Implementing a Mobile Health System to Integrate the Treatment of Addiction Into Primary Care: A Hybrid Implementation-Effectiveness Study

Abstract

BackgroundDespite the near ubiquity of mobile phones, little research has been conducted on the implementation of mobile health (mHealth) apps to treat patients in primary care. Although primary care clinicians routinely treat chronic conditions such as asthma and diabetes, they rarely treat addiction, a common chronic condition. Instead, addiction is most often treated in the US health care system, if it is treated at all, in a separate behavioral health system. mHealth could help integrate addiction treatment in primary care.ObjectiveThe objective of this paper was to report the effects of implementing an mHealth system for addiction in primary care on both patients and clinicians.MethodsIn this implementation research trial, an evidence-based mHealth system named Seva was introduced sequentially over 36 months to a maximum of 100 patients with substance use disorders (SUDs) in each of three federally qualified health centers (FQHCs; primary care clinics that serve patients regardless of their ability to pay). This paper reports on patient and clinician outcomes organized according to the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework.ResultsThe outcomes according to the RE-AIM framework are as follows: Reach—Seva reached 8.31% (268/3226) of appropriate patients. Reach was limited by our ability to pay for phones and data plans for a maximum of 100 patients per clinic. Effectiveness—Patients who were given Seva had significant improvements in their risky drinking days (44% reduction, (0.7-1.25)/1.25, P=.04), illicit drug-use days (34% reduction, (2.14-3.22)/3.22, P=.01), quality of life, human immunodeficiency virus screening rates, and number of hospitalizations. Through Seva, patients also provided peer support to one another in ways that are novel in primary care settings. Adoption—Patients sustained high levels of Seva use—between 53% and 60% of the patients at the 3 sites accessed Seva during the last week of the 12-month implementation period. Among clinicians, use of the technology was less robust than use by patients, with only a handful of clinicians using Seva in each clinic and behavioral health providers making most referrals to Seva in 2 of the 3 clinics. Implementation—At 2 sites, implementation plans were realized successfully; they were delayed in the third. Maintenance—Use of Seva dropped when grant funding stopped paying for the mobile phones and data plans. Two of the 3 clinics wanted to maintain the use of Seva, but they struggled to find funding to support this.ConclusionsImplementing an mHealth system can improve care among primary care patients with SUDs, and patients using the system can support one another in their recovery. Among clinicians, however, implementation requires figuring out how information from the mHealth system will be used and making mHealth data available in the electronic health (eHealth) record. In addition, paying for an mHealth system remains a challenge.

User‐centered design for technology‐enabled services for eating disorders

“…One reason for limited reach and suboptimal engagementproblems that are not unique to eating disorders (e.g., Arean et al, 2016;Gilbody et al, 2015;Hermes & Rosenheck, 2016;Localytics, 2017;Owen et al, 2015;Quanbeck et al, 2018;Shaw et al, 2016)-is that technology-enabled services are not designed for the users and contexts in which they will be implemented (Lyon & Koerner, 2016;Mohr, Lyon, Lattie, Reddy, & Schueller, 2017;Mohr, Riper, & Schueller, 2018;Mohr, Weingardt, Reddy, & Schueller, 2017). A clinical review of mental health smartphone apps cited app design as a primary problem leading to low engagement: many apps are not designed with service users in mind and do not solve problems about which users care most (Torous, Nicholas, Larsen, Firth, & Christensen, 2018).…”

Detecting Recovery Problems Just in Time: Application of Automated Linguistic Analysis and Supervised Machine Learning to an Online Substance Abuse Forum (Preprint)

“…The intervention has been described in detail elsewhere, and demonstrated efficacy in reducing risky drinking days by more than half relative to a control group [8]. We use data from two studies of the system: 1) a clinical trial involving individuals discharged from alcohol treatment (Study 1) [8], and 2) an implementation study in primary care, involving individuals who used either alcohol or illicit drugs (Study 2) [59]. The IRBs at the study sites approved both studies.…”

Measuring the Implementation of Behavioral Intervention Technologies: Recharacterization of Established Outcomes (Preprint)

“…BIT usability and its relationship with implementation outcomes is discussed more extensively below. funding for an implementation trial ended [39] Implementation Outcomes Acceptability Acceptability is the extent to which an innovation is agreeable, palatable, or satisfactory to a stakeholder. [23] For BITs, acceptability can be evaluated among a variety of stakeholders (consumers, providers, administrators, policy makers, etc.)…”

“…For instance, Quanbeck and colleagues measured system operating costs, which they saw as distinct from implementation costs including staff time for introducing the BIT to patients and monitoring data produced by the BIT, technical support costs, and ongoing costs such as server hosting. [39] Implementation strategy costs are those incurred directly from the strategies used to employ the BIT, and vary depending on where the BIT lies on the continuum of support defined in Figure 1.[54] Guided BITs may require more time to educate providers and consumers, facilitate engagement, and support use. In addition, resources such as advertising materials, educational materials, registries, clinical reminders, and decision support tools may be needed.…”