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. Author manuscript; available in PMC: 2026 Sep 2.
Published in final edited form as: Drug Alcohol Depend. 2026 Apr 18;284:113165. doi: 10.1016/j.drugalcdep.2026.113165

Implementation of Addiction Consult Services and Bridge Clinics in the HEALing Communities Study in Massachusetts

Zoe M Weinstein 1, Elizabeth Jadovich 1, Mari-Lynn Drainoni 2,3,4, Marc LaRochelle 1, Shapei Yan 1, Jungwun Lee 5, Amy Bettano 6, Donna Beers 1, Alexander Y Walley 1, Jeffrey H Samet 1
PMCID: PMC13533503  NIHMSID: NIHMS2201036  PMID: 42033894

Abstract

Introduction

Addiction Consult Services (ACS) and bridge clinics are hospital-based intervention strategies to expand access to medication for opioid use disorder (MOUD) which many Massachusetts communities adopted during the HEALing Communities Study (HCS). This study describes the implementation of HCS hospital-based MOUD intervention strategies using the RE-AIM framework.

Methods

Post-hoc analysis of community ACS and bridge clinic implementation as part of a waitlist cluster randomized controlled trial of sixteen Massachusetts HCS communities across Wave 1 (randomized to receive intervention first) and Wave 2 (wait-list controls received the intervention second). The intervention was ACS and/or bridge clinics. Main measures include Adoption (planned to implement); Implementation (served at least one patient); Reach (median number of patients served per month); Effectiveness (receipt of MOUD within 14 days of opioid-related emergency or hospital encounter using Massachusetts Public Health Data Warehouse) and Maintenance (serving patients in the year post-study intervention).

Results

Eleven communities adopted 17 ACS and bridge clinics. Among the 17 adopted, 14 (7 ACS and 7 bridge clinics) were implemented. ACS reached a median of 16 patients/month and bridge clinics a median of 51 patients/month. Hospitals that implemented ACS and bridge clinics first did not have significantly higher 14-day post hospital MOUD rates compared to waitlisted hospitals. All 14 implemented ACS and bridge clinics were maintained post-intervention.

Conclusions

ACS and bridge clinics were widely adopted intervention strategies, and the majority of programs were implemented and sustained, demonstrating the perceived value of these services to their communities.

Keywords: opioid use disorder, consult service, methadone, buprenorphine

1.0. Introduction

Addiction Consult Services (ACS) and bridge clinics are evidence-based interventions within hospitals that can increase initiation and optimize the delivery of medications for opioid use disorder (MOUD) (Danovitch et al., 2024; McNeely et al., 2024; Roy et al., 2020; R. D. Shearer et al., 2024; Taylor et al., 2022). Given that 23% of patients with opioid use disorder (OUD) have been hospitalized in the past year, ACS and bridge clinics present excellent opportunities for individuals with OUD to access care (Bernstein et al., 2025; Larochelle et al., 2019). ACS impact the culture throughout a hospital, knowledge of providers (Englander et al., 2018; Wakeman et al., 2013), decrease mortality (Wilson et al., 2022) and are cost effective (Barocas et al., 2021), however; the structure of ACS are heterogeneous (Englander et al., 2022). Bridge clinics are a comparatively recent addition to the addiction treatment landscape that fill important gaps in care linkage, especially for complex patients (Incze et al., 2024; Taylor et al., 2023) and have emerging evidence to support their impact (Casey et al., 2024; Laks et al., 2021; Marcovitz et al., 2021). Despite the recent growth of these two hospital-based addiction treatment programs, much remains unknown about their optimal implementation and impact.

Historically, ACS and bridge clinics were established due to need recognized by local clinician champions, (e.g., addiction medicine physicians and psychiatry consult liaisons), with varying levels of support from institutional leadership (Murphy et al., 2009; O’Toole et al., 2006; Taylor et al., 2023; Trowbridge et al., 2017; Wakeman et al., 2014). More recently, ACS and bridge clinics have been effectively implemented as part of research studies to increase access to MOUD (McNeely et al., 2024; Ober et al., 2025). However, these studies implemented services in a relatively homogenous fashion and were not designed for the needs of specific communities. Community-based programs driven by communities themselves are more likely to be sustained (Ackland et al., 2023; Bangham et al., 2023; Haldane et al., 2019; Holt and Chambers, 2017; Ramanadhan et al., 2020).

This study drew on quantitative data from the NIH-funded HEALing Communities Study (HCS) to describe the implementation of hospital-based MOUD ACS and bridge clinics in Massachusetts. HCS worked with communities across four states (Kentucky, Massachusetts, New York, and Ohio) with the goal of decreasing opioid overdose deaths by implementing the Communities that HEAL (CTH) intervention with community coalitions being the primary implementation strategy of the HCS (HEALing Communities Study Consortium et al., 2024). Implementation was guided by the PRISM-RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework.(Drainoni et al., 2022; Knudsen et al., 2020; McAlearney et al., 2023) A key pillar of the CTH was the Overdose Reduction Continuum of Care Approach which consisted of three menus of intervention strategies that could be selected based on individual community’s needs. Communities were required to select to implement at least five intervention strategies: one to increase overdose education and naloxone distribution, three to increase effective delivery of MOUD, and one to increase safer prescribing and dispensing of opioids. Of the three MOUD intervention strategies, one was required to expand availability of MOUD treatment, one was required to link individuals to MOUD, and one was required to increase engagement and retention with MOUD (HEALing Communities Study Consortium, 2020; Winhusen et al., 2020). Communities could request up to $800,000 annually during their intervention period.

We aimed to describe the implementation and impact of hospital-based MOUD intervention strategies (ACS and bridge clinics) on MOUD provision outcomes in Massachusetts using the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) evaluation framework (Glasgow et al., 1999). This study employed a novel integrative quantitative approach utilizing a combination of community-level self-reported measures of implementation processes, and state-level data on care utilization among adults with OUD available to us through the Public Health Data Warehouse managed by the Massachusetts Department of Public Health (Bayly et al., 2025).

2.0. Methods

2.1. Study overview

Sixteen Massachusetts HCS communities were randomized to either participate in Wave 1 (intervention group) or Wave 2 (wait-list control). The intervention period occurred for all communities during their respective wave (wave 1: January 2020 to June 2022 and Wave 2: July 2022 to December 2023). During the intervention period, community coalitions were the primary implementation strategy used to select each community’s evidence-based intervention strategies.

In the current manuscript we described and assessed both the planning and evaluation of hospital-based MOUD intervention strategies through the five RE-AIM constructs. The HCS study used the unified PRISM/RE-AIM framework to guide the intervention and analysis. Several manuscripts have been published that describe the use of the framework in the analyses of HCS results.(Drainoni et al., 2022; Knudsen et al., 2020; McAlearney et al., 2023) In chronological order, we explain the adoption, implementation, reach, effectiveness, and maintenance of hospital-based MOUD intervention strategies.

2.2. RE-AIM constructs

2.2.1. Adoption

Prior to the intervention period, community coalitions created Community Action Plans that identified and outlined a plan for each intervention strategy that was selected and approved by the research team. For this paper, adoption was measured at the community level and was defined as the presence of at least one ACS or bridge clinic on the Community Action Plan. Communities reported to the study team which intervention strategies they had chosen from Overdose Reduction Continuum of Care Approach menu. Communities exercised substantial leeway in selecting and designing of their community-level intervention. For reporting purposes communities were provided with the following operational definition of bridge clinics to ensure accurate coding of the intervention: “Usually based in emergency departments or outpatient settings, bridge clinics provide rapid initiation of medications for OUD (MOUD), stabilization during high-risk transitions, harm reduction services, and linkage to long-term treatment providers.” (Taylor et al., 2023) Additionally, when communities reported to the study team about bridge clinics, they were instructed to categorize them as an intervention focused on care linkage, distinct from purely expansion for example of primary care MOUD capacity. Communities were asked to confirm the location of the bridge clinic (hospital-associated or community-based) on their reports to the study team.

2.2.2. Implementation

For each intervention strategy, participating hospitals collected data about the number of individuals reached monthly and reported this number to the research team. Implementation was defined as the delivery of services by an adopted ACS or bridge clinic to at least one client in the community. We defined time to implementation as the number of months between the first month a community was eligible to begin implementation, and the first month that an ACS or bridge clinic delivered a service to at least one individual.

2.2.3. Reach

Among ACS and bridge clinics that were successfully implemented (delivered services to at least one individual), the reach of the program was defined as the median number of individuals who received services from that program monthly. These data were reported by participating hospitals to the research team each month over the course of the intervention period. The median reach of each program was then calculated from the total number of individuals reached over the number of months from first implementation to the end of the intervention period.

2.2.4. Effectiveness

Effectiveness was defined as the number of individuals receiving MOUD within 14 days of discharge from an opioid-related emergency department or inpatient encounter at a hospital that implemented an ACS or bridge clinic. Unlike the other RE-AIM measures for this analysis, for the effectiveness outcome, data was from the Massachusetts Public Health Data Warehouse (PHD), a statewide database with individually-linkable data across more than 30 datasets (Bharel et al., 2020). The PHD includes Massachusetts residents with health insurance captured in the state’s All-Payer Claims Database. The Acute Care Hospital Case Mix file was used, including inpatient, observation and emergency department discharge data, to generate aggregate, suppressed information about adults discharged alive from an implementing hospital with diagnoses of opioid dependence or abuse, or opioid poisoning/adverse effects (as proxy for OUD) (McNeely et al., 2024). Discharges occurring during the pre-implementation period (January 1, 2019 through December 31, 2019) and the evaluation period (July 1, 2021 through June 30, 2022) were included to cover when Wave 1 hospitals had already completed active implementation and Wave 2 hospitals were waitlisted. Multiple encounters per individual were permitted. Information relating to the receipt of any MOUD, included buprenorphine, methadone, or naltrexone were obtained from the following: Prescription Monitoring Program (PMP)(buprenorphine only); state All-Payer Claims Database; and the Bureau of Substance Addiction Services treatment records. We used National Drug Codes (NDC) to exclude transdermal and buccal buprenorphine formulations FDA approved for pain. We assumed buprenorphine and naltrexone were received for OUD versus off-label use for pain or for alcohol use disorder respectively because the cohort was restricted to individuals hospitalized with diagnoses highly suggestive of OUD.A Difference-in-differences analysis was conducted to estimate the impact of ACS/bridge clinics on the outcome by comparing rates before and after implementation in Wave 1 hospitals, using Wave 2 hospitals that were waitlisted, and not yet implemented ACS/bridge clinics, as the control. A pooled hierarchical logistic regression model with a random effect at the patient-level was used to account for clusters of patients within the same hospital. Binary indicators for Wave, and time-period (pre- vs post-implementation) and the interaction of Wave and time-period were used and controlled for patient-level factors of age, sex, race/ethnicity, and receipt of MOUD in the 30 days prior to admission. A sensitivity analysis excluded individuals who received MOUD in the 30 days prior to admission to determine the impact on MOUD initiation. As second sensitivity analysis to focus on the impact of an ACS, limited the analysis to hospitals implementing an ACS and excluded emergency department discharges, focusing on inpatient and observation stays served by ACS.

2.2.5. Maintenance

Maintenance was defined as service delivery to at least one person during at least one month in the 12-month period post-intervention period. Some Wave 1 hospitals continued to formally submit Reach data to the research site after the end of the intervention period. For Wave 1 hospitals that did not report post-intervention data and all Wave 2 hospitals, research staff contacted each to inquire about maintenance status.

The study was approved by Advarra, acting as the single site Institutional Review Board. A waiver of consent and full waiver of HIPAA authorization were granted for secondary data analysis. Data in this trial are protected by multiple data-use agreements among researchers and agencies providing the data. Reporting conforms to the CONSORT Reporting Guidelines.

3.0. Results

Out of all 16 Massachusetts HCS communities, 11 communities adopted at least one hospital-based MOUD intervention strategy (ACS or bridge clinic). No communities reported a bridge clinic which was unaffiliated with a hospital (e.g. co-located with a syringe service program). Because some communities adopted more than one hospital-based MOUD program, there were a total of 17 adopted (9 ACS, and 8 bridge clinics). Of these 17 adopted intervention strategies, 14 were implemented (7 ACS, and 7 bridge clinics). The median time to implementation among ACS and bridge clinics was 5 months with a range of 0–14 months. The median number of individuals served by ACS monthly was 16, while the median number of individuals served by bridge clinics monthly was 51. All hospital-based MOUD intervention strategies (100%) were maintained, meaning they served at least one person during at least one month in the 12-month period post-intervention period (Figure 1, Table 1).

Figure 1.

Figure 1.

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Diagram describing the process of adoption, implementation, reach, and maintenance of all ACS and Bridge Clinic strategies selected in Wave 1 and Wave 2 Massachusetts communities.

Table 1:

Adoption, Implementation, Reach and Maintenance of Each Hospital-Based Medication for Opioid Use Disorder Expansion Strategy in Each Community in Massachusetts**

Community Urban or Rural Adoption ACS or Bridge Implementation (y/n) Months to implementation Reach (Median # served/month) Maintenance (y/n)
Community #1 Rural Yes Bridge Clinic No N/A N/A N/A
ACS No N/A N/A N/A
Community #2 Urban Yes ACS Yes 14 21 Yes
Community #3 Urban Yes ACS Yes 3 3 Yes
Community #4 Urban Yes ACS Yes 5 20.5 Yes
Community #5 Urban Yes ACS Yes 5 18.5 Yes
Bridge Clinic Yes 8 49 Yes
Community #6 Urban Yes ACS No N/A N/A N/A
Community #7 Urban Yes ACS Yes 2 46 Yes
Bridge Clinic Yes 2 187 Yes
Community #8 Rural Yes Bridge Clinic Yes 12 18 Yes
Bridge Clinic Yes 12 Unavailable Yes
Community # 9 Rural No N/A N/A N/A N/A N/A
Community #10 Rural No N/A N/A N/A N/A Yes
Community #11 Rural Yes ACS Yes 0 17 Yes
Bridge Clinic Yes 2 14 Yes
Community #12 Urban Yes ACS/Bridge Clinic* Yes 5 6 Yes
Community #13 Urban No N/A N/A N/A N/A N/A
Community #14 Urban No N/A N/A N/A N/A N/A
Community #15 Urban No N/A N/A N/A N/A N/A
Community #16 Urban Yes Bridge Clinic Yes 4 31 Yes
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*

ACS and Bridge Clinic data cannot be disaggregated

**

Effectiveness presented in separate table

3.1. Effectiveness

Among hospitals implementing an ACS or bridge clinic, during the pre-implementation period there were 7,023 opioid-related discharges among Wave 1 hospitals and 3,566 discharges among Wave 2 hospitals. During the evaluation period, there were 6,594 and 3,143 discharges among Wave 1 and Wave 2 hospitals respectively (Table 2). The majority of discharges were among males. There were differences in race/ethnicity by Wave: at baseline, Hispanic individuals represented 20% and 12% of discharges in Waves 1 and 2 respectively; and, White non-Hispanic individuals represented 75% and 84% of discharges in Waves 1 and 2 respectively. Rates of MOUD receipt in the 30 days prior to admission were higher in Wave 1 communities and increased from the baseline to evaluation periods in both Wave 1 and Wave 2 communities (48% to 55% in Wave 1 and 42% to 50% in Wave 2).

Table 2:

Characteristics of opioid-related discharges from emergency department or inpatient encounters at hospitals that implemented an Addiction Consult Service (ACS) or Bridge Clinic during HCS in Wave 1 or Wave 2 communities* during the baseline and evaluation periods.†

Baseline (Jan 1, 2019 – Dec 31, 2019) Evaluation (Jul 1, 2021 – Jun 30, 2022)
Wave 1 Wave 2 Wave 1 Wave 2
(n=7,023) (n=3,566) (n=6,594) (n=3,143)
Male 4,239 (60.4) 2,045 (57.4) 4,100 (62.2) 1,832 (58.3)
Age mean (SD) 42.7 (14.0) 44.5 (15.1) 44.4 (13.4) 46.3 (14.7)
Race/Ethnicity
 American Indian or other non-Hispanic 31 (0.4) 26 (0.7) 32 (0.5) 19 (0.6)
 Asian/PI non-Hispanic 37 (0.5) 19 (0.5) 52 (0.8) 13 (0.4)
 Black non-Hispanic 324 (4.6) 86 (2.4) 446 (6.8) 133 (4.2)
 Hispanic 1,391 (19.8) 438 (12.3) 1,221 (18.5) 326 (10.4)
 White non-Hispanic 5,240 (74.6) 2,997 (84.0) 4,843 (73.5) 2,651 (84.4)
MOUD within 30 days prior to admission 3,343 (47.6) 1,478 (41.5) 3,622 (54.9) 1,557 (49.6)
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*

Wave 1 communities received the intervention first and implemented their ACS or Bridge Clinic prior to the evaluation period. Wave 2 communities received the intervention second and implemented their ACS or Bridge Clinic after the evaluation period.

†

Unless otherwise specified, reported values are n (%).

The proportion receiving MOUD within 14 days of discharge increased from 47% to 54% for Wave 1 hospitals and 42% to 50% for Wave 2 hospitals (Figure 2). The difference-in-differences estimator did not detect a difference between the proportion of discharges with MOUD receipt within 14 days between Wave 1 and Wave 2 hospitals (assessed after Wave 1 but before Wave 2 hospitals had implemented the ACS or bridge clinics; p=0.53). Results were similar across sensitivity analyses excluding discharges where individuals had received MOUD within 30 days of admission or limiting to inpatient discharges among hospitals implementing an ACS (Table 3; Supplemental Tables 1-3).

Figure 2.

Figure 2.

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Proportion of discharges with receipt of MOUD within 14 days of discharge among opioid-related emergency department and inpatient encounters during baseline and evaluation periods for Wave 1 and Wave 2 hospitals that implemented an ACS or Bridge Clinic in Massachusetts.

* The difference-in-differences estimator tested for whether or not the outcome changed from baseline to evaluation periods, after Wave 1 but not Wave 2 hospitals had implemented an ACS or Bridge Clinic. The estimator controlled for age, sex, race/ethnicity and receipt of MOUD within 30 days of admission.

Table 3.

Proportion receiving MOUD within 14 days of discharge from emergency department or inpatient encounters at hospital implementing an Addiction Consult Service or Bridge Clinic during HCS for main and sensitivity analyses.

Baseline
(Jan 1 2019 – Dec 31 2019)		 Evaluation
(Jul 1 2021 – Jun 30 2022)		 p-value*
Intervention Control Intervention Control
Main Analysis: ED or Inpatient Encounter
n 7,023 3,566 6,594 3,142
Post MOUD n (%) 3,285 (46.8) 1,481 (41.5) 3,579 (54.3) 1,573 (50.1) 0.53
Sensitivity Analysis: ED or Inpatient Encounter excluding MOUD within 30 days of admission
n 3,680 2,088 2,972 1,585
Post MOUD n (%) 340 (9.2) 181 (8.7) 351 (11.8) 184 (11.6) 0.77
Sensitivity Analysis: Inpatient Encounters at sites with Addiction Consult Service
n 2,851 828 3,068 687
Post MOUD n (%) 1404 (49.3) 391 (47.2) 1768 (57.6) 363 (52.8) 0.25
Sensitivity Analysis: Inpatient Encounters at sites with Addiction Consult Service excluding encounters with MOUD within 30 days of admission
n 1421 446 1335 331
Post MOUD n (%) 124 (8.7) 55 (12.3) 182 (13.6) 50 (15.1) 0.25
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*

p-value for the difference-in-differences estimator that tested for whether or not the outcome changed from baseline to evaluation periods, after Wave 1 but not Wave 2 hospitals had implemented an ACS or Bridge Clinic. The estimator controlled for age, sex, race/ethnicity and receipt of MOUD within 30 days of admission.

4.0. Discussion

The majority of HCS community coalitions in Massachusetts planned to adopt an ACS or bridge clinic in a local hospital. Among those that adopted an ACS or bridge clinic, the vast majority (82%) did eventually implement the intervention strategy, most within a few months. While many communities adopted and implemented ACS and bridge clinics, there was not an increase in 14-day post hospital MOUD engagement when comparing those hospitals that implemented their ACS or bridge clinic during Wave 1 compared to those hospitals implementing later in Wave 2. All ACS and bridge clinics which were implemented were sustained beyond the intervention period.

The high level of adoption of ACS and bridge clinics, with most (69%) communities adopting at least one, and six out of 16 adopting both, likely demonstrates a strong perceived need within the communities for hospital-based MOUD expansion. Lack of universal adoption may have been driven by two opposing factors: communities perceived that hospital-based MOUD services were adequate and did not see a need to expand, or communities may have perceived too many challenges in implementing MOUD in hospital settings and thus prioritized adoption of other strategies; however, this study did not collect data on how communities weighed the pros and cons (e.g. lead time, cost, effectiveness) of various strategies when choosing which to adopt (R. Shearer et al., 2024a).

Implementation of adopted ACS and bridge clinics was also high (82%) but not universal. Prior research has demonstrated that successful implementation of ACS where MOUD is initiated at high rates appears to depend on some hospital-level factors such as a strong champion and high staffing levels, as well as ease of access to outpatient buprenorphine clinics, factors not assessed in this analysis.(Stevens et al., 2025) Collection of hospital level data beyond the reach data may have provided us with more information about acceptability of ACS and bridge clinics within specific organizations and organizational structures. Implementation may reflect community readiness to embrace MOUD and ease of access to outpatient MOUD. Literature is emerging describing barriers and facilitators to implementation of hospital-based MOUD initiation, including competing priorities, tension around change, and available resources; however, it is unknown which of these factors contributed to incomplete implementation in this study (R. Shearer et al., 2024b).

The reach of implemented ACS and bridge clinics (median 21 patients served per month) was modest relative to the >3000 patients with opioid-related discharges per year at the hospitals with ACS or bridge clinics. Newly established ACS have been shown to see only approximately 11% of hospitalized patients with substance use disorders (D’Amico et al., 2019). There was also a large range of time to implementation of each hospital-based MOUD program. Also notably the period of Wave 1 was during the beginning of the COVID-19 pandemic, which not only greatly disrupted general medical care but also the HCS which likely impacted the effectiveness and reach of novel programs which were starting during a time of extreme challenges to society and healthcare.(Becker and Fiellin, 2020; Walters et al., 2021) Given these disruptions, the intervention period was extended for and additional 6 months the Wave 1 communities in an effort to mitigate the impact of COVID-19 and better balance between the two waves.(Walters et al., 2021) Longer follow-up may have identified further reach of these nascent programs.

Given that the reach of ACS and bridge clinics during the study period was relatively modest and the programs may not have yet been fully scaled up, it may not be surprising that this study did not identify an increase in post-hospital MOUD among hospitals which had implemented these MOUD-expansion intervention strategies as compared to hospitals which had not yet implemented. The outcome measure in this study measures the impact of these services for all patients with opioid-related encounters and could not specifically account for only those patients seen by ACS or bridge clinics. Understanding how to extend the reach of ACS is important as they have been shown to increase MOUD uptake among patients actually seen by them (Calcaterra et al., 2024; Englander et al., 2020, 2019; Lambert et al., 2025; Rostam-Abadi et al., 2024) and bridge clinics are highly effective at MOUD initiation and linkage (Casey et al., 2024; Laks et al., 2021).

While a randomized control trial in six New York Health and Hospitals showed the significant impact of ACS on MOUD initiation, the absolute increase in MOUD was small (11% MOUD initiation rate in the intervention group vs 6.7% in the waitlist control) (McNeely et al., 2024). In addition, in the New York Health and Hospitals trial, there was a strong top-down organizational commitment to comprehensive implementation of ACS with dedicated funding for a multidisciplinary staff and long-term intensive training and support (McNeely et al., 2019). Although both studies included only one year of post-implementation data, the study set in New York Health and Hospitals required a high level of preparation for their hospitals prior to launch (e.g. hiring staff, technology system updates, and strengthening or creating outpatient treatment options) (McNeely et al., 2019).

In addition, in the Massachusetts’ hospitals included in this study, more than 40% of patients were receiving MOUD prior to their hospital or ED encounter and there was a secular trend of increasing community rates of MOUD over time. Given this very high level of community access to MOUD, it is possible that patients who were not as complex to link and engage in MOUD had already been connected to care prior to hospital contact. While ACS and bridge clinics are uniquely poised to support patients who are more challenging to engage and not actively treatment seeking, it may be that these new services were not yet sufficiently established to move the needle. Similar to other studies conducted in areas with high levels of pre-hospital MOUD, it is possible the impact of these services would have been larger in a state with less access to outpatient MOUD (Englander, 2024).

However, despite high levels of community access to MOUD and communities adopting and implementing programs dedicated to expanding MOUD, a large proportion of eligible patients were not on MOUD after their hospital encounter. This highlights an essential question in the field: Why are so many of those with OUD not on MOUD? It is clear that creating and scaling up MOUD services is necessary but not sufficient, (Walters et al., 2024) especially for medically complex patients (Englander, 2024). There are likely a large proportion of patients who may feel current treatment offerings, especially in the era of fentanyl and within the siloed United States treatment system are not meeting their needs (Gryczynski et al., 2014; Simon et al., 2022). Collection of individual patient level data, while beyond the scope of this study, may have helped to understand the populations that were more easily or quickly reached within the scope of a brief (12–18 month) intervention compared to populations that are less easily reached. Given higher rates of MOUD engagement and lower rates of overdose in other nations, providers and policy makers in the United States must continue to push for additional care innovations.

Notably, all ACS and bridge clinics that were implemented were sustained, even after the end of study funding, which demonstrates that hospitals and communities felt these programs were serving a perceived need, and that these services impact hospital culture and clinical practice long-term. The Massachusetts’ Department of Public Health created new grant funding to support the continuation or expansion of these programs, eventually providing some funding for hospitals across the state to build or expand ACS, bridge clinics or both(Executive Office of Health and Human Services, Commonwealth of Massachusetts, 2024; “Mercy awarded grant for substance use disorder program,” 2024). While these care models have become more common in hospital-based care, they are still not the norm (Franz et al., 2024).

4.1. Strengths and Limitations

This study reflects real-world implementation of hospital-based MOUD intervention strategies in a diverse array of Massachusetts communities highly impacted by opioid overdose deaths. These implementations represent the work and values of their communities (Chandler et al., 2023; Young et al., 2022). The control group consisted of hospitals that not only participated in HCS but went on to implement similar intervention strategies. This study does have a few notable limitations. This study describes the experience within a single state and may not be generalizable to states with less robust MOUD care, especially as office-based addiction treatment (OBAT) programs are widespread in Massachusetts including in HCS communities.(HEALing Communities Study Consortium, 2020; LaBelle et al., 2016) Most of the outcomes reported were provided by community partner organizations, which varied in their experience and capacity to collect and report data reliably. Outcomes were reported at the community level as this was a community level intervention; however, hospital and individual level data would have aided in the interpretation of our findings. For the effectiveness outcome, this study could not account for whether a given patient was seen by the ACS or at a bridge clinic. While the effectiveness outcome covariates were limited to age, sex, and race/ethnicity, the difference-in-differences design mitigates the potential impact under the assumption that these characteristics did not shift substantially in the population with OUD over the 3.5 years of this study.

4.2. Conclusions

ACS and bridge clinics were widely adopted evidence-based interventions during the HEALing Communities Study in Massachusetts, demonstrating a perceived value and need for these programs as communities attempted to expand access to MOUD and prevent opioid overdose deaths. Most adopted programs were then implemented and later sustained beyond the end of the intervention period. Despite the promise of these clinical programs, most patients with OUD remain off MOUD, before and after their hospital encounters. While ACS and bridge clinics represent critical encounters for patients with OUD, there is much work to be done to optimize their impact. Further innovation is needed to improve the accessibility and acceptability of current MOUD treatment options in the United States.

Supplementary Material

1

Highlights.

  • Addiction consult services and bridge clinics were widely adopted in HEALing Communities

  • 11of the 16 communities adopted 17 addiction consult services and bridge clinics

  • Among the 17, 14 were implemented and maintained in the year post-intervention

  • Early versus waitlist implementation was not associated with post-hospital medication

  • The majority of patients remain off of medications for opioid use disorder

Acknowledgements

This research was supported by the National Institutes of Health through the NIH HEAL: UM1DA049394, and UM1DA049412. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or its NIH HEAL Initiative. The authors acknowledge the Massachusetts Department of Public Health for creating the unique, cross-sector database used for this project and for providing technical support for the analysis. The authors also acknowledge and thank the community teams and organizations who managed and provided the data presented in this paper. Preliminary findings from this study were presented at the 17th Annual Conference on the Science of Dissemination and Implementation in Health on December 10th 2024.

Footnotes

Declaration of Competing Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

All authors report financial support was provided by National Institutes of Health HEAL Initiative: UM1DA049394, and UM1DA049412. They have no additional known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Disclosure Statement

The authors report that there are no conflicts of interest to declare.

Human Ethics and Consent to Participate Declarations

Not applicable

Declaration of generative AI use: There was no use of AI in the preparation of this manuscript

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Data Availability

The datasets generated during and/or analyzed during the current study are not publicly available because the Massachusetts Public Health Data Warehouse (PHD) is restricted and managed by the Massachusetts Department of Public Health (DPH). Requests for access to this data source should be directed to DPH (DPH.PHD@mass.gov). General information about PHD, including technical documentation, is available at: https://www.mass.gov/public-health-data-warehouse-phd

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS2201036-supplement-1.docx (20.5KB, docx)

Data Availability Statement

The datasets generated during and/or analyzed during the current study are not publicly available because the Massachusetts Public Health Data Warehouse (PHD) is restricted and managed by the Massachusetts Department of Public Health (DPH). Requests for access to this data source should be directed to DPH (DPH.PHD@mass.gov). General information about PHD, including technical documentation, is available at: https://www.mass.gov/public-health-data-warehouse-phd

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