Implementing a bar-code assisted medication administration system: effects on the dispensing process and user perceptions.

PURPOSE: We assessed the effects of a bar-code assisted medication administration system used without the support of computerised prescribing (stand-alone BCMA), on the dispensing process and its users. METHODS: The stand-alone BCMA system was implemented in one ward of a teaching hospital. The number of dispensing steps, dispensing time and potential dispensing errors (PDEs) were directly observed one month before and eight months after the intervention. Attitudes of pharmacy and nursing staff were assessed using a questionnaire (Likert scale) and interviews. RESULTS: Among 1291 and 471 drug items observed before and after the introduction of the technology respectively, the number of dispensing steps increased from five to eight and time (standard deviation) to dispense one drug item by one staff personnel increased from 0.8 (0.09) to 1.5 (0.12) min. Among 2828 and 471 drug items observed before and after the intervention respectively, the number of PDEs increased significantly (P<0.001). 'Procedural errors' and 'missing drug items' were the frequently observed PDEs in the after study. 'Perceived usefulness' and 'job relevance' of the technology decreased significantly (P=0.003 and P=0.004 respectively) among users who participated in the before (N=16) and after (N=16) questionnaires surveys. Among the interviewees, pharmacy staff felt that the system offered less benefit to the dispensing process (9/16). Nursing staff perceived the system as useful in improving the accuracy of drug administration (7/10). CONCLUSION: Implementing a stand-alone BCMA system may slow down and complicate the dispensing process. Nursing staff believe the stand-alone BCMA system could improve the drug administration process but pharmacy staff believes the technology would be more helpful if supported by computerised prescribing. However, periodical assessments are needed to identify weaknesses in the process after implementation, and all users should be educated on the benefits of using this technology.

The pattern of the discovery of medication errors in a tertiary hospital in Hong Kong.

BACKGROUND: The primary goal of reducing medication errors is to eliminate those that reach the patient. OBJECTIVE: We aimed to study the pattern of interceptions to tackle medication errors along the medication use processes. SETTING: Tertiary care hospital in Hong Kong. METHOD: The 'Swiss Cheese Model' was used to explain the interceptions targeting medication error reporting over 5 years (2006-2010). MAIN OUTCOME MEASURES: Proportions of prescribing, dispensing and drug administration errors intercepted by pharmacists and nurses; proportions of prescribing, dispensing and drug administration errors that reached the patient. RESULTS: Our analysis included 1,268 in-patient medication errors, of which 53.4% were related to prescribing, 29.0% to administration and 17.6% to dispensing. 34.1% of all medication errors (4.9% prescribing, 26.8% drug administration and 2.4% dispensing) were not intercepted. Pharmacy staff intercepted 85.4% of the prescribing errors. Nurses detected 83.0% of dispensing and 5.0% of prescribing errors. However, 92.4% of all drug administration errors reached the patient. CONCLUSIONS: Having a preventive measure at each stage of the medication use process helps to prevent most errors. Most drug administration errors reach the patient as there is no defense against these. Therefore, more interventions to prevent drug administration errors are warranted.

Technology-related medication errors in a tertiary hospital: a 5-year analysis of reported medication incidents.

PURPOSE: Healthcare technology is meant to reduce medication errors. The objective of this study was to assess unintended errors related to technologies in the medication use process. METHODS: Medication incidents reported from 2006 to 2010 in a main tertiary care hospital were analysed by a pharmacist and technology-related errors were identified. Technology-related errors were further classified as socio-technical errors and device errors. This analysis was conducted using data from medication incident reports which may represent only a small proportion of medication errors that actually takes place in a hospital. Hence, interpretation of results must be tentative. RESULTS: 1538 medication incidents were reported. 17.1% of all incidents were technology-related, of which only 1.9% were device errors, whereas most were socio-technical errors (98.1%). Of these, 61.2% were linked to computerised prescription order entry, 23.2% to bar-coded patient identification labels, 7.2% to infusion pumps, 6.8% to computer-aided dispensing label generation and 1.5% to other technologies. The immediate causes for technology-related errors included, poor interface between user and computer (68.1%), improper procedures or rule violations (22.1%), poor interface between user and infusion pump (4.9%), technical defects (1.9%) and others (3.0%). In 11.4% of the technology-related incidents, the error was detected after the drug had been administered. CONCLUSIONS: A considerable proportion of all incidents were technology-related. Most errors were due to socio-technical issues. Unintended and unanticipated errors may happen when using technologies. Therefore, when using technologies, system improvement, awareness, training and monitoring are needed to minimise medication errors.