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Updated: Sep 28, 2025
Improving IV Insulin Administration in a Community Hospital
Published on: June 11, 2012
The Impact of Smart Pump Interoperability on Errors in Intravenous Infusion Administrations: A Multihospital Before
Jilian Skog1, Sally Rafie2, Kumiko O Schnock3
1From the Sharp Healthcare, Chula Vista.
Objective:
The objective of this study was to assess the frequency, type, and severity of errors associated with intravenous medication administration before and after smart pump interoperability.
Methods:
We conducted an observational study at a community healthcare system before and after implementing smart pump interoperability. Point prevalence methodology was used to collect data on medication administration and errors in adult inpatient settings.
Results:
Observations were completed for 350 infusions preintervention (178 patients) and 367 postintervention (200 patients). Total errors significantly decreased from 401 (114.6 per 100 infusions) to 354 (96.5 per 100 infusions, P = 0.02). Administration errors decreased from 144 (41.1 per 100 infusions) to 119 (32.4 per 100 infusions, P = 0.12). Expired medication errors significantly reduced from 11 (3.1 per 100 infusions) to 2 (0.5 per 100 infusions, P = 0.02). Errors involving high-risk medications significantly reduced from 45 (12.8 per 100 infusions) to 25 (6.8 per 100 infusions, P = 0.01). Errors involving continuous medications significantly reduced from 44 (12.6 per 100 infusions) to 22 (6.0 per 100 infusions, P = 0.005). When comparing programming type, manual programming resulted in 115 (77.2%) of administration and user documentation errors compared with 34 errors (22.8%) that occurred when autoprogramming was used. Of these, errors involving high-risk medications reduced from 21 (84.0%) to 4 (16.0%) after using autoprogramming.
Conclusions:
Smart pump interoperability resulted in a 16% reduction in medication administration errors. Despite using dose error reduction software and autoprogramming, some types of errors persisted. Further studies are needed to understand how technology use can be optimized.
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