Chrome Extension
WeChat Mini Program
Use on ChatGLM

Investigating Safety Incidents with High-Risk Medications: Insights from the National Reporting and Learning System (NRLS) on Opioids, Insulins, and Anticoagulants

Abdulrhman Alrowily,Khalid Alfaraidy, Saleh Almutairi, Abdullah Alamri, Wejdan Alrowily,Mohammed Abutaleb, Mohammad Zaitoun,Wedad Sarawi, Mashael Aljead

crossref(2024)

Cited 0|Views8
No score
Abstract
Abstract Background Ensuring patient safety is paramount in any healthcare system. Rising concerns about medical errors in the UK have necessitated greater focus to be placed on studying the nature of such errors, particularly those involving high-risk medications. This research aims to conduct a retrospective analysis of incidents related to patient safety in the UK, based on data from the NRLS. Methods This research was conducted based on the review of NRLS patient safety reports published during the period January 1st, 2015 to December 31st, 2015. NHS Improvement provided details regarding the incidents, following approval using a data-sharing agreement. In total, 1,500 incidents were analyzed, equally divided among three categories of high-risk drugs; opioids, insulin and anticoagulants. Excel features and deductive reasoning (thematic analysis) were used in the data analysis. Results The results showed that the insulin category had both the highest risk and the most errors compared to anticoagulants and opioids. These errors primarily resulted from issues in administering, prescribing, and dispensing drugs. Inadequate drug checks, communication difficulties among staff and with patients, and high staff workload were often linked to these errors. Conclusion This study confirms that the NRLS database is a valuable source of data, while the suggestions put forth, based on these results, could contribute to the formulation of measures that diminish the occurrence of errors related to high-risk drugs in healthcare settings. Information technology should enhance medication safety by tracking the processing of medication use.
More
Translated text
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined