BackgroundKnowledge brokers (KBs) work collaboratively with key stakeholders to facilitate the transfer and exchange of information in a given context. Currently, there is a perceived lack of evidence about the effectiveness of knowledge brokering and the factors that influence its success as a knowledge translation (KT) mechanism. Thus, the goal of this review was to systematically gather evidence regarding the nature of knowledge brokering in health-related settings and determine if KBs effectively contributed to KT in these settings.MethodsA systematic review was conducted using a search strategy designed by a health research librarian. Eight electronic databases (MEDLINE, Embase, PsycINFO, CINAHL, ERIC, Scopus, SocINDEX, and Health Business Elite) and relevant grey literature sources were searched using English language restrictions. Two reviewers independently screened the abstracts, reviewed full-text articles, extracted data, and performed quality assessments. Analysis included a confirmatory thematic approach. To be included, studies must have occurred in a health-related setting, reported on an actual application of knowledge brokering, and be available in English.ResultsIn total, 7935 records were located. Following removal of duplicates, 6936 abstracts were screened and 240 full-text articles were reviewed. Ultimately, 29 articles, representing 22 unique studies, were included in the thematic analysis. Qualitative (n = 18), quantitative (n = 1), and mixed methods (n = 6) designs were represented in addition to grey literature sources (n = 4). Findings indicated that KBs performed a diverse range of tasks across multiple health-related settings; results supported the KB role as a ‘knowledge manager’, ‘linkage agent’, and ‘capacity builder’. Our systematic review explored outcome data from a subset of studies (n = 8) for evidence of changes in knowledge, skills, and policies or practices related to knowledge brokering. Two studies met standards for acceptable methodological rigour; thus, findings were inconclusive regarding KB effectiveness.ConclusionsAs knowledge managers, linkage agents, and capacity builders, KBs performed many and varied tasks to transfer and exchange information across health-related stakeholders, settings, and sectors. How effectively they fulfilled their role in facilitating KT processes is unclear; further rigourous research is required to answer this question and discern the potential impact of KBs on education, practice, and policy.Electronic supplementary materialThe online version of this article (doi:10.1186/s13012-015-0351-9) contains supplementary material, which is available to authorized users.
ObjectiveTo determine how machine learning has been applied to prediction applications in population health contexts. Specifically, to describe which outcomes have been studied, the data sources most widely used and whether reporting of machine learning predictive models aligns with established reporting guidelines.DesignA scoping review.Data sourcesMEDLINE, EMBASE, CINAHL, ProQuest, Scopus, Web of Science, Cochrane Library, INSPEC and ACM Digital Library were searched on 18 July 2018.Eligibility criteriaWe included English articles published between 1980 and 2018 that used machine learning to predict population-health-related outcomes. We excluded studies that only used logistic regression or were restricted to a clinical context.Data extraction and synthesisWe summarised findings extracted from published reports, which included general study characteristics, aspects of model development, reporting of results and model discussion items.ResultsOf 22 618 articles found by our search, 231 were included in the review. The USA (n=71, 30.74%) and China (n=40, 17.32%) produced the most studies. Cardiovascular disease (n=22, 9.52%) was the most studied outcome. The median number of observations was 5414 (IQR=16 543.5) and the median number of features was 17 (IQR=31). Health records (n=126, 54.5%) and investigator-generated data (n=86, 37.2%) were the most common data sources. Many studies did not incorporate recommended guidelines on machine learning and predictive modelling. Predictive discrimination was commonly assessed using area under the receiver operator curve (n=98, 42.42%) and calibration was rarely assessed (n=22, 9.52%).ConclusionsMachine learning applications in population health have concentrated on regions and diseases well represented in traditional data sources, infrequently using big data. Important aspects of model development were under-reported. Greater use of big data and reporting guidelines for predictive modelling could improve machine learning applications in population health.Registration numberRegistered on the Open Science Framework on 17 July 2018 (available at https://osf.io/rnqe6/).
Across jurisdictions, government and health insurance providers hold a large amount of data from patient interactions with the healthcare system. We aimed to develop a machine learning-based model for predicting adverse outcomes due to diabetes complications using administrative health data from the single-payer health system in Ontario, Canada. A Gradient Boosting Decision Tree model was trained on data from 1,029,366 patients, validated on 272,864 patients, and tested on 265,406 patients. Discrimination was assessed using the AUC statistic and calibration was assessed visually using calibration plots overall and across population subgroups. Our model predicting three-year risk of adverse outcomes due to diabetes complications (hyper/hypoglycemia, tissue infection, retinopathy, cardiovascular events, amputation) included 700 features from multiple diverse data sources and had strong discrimination (average test AUC = 77.7, range 77.7–77.9). Through the design and validation of a high-performance model to predict diabetes complications adverse outcomes at the population level, we demonstrate the potential of machine learning and administrative health data to inform health planning and healthcare resource allocation for diabetes management.
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