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

Abstract

Background: Most evidence-based practices (EBPs) do not find their way into clinical use, including evidence-based mobile health (mHealth) technologies. The literature offers implementers little practical guidance for successfully integrating mHealth into health care systems.

Objective: The goal of this research was to describe a novel decision-framing model that gives implementers a method of eliciting the considerations of different stakeholder groups when they decide whether to implement an EBP.

Methods: The decision-framing model can be generally applied to EBPs, but was applied in this case to an mHealth system (Seva) for patients with addiction. The model builds from key insights in behavioral economics and game theory. The model systematically identifies, using an inductive process, the perceived gains and losses of different stakeholder groups when they consider adopting a new intervention. The model was constructed retrospectively in a parent implementation research trial that introduced Seva to 268 patients in 3 US primary care clinics. Individual and group interviews were conducted to elicit stakeholder considerations from 6 clinic managers, 17 clinicians, and 6 patients who were involved in implementing Seva. Considerations were used to construct decision frames that trade off the perceived value of adopting Seva versus maintaining the status quo from each stakeholder group's perspective. The face validity of the decision-framing model was assessed by soliciting feedback from the stakeholders whose input was used to build it.

Results: Primary considerations related to implementing Seva were identified for each stakeholder group. Clinic managers perceived the greatest potential gain to be better care for patients and the greatest potential loss to be cost (ie, staff time, sustainability, and opportunity cost to implement Seva). All clinical staff considered time their foremost consideration-primarily in negative terms (eg, cognitive burden associated with learning a new system) but potentially in positive terms (eg, if Seva could automate functions done manually). Patients considered safety (anonymity, privacy, and coming from a trusted source) to be paramount. Though payers were not interviewed directly, clinic managers judged cost to be most important to payers-whether Seva could reduce total care costs or had reimbursement mechanisms available. This model will be tested prospectively in a forthcoming mHealth implementation trial for its ability to predict mHealth adoption. Overall, the results suggest that implementers proactively address the cost and burden of implementation and seek to promote long-term sustainability.

Conclusions: This paper presents a model implementers may use to elicit stakeholders' considerations when deciding to adopt a new technology, considerations that may then be used to adapt the intervention and tailor implementation, potentially increasing the likelihood of implementation success.

Trial registration: ClinicalTrials.gov NCT01963234; https://ichgcp.net/clinical-trials-registry/NCT01963234 (Archived by WebCite at http://www.webcitation.org/78qXQJvVI).

Keywords: behavioral economics; decision-framing; game theory; implementation models; implementation strategies; mHealth; primary care; stakeholder engagement.

Conflict of interest statement

Conflicts of Interest: AQ has a shareholder interest in CHESS Health, a public benefit corporation that disseminates Web-based health care intervention for patients and family members struggling with addiction. This relationship is extensively managed by the author and the University of Wisconsin–Madison’s Conflict of Interest Committee.

©Andrew Quanbeck. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 07.06.2019.

Figures

Figure 1

Schematic representation of decision framing in terms of gains and losses (adapted from Tversky and Kahnamen [15]).

Figure 2

Decision-framing model. EBP: evidence-based practice.

Figure 3

Illustration of prospective ranking and rating procedures.