Testing support models for implementing an evidence-based digital intervention for alcohol use disorder: results of a pragmatic hybrid implementation-effectiveness trial.

This paper reports results of a hybrid effectiveness-implementation randomized trial that systematically varied levels of human oversight required to support implementation of a digital medicine intervention for persons with mild to moderate alcohol use disorder (AUD). Participants were randomly assigned to three groups representing possible digital health support models within a health system: self-monitored use (n = 185), peer-supported use (n = 186), or a clinically integrated model (n = 187). Across all three groups, percentage of risky drinking days dropped from 38.4% at baseline (95%CI [35.8%, 41%]) to 22.5% (19.5%, 25.5%) at 12 months. The clinically integrated group showed significant improvements in mental health quality of life compared to the self-monitoring group (p = 0.011). However, higher rates of attrition in the clinically integrated group warrants consideration in interpreting this result. Results suggest that making a self-guided digital intervention available to patients may be a viable option for health systems looking to promote alcohol risk reduction.

Feasibility of implementing a screening tool for risk of opioid misuse in a trauma surgical population.

Background: As the opioid crisis continues to affect communities across the United States, new interventions for screening and prevention are needed to mitigate its impact. Mental health diagnoses have been identified as a risk factor for opioid misuse, and surgical populations and injury survivors are at high risk for prolonged opioid use and misuse. This study investigated the implementation of a novel opioid risk screening tool that incorporated putative risk factors from a recent study in four trauma units across Wisconsin. Method: The screening tool was implemented across a 6-month period at four sites. Data was collected via monthly meeting notes and "Plan, Do, Study, Act" (PDSA) forms. Following implementation, focus groups reflected on the facilitators and barriers to implementation. Meeting notes, PDSA forms, and focus group data were analyzed using the consolidated framework for implementation research, followed by thematic analyses, to generate themes surrounding the facilitators and barriers to implementing an opioid misuse screener. Results: Implementation facilitators included ensuring patient understanding of the screener, minimizing staff burden from screening, and educating staff to encourage engagement. Barriers included infrastructure limitations that prevented seamless administration of the screener within current workflows, overlap of the screener with existing measures, and lack of guidance surrounding treatment options corresponding to risk. Recommended solutions to address barriers include careful timing of screener administration, accommodating workflows, integration of the screening tool within the electronic health record, and evidence-based interventions guided by screener results. Conclusion: Four trauma centers across Wisconsin successfully implemented a pilot opioid misuse screening tool. Trauma providers and unit staff members believe that this tool would be a beneficial addition to their repertoire if their recommendations were adopted. Future research should refine opioid misuse risk factors and ensure screening items are well-validated with psychometric research supporting treatment responses to screener-indicated risk categories.

As the opioid crisis continues to affect communities across the United States, new interventions for early screening and prevention are needed to minimize the related harms. Prior research has identified risk factors associated with opioid misuse among a trauma surgical patient population, with the highest risk associated with distress-related posttraumatic stress disorder symptoms. A pilot screening tool was created based on this prior research, which was then administered at four trauma surgical units across the state of Wisconsin. Each of the four trauma units successfully implemented the pilot screening tool, and each identified a number of facilitators and barriers to the implementation process. Recommendations for improvement of the implementation process were also gathered. If their recommended changes were to be adopted, trauma providers and trauma unit staff members believed that such a screener for opioid misuse would be a beneficial addition to their current workflow among traumatic injury patients. Future research should refine opioid misuse risk factors and develop a psychometrically sound, validated screener to detect varying levels of risk and tailor treatment approaches based on a patient's risk score. Additionally, future research in the field of opioid misuse prevention should prioritize the recruitment of a more diverse population to support the translation of study findings across populations.

Testing a Digital Health App for Patients With Alcohol-Associated Liver Disease: Mixed Methods Usability Study.

BACKGROUND: Alcohol-associated liver disease (ALD) is increasingly common and associated with serious and costly health consequences. Cessation of drinking can improve ALD morbidity and mortality; however, support for cessation is not routinely offered to those diagnosed with ALD, and continued drinking or resumption of drinking after diagnosis is common. Mobile health (mHealth) has the potential to offer convenient and scalable support for alcohol cessation to those diagnosed with ALD, but mHealth interventions for alcohol cessation have not been designed for or evaluated in a population with ALD. OBJECTIVE: This study aims to understand how individuals with ALD would perceive and use an mHealth tool for alcohol cessation and to gather their perspectives on potential refinements to the tool that would allow it to better meet their needs. METHODS: We interviewed 11 individuals who attended clinic visits related to their ALD to elicit their needs related to support for alcohol cessation and views on how mHealth could be applied. After completing initial interviews (pre), participants were provided with access to an mHealth app designed for alcohol cessation, which they used for 1 month. Afterward, they were interviewed again (post) to give feedback on their experiences, including aspects of the app that met their needs and potential refinements. We applied a mixed methods approach, including a qualitative analysis to identify major themes from the interview transcripts and descriptive analyses of use of the app over 1 month. RESULTS: First, we found that a diagnosis of ALD is perceived as a motivator to quit drinking but that patients had difficulty processing the overwhelming amount of information about ALD they received and finding resources for cessation of alcohol use. Second, we found that the app was perceived as usable and useful for supporting drinking recovery, with patients responding favorably to the self-tracking and motivational components of the app. Finally, patients identified areas in which the app could be adapted to meet the needs of patients with ALD, such as providing information on the medical implications of an ALD diagnosis and how to care for their liver as well as connecting individuals with ALD to one another via a peer-to-peer support forum. Rates of app use were high and sustained across the entire study, with participants using the app a little more than half the days during the study on average and with 100% (11/11) of participants logging in each week. CONCLUSIONS: Our results highlight the need for convenient access to resources for alcohol cessation after ALD diagnosis and support the potential of an mHealth approach to integrate recovery support into care for ALD. Our findings also highlight the ways the alcohol cessation app should be modified to address ALD-specific concerns.

A structured approach to applying systems analysis methods for examining implementation mechanisms.

BACKGROUND: It is challenging to identify and understand the specific mechanisms through which an implementation strategy affects implementation outcomes, as implementation happens in the context of complex, multi-level systems. These systems and the mechanisms within each level have their own dynamic environments that change frequently. For instance, sequencing may matter in that a mechanism may only be activated indirectly by a strategy through another mechanism. The dosage or strength of a mechanism may vary over time or across different health care system levels. To elucidate the mechanisms relevant to successful implementation amidst this complexity, systems analysis methods are needed to model and manage complexity. METHODS: The fields of systems engineering and systems science offer methods-which we refer to as systems analysis methods-to help explain the interdependent relationships between and within systems, as well as dynamic changes to systems over time. When applied to studying implementation mechanisms, systems analysis methods can help (i) better identify and manage unknown conditions that may or may not activate mechanisms (both expected mechanisms targeted by a strategy and unexpected mechanisms that the methods help detect) and (ii) flexibly guide strategy adaptations to address contextual influences that emerge after the strategy is selected and used. RESULTS: In this paper, we delineate a structured approach to applying systems analysis methods for examining implementation mechanisms. The approach includes explicit steps for selecting, tailoring, and evaluating an implementation strategy regarding the mechanisms that the strategy is initially hypothesized to activate, as well as additional mechanisms that are identified through the steps. We illustrate the approach using a case example. We then discuss the strengths and limitations of this approach, as well as when these steps might be most appropriate, and suggest work to further the contributions of systems analysis methods to implementation mechanisms research. CONCLUSIONS: Our approach to applying systems analysis methods can encourage more mechanisms research efforts to consider these methods and in turn fuel both (i) rigorous comparisons of these methods to alternative mechanisms research approaches and (ii) an active discourse across the field to better delineate when these methods are appropriate for advancing mechanisms-related knowledge.

Barriers to Type 2 Diabetes Mellitus Management for Older Hmong Patients with Minimal English Language Skills: Accounts from Caregivers, Case Managers, and Clinicians.

Type 2 diabetes mellitus prevalence rates for Hmong Americans in Wisconsin are more than double that of non-Hispanic Whites. The Hmong's history, lifestyle (dietary and behavioral patterns), and reliance on traditional medicine contribute to their increased risk of diabetes. This qualitative study aimed to better understand the barriers challenging older Hmong patients' ability to manage diabetes. Asian Americans have long been overlooked in health-related research, but recent disaggregated data of specific ethnic groups reveal significant health inequities. Among the different ethnic groups, there is a significant lack of research on the Hmong Americans. Three participant groups (Hmong American family caregivers, Hmong American case managers, and clinicians from different racial backgrounds who provide care for Hmong patients) were recruited from the community and interviewed to understand the barriers experienced by older Hmong patients with minimal English language skills in managing their diabetes. Directed content analysis of the data resulted in three major themes: adherence to culture, health inequity, and managing diabetes. Subthemes included Hmong herbs and shamans, lack of trust in Western medicine, the significance of rice, language barriers, lack of cultural sensitivity, health literacy, monitoring glucose, medicine compliance, and nutrition. Minimal English language skills and low literacy rates (health and education) contribute to their strong adherence to cultural practices which challenges Western medicine, creating difficulty for older Hmong patients to manage their diabetes. Recognizing cultural differences and barriers will enable healthcare providers to improve and cater the treatment options, bridging the gap between older Hmong patients and Western medicine.

Applying concepts from "rapid" and "agile" implementation to advance implementation research.

BACKGROUND: The translation of research findings into practice can be improved to maximize benefits more quickly and with greater flexibility. To expedite translation, researchers have developed innovative approaches to implementation branded as "rapid" and "agile" implementation. Rapid implementation has roots in precision medicine and agile implementation has roots in systems engineering and software design. Research has shown that innovation often derives from learning and applying ideas that have impacted other fields. IMPLICATIONS FOR IMPLEMENTATION RESEARCHERS: This commentary examines "rapid" and "agile" approaches to implementation and provides recommendations to implementation researchers stemming from these approaches. Four key ideas are synthesized that may be broadly applicable to implementation research, including (1) adopting a problem orientation, (2) applying lessons from behavioral economics, (3) using adaptive study designs and adaptive interventions, and (4) using multi-level models to guide implementation. Examples are highlighted from the field where researchers are applying these key ideas to illustrate their potential impact. CONCLUSIONS: "Rapid" and "agile" implementation approaches to implementation stem from diverse fields. Elements of these approaches show potential for advancing implementation research, although adopting them may entail shifting scientific norms in the field.

The economics of adaptations to evidence-based practices.

BACKGROUND: Evidence-based practices (EBPs) are frequently adapted in response to the dynamic contexts in which they are implemented. Adaptation is defined as the degree to which an EBP is altered to fit the setting or to improve fit to local context and can be planned or unplanned. Although adaptations are common and necessary to maximizing the marginal impact of EBPs, little attention has been given to the economic consequences and how adaptations affect marginal costs. DISCUSSION: In assessing the economic consequences of adaptation, one should consider its impact on core components, the planned adaptive periphery, and the unplanned adaptive periphery. Guided by implementation science frameworks, we examine how various economic evaluation approaches accommodate the influence of adaptations and discuss the pros and cons of these approaches. Using the Framework for Reporting Adaptations and Modifications to Evidence-based interventions (FRAME), mixed methods can elucidate the economic reasons driving the adaptations. Micro-costing approaches are applied in research that integrates the adaptation of EBPs at the planning stage using innovative, adaptive study designs. In contrast, evaluation of unplanned adaptation is subject to confounding and requires sensitivity analysis to address unobservable measures and other uncertainties. A case study is presented using the RE-AIM framework to illustrate the costing of adaptations. In addition to empirical approaches to evaluating adaptation, simulation modeling approaches can be used to overcome limited follow-up in implementation studies. CONCLUSIONS: As implementation science evolves to improve our understanding of the mechanisms and implications of adaptations, it is increasingly important to understand the economic implications of such adaptations, in addition to their impact on clinical effectiveness. Therefore, explicit consideration is warranted of how costs can be evaluated as outcomes of adaptations to the delivery of EBPs.

Screening in Trauma for Opioid Misuse Prevention (STOMP): Results from a prospective cohort of victims of traumatic injury.

BACKGROUND: Traumatic injury frequently requires opioid analgesia to manage pain and avoid catastrophic complications. Risk screening for opioid misuse and the development of use disorder remains uninvestigated. METHODS: Participants were Trauma/Orthopedic Surgical Services patients at a Level I Trauma Center who were English speaking, aged 18-75, received an opioids prescription at discharge, and were under control of their own medications at discharge. Baseline measures included validated self-report instruments for psychosocial factors, such as anxiety, depression, pain coping, and social support. Health record data included diagnosis codes, procedures, Injury Severity Score, and pain severity (0-10 scale). Opioid use disorder (by Clinical International Diagnostic Interview-Substance Abuse Module) or opioid misuse (Current Opioid Misuse Measure (COMM) and survey items) were assessed at 24 weeks post-discharge. RESULTS: 295 patients enrolled with 237 completing the 24 week assessments. Stepwise regression modeling demonstrated pre-injury PTSD symptoms, Opioid Risk score, medication use behaviors, social support, and length of stay predicted opioid misuse. Pre-injury PTSD symptoms, pain coping, and length of stay predicted use disorder. The final regression models for opioid misuse by COMM, opioid misuse via survey items, and for opioid use disorder had highly favorable areas under the receiver operating curve (0.880, 0.790, and 0.943 respectively). CONCLUSIONS: Pre-injury presence of PTSD-related symptoms, impaired pain coping, social support, and hospitalization > 6 days predicted opioid misuse and opioid addiction at 6 months after hospital discharge. Behavioral screening and management strategies appear warranted in the population of traumatic injury victims to reduce opioid-related risks.

Testing an mHealth System for Individuals With Mild to Moderate Alcohol Use Disorders: Protocol for a Type 1 Hybrid Effectiveness-Implementation Trial.

BACKGROUND: The extent of human interaction needed to achieve effective and cost-effective use of mobile health (mHealth) apps for individuals with mild to moderate alcohol use disorder (AUD) remains largely unexamined. This study seeks to understand how varying levels of human interaction affect the ways in which an mHealth intervention for the prevention and treatment of AUDs works or does not work, for whom, and under what circumstances. OBJECTIVE: The primary aim is to detect the effectiveness of an mHealth intervention by assessing differences in self-reported risky drinking patterns and quality of life between participants in three study groups (self-monitored, peer-supported, and clinically integrated). The cost-effectiveness of each approach will also be assessed. METHODS: This hybrid type 1 study is an unblinded patient-level randomized clinical trial testing the effects of using an evidence-based mHealth system on participants' drinking patterns and quality of life. There are two groups of participants for this study: individuals receiving the intervention and health care professionals practicing in the broader health care environment. The intervention is a smartphone app that encourages users to reduce their alcohol consumption within the context of integrative medicine using techniques to build healthy habits. The primary outcomes for quantitative analysis will be participant data on their risky drinking days and quality of life as well as app use from weekly and quarterly surveys. Cost measures include intervention and implementation costs. The cost per participant will be determined for each study arm, with intervention and implementation costs separated within each group. There will also be a qualitative assessment of health care professionals' engagement with the app as well as their thoughts on participant experience with the app. RESULTS: This protocol was approved by the Health Sciences Minimal Risk Institutional Review Board on November 18, 2019, with subsequent annual reviews. Recruitment began on March 6, 2020, but was suspended on March 13, 2020, due to the COVID-19 pandemic restrictions. Limited recruitment resumed on July 6, 2020. Trial status as of November 17, 2021, is as follows: 357 participants were enrolled in the study for a planned enrollment of 546 participants. CONCLUSIONS: The new knowledge gained from this study could have wide and lasting benefits related to the integration of mHealth systems for individuals with mild to moderate AUDs. The results of this study will guide policy makers and providers toward cost-effective ways to incorporate technology in health care and community settings. TRIAL REGISTRATION: ClinicalTrials.gov NCT04011644; https://clinicaltrials.gov/ct2/show/NCT04011644. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/31109.

Efficacy and comparative effectiveness of telephone and smartphone remote continuing care interventions for alcohol use disorder: a randomized controlled trial.

BACKGROUND AND AIMS: Management of alcohol use disorder (AUD) could be enhanced by effective remote treatments. This study tested whether supplementing intensive outpatient programs (IOPs) with continuing care delivered via (1) telephone, (2) smartphone or (3) their combination improves outcomes relative to (4) IOP only. Continuing care conditions were also compared. DESIGN: Randomized controlled trial of four groups with 3-, 6-, 9-, 12- and 18-month follow-ups. SETTING: University research center in Philadelphia, PA, USA. PARTICIPANTS: Participants (n = 262) met DSM-V criteria for AUD, were largely male (71%) and African American (82%). INTERVENTIONS AND COMPARATOR: Telephone monitoring and counseling (TMC; n = 59), addiction comprehensive health enhancement support system (ACHESS; n = 68) and TMC + ACHESS (n = 70) provided for 12 months. The control condition received IOP only (TAU; n = 65). MEASUREMENT: The primary outcome was percentage of days heavy drinking (PDHD) in months 1-12. Secondary outcomes were any drinking, any drug use, drinking consequences and quality of life. FINDINGS: Mean PDHD in months 1-12 was 10.29 in TAU, 5.41 in TMC, 6.80 in ACHESS and 5.99 in TMC + ACHESS. PDHD was lower in TMC [Cohen's d = 0.35, P = 0.018, 95% confidence interval (CI) = (-1.42, -0.20)], ACHESS [d = 0.31, P = 0.031, 95% CI = (-1.27, -0.06)] and TMC + ACHESS [d = 0.36, P = 0.009, 95% CI = (-1.40, -0.20)] than in TAU. Differences between TMC + ACHESS, TMC and ACHESS were small (d ≤ 0.06) and non-significant. Findings were inconclusive as to whether or not the treatment conditions differed on PDHD at 18 months. A significant effect was obtained on any drinking, which was higher in months 1-12 in TAU than in TMC [odds ratio (OR) = 3.02, standard error (SE) = 0.43, 95% CI = (1.30, 6.99), P = 0.01] and TMC + ACHESS [OR = 2.43, SE = 0.39, 95% CI = (1.12, 5.27), P = 0.025). No other significant effects were obtained on other secondary outcomes during or after treatment. CONCLUSIONS: A telephone-delivered intervention and a smartphone-delivered intervention, alone and in combination, provided effective remote continuing care for alcohol use disorder. The combination of both interventions was not superior to either alone and effects did not persist post-treatment.

Encouraging the scale-up of proven interventions: Infrastructure development for the "Evidence-to-Implementation" award.

BACKGROUND/OBJECTIVE: Although most research universities offer investigators help in obtaining patents for inventions, investigators generally have few resources for scaling up non-patentable innovations, such as health behavior change interventions. In 2017, the dissemination and implementation (D & I) team at the University of Wisconsin's Clinical and Translational Science Award (CTSA) created the Evidence-to-Implementation (E2I) award to encourage the scale-up of proven, non-patentable health interventions. The award was intended to give investigators financial support and business expertise to prepare evidence-based interventions for scale-up. METHODS: The D & I team adapted a set of criteria named Critical Factors Assessment, which has proven effective in predicting the success of entrepreneurial ventures outside the health care environment, to use as review criteria for the program. In March 2018 and February 2020, multidisciplinary panels assessed proposals using a review process loosely based on the one used by the NIH for grant proposals, replacing the traditional NIH scoring criteria with the eight predictive factors included in Critical Factors Assessment. RESULTS: two applications in 2018 and three applications in 2020 earned awards. Funding has ended for the first two awardees, and both innovations have advanced successfully. CONCLUSION: Late-stage translation, though often overlooked by the academic community, is essential to maximizing the overall impact of the science generated by CTSAs. The Evidence-to-implementation award provides a working model for supporting late-stage translation within a CTSA environment.

Implementation science issues in understanding, collecting, and using cost estimates: a multi-stakeholder perspective.

Understanding the resources needed to achieve desired implementation and effectiveness outcomes is essential to implementing and sustaining evidence-based practices (EBPs). Despite this frequent observation, cost and economic measurement and reporting are rare, but becoming more frequent in implementation science, and when present is seldom reported from the perspective of multiple stakeholders (e.g., the organization, supervisory team), including those who will ultimately implement and sustain EBPs.Incorporating a multi-level framework is useful for understanding and integrating the perspectives and priorities of the diverse set of stakeholders involved in implementation. Stakeholders across levels, from patients to delivery staff to health systems, experience different economic impacts (costs, benefit, and value) related to EBP implementation and have different perspectives on these issues. Economic theory can aid in understanding multi-level perspectives and approaches to addressing potential conflict across perspectives.This paper provides examples of key cost components especially important to different types of stakeholders. It provides specific guidance and recommendations for cost assessment activities that address the concerns of various stakeholder groups, identifies areas of agreement and conflict in priorities, and outlines theoretically informed approaches to understanding conflicts among stakeholder groups and processes to address them. Involving stakeholders throughout the implementation process and presenting economic information in ways that are clear and meaningful to different stakeholder groups can aid in maximizing benefits within the context of limited resources. We posit that such approaches are vital to advancing economic evaluation in implementation science. Finally, we identify directions for future research and application.Considering a range of stakeholders is critical to informing economic evaluation that will support appropriate decisions about resource allocation across contexts to inform decisions about successful adoption, implementation, and sustainment. Not all perspectives need to be addressed in a given project but identifying and understanding perspectives of multiple groups of key stakeholders including patients and direct implementation staff not often explicitly considered in traditional economic evaluation are needed in implementation research.

Building capacity for dissemination and implementation to maximize research impact in a CTSA: The University of Wisconsin story.

We report results of an 8-year process of stakeholder engagement aimed at building capacity in Dissemination and Implementation (D&I) research at the University of Wisconsin as part of the National Institutes of Health's Clinical and Translational Science Award (CTSA). Starting in 2008, annual individual interviews were held with leaders of the Wisconsin CTSA's community engagement core for strategic planning purposes. Interviews were followed by annual planning meetings that employed a facilitated group decision-making process aimed at identifying and prioritizing gaps in the translational research spectrum. In 2011, the stakeholder engagement process identified D&I as a primary gap limiting overall impact of the institution's research across the translational spectrum. Since that time, our CTSA has created an array of D&I resources falling into four broad categories: (1) relationship building with D&I partners, (2) D&I skill building, (3) translational research resources, and (4) resources to support D&I activities. Our systematic process of stakeholder engagement has increased the impact of research by providing D&I resources to meet investigator and community needs. CTSAs could engage with leaders of their community engagement cores, which are common to all CTSAs, to adapt or adopt these resources to build D&I capacity.

The Balanced Opioid Initiative: protocol for a clustered, sequential, multiple-assignment randomized trial to construct an adaptive implementation strategy to improve guideline-concordant opioid prescribing in primary care.

BACKGROUND: Rates of opioid prescribing tripled in the USA between 1999 and 2015 and were associated with significant increases in opioid misuse and overdose death. Roughly half of all opioids are prescribed in primary care. Although clinical guidelines describe recommended opioid prescribing practices, implementing these guidelines in a way that balances safety and effectiveness vs. risk remains a challenge. The literature offers little help about which implementation strategies work best in different clinical settings or how strategies could be tailored to optimize their effectiveness in different contexts. Systems consultation consists of (1) educational/engagement meetings with audit and feedback reports, (2) practice facilitation, and (3) prescriber peer consulting. The study is designed to discover the most cost-effective sequence and combination of strategies for improving opioid prescribing practices in diverse primary care clinics. METHODS/DESIGN: The study is a hybrid type 3 clustered, sequential, multiple-assignment randomized trial (SMART) that randomizes clinics from two health systems at two points, months 3 and 9, of a 21-month intervention. Clinics are provided one of four sequences of implementation strategies: a condition consisting of educational/engagement meetings and audit and feedback alone (EM/AF), EM/AF plus practice facilitation (PF), EM/AF + prescriber peer consulting (PPC), and EM/AF + PF + PPC. The study's primary outcome is morphine-milligram equivalent (MME) dose by prescribing clinicians within clinics. The study's primary aim is the comparison of EM/AF + PF + PPC versus EM/AF alone on change in MME from month 3 to month 21. The secondary aim is to derive cost estimates for each of the four sequences and compare them. The exploratory aim is to examine four tailoring variables that can be used to construct an adaptive implementation strategy to meet the needs of different primary care clinics. DISCUSSION: Systems consultation is a practical blend of implementation strategies used in this case to improve opioid prescribing practices in primary care. The blend offers a range of strategies in sequences from minimally to substantially intensive. The results of this study promise to help us understand how to cost effectively improve the implementation of evidence-based practices. TRIAL REGISTRATION: NCT04044521 (ClinicalTrials.gov). Registered 05 August 2019.

Engaging Patients in Primary Care Quality Improvement Initiatives: Facilitators and Barriers.

Health care transformation calls for patient engagement in quality improvement (PEQI), yet practice participation remains low. This pilot study of 8 primary care clinics at 7 statewide locations sought to determine the most effective strategies for disseminating a previously successful single-system PEQI intervention. Qualitative data were obtained through site visits, interviews, observations, and journaling. All material pertaining to barriers, recruitment/retention, and implementation was extracted, compared, and categorized. Five teams partially completed the intervention and 3 finished. These 3 teams did not ask for shorter trainings and were assigned a quality improvement (QI) coach. Multiple barriers to recruitment, implementation, and retention were noted at the organizational and clinic/team level, including turnover, shifting priorities, cross-level communication difficulties, lack of QI knowledge, and confusion between patient engagement and patient activation. These findings suggest that QI facilitation and dedicated time can help primary care teams identify and overcome barriers to PEQI.

Using Stakeholder Values to Promote Implementation of an Evidence-Based Mobile Health Intervention for Addiction Treatment in Primary Care Settings.

BACKGROUND: Most evidence-based practices (EBPs) do not find their way into clinical use, including evidence-based mobile health (mHealth) technologies. The literature offers implementers little practical guidance for successfully integrating mHealth into health care systems. OBJECTIVE: The goal of this research was to describe a novel decision-framing model that gives implementers a method of eliciting the considerations of different stakeholder groups when they decide whether to implement an EBP. METHODS: The decision-framing model can be generally applied to EBPs, but was applied in this case to an mHealth system (Seva) for patients with addiction. The model builds from key insights in behavioral economics and game theory. The model systematically identifies, using an inductive process, the perceived gains and losses of different stakeholder groups when they consider adopting a new intervention. The model was constructed retrospectively in a parent implementation research trial that introduced Seva to 268 patients in 3 US primary care clinics. Individual and group interviews were conducted to elicit stakeholder considerations from 6 clinic managers, 17 clinicians, and 6 patients who were involved in implementing Seva. Considerations were used to construct decision frames that trade off the perceived value of adopting Seva versus maintaining the status quo from each stakeholder group's perspective. The face validity of the decision-framing model was assessed by soliciting feedback from the stakeholders whose input was used to build it. RESULTS: Primary considerations related to implementing Seva were identified for each stakeholder group. Clinic managers perceived the greatest potential gain to be better care for patients and the greatest potential loss to be cost (ie, staff time, sustainability, and opportunity cost to implement Seva). All clinical staff considered time their foremost consideration-primarily in negative terms (eg, cognitive burden associated with learning a new system) but potentially in positive terms (eg, if Seva could automate functions done manually). Patients considered safety (anonymity, privacy, and coming from a trusted source) to be paramount. Though payers were not interviewed directly, clinic managers judged cost to be most important to payers-whether Seva could reduce total care costs or had reimbursement mechanisms available. This model will be tested prospectively in a forthcoming mHealth implementation trial for its ability to predict mHealth adoption. Overall, the results suggest that implementers proactively address the cost and burden of implementation and seek to promote long-term sustainability. CONCLUSIONS: This paper presents a model implementers may use to elicit stakeholders' considerations when deciding to adopt a new technology, considerations that may then be used to adapt the intervention and tailor implementation, potentially increasing the likelihood of implementation success. TRIAL REGISTRATION: ClinicalTrials.gov NCT01963234; https://clinicaltrials.gov/ct2/show/NCT01963234 (Archived by WebCite at http://www.webcitation.org/78qXQJvVI).

Barriers to Buprenorphine Expansion in Ohio: A Time-Elapsed Qualitative Study.

Buprenorphine partial opioid agonist pharmacotherapy, a key treatment for opioid use disorders (OUDs), is underutilized in the United States. Qualitative interviews, conducted in 2012/2013 and repeated in 2015, identified systemic barriers to providing buprenorphine treatment in Ohio. A representative sample of Ohio's Alcohol, Drug Abuse and Mental Health Services (ADAMHS) county boards (n = 18) was selected based on percentage of OUD admissions, density of buprenorphine prescribers, and county board area population. Boards reported that the barriers to the use of buprenorphine in 2012/2013 included (1) negative attitudes toward the use of buprenorphine among substance use disorder treatment providers; (2) a lack of prescribers; and (3) lack of funding. The 2015 interviews suggested that the lack of prescribers surpassed lack of funding as the main impediment to buprenorphine expansion. Negative provider attitudes were no longer problematic. Concerns about buprenorphine diversion, however, had emerged as a new barrier. This article offers recommendations for future policy efforts to overcome these barriers and expand the use of evidence-based opioid treatments. It highlights the need for payers and policymakers to increase the number of buprenorphine prescribers to make best use of funding available to fight the opioid epidemic.

Systems consultation for opioid prescribing in primary care: a qualitative study of adaptation.

BACKGROUND: In order to promote guideline-concordant opioid prescribing practices, a blended implementation strategy called systems consultation was pilot tested in four primary care clinics in one US health system. OBJECTIVES: To describe (1) how systems consultation worked during the pilot test and (2) the modifications necessary to adapt this implementation strategy to primary care. METHODS: A team of investigators conducted observations (n=24), focus groups (n=4) and interviews (n=2). The team; kept contact logs documenting all interactions with the intervention clinics and preserved all work products resulting from the intervention. Initial analysis was concurrent with data collection and findings were used to modify the intervention in real time. At the conclusion of the pilot test, a pragmatic descriptive analysis of all data was performed to explore key modifications. RESULTS: Time constraints, entrenched hierarchical structures and a lack of quality improvement skills among clinical staff were the main barriers to implementing systems consultation. Modifications made to address these conditions included creating a consulting team, giving change teams more direction, revising process improvement tools, supporting the use of electronic health record (EHR) functionalities and providing opportunities for shared learning among clinics. DISCUSSION AND CONCLUSION: With the lessons of this research in mind, our goal in future iterations of systems consultation is to give clinics a combination of clinical, organisational change and EHR expertise optimised according to their needs. We believe a streamlined process for assessing the key characteristics identified in this study can be used to develop a plan for this kind of optimisation, or tailoring, and we will be developing such a process as part of an upcoming clinical trial.

Effects of automated smartphone mobile recovery support and telephone continuing care in the treatment of alcohol use disorder: study protocol for a randomized controlled trial.

BACKGROUND: New smartphone communication technology provides a novel way to provide personalized continuing care support following alcohol treatment. One such system is the Addiction version of the Comprehensive Health Enhancement Support System (A-CHESS), which provides a range of automated functions that support patients. A-CHESS improved drinking outcomes over standard continuing care when provided to patients leaving inpatient treatment. Effective continuing care can also be delivered via telephone calls with a counselor. Telephone Monitoring and Counseling (TMC) has demonstrated efficacy in two randomized trials with alcohol-dependent patients. A-CHESS and TMC have complementary strengths. A-CHESS provides automated 24/7 recovery support services and frequent assessment of symptoms and status, but does not involve regular contact with a counselor. TMC provides regular and sustained contact with the same counselor, but no ongoing support between calls. The future of continuing care for alcohol use disorders is likely to involve automated mobile technology and counselor contact, but little is known about how best to integrate these services. METHODS/DESIGN: To address this question, the study will feature a 2 × 2 design (A-CHESS for 12 months [yes/no] × TMC for 12 months [yes/no]), in which 280 alcohol-dependent patients in intensive outpatient programs (IOPs) will be randomized to one of the four conditions and followed for 18 months. We will determine whether adding TMC to A-CHESS produces fewer heavy drinking days than TMC or A-CHESS alone and test for TMC and A-CHESS main effects. We will determine the costs of each of the four conditions and the incremental cost-effectiveness of the three active conditions. Analyses will also examine secondary outcomes, including a biological measure of alcohol use, and hypothesized moderation and mediation effects. DISCUSSION: The results of the study will yield important information on improving patient alcohol use outcomes by integrating mobile automated recovery support and counselor contact. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02681406 . Registered on 2 September 2016.

Implementing a Mobile Health System to Integrate the Treatment of Addiction Into Primary Care: A Hybrid Implementation-Effectiveness Study.

BACKGROUND: Despite the near ubiquity of mobile phones, little research has been conducted on the implementation of mobile health (mHealth) apps to treat patients in primary care. Although primary care clinicians routinely treat chronic conditions such as asthma and diabetes, they rarely treat addiction, a common chronic condition. Instead, addiction is most often treated in the US health care system, if it is treated at all, in a separate behavioral health system. mHealth could help integrate addiction treatment in primary care. OBJECTIVE: The objective of this paper was to report the effects of implementing an mHealth system for addiction in primary care on both patients and clinicians. METHODS: In this implementation research trial, an evidence-based mHealth system named Seva was introduced sequentially over 36 months to a maximum of 100 patients with substance use disorders (SUDs) in each of three federally qualified health centers (FQHCs; primary care clinics that serve patients regardless of their ability to pay). This paper reports on patient and clinician outcomes organized according to the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. RESULTS: The outcomes according to the RE-AIM framework are as follows: Reach-Seva reached 8.31% (268/3226) of appropriate patients. Reach was limited by our ability to pay for phones and data plans for a maximum of 100 patients per clinic. Effectiveness-Patients who were given Seva had significant improvements in their risky drinking days (44% reduction, (0.7-1.25)/1.25, P=.04), illicit drug-use days (34% reduction, (2.14-3.22)/3.22, P=.01), quality of life, human immunodeficiency virus screening rates, and number of hospitalizations. Through Seva, patients also provided peer support to one another in ways that are novel in primary care settings. Adoption-Patients sustained high levels of Seva use-between 53% and 60% of the patients at the 3 sites accessed Seva during the last week of the 12-month implementation period. Among clinicians, use of the technology was less robust than use by patients, with only a handful of clinicians using Seva in each clinic and behavioral health providers making most referrals to Seva in 2 of the 3 clinics. Implementation-At 2 sites, implementation plans were realized successfully; they were delayed in the third. Maintenance-Use of Seva dropped when grant funding stopped paying for the mobile phones and data plans. Two of the 3 clinics wanted to maintain the use of Seva, but they struggled to find funding to support this. CONCLUSIONS: Implementing an mHealth system can improve care among primary care patients with SUDs, and patients using the system can support one another in their recovery. Among clinicians, however, implementation requires figuring out how information from the mHealth system will be used and making mHealth data available in the electronic health (eHealth) record. In addition, paying for an mHealth system remains a challenge.