Purpose: The implementation of a quality-monitoring program that identifies and corrects problems associated with using a bar-code-assisted medication administration (BCMA) system is described.
Summary: In November 2004, the Bar Code Resource Office assembled a work group to develop a quality program to improve machine-readable, bar-coded medications. The project scope involved the development of a pharmacy-based quality program for unit-dose packaging and bar-code labeling to improve the scanning success rate of bar-coded medications at the point-of-care. Data were collected from facility-based BCMA coordinators at each medical center regarding specific reasons for bedside scanning circumvention, as well as successful scan rates in the pharmacy and at the bedside. The pharmacy and bedside scanning data were aggregated and the baseline of successful scans was determined to be 95%. The reported reasons for scanning circumvention were grouped into six categories: bar-code labeling, missing doses, labels not scanning, error messages, mispackaged medications, and mislabeled medications. The work group developed strategies to mitigate problems in each of the areas. As a result of this effort, the Department of Veterans Affairs created a directive that outlined the best practices for unit-dose packaging and labeling, as well as requirements for ongoing data collection and reporting.
Conclusion: A quality-monitoring program that identified and provided best-practice recommendations corrected problems associated with using a BCMA system and improved bar-code labeling processes.
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http://dx.doi.org/10.2146/ajhp080172 | DOI Listing |
J Environ Manage
January 2025
Chongqing Environmental Consulting Co., Ltd., CISDI Group Co., Ltd., Chongqing, China. Electronic address:
To deal with the increasingly severe climate crisis and environmental pollution, China launched a nationwide real-time air quality monitoring program in three batches, a milestone moment in its environmental governance history. Using the time-varying difference-in-differences model, this study explores the synergies of this program across 284 cities from 2009 to 2019. The findings are as follows: (1) With environmental information disclosed, the national air quality monitoring program can reduce the outdoor fine particulate matter concentration by an overall effect of 3.
View Article and Find Full Text PDFSci Adv
January 2025
Doerr School of Sustainability, Stanford University, Stanford, CA, USA.
Poor ambient air quality poses a substantial global health threat. However, accurate measurement remains challenging, particularly in countries such as India where ground monitors are scarce despite high expected exposure and health burdens. This lack of precise measurements impedes understanding of changes in pollution exposure over time and across populations.
View Article and Find Full Text PDFIntegr Environ Assess Manag
January 2025
Programa de Biologia Marinha e Ambientes Costeiros, Universidade Federal Fluminense (PBMAC-UFF), Niterói, Rio de Janeiro, Brazil.
Road activities are recognized sources of pollution that affect the hydrochemistry of nearby water bodies. This study evaluated the Water Quality Monitoring Program in the Soberbo and Iconha rivers in the Guapi-Macacu watershed, which is affected by the BR-116 highway. The Rio-Teresópolis Concessionaire from 2009 to 2016 carried out quarterly sampling.
View Article and Find Full Text PDFAm J Pharm Educ
January 2025
UNC Eshelman School of Pharmacy, UNC Chapel Hill, Chapel Hill, NC. Electronic address:
Objective: To utilize the Consolidated Framework for Implementation Research (CFIR) to identify key determinants that impact successful integration of cultural intelligence trainings in PharmD classes and develop recommendations to address barriers to these trainings.
Methods: Terms related to cultural intelligence were searched in PubMed, Embase, CINAHL, Scopus, ProQuest Dissertations and Theses, ERIC, and PsycInfo. Articles were imported into Covidence and screened for content related to cultural intelligence in PharmD programs, specifically in classroom settings.
Bioengineering (Basel)
December 2024
Jiangsu Key Laboratory of Intelligent Medical Image Computing, Nanjing 210044, China.
The pivotal role of sleep has led to extensive research endeavors aimed at automatic sleep stage classification. However, existing methods perform poorly when classifying small groups or individuals, and these results are often considered outliers in terms of overall performance. These outliers may introduce bias during model training, adversely affecting feature selection and diminishing model performance.
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