The KfW, which together with its subsidiaries DEG, KfW IPEX-Bank and FuB forms the KfW Bankengruppe ("banking group"), is a German state-owned investment and development bank, based in Frankfurt. As of 2014, it is the world's largest national development bank and as of 2018 Germany’s third largest bank by balance sheet. Its name originally comes from Kreditanstalt für Wiederaufbau ("Credit Institute for Reconstruction"). It was formed in 1948 after World War II as part of the Marshall Plan.
In this tool demonstration we present a prototype at the KfW bank, which generates product information documents. It can generate reader-ready documents with well-formed sentences, a standardized structure and layout, while using only a feature model with annotated text. Our prototype uses the FeatureIDE third party library to process feature model and configuration. The generated texts in markdown format are transformed using pandoc into OfficeOpen XML format as the final product information documents. Two new features are presented, which complement our initial implementation: A script interpreter based on LuaJ, which produces feature text snippets for a given Lua script, using the feature model and its configuration object. And secondly, QR-Codes for the product documents, where the QR-Code contains the given configuration of the generated document as unique url to link back to our feature model based web configurator “YAP”.
Cardiovascular diseases (CVD) are the leading cause of death in Cambodia. However, it is unknown whether clinicians in Cambodia provide evidence-based CVD preventive care actions. We address this important gap and provide one of the first assessments of clinical care for CVD prevention in an LMIC context. We determined the proportion of primary care visits by adult patients that resulted in evidence-based CVD preventive care actions, identified which care actions were most frequently missed, and estimated the know-do gap for each clinical action. We used data on 190 direct clinician-patient observations and 337 clinician responses to patient vignettes from 114 public primary care health facilities. Our main outcomes were the proportion of patient consultations and responses to care vignettes where clinicians measured blood pressure, blood glucose, body mass index, and asked questions regarding alcohol, tobacco, physical activity, and diet. There were very large clinical care shortfalls for all CVD care actions. Just 6.4% (95% CI: 3.0%, 13.0%) of patients had their BMI measured, 8.0% (4.6%, 13.6%) their blood pressure measured at least twice, only 4.7% (1.9%, 11.2%) their blood glucose measured. Less than 21% of patients were asked about their physical activity (11.7% [7.0%, 18.9%]), smoking (18.0% [11.8%, 26.5%]), and alcohol-related behaviors (20.2% [13.7%, 28.9%]). We observed the largest know-do gaps for blood glucose and BMI measurements with smaller but important know-do gaps for the other clinical actions. CVD care did not vary across clinician cadre or by years of experience. We find large CVD care delivery gaps in primary-care facilities across Cambodia. Our results suggest that diabetes is being substantially underdiagnosed and that clinicians are losing CVD prevention potential by not identifying individuals who would benefit from behavioral changes. The large overall and know-do gaps suggest that interventions for improving preventive care need to target both clinical knowledge and the bottlenecks between knowledge and care behavior.
