METHODS: A retrospective review of reports received from 1 January 2009 to 31 December 2012 was undertaken. Descriptive statistics method was applied.
RESULTS: A total of 17,357 MEs reported were reviewed. The majority of errors were from public-funded hospitals. Near misses were classified in 86.3 % of the errors. The majority of errors (98.1 %) had no harmful effects on the patients. Prescribing contributed to more than three-quarters of the overall errors (76.1 %). Pharmacists detected and reported the majority of errors (92.1 %). Cases of erroneous dosage or strength of medicine (30.75 %) were the leading type of error, whilst cardiovascular (25.4 %) was the most common category of drug found.
CONCLUSIONS: MERS provides rich information on the characteristics of reported MEs. Low contribution to reporting from healthcare facilities other than government hospitals and non-pharmacists requires further investigation. Thus, a feasible approach to promote MERS among healthcare providers in both public and private sectors needs to be formulated and strengthened. Preventive measures to minimise MEs should be directed to improve prescribing competency among the fallible prescribers identified.
METHODS: The literature relating to MEs in Southeast Asian countries was systematically reviewed in December 2014 by using; Embase, Medline, Pubmed, ProQuest Central and the CINAHL. Inclusion criteria were studies (in any languages) that investigated the incidence and the contributing factors of ME in patients of all ages.
RESULTS: The 17 included studies reported data from six of the eleven Southeast Asian countries: five studies in Singapore, four in Malaysia, three in Thailand, three in Vietnam, one in the Philippines and one in Indonesia. There was no data on MEs in Brunei, Laos, Cambodia, Myanmar and Timor. Of the seventeen included studies, eleven measured administration errors, four focused on prescribing errors, three were done on preparation errors, three on dispensing errors and two on transcribing errors. There was only one study of reconciliation error. Three studies were interventional.
DISCUSSION: The most frequently reported types of administration error were incorrect time, omission error and incorrect dose. Staff shortages, and hence heavy workload for nurses, doctor/nurse distraction, and misinterpretation of the prescription/medication chart, were identified as contributing factors of ME. There is a serious lack of studies on this topic in this region which needs to be addressed if the issue of ME is to be fully understood and addressed.
METHODS: A cross-sectional study was conducted over the period of 9 weeks in patients who visited the ED of Hospital Universiti Sains Malaysia (HUSM), Kelantan, Malaysia. Data on patient medication orders and demographic information was collected from the doctor's clerking sheet. Observations were made on nursing activities and these were documented in the data collection form. Other information related to the administration of medications were obtained from the nursing care records.
RESULTS: Observations and data collections were made for 547 patients who fulfilled the study criteria. From these, 311 patient data were randomly selected for analysis. Ninety-five patients had at least one ME. The prevalence of ME was calculated to be 30.5%. The most common types of ME were wrong time error (46.9%), unauthorized drug error (25.4%), omission error (18.5%) and dose error (9.2%). The most frequently drug associated with ME was analgesics. No adverse event was observed.
CONCLUSIONS: The prevalence of ME in our ED setting was moderately high. However, the majority of them did not result in any adverse event. Intervention measures are needed to prevent further occurrence.
DESIGN/METHODOLOGY/APPROACH: A literature review was performed on issues, sources, management and approaches to HISs-induced errors. A critical review of selected models was performed in order to identify medical error dimensions and elements based on human, process, technology and organisation factors.
FINDINGS: Various error classifications have resulted in the difficulty to understand the overall error incidents. Most classifications are based on clinical processes and settings. Medical errors are attributed to human, process, technology and organisation factors that influenced and need to be aligned with each other. Although most medical errors are caused by humans, they also originate from other latent factors such as poor system design and training. Existing evaluation models emphasise different aspects of medical errors and could be combined into a comprehensive evaluation model.
RESEARCH LIMITATIONS/IMPLICATIONS: Overview of the issues and discourses in HIS-induced errors could divulge its complexity and enable its causal analysis.
PRACTICAL IMPLICATIONS: This paper helps in understanding various types of HIS-induced errors and promising prevention and management approaches that call for further studies and improvement leading to good practices that help prevent medical errors.
ORIGINALITY/VALUE: Classification of HIS-induced errors and its management, which incorporates a socio-technical and multi-disciplinary approach, could guide researchers and practitioners to conduct a holistic and systematic evaluation.
MATERIALS AND METHODS: We investigated Google Trends® for popular search relating to medication errors, risk management and shift work. Relative search volumes (RSVs) were evaluated from 2008 to 2018. A comparison between RSV curves related to medication errors, risk management and shift work was carried out. Then, we compared the world to Italian search.
RESULTS: RSVs were persistently higher for risk management than for medication errors (mean RSVs 069 vs. 48%) and RSVs were stably higher for medication errors than shift work (mean RSVs 48 vs. 22%). In Italy, RSVs were much lower compared to the rest of the world, and RSVs for medication errors during the study period were negligible. Mean RSVs for risk management and shift work were 3 and 25%, respectively. RSVs related to medication errors and clinical risk management were correlated (r=0.520, p<0.0001).
CONCLUSIONS: Google Trends® search query volumes related to medication errors, risk management and shift work are different. RSVs for risk management are higher, and they are correlated with medication errors. Also, shift work search appears to be lower. These results should be interpreted in order to correctly evaluate how to decrease the number of medication errors in different health care related setting.