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Abstract Background The first attempt to implement a new tool or practice does not always lead to the desired outcome. Re-implementation, which we define as the systematic process of reintroducing an intervention in the same environment, often with some degree of modification, offers another chance at implementation with the opportunity to address failures, modify, and ultimately achieve the desired outcomes. This article proposes a definition and taxonomy for re-implementation informed by case examples in the literature. Main body We conducted a scoping review of the literature for cases that describe re-implementation in concept or practice. We used an iterative process to identify our search terms, pilot testing synonyms or phrases related to re-implementation. We searched PubMed and CINAHL, including articles that described implementing an intervention in the same environment where it had already been implemented. We excluded articles that were policy-focused or described incremental changes as part of a rapid learning cycle, efforts to spread, or a stalled implementation. We assessed for commonalities among cases and conducted a thematic analysis on the circumstance in which re-implementation occurred. A total of 15 articles representing 11 distinct cases met our inclusion criteria. We identified three types of circumstances where re-implementation occurs: (1) failed implementation, where the intervention is appropriate, but the implementation process is ineffective, failing to result in the intended changes; (2) flawed intervention, where modifications to the intervention itself are required either because the tool or process is ineffective or requires tailoring to the needs and/or context of the setting where it is used; and (3) unsustained intervention, where the initially successful implementation of an intervention fails to be sustained. These three circumstances often co-exist; however, there are unique considerations and strategies for each type that can be applied to re-implementation. Conclusions Re-implementation occurs in implementation practice but has not been consistently labeled or described in the literature. Defining and describing re-implementation offers a framework for implementation practitioners embarking on a re-implementation effort and a starting point for further research to bridge the gap between practice and science into this unexplored part of implementation.

Objectives This research aimed to understand the barriers and facilitators clinicians face in using a digital clinical decision support tool—UpToDate—around the globe.Design We used a mixed-methods cohort study design that enrolled 1681 clinicians (physicians, surgeons or physician assistants) who applied for free access to UpToDate through our established donation programme during a 9-week study enrolment period. Eligibility included working outside of the USA for a limited-resource public or non-profit health facility, serving vulnerable populations, having at least intermittent internet access, completing the application in English; and not being otherwise able to afford the subscription.Intervention After consenting to study participation, clinicians received a 1-year subscription to UpToDate. They completed a series of surveys over the year, and we collected clickstream data tracking their use of the tool.Primary and secondary outcome measures (1) The variation in use by demographic; (2) the prevalence of barriers and facilitators of use; and (3) the relationship between barriers, facilitators and use.Results Of 1681 study enrollees, 69% were men and 71% were between 25 and 35 years old, with the plurality practicing general medicine and the majority in sub-Saharan Africa or Southeast Asia. Of the 11 barriers we assessed, fitting the tool into the workflow was a statistically significant barrier, making clinicians 50% less likely to use it. Of the 10 facilitators we assessed, a supportive professional context and utility were significant drivers of use.Conclusions There are several clear barriers and facilitators to promoting the use of digital clinical decision support tools in practice. We recommend tools like UpToDate be implemented with complementary services. These include generating a supportive professional context, helping clinicians realise the tools’ use and working with health systems to better integrate digital, clinical decision support tools into workflows.

BackgroundEvidence-based digital health tools allow clinicians to keep up with the expanding medical literature and provide safer and more accurate care. Understanding users’ online behavior in low-resource settings can inform programs that encourage the use of such tools. Our program collaborates with digital tool providers, including UpToDate, to facilitate free subscriptions for clinicians serving in low-resource settings globally. ObjectiveWe aimed to define segments of clinicians based on their usage patterns of UpToDate, describe the demographics of those segments, and relate the segments to self-reported professional climate measures. MethodsWe collected 12 months of clickstream data (a record of users’ clicks within the tool) as well as repeated surveys. We calculated the total number of sessions, time spent online, type of activity (navigating, reading, or account management), calendar period of use, percentage of days active online, and minutes of use per active day. We defined behavioral segments based on the distributions of these statistics and related them to survey data. ResultsWe enrolled 1681 clinicians from 75 countries over a 9-week period. We based the following five behavioral segments on the length and intensity of use: short-term, light users (420/1681, 25%); short-term, heavy users (252/1681, 15%); long-term, heavy users (403/1681, 24%); long-term, light users (370/1681, 22%); and never-users (252/1681, 15%). Users spent a median of 5 hours using the tool over the year. On days when users logged on, they spent a median of 4.4 minutes online and an average of 71% of their time reading medical content as opposed to navigating or managing their account. Over half (773/1432, 54%) of the users actively used the tool for 48 weeks or more during the 52-week study period. The distribution of segments varied by age, with lighter and less use among those aged 35 years or older compared to that among younger users. The speciality of medicine had the heaviest use, and emergency medicine had the lightest use. Segments varied strongly by geographic region. As for professional climate, most respondents (1429/1681, 85%) reported that clinicians in their area would view the use of a online tool positively, and compared to those who reported other views, these respondents were less likely to be never-users (286/1681, 17% vs 387/1681, 23%) and more likely to be long-term users (655/1681, 39% vs 370/1681, 22%). ConclusionsWe believe that these behavioral segments can help inform the implementation of digital health tools, identify users who may need assistance, tailor training and messaging for users, and support research on digital health efforts. Methods for combining clickstream data with demographic and survey data have the potential to inform global health implementation. Our forthcoming analysis will use these methods to better elucidate what drives digital health tool use.

Abstract Background The BetterBirth trial tested the effect of a peer coaching program around the WHO Safe Childbirth Checklist for birth attendants in primary-level facilities in Uttar Pradesh, India on a composite measure of perinatal and maternal mortality and maternal morbidity. This study aimed to examine the adherence to essential birth practices between two different cadres of birth attendants—nurses and auxiliary nurse midwives (ANMs)—during and after a peer coaching intervention for the WHO Safe Childbirth Checklist. Methods This is a secondary analysis of birth attendant characteristics, coaching visits, and behavior uptake during the BetterBirth trial through birth attendant surveys, coach observations, and independent observations. Descriptive statistics were calculated overall, and by staffing cadre (staff nurses and ANMs) for demographic characteristics. Logistic regression using the Pearson overdispersion correction (to account for clustering by site) was used to assess differences between staff nurses and ANMs in the intervention group during regular coaching (2-month time point) and 4 months after the coaching program ended (12-month time point). Results Of the 570 birth attendants who responded to the survey in intervention and control arms, 474 were staff nurses (83.2%) and 96 were ANMs (16.8%). In the intervention arm, more staff nurses (240/260, 92.3%) received coaching at all pause points compared to ANMs (40/53, 75.5%). At baseline, adherence to practices was similar between ANMs and staff nurses (~ 30%). Overall percent adherence to essential birth practices among ANMs and nurses was highest at 2 months after intervention initiation, when frequent coaching visits occurred (68.1% and 64.1%, respectively, p = 0.76). Practice adherence tapered to 49.2% among ANMs and 56.1% among staff nurses at 12 months, which was 4 months after coaching had ended (p = 0.68). Conclusions Overall, ANMs and nurses responded similarly to the coaching intervention with the greatest increase in percent adherence to essential birth practices after 2 months of coaching and subsequent decrease in adherence 4 months after coaching ended. While coaching is an effective strategy to support some aspects of birth attendant competency, the structure, content, and frequency of coaching may need to be customized according to the birth attendant training and competency. Trial registration ClinicalTrials.gov: NCT2148952; Universal Trial Number: U1111–1131-5647.

Summary: Background: A coaching-based implementation of the WHO Safe Childbirth Checklist in Uttar Pradesh, India, improved adherence to evidence-based practices, but did not reduce perinatal mortality, maternal morbidity, or maternal mortality. We examined facility-level correlates of the outcomes, which varied widely across the 120 study facilities. Methods: We did a post-hoc analysis of the coaching-based implementation of the WHO Safe Childbirth Checklist in Uttar Pradesh. We used multivariable modelling to identify correlations between 30 facility-level characteristics and each health outcome (perinatal mortality, maternal morbidity, or maternal mortality). To identify contexts in which the intervention might have had an effect, we then ran the models on data restricted to the period of intensive coaching and among patients not referred out of the facilities. Findings: In the multivariable context, perinatal mortality was associated with only 3 of the 30 variables: female literacy at the district level, geographical location, and previous neonatal mortality. Maternal morbidity was only associated with geographical location. No facility-level predictors were associated with maternal mortality. Among facilities in the lowest tertile of birth volume (