Innovative Implementation Studies Conducted in US Safety Net Health Care Settings: A Systematic Review

Abstract

Little is known about dissemination and implementation in safety net settings. The authors conducted a literature review of innovation/implementation studies in US safety net health care settings between 2008 and 2017. Each article was coded for (1) intervention characteristics, (2) implementation stage, (3) internal versus external ownership, and (4) prespecified implementation outcomes (eg, acceptability and fidelity). Twenty studies were identified; the majority were implemented within community clinics or integrated safety net systems (15 articles), most involved care process improvements (13 articles), and most were internally developed (13 articles). The internally developed innovations reported fewer barriers to acceptability among staff/providers, higher leadership involvement and organizational alignment, greater amounts of customization to the local setting, and better sustainment. Future work should harness the high levels of alignment and acceptability in implementation research within safety net settings, with an eye toward maintaining fidelity to facilitate dissemination across sites.

mHealth app using machine learning to increase physical activity in diabetes and depression: clinical trial protocol for the DIAMANTE Study

“…However, in the USA, low-income minority patients frequently receive their care in safety net settings (services in the public sector for those unable to attain private health insurance), where novel mobile technologies are not often designed, developed or implemented. 19 This translational gap increases the probability that these interventions will ultimately fail when implemented in actual clinical settings that serve vulnerable populations.…”

Real-world insights from launching remote peer-to-peer mentoring in a safety net healthcare delivery setting

“…In summary, our study demonstrates several key lessons, aligned with previously published implementation outcome frameworks to help move digital health tools into wider adoption and spread. 19 , 27 First, there appeared to be successful alignment of the digital health company’s mission/values and business plan to serve the diverse population at Montefiore, as evidenced by: the focus on social determinants of health (in addition to traditional diabetes education), careful attention to language access, flexibility according to the digital and health literacy needs of end users, and heightened attention to peer-matching of mentors and patients by characteristics such as medical and social experiences. Second, there was a transparency of data sharing from both the healthcare system perspective (real-time and often messy lab data collected via the health system) and from InquisitHealth (raw data about patients reached, enrolled, and mentored).…”

“…Third, it is critical in this type of partnership to focus on both internal validity and effectiveness (HbA1c improvement), as well as implementation process—that is, reach and engagement (eg, number of calls/contacts) overall and among key patient subgroups. 15 , 19 , 28 , 29 Too often, pilots are overly focused on effectiveness among a narrow group of enrolled participants which could represent selection bias among healthier or “worried well” individuals taking advantage of new remote/digital solutions first. 30 Moving forward, more real-world evaluations are needed to unpack the successful components for widespread dissemination, especially the rich insights for the remote care of diverse populations in the time of COVID-19.…”

“…Within the broader digital health ecosystem, private sector solutions are particularly important to examine, as digital health companies may be better suited to cost-efficiently scale these technologies to multiple markets simultaneously. 18 Because real-world evidence about implementation of remote peer mentoring solutions is lacking (especially with respect to diverse populations), 19 , 20 we describe here early implementation and effectiveness data from the rollout at a large academic medical center.…”

An mHealth app using machine learning to increase physical activity in diabetes and depression: clinical trial protocol for the DIAMANTE Study

“…However, in the U.S., low-income minority patients frequently receive their care in safety net settings (services in the public sector for those unable to attain private health insurance), where novel mobile technologies are not often designed, developed or implemented (19). This translational gap increases the probability that these interventions will ultimately fail when implemented in actual clinical settings that serve vulnerable populations.…”