Highly prevalent conditions with multiple and complex underlying etiologies are a challenge to public health. Undernutrition, for example, affects 20% of children in the developing world. The cause and consequence of poor nutrition are multifaceted. Undernutrition has been associated with half of all deaths worldwide in children aged <5 years; in addition, its pernicious long-term effects in early childhood have been associated with cognitive and physical growth deficits across multiple generations and have been thought to suppress immunity to further infections and to reduce the efficacy of childhood vaccines. The Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health (MAL-ED) Study, led by the Fogarty International Center of the National Institutes of Health and the Foundation for the National Institutes of Health, has been established at sites in 8 countries with historically high incidence of diarrheal disease and undernutrition. Central to the study is the hypothesis that enteropathogen infection contributes to undernutrition by causing intestinal inflammation and/or by altering intestinal barrier and absorptive function. It is further postulated that this leads to growth faltering and deficits in cognitive development. The effects of repeated enteric infection and undernutrition on the immune response to childhood vaccines is also being examined in the study. MAL-ED uses a prospective longitudinal design that offers a unique opportunity to directly address a complex system of exposures and health outcomes in the community-rather than the relatively rarer circumstances that lead to hospitalization-during the critical period of development of the first 2 years of life. Among the factors being evaluated are enteric infections (with or without diarrhea) and other illness indicators, micronutrient levels, diet, socioeconomic status, gut function, and the environment. MAL-ED aims to describe these factors, their interrelationships, and their overall impact on health outcomes in unprecedented detail, and to make individual, site-specific, and generalized recommendations regarding the nature and timing of possible interventions aimed at improving child health and development in these resource-poor settings.

BackgroundIn healthcare change interventions, on-the-ground learning about the implementation process is often lost because of a primary focus on outcome improvements. This paper describes the Learning Evaluation, a methodological approach that blends quality improvement and implementation research methods to study healthcare innovations.MethodsLearning Evaluation is an approach to multi-organization assessment. Qualitative and quantitative data are collected to conduct real-time assessment of implementation processes while also assessing changes in context, facilitating quality improvement using run charts and audit and feedback, and generating transportable lessons. Five principles are the foundation of this approach: (1) gather data to describe changes made by healthcare organizations and how changes are implemented; (2) collect process and outcome data relevant to healthcare organizations and to the research team; (3) assess multi-level contextual factors that affect implementation, process, outcome, and transportability; (4) assist healthcare organizations in using data for continuous quality improvement; and (5) operationalize common measurement strategies to generate transportable results.ResultsLearning Evaluation principles are applied across organizations by the following: (1) establishing a detailed understanding of the baseline implementation plan; (2) identifying target populations and tracking relevant process measures; (3) collecting and analyzing real-time quantitative and qualitative data on important contextual factors; (4) synthesizing data and emerging findings and sharing with stakeholders on an ongoing basis; and (5) harmonizing and fostering learning from process and outcome data. Application to a multi-site program focused on primary care and behavioral health integration shows the feasibility and utility of Learning Evaluation for generating real-time insights into evolving implementation processes.ConclusionsLearning Evaluation generates systematic and rigorous cross-organizational findings about implementing healthcare innovations while also enhancing organizational capacity and accelerating translation of findings by facilitating continuous learning within individual sites. Researchers evaluating change initiatives and healthcare organizations implementing improvement initiatives may benefit from a Learning Evaluation approach.Electronic supplementary materialThe online version of this article (doi:10.1186/s13012-015-0219-z) contains supplementary material, which is available to authorized users.

BackgroundThe application of complexity science to understanding healthcare system improvement highlights the need to consider interdependencies within the system. One important aspect of the interdependencies in healthcare delivery systems is how individuals relate to each other. However, results from our observational and interventional studies focusing on relationships to understand and improve outcomes in a variety of healthcare settings have been inconsistent. We sought to better understand and explain these inconsistencies by analyzing our findings across studies and building new theory.MethodsWe analyzed eight observational and interventional studies in which our author team was involved as the basis of our analysis, using a set theoretical qualitative comparative analytic approach. Over 16 investigative meetings spanning 11 months, we iteratively analyzed our studies, identifying patterns of characteristics that could explain our set of results.Our initial focus on differences in setting did not explain our mixed results. We then turned to differences in patient care activities and tasks being studied and the attributes of the disease being treated. Finally, we examined the interdependence between task and disease.ResultsWe identified system-level uncertainty as a defining characteristic of complex systems through which we interpreted our results. We identified several characteristics of healthcare tasks and diseases that impact the ways uncertainty is manifest across diverse care delivery activities. These include disease-related uncertainty (pace of evolution of disease and patient control over outcomes) and task-related uncertainty (standardized versus customized, routine versus non-routine, and interdependencies required for task completion).ConclusionsUncertainty is an important aspect of clinical systems that must be considered in designing approaches to improve healthcare system function. The uncertainty inherent in tasks and diseases, and how they come together in specific clinical settings, will influence the type of improvement strategies that are most likely to be successful. Process-based efforts appear best-suited for low-uncertainty contexts, while relationship-based approaches may be most effective for high-uncertainty situations.Electronic supplementary materialThe online version of this article (doi:10.1186/s13012-014-0165-1) contains supplementary material, which is available to authorized users.