Information Presentation Features and Comprehensibility of Hospital Report Cards: Design Analysis and Online Survey Among Users

Background: Improving the transparency of information about the quality of health care providers is one way to improve health care quality. It is assumed that Internet information steers patients toward better-performing health care providers and will motivate providers to improve quality. However, the effect of public reporting on hospital quality is still small. One of the reasons is that users find it difficult to understand the formats in which information is presented. Objective: We analyzed the presentation of risk-adjusted mortality rate (RAMR) for coronary angiography in the 10 most commonly used German public report cards to analyze the impact of information presentation features on their comprehensibility. We wanted to determine which information presentation features were utilized, were preferred by users, led to better comprehension, and had similar effects to those reported in evidence-based recommendations described in the literature. Methods: The study consisted of 5 steps: (1) identification of best-practice evidence about the presentation of information on hospital report cards; (2) selection of a single risk-adjusted quality indicator; (3) selection of a sample of designs adopted by German public report cards; (4) identification of the information presentation elements used in public reporting initiatives in Germany; and (5) an online panel completed an online questionnaire that was conducted to determine if respondents were able to identify the hospital with the lowest RAMR and if respondents’ hospital choices were associated with particular information design elements. Results: Evidence-based recommendations were made relating to the following information presentation features relevant to report cards: evaluative table with symbols, tables without symbols, bar charts, bar charts without symbols, bar charts with symbols, symbols, evaluative word labels, highlighting, order of providers, high values to indicate good performance, explicit statements of whether high or low values indicate good performance, and incomplete data (“N/A” as a value). When investigating the RAMR in a sample of 10 hospitals’ report cards, 7 of these information presentation features were identified. Of these, 5 information presentation features improved comprehensibility in a manner reported previously in literature. Conclusions: To our knowledge, this is the first study to systematically analyze the most commonly used public reporting card designs used in Germany. Best-practice evidence identified in international literature was in agreement with 5 findings about German report card designs: (1) avoid tables without symbols, (2) include bar charts with symbols, (3) state explicitly whether high or low values indicate good performance or provide a “good quality” range, (4) avoid incomplete data (N/A given as a value), and (5) rank hospitals by performance. However, these findings are preliminary and should be subject of further evaluation. The implementation of 4 of these recommendations should not present insurmountable obstacles. However, ranking hospitals by performance may present substantial difficulties.

Genometa - A Fast and Accurate Classifier for Short Metagenomic Shotgun Reads

Metagenomic studies use high-throughput sequence data to investigate microbial communities in situ. However, considerable challenges remain in the analysis of these data, particularly with regard to speed and reliable analysis of microbial species as opposed to higher level taxa such as phyla. We here present Genometa, a computationally undemanding graphical user interface program that enables identification of bacterial species and gene content from datasets generated by inexpensive high-throughput short read sequencing technologies. Our approach was first verified on two simulated metagenomic short read datasets, detecting 100% and 94% of the bacterial species included with few false positives or false negatives. Subsequent comparative benchmarking analysis against three popular metagenomic algorithms on an Illumina human gut dataset revealed Genometa to attribute the most reads to bacteria at species level (i.e. including all strains of that species) and demonstrate similar or better accuracy than the other programs. Lastly, speed was demonstrated to be many times that of BLAST due to the use of modern short read aligners. Our method is highly accurate if bacteria in the sample are represented by genomes in the reference sequence but cannot find species absent from the reference. This method is one of the most user-friendly and resource efficient approaches and is thus feasible for rapidly analysing millions of short reads on a personal computer.