JMIR Publications

ABSTRACT

Background:

The majority of evidence-based practices do not find their way into clinical use, including mobile health (mHealth) technologies. The literature offers implementers little guidance for successfully integrating mHealth into healthcare systems.

Objective:

The goal of this research is to describe a novel decision-framing model that gives implementers a method of eliciting and addressing the considerations of different stakeholder groups when they face the decision of whether to implement an evidence-based practice.

Methods:

The decision-framing model integrates key insights from behavioral economics and game theory. It systematically identifies the perceived gains and losses that members of different stakeholder groups consider when deciding whether to adopt a new intervention. The approach was applied retrospectively in a parent implementation research trial that introduced an mHealth system to 268 patients in three U.S. clinics offering primary and behavioral healthcare services. The mHealth system, called Seva, supports patients with addiction. Individual and group interviews were conducted to elicit stakeholder considerations from 23 clinic staff members (6 managers and 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 vs. 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 with respect to implementing Seva were identified for each stakeholder group. Clinic managers perceived the greatest potential gain to be providing better care for patients, and the greatest potential loss to be cost, expressed in terms of staff time, sustainability, and opportunity cost to implement Seva. All clinical staff considered time their foremost consideration—primarily in negative terms (e.g., cognitive burden associated with learning a new system) but potentially in positive terms (e.g., if Seva could automate functions done manually). Patients considered safety (anonymity, privacy, and coming from a trusted source) to be paramount. Though not assessed directly, payers considered impact on cost foremost—whether Seva could reduce total care costs and whether reimbursement mechanisms were available. When considerations were compiled into decision frames that traded off the gains and losses associated with adopting Seva, only one stakeholder group—patients—expressed a positive overall value, and these were the stakeholders who used Seva most extensively. 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 systematic method of inquiry that implementers may use to elicit stakeholders’ considerations when deciding to adopt a new technology. Stakeholder considerations may be used to adapt mHealth interventions and tailor implementation, potentially increasing the likelihood of implementation success for various evidence-based practices and technologies. Clinical Trial: ClinicalTrials.gov (NCT01963234). https://clinicaltrials.gov/ct2/show/NCT01963234 Registered October 9, 2013