Pillar 3-Pre-processed web server log file dataset of the banking institution

The dataset presented in this article represents the pre-processed web server log file of the commercial bank. The source of data is the web server of the bank and keeps access of web users starting the year 2009 till 2012. It contains accesses to the bank website during and after the financial crisis. Unnecessary data saved by the web server was removed to keep the focus only on the textual content of the website. Many variables were added to the original log file to make the analysis workable. To keep the privacy of website users, sensitive information in the log file were anonymized. The dataset offers a way to understand the behavior of stakeholders during and after the crisis and how they comply with the Basel regulations. The behavior of users can be modeled using the multinomial logit model, which is in detail described in the research article [1] related to this data article.

Health care public reporting utilization - user clusters, web trails, and usage barriers on Germany's public reporting portal Weisse-Liste.de

BACKGROUND

Quality of care public reporting provides structural, process and outcome information to facilitate hospital choice and strengthen quality competition. Yet, evidence indicates that patients rarely use this information in their decision-making, due to limited awareness of the data and complex and conflicting information. While there is enthusiasm among policy makers for public reporting, clinicians and researchers doubt its overall impact. Almost no study has analyzed how users behave on public reporting portals, which information they seek out and when they abort their search.

METHODS

This study employs web-usage mining techniques on server log data of 17 million user actions from Germany's premier provider transparency portal Weisse-Liste.de (WL.de) between 2012 and 2015. Postal code and ICD search requests facilitate identification of geographical and treatment area usage patterns. User clustering helps to identify user types based on parameters like session length, referrer and page topic visited. First-level markov chains illustrate common click paths and premature exits.

RESULTS

In 2015, the WL.de Hospital Search portal had 2,750 daily users, with 25% mobile traffic, a bounce rate of 38% and 48% of users examining hospital quality information. From 2013 to 2015, user traffic grew at 38% annually. On average users spent 7 min on the portal, with 7.4 clicks and 54 s between clicks. Users request information for many oncologic and orthopedic conditions, for which no process or outcome quality indicators are available. Ten distinct user types, with particular usage patterns and interests, are identified. In particular, the different types of professional and non-professional users need to be addressed differently to avoid high premature exit rates at several key steps in the information search and view process. Of all users, 37% enter hospital information correctly upon entry, while 47% require support in their hospital search.

CONCLUSIONS

Several onsite and offsite improvement options are identified. Public reporting needs to be directed at the interests of its users, with more outcome quality information for oncology and orthopedics. Customized reporting can cater to the different needs and skill levels of professional and non-professional users. Search engine optimization and hospital quality advocacy can increase website traffic.