Abstract
Objectives: To assess the nature of errors associated with e-prescribing and automated dispensing cabinets, two technologies in wide-spread use in pharmacy settings. |Study design: The Dyke Anderson Patient Safety Database (DAPSD) was developed from the responses of 535 licensed pharmacists in Nebraska to an institutional review board approved (exempt) cross-sectional survey conducted previously. Pharmacists reported safety issues in the form of errors prevented and errors observed about specific technologies as descriptive, open-ended text responses. Investigators prepared for the data analysis by performing direct observation of the e-prescribing and automated dispensing process in local health system pharmacy. The e-prescribing technology and automated dispensing cabinets (ADCs) data was selected from the larger data set and studied. Text statements were individually evaluated and transformed using SPSS text analysis to generate precise error subtype categories and causes of these subtypes. Descriptive statistics were performed to compute frequency of each subtype of error either prevented or observed and the knowledge gap about cause for each.|Primary findings: E-prescribing: 1) Qualitative proposition- Medical Decision Support (MDS) must be made mandatory in all e-prescribing systems. 2) Data transformation: Six prevented error type categories and seven observed error type categories generated. 3) Quantitative findings: 207 pharmacists reporting 227 error types prevented whereas 191 pharmacists reported 199 error types observed. There were 11 causes reported out of 227 error types prevented reports and 93 causes reported out of 199 error types observed reports. ADCs: 1) Qualitative proposition - A manual double check is required before dispensing medication. 2) Data transformation: Seven prevented error type categories and six observed error type categories were generated. 3) Quantitative findings: 171 pharmacists reporting 189 error types prevented whereas 156 pharmacists reported 163 error types observed. There were 18 causes reported out of 189 error types prevented reports and 39 causes reported out of 163 error types observed reports. |Conclusions: There are new error types arising due to use of technology and also persistent error types that exist with or without use of technology. A cause needs to be identified to design effective solutions. We need to shift our paradigm of inquiry to focus on local and specific safety risks generated by each technology individually, within the context of the technology-human interface.