Search

Background: The knowledge of medication errors is an essential prerequisite for better healthcare delivery. Medication errors are potential errors and impart a significant burden on patient quality of life as well as economic status. Objectives: The study was conducted with objective to determine the prevalence and nature of medication errors in pediatric out-patients and also to study their prescribing pattern. Methods: A quantitative cross-sectional study was conducted in Outpatient Pediatric Department of Public Sector Hospital, Lahore, Pakistan over a period of one month during which 240 prescriptions were evaluated to identify medication errors and prescribing pattern. Data was collected by using a self-designed data collection tool and analyzed using SPSS version 20. Results: Current study evaluated 240 outpatient prescription orders out of which 234 were found to have medication errors. Highest percentage of errors observed in studied population including prescription errors (missed prescriber and patient information) (86.8%), prescribing errors (7.6%) and dispensing errors (3.6%) respectively. Upper Respiratory Tract Infection was the extensive complaint of majority of patients and the drugs of concern were mainly antibiotics, antihistamines, analgesics and anti-inflammatory and expectorants. Conclusion: The current detection of medication errors reveals about recurrent basis of prescribing errors in addition to prescription parts errors. These findings emphasizes that prescribers need to work in alliance with pharmacist to improve patient’s quality of life in primary care. Key words: Medication error, Pediatric, Outpatient, Prescription.

application/pdf, The Trans-Pacific Partnership (TPP), described as the 21st century agreement, was signed on 4 February 2016. Chapter 14 of the trade agreement sets rules and procedures for electronic commerce and digital economy. Many argue that this chapter was drafted with tech companies in mind and that they are the real beneficiary of it. Some of the rules relate, directly and indirectly, to the privacy and data protection aspect. This article seeks to briefly analyze those rules.

In order to investigate how artificial neural networks (ANNs) have been applied for partial discharge (PD) pattern recognition, this paper reviews recent progress made on ANN development for PD classification by a literature survey. Contributions from several authors have been presented and discussed. High recognition rate has been recorded for several PD faults, but there are still many factors that hinder correct recognition of PD by the ANN, such as high-amplitude noise or wide spectral content typical from industrial environments, trial and error approaches in determining an optimum ANN, multiple PD sources acting simultaneously, lack of comprehensive and up to date databank of PD faults, and the appropriate selection of the characteristics that allow a correct recognition of the type of source which are currently being addressed by researchers. Several suggestions for improvement are proposed by the authors include: (1) determining the optimum weights in training the ANN; (2) using PD data captured over long stressing period in training the ANN; (3) ANN recognizing different PD degradation levels; (4) using the same resolution sizes of the PD patterns when training and testing the ANN with different PD dataset; (5) understanding the characteristics of multiple concurrent PD faults and effectively recognizing them; and (6) developing techniques in order to shorten the training time for the ANN as applied for PD recognition Finally, this paper critically assesses the suitability of ANNs for both online and offline PD detections outlining the advantages to the practitioners in the field. It is possible for the ANNs to determine the stage of degradation of the PD, thereby giving an indication of the seriousness of the fault.