Risk models to improve safety of dispensing high-alert medications in community pharmacies.

摘要

To determine whether sociotechnical probabilistic risk assessment can create accurate approximations of detailed risk models that describe error pathways, estimate the incidence of preventable adverse drug events (PADEs) with high-alert medications, rank the effectiveness of interventions, and provide a more informative picture of risk in the community pharmacy setting than is available currently. Developmental study. 22 community pharmacies representing three U.S. regions. Model-building group: six pharmacists and three technicians. Model validation group: 11 pharmacists; staff at two pharmacies observed. A model-building team built 10 event trees that estimated the incidence of PADEs for four high-alert medications: warfarin, fentanyl transdermal systems, oral methotrexate, and insulin analogs. Validation of event tree structure and incidence of defined PADEs with targeted medications. PADEs with the highest incidence included dispensing the wrong dose/strength of warfarin as a result of data entry error (1.83/1,000 prescriptions), dispensing warfarin to the wrong patient (1.22/1,000 prescriptions), and dispensing an inappropriate fentanyl system dose due to a prescribing error (7.30/10,000 prescriptions). PADEs with the lowest incidence included dispensing the wrong drug when filling a warfarin prescription (9.43/1 billion prescriptions). The largest quantifiable reductions in risk were provided by increasing patient counseling (27-68% reduction), conducting a second data entry verification process during product verification (50-87% reduction), computer alerts that can't be bypassed easily (up to 100% reduction), opening the bag at the point of sale (56% reduction), and use of barcoding technology (almost a 100,000% increase in risk if technology not used). Combining two or more interventions resulted in further overall reduction in risk. The risk models define thousands of ways process failures and behavioral elements combine to lead to PADEs. This level of detail is unavailable from any other source.