Investigating inpatient medication administration using the theory of planned behavior

Joseph W. Rogers, Marc Fleming, Joyce Tipton, Angela Ward, Kevin W. Garey, Elizabeth P. Pitman

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Purpose. Results of a study to determine behavioral factors that help explain why nurses often do not obtain and administer medications from automated dispensing cabinets (ADCs) “one patient at a time” are reported. Methods. To investigate nurses' frequent failure to adhere to best-practice standards for ADC use, a 12-item questionnaire developed using information obtained from an elicitation study and a focus group session was e-mailed to 755 nurses at an academic medical center. A model based on constructs of the theory of planned behavior (attitude, subjective norm, and perceived behavioral control) was used to evaluate nurses' intent to follow ADC best practices through univariate and multivariate analyses. Results. Of the 755 nurses targeted in the survey, 271 (34%) submitted usable responses. A favorable attitude toward safe ADC dispensing practices and a supportive subjective norm were strongly associated with intent to use ADCs properly; those factors remained highly correlated with intent (p < 0.001 for both) after controlling for demographic and work-related variables such as years in practice and patient workload. Eighty percent of observed variability of intent was explained by the evaluated constructs and variables (p < 0.001), suggesting that direct interventions at the patient care unit level and peer influence would likely be effective in promoting safe ADC use. Conclusion. A survey based on the theory of planned behavior demonstrated that attitude and subjective norm were important independent predictors of nurse intent to use ADCs properly.

Original languageEnglish
Pages (from-to)2065-2070
Number of pages6
JournalAmerican Journal of Health-System Pharmacy
Issue number24
StatePublished - 15 Dec 2017


  • Healthcare utilization
  • Patient safety
  • Prediction models
  • Qualitative research


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