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The collaborative care model (CoCM) is a well-established system of behavioral health care in primary care settings. There is potential for digital and mobile technology to augment the CoCM to improve access, scalability, efficiency, and clinical outcomes.
This study aims to conduct a scoping review to synthesize the evidence available on digital and mobile health technology in collaborative care settings.
This review included cohort and experimental studies of digital and mobile technologies used to augment the CoCM. Studies examining primary care without collaborative care were excluded. A literature search was conducted using 4 electronic databases (MEDLINE, Embase, Web of Science, and Google Scholar). The search results were screened in 2 stages (title and abstract screening, followed by full-text review) by 2 reviewers.
A total of 3982 nonduplicate reports were identified, of which 20 (0.5%) were included in the analysis. Most studies used a combination of novel technologies. The range of digital and mobile health technologies used included mobile apps, websites, web-based platforms, telephone-based interactive voice recordings, and mobile sensor data. None of the identified studies used social media or wearable devices. Studies that measured patient and provider satisfaction reported positive results, although some types of interventions increased provider workload, and engagement was variable. In studies where clinical outcomes were measured (7/20, 35%), there were no differences between groups, or the differences were modest.
The use of digital and mobile health technologies in CoCM is still limited. This study found that technology was most successful when it was integrated into the existing workflow without relying on patient or provider initiative. However, the effect of digital and mobile health on clinical outcomes in CoCM remains unclear and requires additional clinical trials.
There are more people who could benefit from behavioral health services than can be served by the currently existing resources for care [
The collaborative care model (CoCM) aims to meet this vast need [
Pitfalls along every aspect of collaborative care (CC) may contribute to unsuccessful implementation. For example, as the CoCM is a specialized multicomponent service, robust adoption requires provider training and stakeholder buy-in [
Digital and mobile health technologies have the potential to support multiple components of CoCM [
Technology has been recognized as important for the optimal functioning of the CoCM from an early stage. For example, clinicians and researchers recognized the necessity of using caseload registries to manage patient information, track outcomes, and have easy access to information from assessments and follow-up appointments [
This review aims to summarize the current state of research into the ability of digital and mobile health to augment the CoCM, highlight important challenges and limitations, and explore areas for further investigation.
The primary aim of this review is to synthesize the evidence available on digital and mobile health technology in CC settings.
The topic of CC augmented with technology includes a wide variety of potential interventions, both patient-facing and provider-facing, and a wide variety of psychiatric disorders, including depression, anxiety, posttraumatic stress disorder (PTSD), bipolar disorder, and others. Thus, we chose to conduct a scoping review on this broad and emerging topic using the methodology recommended by Arksey and O’Malley [
We attempted to answer 3 research questions with this scoping review. First, what digital and mobile health technologies have been studied in CC, and at what levels have they been implemented (patient- vs provider-facing)? Second, what is known about the acceptability and feasibility of digital and mobile health use in the CC context? Third, what, if anything, is known about the impact of these technologies on clinical outcomes in this setting?
We searched 3 web-based databases (Web of Science, MEDLINE, and Embase) during February 2020 with search terms related to CC (sometimes termed integrated care) and various technology interventions, including mobile apps, sensors, social media, and wearable devices. Scans of dark literature from Google Scholar were also completed. Scoping reviews on similar topics were consulted in the search strategy development [
Articles were determined eligible for inclusion if they described original research on digital and mobile health to augment the CoCM for the treatment of common behavioral health conditions. Novel technologies such as mobile apps, web-based platforms, ambient or wearable sensors, and social media were included. Technologies that are already well-established in the CoCM and in medicine broadly, such as electronic medical records, caseload registries, or telemedicine, were excluded. Opinion pieces, reviews, books, book chapters, protocols, and commentaries were excluded. Post hoc analyses of the trials included in this review were only included if they helped answer the questions posed by this review; otherwise, they were excluded. Articles written in languages other than English were also excluded. Studies were excluded if they recruited patients from a primary care population or other general medical populations that did not participate in a CoCM, as described previously in this paper. Studies in which substance abuse was the primary diagnosis were excluded.
Owing to the large volume of articles obtained in the initial screening, the articles were divided and screened by 2 independent reviewers (KM and MS). In the initial stage of screening, articles were excluded based on the title and abstract. In the second stage, the remaining papers were excluded based on their full text. At the stage of full-text screening, conflicts were resolved by consensus between the reviewers.
The initial search identified 3817 reports; 661 additional reports were identified in December 2020, and 489 were identified in September 2021. The study selection process is summarized in
Study selection diagram.
Characteristics of the included studies (N=20).
Characteristics | Studies, n (%) | ||||
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Randomized trial | 8 (40) | ||
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Quasi-experiment or nonrandomized trial with comparator group | 2 (10) | ||
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Qualitative or mixed methods | 6 (30) | ||
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Post hoc analysis | 4 (20) | ||
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2018 or newer | 15 (75) | ||
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2015-18 | 4 (20) | ||
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2014 or before | 1 (5) | ||
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≤10 | 1 (5) | ||
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10-50 | 5 (25) | ||
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50-200 | 2 (10) | ||
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200-500 | 4 (20) | ||
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>500 | 7 (35) | ||
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Not specified | 1 (5) | ||
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Anxiety disorders | 4 (20) | ||
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Depressive disorders | 14 (70) | ||
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Bipolar disorder | 1 (5) | ||
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Posttraumatic stress disorder | 3 (15) | ||
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Patient-facing | 13 (65) | ||
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Provider-facing | 3 (15) | ||
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Combination | 5 (25) | ||
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SMS text messaging | 3 (15) | |||
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Interactive voice recording | 5 (25) | |||
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Mobile app | 5 (25) | |||
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Web-based platform | 7 (35) | |||
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Videoconferencing | 1 (5) | |||
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Other | 4 (20) | |||
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Adherence | 3 (15) | |||
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Acceptability | 7 (35) | |||
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Feasibility | 9 (45) | |||
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Clinical improvement | 10 (50) | |||
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Other | 3 (15) | |||
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Collaborative care | 4 (20) | |||
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Primary care | 3 (15) | |||
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Collaborative care and primary care | 4 (20) | |||
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Other | 2 (10) | |||
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No comparator | 7 (35) |
We have summarized the results of our review in light of the research questions that we posed. We first describe which digital and mobile health technologies have been studied in CC and the nodes of the CC workflow that have been implemented. Next, we describe what is known about the acceptability and feasibility of digital and mobile health use in CC settings. Finally, we describe what is known about the impact of these technologies on the clinical outcomes in this setting.
Most studies identified by this review implemented or assessed multiple technologies at once rather than a single intervention. Of the 20 studies, 5 (25%) [
The studies considered in this review encompassed all aspects of the CC workflow. Of the 20 studies, 3 (15%) used technology to augment BHCM training in the skills and implementation of the model [
The overall response to the technology presented in these studies was positive for both patients and providers. Patients enjoyed having the option of using technology for psychoeducation as therapy extenders and ways of communicating with their providers [
Of the 20 studies, 8 (40%) studies examined clinical outcomes [
In this scoping review, we investigated the use of digital and mobile health technology in CC settings. This study builds on previous research highlighting the potential of technology in improving behavioral health care [
Our results suggest that the implementation of digital and mobile health technology in CC is currently in its early stages in both clinical research and practice. For example, of the 20 studies, only 1 (5%) study using an app was a large trial [
We believe that digital technology has the potential to support the delivery and scale of the CoCM by mitigating several common challenges to their effective implementation, ranging from provider training, patient screening and referral, monitoring and treatment, and sustainability of the practice. We also believe that the CoCM is especially suited to absorb such changes because of its forward-looking, team-based, and measurement-guided approach. We have used the results of our review to scope future directions for augmenting CC with digital and mobile health technologies focusing on provider training, screening, monitoring, treatment, and sustainability.
Despite widespread recognition of the merits of the CoCM, adoption may continue to lag in part because of the lack of local expertise and provider training in the implementation of this complex, multicomponent service [
Increasing access to behavioral health care through systematic screening and referral is a core mission of the CoCM, one that technology has a great potential to support. Of the 20 studies, our review identified 1 (5%) research group that successfully used SMS text messaging and IVR to scale the screening of behavioral health disorders [
Although much further into the future, technology has the potential to facilitate screening by using digital biomarkers rather than self-report [
Another core CoCM feature is its reliance on measurement-based care and systematic monitoring to guide clinical decisions. Our results suggest that technology can support this, particularly with the use of mobile apps, which can solicit symptom rating scales from patients at regularly set intervals [
A future direction for digital and mobile health technologies in measurement-based care involves unobtrusive monitoring with connected devices, in particular mobile phones and wearable devices [
Digital and mobile health technology can facilitate and scale treatment as part of the CoCM. For example, providing psychoeducational material is a basic intervention that was implemented in most of the studies in this review [
Digital and mobile health technologies can also help to facilitate more active treatment modules. For example, our review identified the preliminary use of cognitive behavioral therapy–based web-based treatments as part of technological implementation in CC [
Research suggests that the implementation of digital and mobile health technologies in behavioral health care requires the addition of a new team member—a digital health coach—who might help patients and providers navigate digital interventions [
There are several challenges in the implementation of digital and mobile interventions in CC. To provide evidence for efficacy, clinical outcomes in technology-augmented care should be superior to the clinical outcomes of traditional CoCM. The most comprehensive study of app-augmented care to date, by Carleton et al [
Another common challenge in mobile health technology is the lack of engagement and high attrition rates, as demonstrated in several of the reviewed studies [
Finally, implementation of many of the discussed technologies requires addressing challenges with legal, ethical, and privacy concerns about the use of these data at both the patient and provider levels. This is a common challenge in technology, especially in the clinical context of behavioral health. Our review identified early reports of privacy concerns with regards to tracking [
Our review has several limitations. Methodologically, we did not use a librarian as part of the search strategy or calibration of the exclusion and inclusion criteria. Although improving the feasibility of our review, these strategies may have limited the breadth of our search and screening process. In a few years, as more technologies are implemented in CoCM, we expect a systematic review of the literature to be conducted to assess the evidence. In this review, we focused on the treatment of common behavioral health disorders in the CC setting specifically because of the unique approach and structure of this model. Therefore, we excluded studies of digital and mobile health technologies in related settings, such as primary care and substance abuse treatment. Nevertheless, as digital and mobile health technologies are used both in primary care and substance abuse, we drew and built on this research in our discussion.
The use of digital and mobile health technologies in the CoCM is still limited. Digital technology was the most successful when it was integrated into the existing workflow without relying on the patient or provider initiative. The effect of digital and mobile health on clinical outcomes in CoCM remains unclear and requires additional clinical trials. To advance the use of digital and mobile health in CoCM, we have introduced a forward-looking discussion for augmenting CC with a focus on improving access to care, remote patient monitoring, and enhancing treatment.
Search terminology.
Summary of selected studies.
behavioral health care manager
collaborative care
collaborative care model
interactive voice recording
primary care provider
posttraumatic stress disorder
JMK has received fees for consultation or honoraria for lectures from Alkermes, Dainippon Sumitomo, H Lundbeck, Intracellular Therapies, Janssen, Karuna, LB Pharma, Lyndra, Merck, Minerva, Neurocrine, Otsuka, Roche, Saladex, Sunovion, Takeda, and Teva. He is also a shareholder of LB Pharma and The Vanguard Research Group.