scholarly journals Evaluation of medication administration timing variance using information from a large health system’s clinical data warehouse

Author(s):  
Charity M Loput ◽  
Connie Saltsman ◽  
Risa Rahm ◽  
W Dan Roberts ◽  
Sanya Sharma ◽  
...  

Abstract Disclaimer In an effort to expedite the publication of articles related to the COVID-19 pandemic, AJHP is posting these manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. Purpose An analysis to determine the frequency of medication administration timing variances for specific therapeutic classes of high-risk medications using data extracted from a health-system clinical data warehouse (CDW) is presented. Methods This multicenter retrospective, observational analysis of 1 year of medication administration data from 14 hospitals was conducted using a large enterprise health-system CDW. The primary objective was to assess medication administration timing variance for focused therapeutic classes using medication orders and electronic medication administration records data extracted from the electronic health record (EHR). Administration timing variance patterns between standard hospital staffing shifts, within therapeutic drug classes, and for as-needed (PRN) medications were also studied. Calculated variables for delayed medication administration (ie, administration time variance) were created for documented administration time intervals of 30-59, 60-120, and more than 120 minutes before or after medication orders. Results A total of 5,690,770 medication administrations (3,418,275 scheduled and 2,272,495 PRN) were included in the normalized data set. Scheduled medications were frequently subject to delays of ≥60 minutes (15% of administrations, n = 275,257) when scheduled for administration between 9-10 AM and between 9-10 PM. By therapeutic drug class, scheduled administrations of insulins, heparin products, and platelet aggregation inhibitors (most commonly heparin flushes and line-management preparations) were the most commonly delayed. For PRN medications, medications in the anticoagulant and antiplatelet agent class were most likely to be administered early (<60 minutes from the scheduled time of first administration). Conclusion The findings of this study assist in understanding patterns of delayed medication administration. Medication class, time of day of scheduled administration, and frequency were factors that influenced medication administration timing variance.

2006 ◽  
Vol 52 (2) ◽  
pp. 192-197
Author(s):  
Qiyan Zhang ◽  
Yasushi Matsumura ◽  
Tadamasa Teratani ◽  
Sachiko Yoshimoto ◽  
Takahiro Mineno ◽  
...  

2012 ◽  
Vol 19 (5) ◽  
pp. 782-785 ◽  
Author(s):  
Abdelali Boussadi ◽  
Thibaut Caruba ◽  
Eric Zapletal ◽  
Brigitte Sabatier ◽  
Pierre Durieux ◽  
...  

2021 ◽  
pp. 561-569
Author(s):  
Steven A. Eschrich ◽  
Jamie K. Teer ◽  
Phillip Reisman ◽  
Erin Siegel ◽  
Chandan Challa ◽  
...  

PURPOSE The use of genomics within cancer research and clinical oncology practice has become commonplace. Efforts such as The Cancer Genome Atlas have characterized the cancer genome and suggested a wealth of targets for implementing precision medicine strategies for patients with cancer. The data produced from research studies and clinical care have many potential secondary uses beyond their originally intended purpose. Effective storage, query, retrieval, and visualization of these data are essential to create an infrastructure to enable new discoveries in cancer research. METHODS Moffitt Cancer Center implemented a molecular data warehouse to complement the extensive enterprise clinical data warehouse (Health and Research Informatics). Seven different sequencing experiment types were included in the warehouse, with data from institutional research studies and clinical sequencing. RESULTS The implementation of the molecular warehouse involved the close collaboration of many teams with different expertise and a use case–focused approach. Cornerstones of project success included project planning, open communication, institutional buy-in, piloting the implementation, implementing custom solutions to address specific problems, data quality improvement, and data governance, unique aspects of which are featured here. We describe our experience in selecting, configuring, and loading molecular data into the molecular data warehouse. Specifically, we developed solutions for heterogeneous genomic sequencing cohorts (many different platforms) and integration with our existing clinical data warehouse. CONCLUSION The implementation was ultimately successful despite challenges encountered, many of which can be generalized to other research cancer centers.


Author(s):  
Kwang Seob Lee ◽  
Dong‐Gyo Shin ◽  
Jin‐Hee Hwang ◽  
Ranhee Kim ◽  
Chang Hoon Han ◽  
...  

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Dominic Girardi ◽  
Johannes Dirnberger ◽  
Michael Giretzlehner

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