The impact of traditional and smart pump infusion technology on nurse medication administration performance in a simulated inpatient unit

Abstract

ObjectiveAssess the impact of infusion pump technologies (traditional pump vs smart pump vs smart pump with barcode) on nurses' ability to safely administer intravenous medications.DesignExperimental study with a repeated measures design.SettingHigh-fidelity simulated inpatient unit.ResultsThe nurses remedied 60% of “wrong drug” errors. This rate did not vary as a function of pump type. The nurses remedied “wrong patient” errors more often when using the barcode pump (88%) than when using the traditional pump (46%) or the smart pump (58%) (Cochran Q=14.36; p<0.05). The number of nurses who remedied “wrong dose hard limit” errors was higher when using the smart pump (75%) and the barcode pump (79%) than when using the traditional pump (38%) (Cochran Q=12.13; p<0.003). Conversely, there was no difference in remediation of “wrong dose soft limit” errors across pump types. The nurses' pump programming was less accurate when mathematical conversions were required. Success rates on secondary infusions were low (55.6%) and did not vary as a function of pump type.ConclusionsThese findings indicate that soft (changeable) limits in smart infusion pumps had no significant effect in preventing dosing errors. Provided that smart pumps are programmed with hard (unchangeable) limits, they can prevent dosing errors, thereby increasing patient safety. Until barcode pumps are integrated with other systems within the medication administration process, their role in enhancing patient safety will be limited. Further improvements to pump technologies are needed to mitigate risks associated with intravenous infusions, particularly secondary infusions.

Erros Na Administração De Medicamentos: Evidências E Implicações Na Segurança Do Paciente

“…Dentre os tipos de erros na AM encontrados nesta revisão, o principal deles diz respeito à dosagem medicamentosa, presente em 27 estudos (67,5%) (8,(10)(11)(13)(14)(16)(17)(19)(20)(23)(24)(25)(26)(27)(28)(30)(31)(32)34,36,(42)(43)(44)(45)(46)(47)(48)(49) . Corroborando com tal achado, uma pesquisa (50) que teve como um dos objetivos identificar o perfil dos erros cometidos durante o preparo, encontrou que 67,7% dos erros estavam associados a doses preparadas erroneamente.…”

“…Esta realidade também foi encontrada na presente pesquisa, em que a administração da medicação errada foi o segundo erro mais comum entre os estudos analisados e esteve presente em 62,5% (n=25) (8)(9)(10)(11)13,16,(20)(21)(23)(24)(25)(26)(28)(29)(30)(31)(36)(37)(42)(43)(45)(46)(48)(49)53) da amostra, seguido pelo medicamento administrado ao paciente errado (n=21;52,5%) (8)(9)(10)(11)16,18,22,(23)(24)(25)(26)(27)(28)(30)(31)(33)(34)(36)(37)(46)(47) .…”

A Security Analysis of Cyber-Physical Systems Architecture for Healthcare

“…Studies have mentioned that while these drug libraries do help catch many potential problems, there are still sources of errors that have not been fully addressed [23]. These included clinicians overriding soft and hard limits [21] and facilities setting soft warning levels too strictly or incorrectly.…”

Model-Based Dependability Analysis of Programmable Drug Infusion Pumps

“…There have been extensive studies by human factors experts and psychologists on the cognitive factors underlying human error [22,23,36]. Furthermore, the specific types of mistakes that operators commit while operating an infusion pump have also been well-studied and documented [3,9,20,24,28,37,40,47]. Table 2 lists some of the common pump programming errors as reported in the literature.…”

“…Mechanical failures are modeled using failure mode variables and transitions. Our approach incorporates models of pump programming errors based on generic human error models commonly studied in the human factors community [22,27,36] along with the results of studies and reports on the common types of errors committed by caregivers while programming infusion pumps [3,4,28,37,47]. The composition of these models yields an affine hybrid automaton.…”