Class of 2016 Abstract / Objectives: Analyze electronic prescription errors made by a community health center. Determine the time it takes to correct electronic prescription errors in a community pharmacy. Ascertain whether or not a collaborative practice agreement would be beneficial. Methods: The store computer system was used to generate a report of all prescriptions received at a community pharmacy from a community health center during a 6-month period. Using an Excel sheet, one author kept track of how many electronic prescriptions were received, the number and type of errors, and the time it took to get an error corrected. Results: There were 1896 electronic prescriptions sent from a community health center to a community pharmacy; 61 contained an error (3.24%). On average, it took the doctor’s office 111.7 hours to call back and clarify the mistake. Conclusions: There was not a significant amount of prescribing errors that occurred during the data collection period. However, the time it took for the doctor’s office to call back was significant and translates to patients not being able to get their medications on time.

Magister Pharmaceuticae - MPharm / The objectives of the present study were to describe the type and frequency of prescribing errors and error frequency, to determine the error frequency for different drug classes, to identify potential drug interactions and drug-disease interactions to point out off-label prescribing and to evaluate risk factors of prescribing errors. Methods: This prospective cross sectional study was conducted over a period of 6 months from July 2012 to December 2012 in 2 specialist wards and 2 general medical wards at Red Cross War Memorial Children’s Hospital in Cape Town in South Africa. Only prescriptions generated by doctors in the above mentioned wards were assessed. Convenience sampling was used to select 200 prescription charts for analysis. Information relating to prescribing error, potential drug interaction, potential drug-disease interactions, off-label prescribing and potential risk factors of prescribing error were entered into excel spreadsheet and analysed using STATA versions 11&12. The mass of the patients was converted into weight-for-age z-score (WAZ) using WHO 2006 child growth standards. Univariate analysis and multiple logistic regression were used to identify risk factors of prescribing errors. Results: Of the 200 children on whom prescribing information was analysed, 40 (20%) were severely underweight and a further 25(12.5%) were moderately underweight. A total of 1402 prescribing errors were documented in 1282 drug items prescribed, a rate of 1.09 errors per drug item prescribed. Incomplete prescription information was the most common type of prescribing error, present in 65.6% of all drug items prescribed. The error frequency was high for all drug classes ranging from 57.9% of all respiratory drug items prescribed to 86.4% of all gastro intestinal system drug items prescribed. The number of potential drug-drug interactions was low i.e. 20 potential pharmacodynamics and 49 potential pharmacokinetic drug interactions were identified. The number of potential drug-disease interactions was also low i.e. 39 or 0.03% per drug item prescribed. Furthermore 57 off-label prescribing incidences were recorded. Senior doctors posed a significant risk factor for prescribing errors, an OR 1.95, 95% CI 1.46 – 2.61. Conversely, prescriptions written up in the general wards compared to the specialty wards (an OR 0.65. 95% CI 0.47-0.90) and prescribing during weekends compared to weekdays (an OR 0.71, 95% CI 0.53-0.96) were associated with lower prescribing error risk. Conclusion: This study provided valuable information about prescribing practices in children at RCWMCH. There is a need to improve prescribers’ practice at RCWMCH considering the type of errors observed viz. missing information, use of wrong drug name, abbreviations, legibility concerns and lack of clarity of the prescriptions, among others. Based on this study results further intervention studies are recommended to investigate the level of medical student’s training w.r.t prescribing practice.

No description available.

Hospital electronic prescribing and medicines administration (HEPMA) system implementation is advocated by national e-health strategies to produce patient safety benefits. No previous study has evaluated HEPMA implementation impacting discharge information communication or assessed discharge prescribing errors. The aims were to assess HEPMA system implementation impact on medicines related discharge communication and prescribing errors, and to gain the perspective of hospital staff involved in the communication process. Following a narrative literature review, a convergent parallel mixed methods was selected, consisting of interpretative phenomenology and experimental before and after study design. Face-to-face semi-structured interviews of a purposive sample of hospital staff involved in discharge information communication were undertaken using the Theoretical Domains Framework (TDF) as a theoretical lens. In addition a quasi experimental retrospective case notes review, both before and after implementation was completed. Pre-implementation, staff described patient safety concerns with traditional discharge communication processes. They cited frequent prescribing errors, and associated adverse events and hospital readmissions. HEPMA implementation was anticipated to improve patient safety and create more efficient discharge communication. Post-implementation staff articulated improved information quality highlighting fewer omitted medicines and improved patient safety. TDF findings of behaviour change highlighted behavioural alteration including adaption of processes to improve discharge quality. Quantitative data collection (n=159 before and after) confirmed qualitative findings; increased compliance with discharge documentation, for example staff grade recorded increased from 40% to 100% (p<0.001). Prescribing error quantity and severity were reduced; errors reduced from 99% to 23% of patients (p<0.001); only 22% of identified errors likely to cause harm. Omitted medicines decreased from 42% to 11% of patients (p<0.001). The findings contribute original knowledge concerning HEPMA implementation impacting discharge information communication and prescribing errors. The study demonstrated reduced prescribing errors and improved patient safety which potentially impacted health and wellbeing. Qualitative findings and quantitative results are transferable and applicable to other NHS organisations or similar healthcare settings.

Background: Prescribing errors are the most frequent type of error in the medication use process. High risk medicines (HRMs) are a sub-class of medications that if used erroneously have potentially devastating consequences which defined by Institute for Safe Medication Practices (ISMP) as the drugs that bear a heightened risk of causing significant patient harm when they are used in error. Therefore, prescribing errors with HRMs are of concern to healthcare professionals that are responsible for ensuring mitigating patient safety. This thesis examines to what extent prescribing errors with HRMs in hospital occur, the causes of prescribing errors with HRMs and the differences to non-HRMs and the prescribing errors with HRMs during the on-call period. Method: The research adopted a mixed methods approach to explore prescribing errors with HRMs in hospitals and three studies were undertaken. The first study was a systematic review of the literature to explore the prevalence and incidence of prescribing errors with HRMs in hospitals. The second study was a secondary analysis of 59 existing interviews with foundation year doctors to explore the causes of prescribing errors with HRMs and compare them to those for non-HRMs reported in the same interviews. The third study was a qualitative study of the challenges of prescribing HRMs safely during the on-call period. This final study involved six focus groups with foundation year doctors (total participants number was 42). Results: Overall, findings demonstrated that there is paucity of studies that explored the prevalence of prescribing errors with HRMs and this literature showed inconsistency in definitions of prescribing errors, HRMs lists, severity scales and study methods (Study One). This resulted in a very wide range of prevalence of prescribing errors with HRMs. In terms of causes of prescribing errors with HRMs (Study Two), prescribing HRMs was considered a complex task for participants, especially those requiring dosage calculations, errors in the legal prescription requirements for controlled medications occurred with HRMs only and the on-call period was a particularly challenging period to prescribe safely especially with HRMs. In Study Three, the reasons found for this include the nature of the on-call period as a fast-paced environment, the methods of communication such as the bleep system, lack of accessibility to patient information and lack of plan from the primary team. Conclusions: HRMs form part of general medications, meaning they share similar traits, but the potentially devastating consequences of HRMs and the complicated task posed by prescribing them makes errors in their prescription profound. Therefore, HRMs need closer attention and more concern from healthcare professionals, researchers and policymakers. Such attention could result in a significant reduction in adverse outcomes and improved patient safety.