BackgroundDifferences in cancer survival exist between countries in Europe. Benchmarking of good practices can assist cancer centers to improve their services aiming for reduced inequalities. The aim of the BENCH-CAN project was to develop a cancer care benchmark tool, identify performance differences and yield good practice examples, contributing to improving the quality of interdisciplinary care. This paper describes the development of this benchmark tool and its validation in cancer centers throughout Europe.MethodsA benchmark tool was developed and executed according to a 13 step benchmarking process. Indicator selection was based on literature, existing accreditation systems, and expert opinions. A final format was tested in eight cancer centers. Center visits by a team of minimally 3 persons, including a patient representative, were performed to verify information, grasp context and check on additional questions (through semi-structured interviews). Based on the visits, the benchmark methodology identified opportunities for improvement.ResultsThe final tool existed of 61 qualitative and 141 quantitative indicators, which were structured in an evaluative framework. Data from all eight participating centers showed inter-organization variability on many indicators, such as bed utilization and provision of survivorship care. Subsequently, improvement suggestions for centers were made; 85% of which were agreed upon.ConclusionA benchmarking tool for cancer centers was successfully developed and tested and is available in an open format. The tool allows comparison of inter-organizational performance. Improvement opportunities were successfully identified for every center involved and the tool was positively evaluated.Electronic supplementary materialThe online version of this article (10.1186/s12913-018-3574-z) contains supplementary material, which is available to authorized users.
The World Health Organization currently recommends that governments scale up testing for COVID-19 infection. We performed health economic analyses projecting whether the additional costs from screening would be offset by the avoided costs with hospitalizations. We analysed Portuguese COVID-19 data up until the 22nd March 2020, and estimated the additional number of cases that would be detected if different testing rates and frequencies of positive results would have been observed. We projected that, in most scenarios, the costs with scaling up COVID-19 tests would be lower than savings with hospitalization costs, rendering large scale testing cost-saving.
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