Implementation and workflow strategies for integrating digital therapeutics for alcohol use disorders into primary care: a qualitative study
Addiction Science & Clinical Practice volume 18, Article number: 27 (2023)
Alcohol use disorders (AUD) are prevalent and often go untreated. Patients are commonly screened for AUD in primary care, but existing treatment programs are failing to meet demand. Digital therapeutics include novel mobile app-based treatment approaches which may be cost-effective treatment options to help fill treatment gaps. The goal of this study was to identify implementation needs and workflow design considerations for integrating digital therapeutics for AUD into primary care.
We conducted qualitative interviews with clinicians, care delivery leaders, and implementation staff (n = 16) in an integrated healthcare delivery system in the United States. All participants had experience implementing digital therapeutics for depression or substance use disorders in primary care. Interviews were designed to gain insights into adaptations needed to optimize existing clinical processes, workflows, and implementation strategies for use with alcohol-focused digital therapeutics. Interviews were recorded and transcribed and then analyzed using a rapid analysis process and affinity diagramming.
Qualitative themes were well represented across health system staff roles. Participants were enthusiastic about digital therapeutics for AUD, anticipated high patient demand for such a resource, and made suggestions for successful implementation. Key insights regarding the implementation of digital therapeutics for AUD and unhealthy alcohol use from our data include: (1) implementation strategy selection must be driven by digital therapeutic design and target population characteristics, (2) implementation strategies should seek to minimize burden on clinicians given the large numbers of patients with AUD who are likely to be interested in and eligible for digital therapeutics, and (3) digital therapeutics should be offered alongside many other treatment options to accommodate individual patients’ AUD severity and treatment goals. Participants also expressed confidence that previous implementation strategies used with other digital therapeutics such as clinician training, electronic health record supports, health coaching, and practice facilitation would be effective for the implementation of digital therapeutics for AUD.
The implementation of digital therapeutics for AUD would benefit from careful consideration of the target population. Optimal integration requires tailoring workflows to meet anticipated patient volume and designing workflow and implementation strategies to meet the unique needs of patients with varying AUD severity.
Contributions to the literature
Provides insights regarding the best ways to implement app-based treatments for alcohol use disorder (AUD) in primary care.
Represents the perspectives of a variety of stakeholders including care delivery leaders, clinicians, and implementation staff.
Identifies the following recommendations to improve the implementation of digital therapeutics for AUD: (1) plan for high patient volume, (2) support teams during implementation, and (3) account for individual patient needs.
Alcohol use disorders (AUD) are prevalent and often go untreated; in 2019, 14 million US adults had AUD and only about 7.3% received any treatment in the past year . Patients are commonly screened for AUD in primary care, and many are open to primary care-based treatment . However, existing primary care-based treatment programs do not have sufficient capacity to meet demand [3, 4].
Digital health includes software that assists with assessment, monitoring, or treatment of health conditions . Digital health may be used in conjunction with other interventions (e.g., counselling) or as a stand-alone intervention . Health systems are increasingly utilizing digital health to improve treatment access, reduce costs, and provide additional options to patients [7, 8]. Digital therapeutics are a specific form of digital health that delivers evidence-based treatments aimed to treat, manage, or prevent disorders [5, 9, 10]. They are often delivered via websites or smartphone apps. Examples of digital therapeutics for AUD include kiosk-delivered brief interventions and psychosocial interventions that are packaged as smartphone apps for the purpose of treatment or ongoing support for AUD [11,12,13,14]. While research has been conducted on the effectiveness of digital therapeutics for AUD, there is limited evidence on the best strategies for their implementation . Without thoughtful implementation, digital therapeutics are unlikely to be widely utilized by clinicians. The focus of this article is digital therapeutics that run on smartphone apps, henceforth “app-based treatments.”
Qualitative and other descriptive research has been used to study the implementation of digital therapeutics in primary care. Prior studies have identified barriers and facilitators to implementation, implementation strategies, delivery approaches, and workflow considerations, resulting in rich information to inform future implementation efforts [16,17,18,19,20,21]. Overall, these studies demonstrate clinician and patient willingness to try digital therapeutics, and the importance of integrating these novel treatments into existing clinician workflows. For example, Graham and colleagues identified a sub-optimal referral process in the electronic health record as a barrier to connecting patients to a digital mental health support . Mares and colleagues identified physician workload as a barrier to implementing an app-based treatment for substance use disorder (SUD), and suggested behavioral health care providers may be better suited to deliver digital therapeutics . In a qualitative user-centered design study, Glass and colleagues found that patients preferred clinicians offer them app-based treatments for drug use disorders during their existing primary care visits whenever possible, as opposed to learning about apps via direct-to-consumer methods (e.g., phone calls, flyers) . While such findings could help inform the implementation of a digital therapeutics for AUD, it is known that implementation strategies and clinical workflows must be tailored to the populations and practice settings in which interventions take place, especially in the case of technology-based interventions which have been traditionally difficult to implement [21,22,23,24].
The goal of this study was to identify implementation needs and strategy design considerations for integrating digital therapeutics for AUD into primary care. This study used qualitative methods to elicit lessons learned from clinicians and health system leaders who had engaged in previous digital therapeutic implementation efforts. Specifically, this study followed a pilot study of the implementation of a digital therapeutic for SUD in primary care, as well as the implementation of apps for depression and anxiety, and used these recent implementation efforts as a reference point to identify (1) barriers and facilitators to implementation and (2) adaptations that would be needed to implement a digital therapeutic for AUD.
Setting and context
This study was conducted at Kaiser Permanente Washington (KPWA), an integrated health system with approximately 700,000 members and 30 primary care clinics. In this system, care for AUD included brief alcohol interventions, referral to integrated mental health specialists, medication, and specialist addiction treatment by referral . All primary care clinics had previous experience implementing digital interventions to support mental health. This included a cognitive-behavioral treatment app for depression and anxiety called Thrive that was offered and facilitated by primary care-based licensed independent clinical social workers (LICSWs) who served as integrated mental health specialists [26, 27], and two apps that patients could download on their own via the health plan’s patient portal website: Calm, a mindfulness and meditation app  and myStrength®, an app that provides support for improving health behaviors and addressing a variety of challenges such as stress, sleep, depression, and anxiety [8, 29]. Two clinics had recently engaged in a quality improvement pilot to implement prescription digital therapeutics, reSET® and reSET-O®, which are for SUD and opioid use disorder (OUD), respectively . Among other features, these SUD apps include cognitive-behavioral skills-based content, quizzes to reinforce concept mastery, and contingency management via electronic gift cards to reward successful progression through one’s treatment plan.
The pilot implementation of reSET and reSET-O in KPWA involved a partnership between care delivery leaders and researchers, using an implementation strategy that care delivery leaders had previously used for the Thrive depression app . This implementation strategy involved live clinician training and a recorded video, workflow specifications, written job aids that describe the new steps required to offer apps within existing clinical workflows, electronic health record documentation templates, and a monthly report of app use by clinicians and patients. Additional strategies were developed by researchers in partnership with care delivery leaders, including implementation support in the form of practice facilitation , a dedicated health coach to support patients and encourage engagement with the app , and audit and feedback reports . LICSWs determined eligibility and offered the app to patients when clinically appropriate. Similar to the way that the health system implemented Thrive, if patients expressed interest in using reSET or reSET-O, LICSWs helped the patient set up the digital therapeutic and facilitated follow-up care to discuss clinical issues and engagement with the app. Because reSET and reSET-O require a prescription, as an additional step LICSWs entered an electronic order which routed a prescription to a clinician with expertise in SUD treatment for approval [35, 36].
This study used content analysis and a pragmatic constructivist approach to understand clinical stakeholder perspectives on how best to integrate digital therapeutics for AUD into the primary care setting . Study participants were invited to reflect on their involvement and experience implementing digital therapeutics for depression, anxiety, and SUD to share lessons learned and to make recommendations for implementing a digital intervention specifically for AUD. The KPWA Institutional Review Board approved all study activities. A completed Standards for Reporting Qualitative Research checklist is available in Additional file 1.
A purposive sample of interview participants was identified based on their involvement with previous digital intervention implementation efforts and AUD treatment expertise. Priority was given to clinicians and health system leaders who had been involved in the recent implementation of the SUD digital therapeutics. We asked interview participants for the names of other KPWA employees with relevant expertise and, from those, recruited 5 additional participants. Participants were recruited between July and November 2021.
A clinical leader (AGM) initiated recruitment with an introductory email to potential participants. Then a study team member followed up with a detailed interview invitation. Non-responders to this message received 2–3 reminder emails. Those who responded with interest were scheduled for a 30-min interview outside of their working hours.
In semi-structured interviews, participants were asked to share successes and challenges from previous implementation efforts for app-based treatments and to reflect on how those learnings could be applied to the implementation of a digital therapeutic for AUD. Particular attention was paid to the recent implementation of the SUD digital therapeutics and adaptations needed for successful implementation of a digital intervention for AUD. Questions regarding adaptations were informed by the Framework for Reporting Adaptations and Modifications to Evidence-based Implementation Strategies . The full interview guide is available in Additional file 2. Interviews were conducted virtually and were audio-recorded for transcription. Interviews lasted about 22 min on average. Interviewers (JM, TM) were masters-level health services researchers trained in qualitative methods and implementation science. One interviewer (JM) was also involved in the implementation of reSET and reSET-O and had worked with several of the participants in her role as a practice facilitator. To avoid biasing participants’ responses, participants whom JM had worked with were interviewed by TM. Participants received a $60 gift card for completing the interview.
During regular study team meetings, interviewers discussed themes and concepts that were arising during interviews and the need to continue recruitment. In these discussions interviewers identified concepts that had been expressed by multiple participants. Because we expected some implementation concepts to be more relevant to participants who had specific roles in the health system, we strategically shifted recruitment efforts based on these discussions to focus on balancing representation from different roles (e.g., care delivery leader vs. clinician). Meanwhile, researchers began the analysis process and confirmed themes were repeating across participants and roles. We determined data saturation had been reached when there was group consensus that many themes were recurring, and an insufficient number of new themes were being generated by new interviews to continue recruitment despite having additional eligible participants in the sample pool.  Recruitment stopped when researchers had completed 16 interviews.
Transcripts were analyzed by JM, TM, and JG using a rapid group analysis process inspired by prior literature  and further refined by a qualitative methodologist (CH). A traditional qualitative analysis process typically involves iterative code development, detailed coding of transcripts, and the creation of coding summaries and analytic memos . This rapid group analysis process involved five steps: (1) the development of a form to capture paraphrased themes and associated quotes from individual transcripts, (2) use of this form to synthesize data, (3) a group analysis process through which researchers grouped related themes from individual transcripts into themes that occurred across transcripts, (4) pulling together themes from the group process and themes and quotes from individual transcripts, and (5) identification and summary of the most salient, or “key” themes.
To complete step #1 above, researchers created a form in Microsoft Excel organized in a similar manner to the interview guide to capture themes and associated quotes from transcripts. A separate copy of the form was used for each transcript. Prior to coding all the transcripts, researchers piloted the form by having JM, TM, and JG all code the same 2 transcripts. This led to some reorganization of the form and the addition of a few clarifications to facilitate consistency in using the form.
The remaining transcripts were divided between JM and TM for synthesis during step #2. To increase coding rigor, for each transcript, JM or TM would fill out the form, then the completed form was reviewed by a second researcher (JM, TM, or JG) along with the transcript to make sure no themes were missed. Discrepancies were discussed and resolved in meetings.
For the group analysis step #3, themes were pulled from the forms into a virtual board organized in the same way as the analysis form. Themes appeared on the board as sticky notes that could be moved around, labelled, and grouped. Sticky notes were color-coded by participant role to visualize potential differences in themes across roles. JM, TM, and JG held a series of video conference meetings to group like themes on the virtual board as a means of identifying emergent themes and themes that occurred across transcripts. This process served as a virtual affinity diagramming process .
To accomplish #4 above, researchers collated documentation from the group and individual analysis processes to create a sortable Microsoft Excel sheet which included the themes that occurred across transcripts, themes from individual transcripts, and associated quotes. This sheet served a function similar to a traditional coding memo .
Finally, to complete step #5, researchers used the virtual board from step #3 and supporting quotes from the Excel sheet created in step #4 to identify the most salient themes. After these “key” themes were identified, as a form of member checking, the lead author (JM) presented themes to the SUD digital intervention implementation team, including three interview participants, to validate whether themes accurately represented their views and experiences .
Out of 28 invited participants, 16 completed interviews (57% response rate). Participant characteristics are provided in Table 1. To maintain participant anonymity, quotes are attributed to care delivery leaders (care delivery leaders and LICSW managers), LICSWs, primary care providers (PCPs), and implementation team members (medical assistants [MAs] and practice facilitator). Additional quotes are provided in Table 2. Nine participants were directly involved in the implementation of reSET and reSET-O including two of the care delivery leaders, three LICSWs, one PCP and all three of the implementation team members. All participants besides the implementation team members had been a part of the implementation of Thrive and the apps available via the health plan’s patient portal website (Calm and myStrength). All but one of the LICSWs and PCPs were from two KPWA medical centers in Seattle, Washington. Care delivery leaders and implementation team members worked across KPWA.
Insights from interviews were grouped into six key themes (presented from more general to more specific): (1) general support for implementing digital therapeutics (n = 16), (2) general implementation strategy and workflow recommendations (n = 16), (3) app design and target population will determine implementation needs (n = 14), (4) implementation adaptations for app-based AUD treatment may not need to be extensive (n = 12), (5) implementation adaptations for app-based AUD treatment to accommodate high patient volume (n = 10) and (6) implementation adaptations for app-based AUD treatment to accommodate variation in AUD severity, motivation to change, and treatment goals (n = 10). Key themes were well represented across provider types (i.e., color-coded sticky notes on the virtual affinity diagramming board did not reveal any patterns between roles, and all of the themes included content from multiple provider types).
General support for implementing digital therapeutics
Participants were supportive of offering digital therapeutics for SUD generally and AUD specifically, describing it as “an extra tool in the toolbox” (#11, care delivery leader) and a way to meet high treatment demand. Participants also said that digital therapeutics were a good fit for this context because clinicians in the health system were already using apps to treat anxiety and depression.
General implementation strategy and workflow recommendations
Reflecting on the recent pilot implementation of a SUD digital therapeutic, 5 participants said that the partnership between care delivery leaders and researchers was a successful strategy because researchers evaluated the evidence-base of the apps and research funding brought in additional resources like practice facilitation, health coaching, and electronic health record programming. Participants also recommended involving clinical leaders and clinical champions in implementation efforts and making sure those responsible for implementation had dedicated time to address clinician questions and problem-solve around implementation barriers.
Participants gave advice on approaches for sharing information about newly implemented digital therapeutics and increasing clinician knowledge about them. Participants advocated for training to describe the evidence-base for the app and information about who is most likely to benefit. They also suggested that managers provide dedicated time for clinicians to test and become familiar with the app. Participants advised clinician-facing information about digital therapeutics should come in multiple forms including email “blasts” (#1, LICSW and #9, care delivery leader), documents that contain a concise written overview of the treatment on a single page, announcements in meetings, and information from clinical champions. Participants also suggested clinicians be given an “elevator pitch” (#7, LICSW), meaning a concise verbal description of the app, that they can share with patients.
To ease implementation, participants suggested clear, simple workflows and electronic health record supports to make it easy to connect patients to the app. For example, during Thrive and reSET and reSET-O implementations, a programmer created auto-populating text about the apps for clinical notes; this made it easy for clinicians to share information about the apps and for patients and clinicians to access the information in the future.
App design and target population will determine implementation needs
Participants explained how an app’s design, and specifically, its target population, would determine the number and characteristics of patients who might be offered the app and the supports they may need, which in turn would determine the ideal implementation strategies. For example, a digital therapeutic might be designed for patients with unhealthy alcohol use (likely a large population) or eligibility may be limited to patients with a clinically recognized AUD diagnosis (likely a much smaller population); successful implementation for these unique target populations would likely require different implementation strategies and supports for app delivery.
In general, participants preferred flexible and inclusive eligibility criteria for digital therapeutics. Participants expressed feeling challenged when digital therapeutics had eligibility criteria that restricted its use to a specific population of patients which they perceived as unnecessarily narrow. One participant remarked,
One of the challenges we've come across so far… is that when patients present and they have alcohol use disorder, it has to be paired with another substance [for them to be eligible for reSET]. Which is really hard, because there are so many patients who have presented that have problems with alcohol use, they want support around it, and then we review and see—oh wait, they don't have another substance they're using so I can't offer them reSET. (#11, care delivery leader)
Although reSET was designed to treat SUD but not AUD alone, participants articulated a preference to make their own determination about who to prescribe the app to. Nine participants said they thought the principles behind treating AUD and other SUDs were similar enough that it would be appropriate to prescribe reSET to patients with AUD who do not use other drugs, even though the app is not indicated for patients who solely use alcohol .
Whatever the eligibility criteria for an app-based treatment may be, participants stressed that messaging during implementation should make it clear to clinicians which patients are eligible and best suited for the app, especially if care teams have access to multiple digital therapeutics.
Implementation adaptations for app-based AUD treatment may not need to be extensive
Most participants (12/16) said that few, if any, modifications would be necessary to use the existing implementation and workflow strategies developed for prior implementations of other apps to implement a digital therapeutic for AUD. Specifically, implementation strategies (e.g., clinician training materials, electronic health record note templates), and workflows for identifying patients and connecting them to the digital therapeutic (e.g., PCP identification of potentially eligible patients and referral to an LICSW) were identified as applicable for the implementation of digital therapeutics for AUD. Participants also felt that procedures for treatment and follow-up needed few modifications to implement a digital therapeutic for AUD as opposed to SUD. For instance, participants thought that an app could be used as an adjunct to usual treatment for AUD, like it had been for SUD.
Though participants generally endorsed the applicability of past implementation strategies, many also recommended changes to account for the large number of patients with AUD and the unique treatment needs of patients with varying AUD severity, motivation to change, and treatment goals. These recommendations are described in detail below.
Implementation adaptations for app-based AUD treatment to accommodate high patient volume
Most participants (10/16) expected more patients would be eligible for and interested in app-based treatment for AUD compared to SUD because AUD is more prevalent. Participants recommended adaptations to help care teams manage higher patient volume. To improve a health system’s capacity to offer the app to more patients, several participants advocated for a ‘no wrong doors’ approach where any care team member could connect the patient to the app. One participant shared,
We talk about clients having rapport with their PCP... that's not always the case. It may be the nurse or the social worker or the therapist who has far more contact with the client… I believe this is something all clinicians should have awareness of in their toolbox, so to speak, so that if they have rapport with their particular client, that they feel comfortable discussing and offering it. (#9, care delivery leader)
On the other hand, others thought that it would be easier to manage high patient volume if there was one dedicated person (e.g., a centralized LICSW or MA) who would be responsible for connecting patients to the app for multiple clinics. This would help ensure that patients could be reached even if clinicians in the local clinic were too busy to offer the app.
Some participants suggested patients should be able to access the app without going through a clinician and had specific ideas for how a digital therapeutic could be paired with existing wellness or treatment resources. For example, patients at the study site can complete an annual health profile online that includes an alcohol screening instrument. When patients are screened for unhealthy alcohol use, they could be offered the app algorithmically based on a positive screening score result. Another participant suggested,
If somebody is prescribed a medication to reduce cravings, [we could] also hand them this brochure [with information about an app]… at minimum, give them this brochure, at maximum have a quick conversation about here's something else we could pair with medication. (#7, LICSW).
The potential for a high volume of patients caused participants to express doubt care teams would have the capacity to actively monitor patient app use. To support care teams in working with patients using digital therapeutics, participants recommended giving clinicians additional dedicated time to care for these patients, including time to view and process information the app collects (if applicable). Participants also suggested adding supports for patients who are engaging in the app such as tech support or access to a health coach who could monitor patient app use.
Implementation adaptations for app-based AUD treatment to accommodate variation in AUD severity, motivation to change, and treatment goals
Interview participants recommended digital therapeutics be offered as one of many options for AUD treatment, depending on the individual patients’ needs. Different treatment options are needed to account for individual patients’ AUD severity, motivation to change, and treatment goals in terms of whether they want to stop versus reduce their drinking. One participant remarked,
I think a key question is if the app is designed and targeted for people who have alcohol use disorder versus just unhealthy alcohol use. If it's just unhealthy alcohol use, that's a huge population and there would need to be something that is completely self-directed and available on the Web…. [But] the population with a use disorder would benefit from having some staff who are supporting people in using the app and connecting to other care providers and supports if they are identifying a need and a desire for that. Because again, you're talking about a group of people who have a use disorder with a lot of morbidity associated with it and even a highly effective app is not likely to be effective in and of itself for most people. (#12, care delivery leader)
Two participants hypothesized that app-based treatments would be best for patients with mild to moderate AUD who do not need formal treatment. One LICSW shared,
There's a lot of people who get in touch with their provider, their provider gets in touch with social worker because they've started to have the conversation around 'maybe I'm drinking a little bit too much, but I'm not drinking so much that I need treatment or that I need to be connected to a substance use therapist, but maybe I just need a little bit of something to help me get back on track with my goals around a healthy relationship with alcohol.' And so, I think those are patients who would be particularly receptive to app-based care. Because to them it doesn't feel like it's a major problem. It's like the level of treatment fits the level of problem. (#6, LICSW)
On the other end of the spectrum, participants were skeptical about the effectiveness of the app for patients with more severe AUD and expressed concerns about what would happen in a crisis. Some conveyed that dangerous withdrawal symptoms are of greater concern with AUD than SUD, and participants stressed that patients at risk of severe withdrawal symptoms should not rely on digital therapeutics alone for AUD treatment. One PCP said, “I'd probably just want to screen for more of those medically concerning signs of withdrawal, so then they could be encouraged to seek medical care if they are happening…” (#10, PCP).
Participants also hypothesized that patient motivation to change is a determining factor in whether a digital therapeutic would be effective. In general, participants did not think app-based treatment would be useful to patients with low motivation to change. For example, one social work manager shared, “I think that the people who are heavily drinking… I think the app might not be powerful enough” (#5, care delivery leader). Another LICSW compared patients with low motivation to change to patients with high motivation to change:
I think there are some patients that don't want to do anything about their drinking. They either don't acknowledge that it's a problem, or they acknowledge it’s a problem, but they don't want to do anything about it. There are other patients that are like 'yeah, I recognize that I have a problem, but I don't want to go to inpatient, I don't want to go to a facility, I don't want to do all of that, I want to be able to do it on my own.' So those patients I think would be appropriate. Yeah, that's great - you have that motivation, you're driven, you want to make a difference, but you want to be able to have it be a little less intensive. I think a resource like that could be really beneficial. (#16, LICSW)
Participants were supportive of offering app-based treatments to patients who wanted to reduce but not stop their drinking, and a few participants emphasized the importance of allowing for goals besides abstinence. One care delivery leader shared,
You would want a tool that would allow patients to have different goals in terms of what they're looking at with their alcohol use, both from a patient-centered standpoint but also from an effectiveness standpoint, because again we know that just helping people significantly reduce their alcohol use has tremendous health benefits for people. (#12, care delivery leader)
Participants were supportive of having multiple apps on hand to offer to patients with AUD (6/16), as long as it is clear to clinicians “who goes where” (#2, PCP), or which patients to connect to which apps.
This study used qualitative methods to elicit perspectives on the implementation of a digital therapeutic for AUD from care delivery leaders, clinicians, and implementation staff who had experience implementing other app-based treatments, including a digital therapeutic for SUD. Overall, participants felt that the strategies and general workflow procedures for implementing digital therapeutics for AUD could be similar to those used for digital therapeutics for SUD. However, participants articulated that the amount of support needed to promote a successful implementation could be much higher for AUD, and also identified important population characteristics (e.g., patient addiction severity) that must be considered when implementing apps to treat AUD.
High demand for digital therapeutics for AUD may necessitate particular implementation supports
One special consideration for implementing a digital therapeutic for AUD identified in this study is the need to accommodate a high volume of patients. AUD is indeed more prevalent than SUD in the United States overall  and in primary care. One multisite study estimated that 13.9% of primary care patients had past-year AUD, which was approximately double that of other individual drug use disorders (e.g., 7.4% cannabis, 5.1% cocaine, 3.3% heroin, and 2.4% prescription opioids) . Participants offered a few different ideas to accommodate high patient volume including (1) training all care team members to be able to offer digital therapeutics to avoid workflow bottlenecks, (2) implementing a digital therapeutic through a dedicated, centralized clinician to avoid bandwidth issues in local clinics, (3) providing opportunities for patients to access the app without going through a clinician, and (4) providing dedicated time for local teams or a dedicated centralized health coach to handle tasks such as helping patients with technical problems and monitoring app use. Increasing staffing of mental health specialists within primary care may be the most supportive way to meet the needs of large numbers of patients, but this may be difficult to accomplish in the near-term given the challenges recruiting for and retaining qualified workers in demanding and generally low-paying behavioral health positions [47,48,49].
The idea that local primary teams may have difficulty offering apps to patients and guiding them through app use is supported by the literature. For example, Graham and colleagues had planned to recruit patients into a digital therapeutic study from primary care by having clinicians recommend the study and the app and placing an order in the electronic health record. However, only 5% of referrals during the study came through this mechanism. Instead, direct-to-consumer techniques (e.g., digital and print media, registry emails) had the highest yield . Other studies have reported needing to change implementation plans due to workload concerns within clinics. For instance, Mares and colleagues reported their implementation plan of a digital therapeutic in Federally Qualified Health Centers was to involve PCPs, but in the end mental health specialists took on the work of connecting patients to app-based treatments . On the other hand, a qualitative study that elicited patient preferences using user-centered design methods found that primary care patients with drug use disorders wanted their own clinician(s) to offer them apps, largely because they felt they could trust these clinicians and benefit from existing relationships with them . Taken together, these findings suggest health systems implementing digital therapeutics should invest in multiple avenues for connecting patients with apps by both providing tangible support to clinicians who are expected to incorporate apps into patient care (such as dedicated time) and developing other pathways for patients to access digital therapeutics (such as a centralized staff member or direct to consumer techniques).
Implementation strategies should consider the unique needs of patients with varying AUD severity
Findings in this study suggest that the severity of a patient’s AUD should inform decisions on whether to offer them digital therapeutics and/or what follow-up would be needed if they choose to engage in app-based treatment. Some participants expressed concern that app-based treatment would be inappropriate for patients with severe AUD. In a prior qualitative study that interviewed primary care patients with depression, participants said that apps may not be suitable for patients experiencing severe depression because they may not be motivated to engage with app-based treatment . However, in another study that interviewed primary care patients about their preferences for using apps for drug use disorder in primary care, participants said that patients with severe drug use disorder could be given an app for treatment provided they were also provided with additional support and follow-up . In the current study, clinicians mentioned alcohol withdrawal as one factor that would not necessarily preclude use of an app-based treatment but would necessitate additional monitoring and follow-up.
While previous qualitative studies have identified the importance of tailoring app delivery to accommodate patient motivation , previous studies have not examined patient motivation as it relates to AUD treatment decisions regarding digital therapeutics. Several clinician participants speculated that app-based treatments may be uniquely suited for patients with high motivation to change who may be unwilling or unable to commit to intensive forms of treatment. Inpatient treatment in particular may be viewed as time consuming and expensive, and previous research has found patients prefer flexible AUD treatment options . Future research may wish to investigate the patient acceptability, effectiveness, and safety of using digital therapeutics with clinician support on an outpatient basis as an alternative to, or as a prelude to more intensive forms of treatment.
Participants also recommended direct-to-consumer techniques to connect patients with unhealthy alcohol use to app-based treatments. This approach is being trialed in New Mexico State. Starting in 2020, the New Mexico Human Services Department launched the 5-Actions Program™ which provides a digital and phone-based support for people seeking care for unhealthy alcohol or substance use . Future research should evaluate the effectiveness of this program and other direct-to-consumer approaches for offering app-based treatments for unhealthy alcohol use in different settings (e.g., state vs. healthcare sponsored).
This study has limitations. Participants were recruited from a single regional integrated health care system in Washington state. Findings may not be generalizable to other geographical areas and types of health care systems. While it is a strength that this study included a diversity of roles (e.g., care delivery leaders, PCPs, and LICSWs), there were low numbers of participants for some roles (i.e., 2 LICSW managers, 2 MAs, 1 practice facilitator). While all participants had experience working in proximity to apps for depression, anxiety, or SUD, only 8 were involved in the implementation of two prescription digital therapeutics (reSET and reSET-O) for SUD. Finally, during analysis it was sometimes difficult to differentiate between participant comments specific to the implementation of apps for AUD and general advice for implementation efforts related to apps for any health condition.
If implemented appropriately, digital therapeutics could be used to provide effective treatment for AUD within primary care. Participants thought that training, electronic health record tools and templates, practice facilitation, health coaching, protected clinician time, and having dedicated clinicians to offer apps could be effective implementation strategies for apps for AUD. The approach for connecting patients to digital therapeutics for AUD must be tailored to accommodate the anticipated high patient volume while minimizing the workload burden of busy care teams. Digital therapeutics and their delivery should also be tailored to meet the needs of patients with varying AUD severity. Findings may be used to inform future efforts to implement digital interventions for AUD into primary care.
Availability of data and materials
The interview materials are provided in Additional File 2. Additional study materials are available from the last author upon reasonable request. Data used in the current study are not publicly available to protect participant privacy.
Alcohol use disorder
Kaiser Permanente Washington
Licensed independent clinical social worker
Opioid use disorder
Primary care provider
Substance use disorder
SAMHSA, Center for Behavioral Health Statistics and Quality. 2019 National Survey of Drug Use and Health (NSDUH-2019-DS0001). Public Data Set [Internet]. [cited 2022 May 3]. Available from: https://www.samhsa.gov/data/release/2019-national-survey-drug-use-and-health-nsduh-releases. Accessed 3 May 2022.
Barry CL, Epstein AJ, Fiellin DA, Fraenkel L, Busch SH. Estimating demand for primary care-based treatment for substance and alcohol use disorders. Addiction. 2016;111(8):1376–84.
Saitz R, Larson MJ, Labelle C, Richardson J, Samet JH. The case for chronic disease management for addiction. J Addict Med. 2008;2(2):55–65.
Glass JE, Hamilton AM, Powell BJ, Perron BE, Brown RT, Ilgen MA. Specialty substance use disorder services following brief alcohol intervention: a meta-analysis of randomized controlled trials. Addiction. 2015;110(9):1404–15.
Digital Therapeutics Alliance. Digital Therapeutics: Combining Technology and Evidence-based Medicine to Transform Personalized Patient Care. 2019.
Hermes ED, Lyon AR, Schueller SM, Glass JE. Measuring the implementation of behavioral intervention technologies: recharacterization of established outcomes. J Med Internet Res. 2019;21(1): e11752.
Khirasaria R, Singh V, Batta A. Exploring digital therapeutics: The next paradigm of modern health-care industry. Perspect Clin Res. 2020;11(2):54–8.
Mordecai D, Histon T, Neuwirth E, Heisler WS, Kraft A, Bang Y, et al. How Kaiser permanente created a mental health and wellness digital ecosystem. NEJM Catalyst. 2021. https://doi.org/10.1056/CAT.20.0295.
Sepah SC, Jiang L, Peters AL. Long-term outcomes of a web-based diabetes prevention program: 2-year results of a single-arm longitudinal study. J Med Internet Res. 2015;17(4): e92.
Dang A, Arora D, Rane P. Role of digital therapeutics and the changing future of healthcare. J Family Med Prim Care. 2020;9(5):2207–13.
Ramsey AT, Satterfield JM, Gerke DR, Proctor EK. Technology-based alcohol interventions in primary care: systematic review. J Med Internet Res. 2019;21(4): e10859.
Lord SE, Campbell ANC, Brunette MF, Cubillos L, Bartels SM, Torrey WC, et al. Workshop on implementation science and digital therapeutics for behavioral health. JMIR Ment Health. 2021;8(1): e17662.
Gustafson DH, McTavish FM, Chih MY, Atwood AK, Johnson RA, Boyle MG, et al. A smartphone application to support recovery from alcoholism: a randomized clinical trial. JAMA Psychiat. 2014;71(5):566–72.
Blonigen DM, Harris-Olenak B, Haber JR, Kuhn E, Timko C, Humphreys K, et al. Customizing a clinical app to reduce hazardous drinking among veterans in primary care. Psychol Serv. 2019;16(2):250–4.
Song T, Qian S, Yu P. Mobile health interventions for self-control of unhealthy alcohol use: systematic review. JMIR Mhealth Uhealth. 2019;7(1): e10899.
Glass JE, Matson TE, Lim C, Hartzler AL, Kimbel K, Lee AK, et al. Approaches for implementing app-based digital treatments for drug use disorders into primary care: a qualitative, user-centered design study of patient perspectives. J Med Internet Res. 2021;23(7): e25866.
Graham AK, Greene CJ, Powell T, Lieponis P, Lunsford A, Peralta CD, et al. Lessons learned from service design of a trial of a digital mental health service: Informing implementation in primary care clinics. Transl Behav Med. 2020;10(3):598–605.
Knowles SE, Lovell K, Bower P, Gilbody S, Littlewood E, Lester H. Patient experience of computerised therapy for depression in primary care. BMJ Open. 2015;5(11):e008581.
Mares ML, Gustafson DH, Glass JE, Quanbeck A, McDowell H, McTavish F, et al. Implementing an mHealth system for substance use disorders in primary care: a mixed methods study of clinicians’ initial expectations and first year experiences. BMC Med Inform Decis Mak. 2016;16(1):126.
Silfee V, Williams K, Leber B, Kogan J, Nikolajski C, Szigethy E, et al. Health care provider perspectives on the use of a digital behavioral health app to support patients: qualitative study. JMIR Form Res. 2021;5(9):e28538.
Graham AK, Lattie EG, Powell BJ, Lyon AR, Smith JD, Schueller SM, et al. Implementation strategies for digital mental health interventions in health care settings. Am Psychol. 2020;75(8):1080–92.
Unertl KM, Novak LL, Johnson KB, Lorenzi NM. Traversing the many paths of workflow research: developing a conceptual framework of workflow terminology through a systematic literature review. J Am Med Inform Assoc. 2010;17(3):265–73.
Powell BJ, Beidas RS, Lewis CC, Aarons GA, McMillen JC, Proctor EK, et al. Methods to improve the selection and tailoring of implementation strategies. J Behav Health Serv Res. 2017;44(2):177–94.
Greenhalgh T, Wherton J, Papoutsi C, Lynch J, Hughes G, A’Court C, et al. Beyond adoption: a new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. J Med Internet Res. 2017;19(11): e367.
Glass JE, Bobb JF, Lee AK, Richards JE, Lapham GT, Ludman E, et al. Study protocol: a cluster-randomized trial implementing sustained patient-centered alcohol-related care (SPARC trial). Implement Sci. 2018;13(1):108.
Whiteside U, Richards J, Steinfeld B, Simon G, Caka S, Tachibana C, et al. Online cognitive behavioral therapy for depressed primary care patients: a pilot feasibility project. Perm J. 2014;18(2):21–7.
Schure M, McCrory B, Tuchscherer Franklin K, Greist J, Weissman RS. Twelve-month follow-up to a fully automated internet-based cognitive behavior therapy intervention for rural adults with depression symptoms: single-arm longitudinal study. J Med Internet Res. 2020;22(10): e21336.
Find Your Calm. Calm—The #1 App for medication and sleep. https://www.calm.com/. Accessed 15 Mar 2023.
myStrength: Hope, Health and Happiness. myStrength, Inc. https://mystrength.com/about. Accessed 15 Mar 2023.
reSET® & reSET-O®. Pear Therapeutics; 2022. https://peartherapeutics.com/products/reset-reset-o/.
Waypoint Health Innovations. Thrive: Personalized care for depression. 2022. https://waypointhealth.com/thrive/. Accessed 1 Nov 2022.
Ritchie MJ, Dollar KM, Miller CJ, Oliver KA, Smith JL, Lindsay JA, et al. Using implementation facilitation to improve care in the Veterans Health Administration (Version 2). Veterans Health Administration, Quality Enhancement Research Initiative (QUERI) for Team-Based Behavioral Health. 2017.
Mohr D, Cuijpers P, Lehman K. Supportive accountability: a model for providing human support to enhance adherence to ehealth interventions. J Med Internet Res. 2011;13(1): e30.
Ivers NM, Sales A, Colquhoun H, Michie S, Foy R, Francis JJ, et al. No more “business as usual” with audit and feedback interventions: towards an agenda for a reinvigorated intervention. Implement Sci. 2014;17(9):14.
An Initial Study of the Implementation of Digital Therapeutics for Substance Use Disorders in Primary Care (DIGITS Pilot) [Internet]. ClinicalTrials.gov. 2021. https://clinicaltrials.gov/ct2/show/NCT04907045. Accessed 28 Sep 2022.
Glass JE, Dorsey CN, Beatty T, Bobb JF, Wong ES, Palazzo L, et al. Study protocol for a factorial-randomized controlled trial evaluating the implementation, costs, effectiveness, and sustainment of digital therapeutics for substance use disorder in primary care (DIGITS Trial). Implementation Sci. 2023;18(1):3.
Patton MQ. Qualitative research and evaluation methods: integrating theory and practice. Thousand Oaks: Sage publications; 2014.
Miller CJ, Barnett ML, Baumann AA, Gutner CA, Wiltsey-Stirman S. The FRAME-IS: a framework for documenting modifications to implementation strategies in healthcare. Implement Sci. 2021;16(1):36.
Saunders B, Sim J, Kingstone T, Baker S, Waterfield J, Bartlam B, et al. Saturation in qualitative research: exploring its conceptualization and operationalization. Qual Quant. 2018;52(4):1893–907.
Vindrola-Padros C, Johnson GA. Rapid techniques in qualitative research: a critical review of the literature. Qual Health Res. 2020;30(10):1596–604.
Saldana J. The coding manual for qualitative research. Thousand Oaks: SAGE; 2009.
Beyer H, Holtzblatt K. Contextual design. San Francisco: Morgan Kaufman; 1998.
Birks M, Chapman Y, Francis K. Memoing in qualitative research: probing data and processes. Nurs Res. 2008;13(1):68–75.
Creswell JW. Educational research: Planning, conducting, and evaluating quantitative and qualitative research, vol. 7. 2nd ed. Supper Saddle River: Prentice Hall; 2005.
Key substance use and mental health indicators in the United States: Results from the 2020 National Survey on Drug Use and Health. Rockville, MD: Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration; 2021. (NSDUH Series H-56). Report No.: HHS Publication No. PEP21–07–01–003. https://www.samhsa.gov/data/
Wu LT, McNeely J, Subramaniam GA, Brady KT, Sharma G, VanVeldhuisen P, et al. DSM-5 substance use disorders among adult primary care patients: results from a multisite study. Drug Alcohol Depend. 2017;179:42–6.
National Projections of Supply and Demand for Behavioral Health Practitioners: 2013–2025. Health Resources and Services Administration/National Center for Health Workforce Analysis; Substance Abuse and Mental Health Services Administration/Office of Policy, Planning, and Innovation. 2015.
Skillman SM, Dunlap B. Washington State’s Behavioral Health Workforce: Examination of Education and Training Needs and Priorities for Future Assessment. Center for Health Workforce Studies, University of Washington. 2022. https://familymedicine.uw.edu/chws/wp-content/uploads/sites/5/2022/11/WA-BH-Education-Training-Assess-FR-2022.pdf. Accessed 15 Mar 2023.
Crowley RA, Kirschner N, for the Health and Public Policy Committee of the American College of Physicians*. The integration of care for mental health, substance abuse, and other behavioral health conditions into primary care: executive summary of an american college of physicians position paper. Ann Intern Med. 2015;163(4):298–9.
Tarp K, Rasmussen J, Mejldal A, Folker MP, Nielsen AS. Blended treatment for alcohol use disorder (Blend-A): explorative mixed methods pilot and feasibility study. JMIR Form Res. 2022;6(4): e17761.
Addictions Treatment Self Guided Roadmap New Mexico 5-Actions Program. https://nm5actions.com/. Accessed 16 Mar 2023.
The authors wish to thank participants of this study and care delivery leaders of Kaiser Permanente Washington for volunteering their time and endorsing this study.
Research reported in this publication was supported by the National Institute On Alcohol Abuse And Alcoholism of the National Institutes of Health under Award Number K01AA023859. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Ethics approval and consent to participate
The Kaiser Permanente Washington Human Subjects Review Office (FWA00002344) determined that this project is exempt from Institutional Review Board review according to federal regulations, per Category 2. This exempt research was conducted in accordance with the principles of the Belmont Report. Verbal informed consent was obtained from all the subjects enrolled in this study. All subjects participated voluntarily and received a small compensation.
Consent for publication
reSET® and reSET-O® are digital therapeutics for substance use disorder marketed by Pear Therapeutics (US), Inc. that are discussed in this manuscript. During a quality improvement project, Pear Therapeutics (US), Inc. provided digital therapeutic prescriptions at no cost to Kaiser Permanente Washington. Pear Therapeutics (US), Inc. was not involved in the current study and has not provided funding to the authors.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Mogk, J.M., Matson, T.E., Caldeiro, R.M. et al. Implementation and workflow strategies for integrating digital therapeutics for alcohol use disorders into primary care: a qualitative study. Addict Sci Clin Pract 18, 27 (2023). https://doi.org/10.1186/s13722-023-00387-w
- Alcohol use disorders
- Digital interventions
- Primary care
- Implementation science