Abstract

BackgroundMost 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.ObjectiveThe 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.MethodsThe 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.ResultsPrimary 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.ConclusionsThis 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 RegistrationClinicalTrials.gov NCT01963234; https://clinicaltrials.gov/ct2/show/NCT01963234 (Archived by WebCite at http://www.webcitation.org/78qXQJvVI)

Highlights

  • As of 2019, the Seva implementation trial was among the most comprehensive mobile health (mHealth) implementation research trials reported in the US health care system, providing an instructive context for examining the emerging topic of mHealth implementation research

  • Previous implementation research has focused on implementation frameworks and strategies [8,9,10], including frameworks related to mHealth [11,12] and frameworks related to the definition and use of specific implementation strategies [10]

  • The subsequent results and discussion should be understood in the context of the parent implementation study [6], which showed that implementation and effectiveness outcomes were largely positive; management supported the use of Seva in all 3 clinics, 1 or more clinic champions emerged at each site to engage and support patients, and patients showed significant reductions in drinking and drug use

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Summary

Introduction

The vast majority of practices shown to be effective by research remain unused in health care. It takes an estimated 17 years for an evidence-based practice (EBP) to be used in clinics, but only 14% of EBPs ever make it into use [1]. As of 2019, the degree to which mHealth technologies have been successfully implemented and integrated into the mainstream health care system in the United States remains limited. 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

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