English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In general, there have been two primary aspects where AHRQ is exploring refinements based on additional data elements: present on admission (POA) and laboratory values. Given the fact Medicare began to require POA for diagnosis codes for most hospitals in the recent past, a number of the AHRQ QIs were subsequently modified to reflect POA in the technical specifications. This requirement of Medicare has resulted in many, but not all, hospital claims containing POA. Thus, a prediction methodology has been developed for claims without POA. For such claims, a Markov chain Monte Carlo method is utilized to predict the likelihood of whether diagnoses are POA.

Recent and upcoming improvements of the AHRQ quality indicator systems and first experience with Present on Admission