Evaluating Adherence to the Sepsis Bundle and the Effectiveness of Best Practice Alerts

PURPOSE: To evaluate the adherence to the CMS sepsis recommendations and sepsis bundle used by the study health system before and after the implementation of Best Practice Alerts (BPAs) and assessing the effect of these alerts on patient outcomes. METHODS: The study was a single-center, process evaluation through a retrospective chart review within a southwest healthcare system. The sample consisted of 73 patients for the pre-implementation period (May 1, 2016-September 7, 2016) and 75 patients for the post-implementation period (September 8, 2016-April 30, 2017). RESULTS: No major differences were found between the two groups with regard to patient age, ethnicity, and time of admission. The post-implementation group had a higher incidence of timely antibiotic administration (p=.008) with 38% receiving initial antibiotic administration in 45 minutes or less of meeting sepsis criteria versus 21% in the pre-implementation group. In the post-implementation group, 89% of patients met sepsis criteria versus 67% in the pre-implementation group. The post-implementation group also collected blood cultures in 30 minutes or less in 61% of patients versus 41% in the pre-implementation group (p=.03). No significant difference was found in regard to antibiotic selection, mortality, or length of stay. CONCLUSION: The post-implementation group achieved more timely antibiotic administration and blood culture collection; however, there was no significant improvement in appropriate antibiotic choice, length of stay, or mortality. BPAs were inconsistent with the time that patients met sepsis criteria. After years of research and protocol changes, outcomes have not improved, indicating a great need for consideration of alternative treatments to improve the care and outcomes of sepsis patients

VarArray Meets t-SOT: Advancing the State of the Art of Streaming Distant Conversational Speech Recognition

This paper presents a novel streaming automatic speech recognition (ASR) framework for multi-talker overlapping speech captured by a distant microphone array with an arbitrary geometry. Our framework, named t-SOT-VA, capitalizes on independently developed two recent technologies; array-geometry-agnostic continuous speech separation, or VarArray, and streaming multi-talker ASR based on token-level serialized output training (t-SOT). To combine the best of both technologies, we newly design a t-SOT-based ASR model that generates a serialized multi-talker transcription based on two separated speech signals from VarArray. We also propose a pre-training scheme for such an ASR model where we simulate VarArray's output signals based on monaural single-talker ASR training data. Conversation transcription experiments using the AMI meeting corpus show that the system based on the proposed framework significantly outperforms conventional ones. Our system achieves the state-of-the-art word error rates of 13.7% and 15.5% for the AMI development and evaluation sets, respectively, in the multiple-distant-microphone setting while retaining the streaming inference capability.Comment: 6 pages, 2 figure, 3 tables, v2: Appendix A has been adde

A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena

Word reordering is one of the most difficult aspects of statistical machine translation (SMT), and an important factor of its quality and efficiency. Despite the vast amount of research published to date, the interest of the community in this problem has not decreased, and no single method appears to be strongly dominant across language pairs. Instead, the choice of the optimal approach for a new translation task still seems to be mostly driven by empirical trials. To orientate the reader in this vast and complex research area, we present a comprehensive survey of word reordering viewed as a statistical modeling challenge and as a natural language phenomenon. The survey describes in detail how word reordering is modeled within different string-based and tree-based SMT frameworks and as a stand-alone task, including systematic overviews of the literature in advanced reordering modeling. We then question why some approaches are more successful than others in different language pairs. We argue that, besides measuring the amount of reordering, it is important to understand which kinds of reordering occur in a given language pair. To this end, we conduct a qualitative analysis of word reordering phenomena in a diverse sample of language pairs, based on a large collection of linguistic knowledge. Empirical results in the SMT literature are shown to support the hypothesis that a few linguistic facts can be very useful to anticipate the reordering characteristics of a language pair and to select the SMT framework that best suits them.Comment: 44 pages, to appear in Computational Linguistic