OBJECTIVE: The study sought to create an online resource that informs the public of coronavirus disease 2019 (COVID-19) outbreaks in their area. MATERIALS AND METHODS: This R Shiny application aggregates data from multiple resources that track COVID-19 and visualizes them through an interactive, online dashboard. RESULTS: The Web resource, called the COVID-19 Watcher, can be accessed online (https://covid19watcher.research.cchmc.org/). It displays COVID-19 data from every county and 188 metropolitan areas in the United States. Features include rankings of the worst-affected areas and auto-generating plots that depict temporal changes in testing capacity, cases, and deaths. DISCUSSION: The Centers for Disease Control and Prevention does not publish COVID-19 data for local municipalities, so it is critical that academic resources fill this void so the public can stay informed. The data used have limitations and likely underestimate the scale of the outbreak. CONCLUSIONS: The COVID-19 Watcher can provide the public with real-time updates of outbreaks in their area.

OBJECTIVES: Electronic trigger detection tools hold promise to reduce Adverse drug event (ADEs) through efficiencies of scale and real-time reporting. We hypothesized that such a tool could automatically detect medication dosing errors as well as manage and evaluate dosing rule modifications. MATERIALS AND METHODS: We created an order and alert analysis system that identified antibiotic medication orders and evaluated user response to dosing alerts. Orders associated with overridden alerts were examined for evidence of administration and the delivered dose was compared to pharmacy-derived dosing rules to confirm true overdoses. True overdose cases were reviewed for association with known ADEs. RESULTS: Of 55 546 orders reviewed, 539 were true overdose orders, which lead to 1965 known overdose administrations. Documentation of loose stools and diarrhea was significantly increased following drug administration in the overdose group. Dosing rule thresholds were altered to reflect clinically accurate dosing. These rule changes decreased overall alert burden and improved the salience of alerts. DISCUSSION: Electronic algorithm-based detection systems can identify antibiotic overdoses that are clinically relevant and are associated with known ADEs. The system also serves as a platform for evaluating the effects of modifying electronic dosing rules. These modifications lead to decreased alert burden and improvements in response to decision support alerts. CONCLUSION: The success of this test case suggests that gains are possible in reducing medication errors and improving patient safety with automated algorithm-based detection systems. Follow-up studies will determine if the positive effects of the system persist and if these changes lead to improved safety outcomes.

OBJECTIVE: The current study aims to fill the gap in available healthcare de-identification resources by creating a new sharable dataset with realistic Protected Health Information (PHI) without reducing the value of the data for de-identification research. By releasing the annotated gold standard corpus with Data Use Agreement we would like to encourage other Computational Linguists to experiment with our data and develop new machine learning models for de-identification. This paper describes: (1) the modifications required by the Institutional Review Board before sharing the de-identification gold standard corpus; (2) our efforts to keep the PHI as realistic as possible; (3) and the tests to show the effectiveness of these efforts in preserving the value of the modified data set for machine learning model development. MATERIALS AND METHODS: In a previous study we built an original de-identification gold standard corpus annotated with true Protected Health Information (PHI) from 3503 randomly selected clinical notes for the 22 most frequent clinical note types of our institution. In the current study we modified the original gold standard corpus to make it suitable for external sharing by replacing HIPAA-specified PHI with newly generated realistic PHI. Finally, we evaluated the research value of this new dataset by comparing the performance of an existing published in-house de-identification system, when trained on the new de-identification gold standard corpus, with the performance of the same system, when trained on the original corpus. We assessed the potential benefits of using the new de-identification gold standard corpus to identify PHI in the i2b2 and PhysioNet datasets that were released by other groups for de-identification research. We also measured the effectiveness of the i2b2 and PhysioNet de-identification gold standard corpora in identifying PHI in our original clinical notes. RESULTS: Performance of the de-identification system using the new gold standard corpus as a training set was very close to training on the original corpus (92.56 vs. 93.48 overall F-measures). Best i2b2/PhysioNet/CCHMC cross-training performances were obtained when training on the new shared CCHMC gold standard corpus, although performances were still lower than corpus-specific trainings. DISCUSSION AND CONCLUSION: We successfully modified a de-identification dataset for external sharing while preserving the de-identification research value of the modified gold standard corpus with limited drop in machine learning de-identification performance.

This paper discusses a template-based approach to aid in introducing the upper-division undergraduate (or first year graduate) to the rapidly emerging message passing parallel computing paradigm. Our template library facilitates an accelerated MPI programming learning environment that can realistically be included as one topic among many in an algorithms course. One template module is based on a backtracking solution to the satisfiablity problem (SAT), which the student first solves in the sequential setting. With the aid of a modified template, the student then develops a simple parallel SAT solver. The template includes such things as I/O functions, allowing the student to focus on the algorithm itself. The parallel part is partially provided by the template, with indicators given in places where the student needs to plug in missing MPI function calls. The students are excited about this hands-on-experience in the increasingly important world of message passing parallel computing, which might be missed if their curriculum does not include a course devoted to this topic.

No abstract available.