (medical providers, social workers, peers with lived experience in recovery) and include detailed appendices of practical tools such as medication protocols (eg, buprenorphine induction), risk assessments (eg, outpatient parenteral antibiotic therapy) and treatment tools (eg, a patient safety care plan to manage patient and staff expectations surrounding risks for in hospital drug use). A case example illustrates how IMPACT works and how tools can be applied. We hope other hospitals can adapt and integrate these tools to support widespread implementation of hospital-based SUD care.

The SUMMIT ambulatory-ICU primary care model for medically and socially complex patients in an urban federally qualified health center: study design and rationale.

BACKGROUND: Medically complex urban patients experiencing homelessness comprise a disproportionate number of high-cost, high-need patients. There are few studies of interventions to improve care for these populations; their social complexity makes them difficult to study and requires clinical and research collaboration. We present a protocol for a trial of the streamlined unified meaningfully managed interdisciplinary team (SUMMIT) team, an ambulatory ICU (A-ICU) intervention to improve utilization and patient experience that uses control populations to address limitations of prior research. METHODS/DESIGN: Participants are patients at a Federally Qualified Health Center in Portland, Oregon that serves patients experiencing homelessness or who have substance use disorders. Participants meet at least one of the following criteria: > 1 hospitalization over past 6 months; at least one medical co-morbidity including uncontrolled diabetes, heart failure, chronic obstructive pulmonary disease, liver disease, soft-tissue infection; and 1 mental health diagnosis or substance use disorder. We exclude patients if they have < 6 months to live, have cognitive impairment preventing consent, or are non-English speaking. Following consent and baseline assessment, we randomize participants to immediate SUMMIT intervention or wait-list control group. Participants receiving the SUMMIT intervention transfer care to a clinic-based team of physician, complex care nurse, care coordinator, social worker, and pharmacist with reduced panel size and flexible scheduling with emphasis on motivational interviewing, patient goal setting and advanced care planning. Wait-listed participants continue usual care plus engagement with community health worker intervention for 6 months prior to joining SUMMIT. The primary outcome is hospital utilization at 6 months; secondary outcomes include emergency department utilization, patient activation, and patient experience measures. We follow participants for 12 months after intervention initiation. DISCUSSION: The SUMMIT A-ICU is an intensive primary care intervention for high-utilizers impacted by homelessness. Use of a wait-list control design balances community and staff stakeholder needs, who felt all participants should have access to the intervention, while addressing research needs to include control populations. Design limitations include prolonged follow-up period that increases risk for attrition, and conflict between practice and research; including partner stakeholders and embedded researchers familiar with the population in study planning can mitigate these barriers. Trial registration ClinicalTrials.gov NCT03224858, Registered 7/21/17 retrospectively registered https://clinicaltrials.gov/ct2/show/NCT03224858.

Planning and Designing the Improving Addiction Care Team (IMPACT) for Hospitalized Adults with Substance Use Disorder.

People with substance use disorders (SUD) have high rates of hospitalization and readmission, long lengths of stay, and skyrocketing healthcare costs. Yet, models for improving care are extremely limited. We performed a needs assessment and then convened academic and community partners, including a hospital, community SUD organizations, and Medicaid accountable care organizations, to design a care model for medically complex hospitalized patients with SUD. Needs assessment showed that 58% to 67% of participants who reported active substance use said they were interested in cutting back or quitting. Many reported interest in medication for addiction treatment (MAT). Participants had high rates of costly readmissions and longer than expected length of stay. Community stakeholders identified long wait times and lack of resources for medically complex patients as key barriers. We developed the Improving Addiction Care Team (IMPACT), which includes an inpatient addiction medicine consultation service, rapid-access pathways to posthospital SUD treatment, and a medically enhanced residential care model that integrates antibiotic infusion and residential addiction care. We developed a business case and secured funding from Medicaid and hospital payers. IMPACT provides one pathway for hospitals, payers, and communities to collaboratively address the SUD epidemic. Journal of Hospital Medicine 2017;12:339-342.

The care transitions innovation (C-TraIn) for socioeconomically disadvantaged adults: results of a cluster randomized controlled trial.

BACKGROUND: Despite growing emphasis on transitional care to reduce costs and improve quality, few studies have examined transitional care improvements in socioeconomically disadvantaged adults. It is important to consider these patients separately as many are high-utilizers, have different needs, and may have different responses to interventions. OBJECTIVE: To evaluate the impact of a multicomponent transitional care improvement program on 30-day readmissions, emergency department (ED) use, transitional care quality, and mortality. DESIGN: Clustered randomized controlled trial conducted at a single urban academic medical center in Portland, Oregon. PARTICIPANTS: Three hundred eighty-two hospitalized low-income adults admitted to general medicine or cardiology who were uninsured or had public insurance. INTERVENTION: Multicomponent intervention including (1) transitional nurse coaching and education, including home visits for highest risk patients; (2) pharmacy care, including provision of 30 days of medications after discharge for those without prescription drug coverage; (3) post-hospital primary care linkages; (4) systems integration and continuous quality improvement. MEASUREMENTS: Primary outcomes included 30-day inpatient readmission and ED use. Readmission data were obtained using state-wide administrative data for all participants (insured and uninsured). Secondary outcomes included quality (3-item Care Transitions Measure) and mortality. Research staff administering questionnaires and assessing outcomes were blinded. RESULTS: There was no significant difference in 30-day readmission between C-TraIn (30/209, 14.4 %) and control patients (27/173, 16.1 %), p = 0.644, or in ED visits between C-TraIn (51/209, 24.4 %) and control (33/173, 19.6 %), p = 0.271. C-TraIn was associated with improved transitional care quality; 47.3 % (71/150) of C-TraIn patients reported a high quality transition compared to 30.3 % (36/119) control patients, odds ratio 2.17 (95 % CI 1.30-3.64). Zero C-TraIn patients died in the 30-day post-discharge period compared with five in the control group (unadjusted p = 0.02). CONCLUSIONS: C-TraIn did not reduce 30-day inpatient readmissions or ED use; however, it improved transitional care quality.

"Did I do as best as the system would let me?" Healthcare professional views on hospital to home care transitions.

BACKGROUND: Patients are vulnerable to poor quality, fragmented care as they transition from hospital to home. Few studies examine the discharge process from the perspectives of multiple healthcare professionals. OBJECTIVE: To understand care transitions from the perspective of diverse healthcare professionals, and identify recommendations for process improvement. DESIGN: Cross sectional qualitative study. PARTICIPANTS AND SETTING: Clinicians, care teams, and administrators from the inpatient general medicine services at one urban, academic hospital; two outpatient primary care clinics; and one Medicaid managed care plan. APPROACH: We conducted 13 focus groups and two in-depth interviews with participants prior to initiating a hospital-funded, multi-component transitional care intervention for uninsured and low-income publicly insured patients, the Care Transitions Innovation (C-TraIn). We used thematic analysis to identify emergent themes and a cross-case comparative analysis to describe variation by participant role and setting. KEY RESULTS: Poor transitional care reflected healthcare system fragmentation, limiting the ability of healthcare professionals to provide optimal patient care. Lack of standardized processes, poor multidisciplinary communication within the hospital, and fragmented communication across settings led to chaotic, unsystematic transitions, poor patient outcomes, and feelings of futility and dissatisfaction among providers. Patients with complex psychosocial needs were especially vulnerable during care transitions. Recommended changes to improve transitional care included improving hospital multidisciplinary hospital rounds, clarifying accountability as patients move across settings, standardizing discharge processes, and providing additional medical staff training. CONCLUSIONS: Hospital to home care transitions are critical junctures that can impact health outcomes, experience of care, and costs. Transitional care quality improvement initiatives must address system fragmentation, reduce communication barriers within and between settings, and ensure adequate professional training.

Planning and designing the care transitions innovation (C-Train) for uninsured and Medicaid patients.

BACKGROUND: Uninsured and Medicaid patients are particularly vulnerable as they transition from hospital to home. Transitional care improvement programs require time and capital, incentives for which may be unclear for those lacking a third-party payor. This article describes our experience developing a hospital-funded transitional care program for uninsured and Medicaid patients. METHODS: We performed an inpatient needs assessment, convened multi-stakeholder work groups, and engaged institutional change-agents to inform program development and a business case. RESULTS: We mapped needs to specific program elements, including a transitional care nurse, pharmacy consult and provision of medications for uninsured patients, medical home linkages including community payment for medical homes, and monthly quality improvement meetings. A business case was informed by local needs and utilization data, and compelled the hospital to invest in up-front resources for this population. DISCUSSION: We are studying our program's impact on 30-day readmission and emergency department rates through a clustered, randomized controlled trial. Lessons from our experience may be useful to others aiming to improve care for socioeconomically disadvantaged patients.

Risk prediction models for hospital readmission: a systematic review.

CONTEXT: Predicting hospital readmission risk is of great interest to identify which patients would benefit most from care transition interventions, as well as to risk-adjust readmission rates for the purposes of hospital comparison. OBJECTIVE: To summarize validated readmission risk prediction models, describe their performance, and assess suitability for clinical or administrative use. DATA SOURCES AND STUDY SELECTION: The databases of MEDLINE, CINAHL, and the Cochrane Library were searched from inception through March 2011, the EMBASE database was searched through August 2011, and hand searches were performed of the retrieved reference lists. Dual review was conducted to identify studies published in the English language of prediction models tested with medical patients in both derivation and validation cohorts. DATA EXTRACTION: Data were extracted on the population, setting, sample size, follow-up interval, readmission rate, model discrimination and calibration, type of data used, and timing of data collection. DATA SYNTHESIS: Of 7843 citations reviewed, 30 studies of 26 unique models met the inclusion criteria. The most common outcome used was 30-day readmission; only 1 model specifically addressed preventable readmissions. Fourteen models that relied on retrospective administrative data could be potentially used to risk-adjust readmission rates for hospital comparison; of these, 9 were tested in large US populations and had poor discriminative ability (c statistic range: 0.55-0.65). Seven models could potentially be used to identify high-risk patients for intervention early during a hospitalization (c statistic range: 0.56-0.72), and 5 could be used at hospital discharge (c statistic range: 0.68-0.83). Six studies compared different models in the same population and 2 of these found that functional and social variables improved model discrimination. Although most models incorporated variables for medical comorbidity and use of prior medical services, few examined variables associated with overall health and function, illness severity, or social determinants of health. CONCLUSIONS: Most current readmission risk prediction models that were designed for either comparative or clinical purposes perform poorly. Although in certain settings such models may prove useful, efforts to improve their performance are needed as use becomes more widespread.