Modeling nurses' acceptance of bar coded medication administration technology at a pediatric hospital

Abstract

Objective: To identify predictors of nurses' acceptance of bar coded medication administration (BCMA).

Design: Cross-sectional survey of registered nurses (N=83) at an academic pediatric hospital that recently implemented BCMA.

Methods: Surveys assessed seven BCMA-related perceptions: ease of use; usefulness for the job; social influence from non-specific others to use BCMA; training; technical support; usefulness for patient care; and social influence from patients/families. An all possible subset regression procedure with five goodness-of-fit indicators was used to identify which set of perceptions best predicted BCMA acceptance (intention to use, satisfaction).

Results: Nurses reported a moderate perceived ease of use and low perceived usefulness of BCMA. Nurses perceived moderate-or-higher social influence to use BCMA and had moderately positive perceptions of BCMA-related training and technical support. Behavioral intention to use BCMA was high, but satisfaction was low. Behavioral intention to use was best predicted by perceived ease of use, perceived social influence from non-specific others, and perceived usefulness for patient care (56% of variance explained). Satisfaction was best predicted by perceived ease of use, perceived usefulness for patient care, and perceived social influence from patients/families (76% of variance explained).

Discussion: Variation in and low scores on ease of use and usefulness are concerning, especially as these variables often correlate with acceptance, as found in this study. Predicting acceptance benefited from using a broad set of perceptions and adapting variables to the healthcare context.

Conclusion: Success with BCMA and other technologies can benefit from assessing end-user acceptance and elucidating the factors promoting acceptance and use.

Figure 1

Study conceptual model and the specific perception, demographic, and acceptance variables used to test it. ^aIn the technology acceptance literature, these are predicted to moderate the perceptions–acceptance relationship. TAM, Technology Acceptance Model.

Figure 2

Fit indices and unstandardized parameter estimates from best subset regression models. *p≤0.05; **p≤0.01. AIC, Akaike information criterion; BCMA, bar coded medication administration; BIC, Bayesian information criterion; Cp, Mallow's Cp statistic; RMSE, root mean square error.