Smart Pumps Improve Medication Safety but Increase Alert Burden in Neonatal Care

Melton, Kristin; Timmons, Kristen; Walsh, Kathleen E.; Meinzen‐Derr, Jareen; Kirkendall, Eric S.

doi:10.21203/rs.2.13639/v1

Cited by 3 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One study reported a high volume of alerts clustered around specific patients and mediations in a NICU. 52 This can potentially lead to a high alert burden and limit DERS safety benefit by desensitizing nurses to these alerts. Another study found that due to line occlusions, drug incompatibilities, and patient factors, alarms from infusion pumps were frequent in the NICU/pediatric ICU.…”

Section: Discussionmentioning

confidence: 99%

Quantifying the Impact of Infusion Alerts and Alarms on Nursing Workflows: A Retrospective Analysis

Obuseh

DeLaurentis

2021

Appl Clin Inform

View full text Add to dashboard Cite

Background Smart infusion pumps affect workflows as they add alerts and alarms in an information-rich clinical environment where alarm fatigue is already a major concern. An analytic approach is needed to quantify the impact of these alerts and alarms on nursing workflows and patient safety. Objectives To analyze a detailed infusion dataset from a smart infusion pump system and identify contributing factors for infusion programming alerts, operational alarms, and alarm resolution times. Methods We analyzed detailed infusion pump data across four hospitals in a health system for up to 1 year. The prevalence of alerts and alarms was grouped by infusion type and a selected list of 32 high-alert medications (HAMs). Logistic regression was used to explore the relationship between a set of risk factors and the occurrence of alerts and alarms. We used nonparametric tests to explore the relationship between alarm resolution times and a subset of predictor variables. Results The study dataset included 745,641 unique infusions with a total of 3,231,300 infusion events. Overall, 28.7% of all unique infusions had at least one operational alarm, and 2.1% of all unique infusions had at least one programming alert. Alarms averaged two per infusion, whereas at least one alert happened in every 48 unique infusions. Eight percent of alarms took over 4 minutes to resolve. Intravenous fluid infusions had the highest rate of error-state occurrence. HAMs had 1.64 more odds for alerts than the rest of the infusions. On average, HAMs had a higher alert rate than maintenance fluids. Conclusion Infusion pump alerts and alarms impact clinical care, as alerts and alarms by design interrupt clinical workflow. Our study showcases how hospital system leadership teams can leverage infusion pump informatics to prioritize quality improvement and patient safety initiatives pertaining to infusion practices.

show abstract

Section: Discussionmentioning

confidence: 99%

Quantifying the Impact of Infusion Alerts and Alarms on Nursing Workflows: A Retrospective Analysis

Obuseh

DeLaurentis

2021

Appl Clin Inform

View full text Add to dashboard Cite

show abstract

“…For these hospitals, introducing unfamiliar brand-new infusion pumps, which are introduced in recent times and not used in many hospitals, can be a risk factor regardless of the performance of the device. 15 In addition, most nurses prefer to use familiar infusion pumps to overcome the necessity to re-train and adapt to the unfamiliar interface of a novel device owing to the fear of accidents from user confusion. 16 Furthermore, many vendors recommend a dedicated IV catheter with their infusion pumps to guarantee the accuracy of infusion.…”

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

View full text Add to dashboard Cite

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.

show abstract

Integrating and Evaluating the Data Quality and Utility of Smart Pump Information in Detecting Medication Administration Errors: Evaluation Study (Preprint)

Ni¹,

Lingren²,

Huth³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

BACKGROUND At present, electronic health records (EHRs) are the central focus of clinical informatics given their role as the primary source of clinical data. Despite their granularity, the EHR data heavily rely on manual input and are prone to human errors. Many other sources of data exist in the clinical setting, including digital medical devices such as smart infusion pumps. When incorpo-rated with prescribing data from the EHRs, smart pump records (SPR) are capable of shedding light on actions taking place during the medication use process. Nevertheless, harmonizing the two sources is hindered by multiple technical challenges, and the usability and utility of SPRs have not been realized. OBJECTIVE In this study we incorporated SPRs with EHR data to evaluate their usability and utility in detecting medication administration errors. Our overarching hypothesis was that SPRs would contribute unique information in the medication use process, enabling more comprehensive detection of discrepancies and potential errors in medication administration. METHODS We evaluated the medication use process of nine high-risk medications for patients admitted to the neonatal intensive care unit during a one-year period. An automated algorithm was developed to align SPRs with their medication orders in the EHRs using patient ID, medication name and timestamp. The aligned data were manually reviewed by a clinical research coordinator and two pediatric physicians to identify discrepancies in medication administration. The usability of SPRs was assessed with the proportion of usable data that were linked to valid EHR orders. To evaluate their utility, we compared the frequency and severity of discrepancies captured by the SPR and EHR data respectively. A novel concordance assessment was also developed to under-stand the detection power and capabilities of SPR and EHR data. RESULTS Approximately 70% of the SPRs contained valid patient IDs and medication names, making them usable for data integration. After combing the two sources, the investigative team re-viewed 2307 medication orders with 10575 medication administration records (MARs) and 23397 SPRs. A total of 321 MAR and 682 SPR discrepancies were identified, with vasopressors showing the highest discrepancy rates, followed by narcotics and total parenteral nutrition. Compared to EHR MARs, substantial dosing discrepancies were more detectable using the SPRs. The concordance analysis showed little overlap between MAR and SPR discrepancies, with most discrepancies captured by the SPR data. CONCLUSIONS We integrated smart infusion pump information with EHR data to make visible the most error-prone phases of the medication lifecycle. The findings suggested that SPRs could be a more re-liable data source for medication error detection. Ultimately, it is imperative to integrate SPR information with EHR data to fully detect and mitigate medication administration errors in the clinical setting.

show abstract

Smart Pumps Improve Medication Safety but Increase Alert Burden in Neonatal Care

Cited by 3 publications

References 16 publications

Quantifying the Impact of Infusion Alerts and Alarms on Nursing Workflows: A Retrospective Analysis

Quantifying the Impact of Infusion Alerts and Alarms on Nursing Workflows: A Retrospective Analysis

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

Integrating and Evaluating the Data Quality and Utility of Smart Pump Information in Detecting Medication Administration Errors: Evaluation Study (Preprint)

Contact Info

Product

Resources

About