OBJECTIVE: To characterize the nature and consequence(s) of interdependent physician electronic health record (EHR) work across inpatient shifts. MATERIALS AND METHODS: Pooled cross-sectional analysis of EHR metadata associated with hospital medicine patients at an academic medical center, January-June 2022. Using patient-day observation data, we use a mixed effects regression model with daytime physician random effects to examine nightshift behavior (handoff time, total EHR time) as a function of behaviors by the preceding daytime team. We also assess whether nighttime patient deterioration is predicted by team coordination behaviors across shifts. RESULTS: We observed 19 671 patient days (N = 2708 encounters). Physicians used the handoff tool consistently, generally spending 8-12 minutes per shift editing patient information. When the day service team was more activated (highest tercile of handoff time, overall EHR time), nightshift experienced increased levels of EHR work and patient risk of overnight decline was elevated. (ie, Busy predicts busy). However, lower levels of dayshift activation were also associated with nightshift spillovers, including higher overnight EHR work and increased likelihood of patient clinical decline. Patient-days in the lowest and highest terciles of dayshift EHR time had a 1 percentage point increased relative risk of overnight decline (baseline prevalence of 4.4%) compared to the middle tercile (P = .04). DISCUSSION: We find evidence of spillovers in EHR work from dayshift to nightshift. Additionally, the lowest and highest levels of dayshift EHR activity are associated with increased risk of overnight patient decline. Results are associational and motivate further examination of additional confounding factors. CONCLUSION: Analyses reveal opportunities to address task interdependence across shifts, using technology to flexibly shape and support collaborative teaming practices in complex clinical environments.

This paper presents our efforts in building a frame-work of automatic sentiment analysis for online user product reviews. The framework consists of web scraping to acquire a corpus of publicly available user product reviews, construction of a reference dictionary for removal of noisy entries such as typos and special characters from the reviews, extraction of Bag-of-Words (BoW) representations, building BoW-based sentiment models for cosine similarity classification, neural network-based classification, and performance evaluation and linguistic analysis modules. In a case study on Amazon user reviews, we analyze the impact of polarity salience- and exponential-based weighting of unigram and bigram BoW vectors and salience weight-driven dimensionality reduction on sentiment prediction accuracy and show that both strategies provide a dramatic reduction of equal error rates. Finally, we use the salience weighting matrix established in the classification experiments to rank-order discriminability of dictionary entries and their bigrams and report our findings in the form of word clouds. The dataset collected for this study, as well as the experimental framework, are made publicly available.

This paper presents our efforts in building a framework of automatic sentiment analysis for online user product reviews. The framework consists of web scraping to acquire a corpus of publicly available user product reviews, construction of a reference dictionary for removal of noisy entries such as typos and special characters from the reviews, extraction of Bagof-Words (BoW) representations, building BoW-based sentiment models for cosine similarity classification, neural networkbased classification, and performance evaluation and linguistic analysis modules. In a case study on Amazon user reviews, we analyze the impact of polarity salience- and exponential-based weighting of unigram and bigram BoW vectors and salience weight-driven dimensionality reduction on sentiment prediction accuracy and show that both strategies provide a dramatic reduction of equal error rates. Finally, we use the salience weighting matrix established in the classification experiments to rank-order discriminability of dictionary entries and their bigrams and report our findings in the form of word clouds. The dataset collected for this study, as well as the experimental framework, are made publicly available.

SUMMARY: Genome sequencing projects annotate protein-coding gene models with multiple transcripts, aiming to represent all of the available transcript evidence. However, downstream analyses often operate on only one representative transcript per gene locus, sometimes known as the canonical transcript. To choose canonical transcripts, Transcript Ranking and Canonical Election (TRaCE) holds an 'election' in which a set of RNA-seq samples rank transcripts by annotation edit distance. These sample-specific votes are tallied along with other criteria such as protein length and InterPro domain coverage. The winner is selected as the canonical transcript, but the election proceeds through multiple rounds of voting to order all the transcripts by relevance. Based on the set of expression data provided, TRaCE can identify the most common isoforms from a broad expression atlas or prioritize alternative transcripts expressed in specific contexts. AVAILABILITY AND IMPLEMENTATION: Transcript ranking code can be found on GitHub at {{https://github.com/warelab/TRaCE}}. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.