Purpose: To establish the baseline prescribing error rate in a tertiary paediatric intensive care unit (PICU) and to determine the impact of a zero tolerance prescribing (ZTP) policy incorporating a dedicated prescribing area and daily feedback of prescribing errors.
Methods: A prospective, non-blinded, observational study was undertaken in a 12-bed tertiary PICU over a period of 134 weeks. Baseline prescribing error data were collected on weekdays for all patients for a period of 32 weeks, following which the ZTP policy was introduced. Daily error feedback was introduced after a further 12 months. Errors were sub-classified as 'clinical', 'non-clinical' and 'infusion prescription' errors and the effects of interventions considered separately.
Results: The baseline combined prescribing error rate was 892 (95 % confidence interval (CI) 765-1,019) errors per 1,000 PICU occupied bed days (OBDs), comprising 25.6 % clinical, 44 % non-clinical and 30.4 % infusion prescription errors. The combined interventions of ZTP plus daily error feedback were associated with a reduction in the combined prescribing error rate to 447 (95 % CI 389-504) errors per 1,000 OBDs (p < 0.0001), an absolute risk reduction of 44.5 % (95 % CI 40.8-48.0 %). Introduction of the ZTP policy was associated with a significant decrease in clinical and infusion prescription errors, while the introduction of daily error feedback was associated with a significant reduction in non-clinical prescribing errors.
Conclusion: The combined interventions of ZTP and daily error feedback were associated with a significant reduction in prescribing errors in the PICU, in line with Department of Health requirements of a 40 % reduction within 5 years.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/s00134-012-2660-7 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A simulation study to quantitatively assess the performance of electronic prescribing systems in English NHS Hospital Trusts.
Sci Rep
January 2025
Pharmacy Department, Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, NE1 4LP, England, UK.
Prescribing errors are a source of preventable harm in healthcare, which may be mitigated using Electronic Prescribing (EP) systems. Anyone who routinely prescribes medication could benefit from digitally assisted automated checks to identify whether a prescription should potentially not be allowed (e.g.
View Article and Find Full Text PDFStandardized Multimodal Pain Protocol Minimizes Inpatient Opioid Administration in Pediatric Orthopedic Surgery Population.
Iowa Orthop J
January 2025
NYU Langone Orthopedic Hospital, New York, New York, USA.
Background: Optimal management of post-operative pain is a critical component of orthopedic surgical care. There is a heightened awareness of narcotic prescribing habits given the current "opioid epidemic." The lack of standardized protocols has led to increased errors, delayed access to prescribed medications, and excessive narcotic prescribing.
View Article and Find Full Text PDFBackground And Aims: Drug-drug interactions (DDIs) are a significant health issue that may adversely affect the health and well-being of patients. This study assesses and compares potential DDI (pDDI) patterns, severity, and associated risk factors in government and private hospitals in Dhaka, Bangladesh.
Methods: A total of 188 and 206 prescriptions were collected from various government and private hospitals' outdoor departments, respectively, by capturing pictures of the prescriptions.
Adaptive Fixed-time tracking control for large-scale nonlinear systems based on improved simplified optimized backstepping strategy.
ISA Trans
January 2025
College of Control Science and Engineering, Bohai University, Jinzhou 121013, Liaoning, China. Electronic address:
This paper investigates the optimal fixed-time tracking control problem for a class of nonstrict-feedback large-scale nonlinear systems with prescribed performance. In the process of optimal control design, the new critic and actor neural network updating laws are proposed by adopting the fixed-time technique and the simplified reinforcement learning algorithm, which both guarantee the simplified optimal control algorithm and accelerate the convergence rate. Furthermore, the prescribed performance method is contemplated simultaneously, which ensures tracking errors can converge within the prescribed performance bounds in fixed time.
View Article and Find Full Text PDFErrors Associated With Medication Administration by a Nurse During Hospitalisation: A Prospective Observational Multicentric Study.
Nurs Open
January 2025
Department of Social and Clinical Pharmacy, Faculty of Pharmacy in Hradec Kralove, Charles University, Hradec Kralove, Czech Republic.
Aims: To explore all medication administration errors (MAEs) throughout the entire process of medication administration by nurses in the inpatient setting, to describe their prevalence, and to analyse associated factors, including deviation from the good practice standards.
Background: Worldwide, MAEs are very common and regarded as a serious risk factor to inpatient safety. Nurses assume an essential role in the hospital setting during the administration of medications.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!