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

Abstract

Objectives: 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.

Design: Developmental study.

Setting: 22 community pharmacies representing three U.S. regions.

Participants: Model-building group: six pharmacists and three technicians. Model validation group: 11 pharmacists; staff at two pharmacies observed.

Intervention: 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.

Main outcome measures: Validation of event tree structure and incidence of defined PADEs with targeted medications.

Results: 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.

Conclusion: 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.