Automated, Web-Based Solution for Bidirectional EHR–Infusion Pump Communication

Griffiths, Steven M.; Sapirstein, Adam; Guzman, Julio C.; Soriano, Zaza; Ravitz, Alan

doi:10.2345/0899-8205-53.1.30

Cited by 3 publications

(4 citation statements)

References 10 publications

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“…Recently, several technical improvements, in terms of device-equipped smart functions and linkage on the electronic medical record (EMR) server, have been made to promptly detect erroneous device settings. 9–14 However, although such previous research demonstrated improvements in patient safety, several obstacles can prevent hospitals from adopting such devices. First, the price of a smart infusion pump is generally two- or three-times higher than that of conventional non-smart devices; therefore, the financial burden of purchasing sufficient smart infusion pumps can be a barrier for most hospitals.…”

Section: Discussionmentioning

confidence: 99%

Convolutional neural network-based ambient light-independent panel digit surveillance technique for infusion pumps

Hwang

Kim

Sung

et al. 2021

Proc Inst Mech Eng H

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For effective patient therapy and improved patient safety, it is critical to administer medication accurately in accordance with doctor’s prescription. However, accidents owing to the erroneous programing of infusion pumps caused by users have been consistently reported in several documents. In this study, the authors propose a novel surveillance technique for infusion pumps to continuously monitor the variations in panel digits using a convolutional neural network model, and evaluate the performance of the implemented technique. During the experimental evaluation, 1st-step ROIs and 2nd-step ROIs were successfully extracted from the frame images regardless of the ambient lighting conditions. The final accuracies of the implemented CNN model are 99.9% for both the training (172,800 images) and validation (1080 images) dataset while the final losses for the training and validation datasets are 0.48 and 0.45 after 13th epoch, respectively. In the 24-h continuous monitoring test, the accuracy of the model for volume recognition considering all the 1440 measurements (960 for day-lighting and 480 for night-lighting) is 95.5%, whereas in day-lighting and night-lighting modes the accuracies of the model are 98.2% and 90.0%, respectively. Based on these experimental results, the proposed surveillance technique incorporating infusion pumps is expected to improve the safety of patients who need long-term treatments via infusion pumps, reducing the burden on the nurses and hospitals.

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Section: Discussionmentioning

confidence: 99%

Convolutional neural network-based ambient light-independent panel digit surveillance technique for infusion pumps

Hwang

Kim

Sung

et al. 2021

Proc Inst Mech Eng H

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“…Technical and clinical process details of the SA system have been previously published. 12 13 The online supplemental appendix includes full information for each scenario. The order of scenarios remained constant across participants.…”

Section: Methodsmentioning

confidence: 99%

“…The SA was developed using a systems engineering approach. 12 We sought to explore the degree to which the SA system could improve safety and efficiency of insulin infusion practices.…”

Section: Introductionmentioning

confidence: 99%

Smart agent system for insulin infusion protocol management: a simulation-based human factors evaluation study

et al. 2021

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ObjectiveTo compare the insulin infusion management of critically ill patients by nurses using either a common standard (ie, human completion of insulin infusion protocol steps) or smart agent (SA) system that integrates the electronic health record and infusion pump and automates insulin dose selection.DesignA within subjects design where participants completed 12 simulation scenarios, in 4 blocks of 3 scenarios each. Each block was performed with either the manual standard or the SA system. The initial starting condition was randomised to manual standard or SA and alternated thereafter.SettingA simulation-based human factors evaluation conducted at a large academic medical centre.SubjectsTwenty critical care nurses.InterventionsA systems engineering intervention, the SA, for insulin infusion management.MeasurementsThe primary study outcomes were error rates and task completion times. Secondary study outcomes were perceived workload, trust in automation and system usability, all measured with previously validated scales.Main resultsThe SA system produced significantly fewer dose errors compared with manual calculation (17% (n=20) vs 0, p<0.001). Participants were significantly faster, completing the protocol using the SA system (p<0.001). Overall ratings of workload for the SA system were significantly lower than with the manual system (p<0.001). For trust ratings, there was a significant interaction between time (first or second exposure) and the system used, such that after their second exposure to the two systems, participants had significantly more trust in the SA system. Participants rated the usability of the SA system significantly higher than the manual system (p<0.001).ConclusionsA systems engineering approach jointly optimised safety, efficiency and workload considerations.

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“…8 The integration of infusion pumps to the EHR presents significant technical challenges that require an established infrastructure, technical resources, and appropriate commitment to the project at the level of the healthcare system. [9][10][11] These systems integrate clinical provider order entry (CPOE)-based infusion orders with IV infusion pump devices. Interoperable infusion pump workflows between pump and EHR can reduce the number of steps by over 50%, primarily by conducting pump programming and EHR documentation for the clinician.…”

Section: Introductionmentioning

confidence: 99%

Automation and interoperability of a nurse-managed insulin infusion protocol as a model to improve safety and efficiency in the delivery of high-alert medications

Barasch

Romig

Demko

et al. 2019

Journal of Patient Safety and Risk Management

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The administration of high-alert medications requires the use of enhanced systems to prevent errors. One commonly used system relies on independent verification of dose changes by a second clinician, a human double check. This system is inefficient, and while it may reduce, it does not eliminate error. We postulate that the ability to integrate interoperable medication infusion pumps and electronic medical records systems with existing dose adjustment algorithms can improve the safety and efficiency in the delivery of high-alert and other medications. We followed step-wise systems engineering practices to develop and build a novel system, the Smart Agent, to semi-autonomously administer intravenous insulin according to our hospital’s protocol for nurse-managed insulin infusion in the intensive care units. The prototype system used a commercial medication infusion pump and our existing electronic health record. We believe that this model of interoperable systems integration can be incrementally and broadly developed in the future to improve the safety and workflow of medication infusions.

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Automated, Web-Based Solution for Bidirectional EHR–Infusion Pump Communication

Cited by 3 publications

References 10 publications

Convolutional neural network-based ambient light-independent panel digit surveillance technique for infusion pumps

Convolutional neural network-based ambient light-independent panel digit surveillance technique for infusion pumps

Smart agent system for insulin infusion protocol management: a simulation-based human factors evaluation study

Automation and interoperability of a nurse-managed insulin infusion protocol as a model to improve safety and efficiency in the delivery of high-alert medications

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