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Today, the term eHealth care is used to describe use of the Internet and related technologies such as computers, tablets and smartphones in the delivery of health care. Individuals on their own may access such technologies so that they can receive psychological therapy self-guided, technology-based therapy or health care providers may access a technology so that they can deliver mental health care partly self-guided, technology-assisted therapy. Though not universal, both short- and long-term positive outcomes have been reported for a diverse range of eMental Health technologies [ 10 , 11 ].

There is a growing consensus, however, that health systems must evolve in order to exploit the potential of technologies to improve mental health care worldwide. Individuals with mental health conditions are increasingly turning to the Internet for information, advice, support, and to share their experiences [ 12 ]. They are increasingly expect eMental Health options as part of their care [ 13 , 14 ] and report high levels of technology device use [ 15 ] in their everyday lives. This multi-level integration and commitment has the potential to improve patient experiences and health outcomes, create operational efficiencies in the delivery of care, and build provider capacity [ 18 — 20 ].

While the increasing evidence base supporting the efficacy and effectiveness of eMental Health care technologies from the patient perspective grows, the implementation of technologies into clinical practice remains slow and fragmented [ 21 — 24 ]. Effective and innovative eMental Health care integration requires sufficient motivational drivers [ 27 ] particularly from health care providers and policy makers.

Previous research has shown that skills in collecting, appraising and disseminating research evidence are not enough for transforming research knowledge into clinical action. The remodeling of workflow and job design across interconnected mental health professionals and processes [ 30 ] is required to realize the potential of eMental Health care for population-level health benefits.

Knowledge transfer among stakeholders would allow patient-level research evidence to be considered together with the perspectives, experiences and outcomes of all groups affected by future implementation decisions [ 31 ]. Barriers to this kind of organizational learning and change persist. Key informants interviewed by Whittaker in on eHealth implementation broadly, not specific to mental health showed that policy makers, administrators and organizational leaders in the United States viewed eHealth care as potentially transformative in patient-centered care [ 32 ]. However, there were differences in informant views on the role of research trials, governance structures, and security solutions in advancing implementation efforts.

Specifically, their study highlighted diverse opinions on the optimal research design methodologies to improve system uptake and the extent to which government agencies should be involved in using ehealth data for population health surveillance. In , Jones and Ashurst conducted an educational online discussion forum with stakeholders self-selected from a website and focused solely on stakeholder concerns [ 33 ].

A major finding of their study was that patient and provider choice in methods of communication online or not was identified as an easy adjunctive way of starting to integrate eHealth for mental health care. We found no peer-reviewed key-informant or stakeholder studies since that have identified and mapped the perspectives capabilities, motivations, and opportunities of decision-makers in the implementation of eMental Health care specifically.

Because technological innovations and how people use them is rapidly evolving, prospective studies of this topic area must be conducted and published regularly to keep up with the ever-changing landscape [ 34 ]. In this paper we build on previous work to ascertain not only what is currently being done internationally to implement eMental Health care, but also reasons for misunderstandings around key implementation issues. We conducted a qualitative study with key stakeholders to address three main objectives: 1 To identify current eMental Health care implementation processes and adoption strategies by public sectors; 2 To explore descriptions of organizational and individual readiness to adopt eMental Health technologies; and 3 To evaluate the frequency and intensity effects of reported implementation factors.

This study involved in-depth individual interviews with a multi-national group of key informants using purposive and snowball sampling strategies. Interviews were conducted between September and November We conducted a three pronged search to identify potential interview participants: 1 targeted Google searches for relevant government, health, and technology organizations in developed countries with the largest eHealth markets Canada, Australia, the Netherlands, New Zealand, the United States, and the United Kingdom [ 35 ], 2 rapid review of recent literature in pertinent topics, and 3 recommendations from members of our research team.

Participants needed to be fluent in English. We identified individuals responsible for leadership, policy, research and information technology that are influential in the use of eMental Health technologies. Many individuals were specifically singled out due to their role as an eMental Health or eHealth advisor on a number of policy related reports and documents.

Overall, we created a list of 91 potential informants. From the organizations and institutions chosen, individuals who were actively developing or have developed eHealth technologies, were high ranking in their particular field e. These individuals were considered to be knowledgeable and experienced in their roles within their specific domain and able to provide intimate knowledge and experience on the subject area.

Four potential informants were chosen from each country and representing each category before approaching additional potential informants. In addition, at the end of each interview, participants were asked if they could recommend additional key informants that we should consider interviewing.

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This was an iterative process until the number of key informants who participated in this qualitative study was the recommended mean number of 30 participants recommended range: 12 to 60 participants [ 36 , 37 ]. Potential participants were contacted directly by e-mail or via e-mail introductions from others.

Based upon a literature review and discussions with the research team, a semi-structured interview guide see Additional file 1 was developed in the absence of a guiding theoretical framework to allow more flexibility in the examination of opinions, attitudes, and perspectives on current eMental Health implementation approaches.

The guide was reviewed by the research team for face and content validity as well as feasibility e. The guide was then pilot tested by one investigator NDG with a convenience sample of 2 experts and 2 non-experts external to the research team. The investigator conducted practice interviews with these individuals to gain ease with asking the interview questions, flow and timing, and feedback was requested on the questions asked.

The first interview provided the opportunity to refine, rephrase and clarify some questions in the guide.


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While in some questions, prompts were added in order to acquire better answers. The pilot test also helped to draw attention to questions that ought to have been included in the guide, for example, a question pertaining directly to governmental policies was added. Attention was also paid to the time required to conduct each interview. The final interview guide was designed to facilitate interviews lasting 30—60 minutes and covering four domains: 1 informant demographics, 2 current eMental Health technologies, 3 implementation strategies and, 4 adoption readiness.

During the study, the interview guide was modified and refined on the basis of previous interviews to further explore emerging key issues in subsequent interviews. All interviews were conducted by one of the investigators NDG. Key informants were free to deviate from the guide and the interviewer intervened only to clarify issues or introduce a new domain.

The guide was e-mailed to key informants two days prior to their scheduled interview to allow time to review the questions. All informants provided verbal informed consent prior to the interview and the study protocol was approved by the University of Alberta Research Ethics Board.

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Phase 1 Thematic Analysis In accordance with Strauss and Corbin [ 38 ], a line-by-line analysis of the transcripts was conducted by one of the investigators NDG in order to generate an initial list of potential themes. Results of this initial reading were discussed with our interdisciplinary team to refine coding procedures. Phase 2 Content Analysis An integrative approach was used to code interview data that included both deductive and inductive methods [ 39 ]. An initial deductive analysis involved the identification and grouping of similar data together into three a priori themes derived from the Behaviour Change Wheel BCW [ 40 ].

The BCW has the benefit of having been derived from 19 other models and classifications already available and therefore covering concepts that have previously been considered to be important to implementation. Specifically, the BCW provided a lens through which to consider the data in terms of the 3 essential conditions for successful behavior change systems: capabilities i. Inductive analysis followed using open coding to categorize data into subthemes.

Constant comparisons of the coded sections of the transcripts with each other and with the emerging themes allowed further refinement. All coding was done by one of the investigators NDG. In addition, several joint coding and analysis sessions involving other investigators LW, ASN were conducted to increase theoretical sensitivity and to ensure high quality of coding. Phase 3 Intensity and Frequency Analysis An analysis of the relationship between reported themes was conducted by calculating of frequency and intensity effect sizes as suggested by Onwuegbuzie [ 41 ] and Sandelowski [ 42 ].

Seeing all of the findings pertaining to one theme together in this way preserves the complexity of the findings and optimizes the descriptive validity of the thematic analysis [ 43 ]. Furthermore, because research suggests that concerns which consume attention often have a disproportionately large impact on the judgment process [ 44 ], it is reasonable to conclude that the frequency and intensity in which topics are identified is useful information for decision-makers.

A frequency effect size, a measure of how dispersed the themes were across interviewees, was computed by taking the number of interviews coded for the theme and dividing this number by the total number of interviews. An intensity effect size , a measure of theme concentration, was derived by dividing the total number of references to each theme by the total number of references across all interviews. Percentile ranks pR were calculated for all effect size to better describe characteristics of the distribution of themes. A summary report of key findings organized into themes and subthemes was provided to key informants for comment, and some amendments were made based on their feedback.

Of the 73 individuals we contacted, 19 did not respond to our request for study participation Of the 38 individuals that agreed to participate, 7 did not complete an interview due to scheduling conflicts. Collectively, the average years working in the eHealth field among the informants was Ensuring that new technologies are streamlined within existing health system workflows was regarded as essential to increasing uptake.

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However, informants suggested the assimilation of eMental Health technology requires extensive change to systems, structures, and individual workflow as innovations have both technical e. This challenge was identified as affecting both the way providers are used to working and the way patients are used to engaging with the health system. The danger perceived by informants was that eMental Health technologies were still seen as isolated from the wider provision of services meaning routinization and full integration of a technology by providers is still only being partially achieved.

Key informants discussed how technology can disrupt or interfere with traditional patient-provider relationships and workflow in a manner that may produce an impersonal and inferior patient-practitioner dynamic.

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As one informant noted:. Hybrid implementation eHealth as adjunct to face-to-face was discussed as a potentially less disruptive approach as it resembles current workflow providing an easier learning curve for providers and patients.

While heterogeneity within broadband i. Most informants identified lack of awareness broadly as a significant barrier to the adoption of technologies to serve mental health conditions. Alternately, informants reported relatively low public awareness of effective eMental Health services and poor marketing of available technologies, which in turn reduces demand. From this perspective, key informants felt that as patients become increasingly aware of eMental Health services, and empowered to choose eHealth options, public engagement will dictate policy reform.

Computer literacy skills and related emotional reactions to job insecurity were identified by key informants as rate-limiting areas of eMental Health implementation.

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Professionals resistant to change, uncertain of the value of technology, and stringent on established processes can generate a digital divide between early adopters and laggards. In this regard, some key informants described the importance of integrating eHealth training early in educational pursuits and career development for all health practitioners, while several other informants described that as the global age raises, these technological shortcomings may dissipate. Key informants identified a knowledge and expertise gap around cyber security as a noteworthy obstacle to large-scale implementation.

These included insufficient privacy protection around personal health information and how this may be viewed and defined differently across governing bodies e. Key informants asserted that health systems are placing a growing priority on eMental Health care as it can increase quality of care in blended care models where technology is used in addition to face-to-face care. In this capacity, eMental Health was seen as ultimately reducing wait times , reaching people who would otherwise be unreached and increasing capacity of health care systems.

But that then probably means we sometimes under deliver therapeutic benefits for patients. Integration with electronic health records was identified as one way to increase delegation and coordination of care. As one informant stated:. So…trying to figure out ways of having a device like a Smartphone, being able to track your movements…be correlated with certain things like certain mood states and what not. Key informants described how opportunities for implementation were limited or advanced by physical e. Key informants posited that the lack of digital infrastructure was an ongoing implementation-limiting factor.

They emphasized that delivering an eHealth service was an ongoing cost, not a one-off. Effective, integrated work between health care and industry to build these sustainable systems was often perceived as uncoordinated. Successful access to eMental Health models was regarded as contingent upon these issues and the need to address interoperability not just at the provider level e.

Key informants conceded unclear reimbursement and licensing models for eMental Health care services as another key feature. At a basic level making sure users can complete transactions or processes online was described as necessary. Although levels of universal health care and health insurance models differed across the countries represented by informants, there was generally reported spotty and vague coverage of eMental Health care, which was viewed by informants as a barrier for patients:.

Findings suggested that unreliable coverage initiates barriers for physicians to refer patients to eMental Health technologies because they do not want the cost to patients to come from out-of-pocket. So you could reward their intention to adopt a new way of working by giving them more time off or giving them more incentives to get used to the new method. There was also considerable discussion about the develop-test-implement cycles of new technologies and persistent difficulties in getting evidence-based treatments scaled up so multiple providers can make use of them:.

If we involved all stakeholders [academics, health providers, end users, private sector] right from the beginning we could probably avoid making a product that we just hope is relevant for someone; I think we forget to add that bit.

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