Dose error reduction software in medication safety risk management - optimising the smart infusion pump dosing limits in neonatal intensive care unit prior to implementation

Optimizing the Use of Dose Error Reduction Software on Intravenous Infusion Pumps

BACKGROUND AND OBJECTIVES

Intravenous infusions have the potential to cause significant harm in patients and are associated with a high rate of adverse drug events and medication errors. Infusion pumps with dose error reduction software (DERS) can be used to reduce errors by establishing safe infusion parameters. In 2019, a quality improvement project was initiated with the aim to increase DERS compliance from 46% to 75% at our specialty institution by October 1, 2022.

METHODS

An interdisciplinary group was tasked with improving compliance with DERS by identifying key drivers, including informed staff, engaged staff, and an accurate smart pump library. We used the Model for Improvement framework to guide this improvement project, and Plan-Do-Study-Act (PDSA) cycles were used to plan for interventions. PDSA cycles included drug library updates, education, and unit-level compliance reporting. Weekly average DERS compliance was monitored as the outcome measure, and weekly pump alerts per 100 infusions were monitored as a balancing measure; statistical process control charts were used to monitor measures from 2018 to 2022.

RESULTS

Over the course of 25 months, 8 PDSA cycles resulted in 5 centerline improvements from a baseline mean of 46% to a final mean of 78%. Pump alerts per 100 infusions decreased from 15.9 to 6.4 with the first PDSA cycle and then continued to decrease to 3.9 with subsequent interventions.

CONCLUSIONS

Although features like DERS can help ensure safe medication administration, continuous improvement efforts to increase DERS compliance without increasing alert burden are needed to ensure that benefits of this technology are optimized.

Medication errors related to high-alert medications in a paediatric university hospital - a cross-sectional study analysing error reporting system data

BACKGROUND

Paediatric patients are prone to medication errors, and only a few studies have explored errors in high-alert medications in children. The present study aimed to investigate the prevalence and nature of medication errors involving high-alert medications and whether high-alert medications are more likely associated with severe patient harm and higher error risk classification compared to other drugs.

METHODS

This study was a cross-sectional report of self-reported medication errors in a paediatric university hospital in 2018-2020. Medication error reports involving high-alert medications were investigated by descriptive quantitative analysis to identify the prevalence of different drugs, Anatomical Therapeutic Chemical groups, administration routes, and the most severe medication errors. Crosstabulation and Pearson Chi-Square (χ2) tests were used to compare the likelihood of more severe consequences to the patient and higher error risk classification between medication errors involving high-alert medications and other drugs.

RESULTS

Among the reported errors (n = 2,132), approximately one-third (34.8%, n = 743) involved high-alert medications (n = 872). The most common Anatomical Therapeutic Chemical subgroups were blood substitutes and perfusion solutions (B05; n = 345/872, 40%), antineoplastic agents (L01; n = 139/872, 16%), and analgesics (N02; n = 98/872, 11%). The majority of high-alert medications were administered intravenously (n = 636/872, 73%). Moreover, IV preparations were administered via off-label routes (n = 52/872, 6%), such as oral, inhalation and intranasal routes. Any degree of harm (minor, moderate or severe) to the patient and the highest risk classifications (IV-V) were more likely to be associated with medication errors involving high-alert medications (n = 743) when compared to reports involving other drugs (n = 1,389).

CONCLUSIONS

Preventive risk management should be targeted on high-alert medications in paediatric hospital settings. In these actions, the use of intravenous drugs, such as parenteral nutrition, concentrated electrolytes, analgesics and antineoplastic agents, and off-label use of medications should be prioritised. Further research on the root causes of medication errors involving high-alert medications and the effectiveness of safeguards is warranted.

Closed-Loop Medication Management with an Electronic Health Record System in U.S. and Finnish Hospitals

Many medication errors in the hospital setting are due to manual, error-prone processes in the medication management system. Closed-loop Electronic Medication Management Systems (EMMSs) use technology to prevent medication errors by replacing manual steps with automated, electronic ones. As Finnish Helsinki University Hospital (HUS) establishes its first closed-loop EMMS with the new Epic-based Electronic Health Record system (APOTTI), it is helpful to consider the history of a more mature system: that of the United States. The U.S. approach evolved over time under unique policy, economic, and legal circumstances. Closed-loop EMMSs have arrived in many U.S. hospital locations, with myriad market-by-market manifestations typical of the U.S. healthcare system. This review describes and compares U.S. and Finnish hospitals' EMMS approaches and their impact on medication workflows and safety. Specifically, commonalities and nuanced differences in closed-loop EMMSs are explored from the perspectives of the care/nursing unit and hospital pharmacy operations perspectives. As the technologies are now fully implemented and destined for evolution in both countries, perhaps closed-loop EMMSs can be a topic of continued collaboration between the two countries. This review can also be used for benchmarking in other countries developing closed-loop EMMSs.

Unnecessary radiation exposure during diagnostic radiography in infants in a neonatal intensive care unit: a retrospective cohort study

Unnecessary radiation exposure (URE) during radiographic examination is an issue among infants in neonatal intensive care units (NICUs). The causes of URE have not been fully explored. This study investigated the incidence and identified the causes of URE in infants during diagnostic radiography in a NICU. This was a retrospective cohort study. We retrieved and analysed requests and radiographs taken at a tertiary NICU between September and November 2018. URE was defined as the rate of discordance between requests and images taken (DisBRI) and unnecessary radiation exposure in irrelevant regions (UREIR) during radiography. We compared the rates of URE between very low-birth-weight (VLBW, birth weight < 1500 g) infants and non-VLBW infants. A total of 306 radiographs from 88 infants were taken. The means ± standard deviations (SDs) of gestational age and birth weight were 35.7 ± 3.6 weeks and 2471 ± 816 g, respectively. Each infant underwent an average of 3.5 radiographs. The DisBRI rate was 1.3% and was mostly related to poor adherence to requests. The UREIR rates in thoraco-abdominal babygrams were 89.6% for the head, 14.8% for the elbows and 18.4% for the knee and were mainly related to improper positioning of and collimation in infants while performing radiography. The UREIR rates for the head, knee and ankle were higher in VLBW infants than in non-VLBW infants (94.6% vs. 85.6%, 27.0% vs. 11.5% and 5.4% vs. 0.7%, respectively, p < 0.05).

CONCLUSIONS

URE during diagnostic radiography is common in sick infants and is mainly related to improper positioning and collimation during examinations. Adherence to protocols when performing radiographic examination or using ultrasonography may be a solution to reduce URE in infants in NICUs.

WHAT IS KNOWN

• The risk of unnecessary radiation exposure (URE) during radiography has been a common and important issue in sick infants in neonatal intensive care units (NICUs). • The new point-of-care ultrasound (POCUS) technique decreases the need for chest films and prevents radiation exposure in neonates.

WHAT IS NEW

• In the NICU, URE is still a common issue in critically ill infants during radiographic examinations. The causes of URE during diagnostic radiography are mainly due to improper positioning and collimation during examinations. • The incidence of URE in irrelevant regions is higher in very low-birth-weight (VLBW) infants than in non-VLBW infants.

Detecting Unusual Intravenous Infusion Alerting Patterns with Machine Learning Algorithms

OBJECTIVE

To detect unusual infusion alerting patterns using machine learning (ML) algorithms as a first step to advance safer inpatient intravenous administration of high-alert medications.

MATERIALS AND METHODS

We used one year of detailed propofol infusion data from a hospital. Interpretable and clinically relevant variables were feature engineered, and data points were aggregated per calendar day. A univariate (maximum times-limit) moving range (mr) control chart was used to simulate clinicians' common approach to identifying unusual infusion alerting patterns. Three different unsupervised multivariate ML-based anomaly detection algorithms (Local Outlier Factor, Isolation Forest, and k-Nearest Neighbors) were used for the same purpose. Results from the control chart and ML algorithms were compared.

RESULTS

The propofol data had 3,300 infusion alerts, 92% of which were generated during the day shift and seven of which had a times-limit greater than 10. The mr-chart identified 15 alert pattern anomalies. Different thresholds were set to include the top 15 anomalies from each ML algorithm. A total of 31 unique ML anomalies were grouped and ranked by agreeability. All algorithms agreed on 10% of the anomalies, and at least two algorithms agreed on 36%. Each algorithm detected one specific anomaly that the mr-chart did not detect. The anomaly represented a day with 71 propofol alerts (half of which were overridden) generated at an average rate of 1.06 per infusion, whereas the moving alert rate for the week was 0.35 per infusion.

DISCUSSION

These findings show that ML-based algorithms are more robust than control charts in detecting unusual alerting patterns. However, we recommend using a combination of algorithms, as multiple algorithms serve a benchmarking function and allow researchers to focus on data points with the highest algorithm agreeability.

CONCLUSION

Unsupervised ML algorithms can assist clinicians in identifying unusual alert patterns as a first step toward achieving safer infusion practices.

Detecting Unusual Intravenous Infusion Alerting Patterns with Machine Learning Algorithms

OBJECTIVE

To detect unusual infusion alerting patterns using machine learning (ML) algorithms as a first step to advance safer inpatient intravenous administration of high-alert medications.

MATERIALS AND METHODS

We used one year of detailed propofol infusion data from a hospital. Interpretable and clinically relevant variables were feature engineered, and data points were aggregated per calendar day. A univariate (maximum times-limit) moving range (mr) control chart was used to simulate clinicians' common approach to identifying unusual infusion alerting patterns. Three different unsupervised multivariate ML-based anomaly detection algorithms (Local Outlier Factor, Isolation Forest, and k-Nearest Neighbors) were used for the same purpose. Results from the control chart and ML algorithms were compared.

RESULTS

The propofol data had 3,300 infusion alerts, 92% of which were generated during the day shift and seven of which had a times-limit greater than 10. The mr-chart identified 15 alert pattern anomalies. Different thresholds were set to include the top 15 anomalies from each ML algorithm. A total of 31 unique ML anomalies were grouped and ranked by agreeability. All algorithms agreed on 10% of the anomalies, and at least two algorithms agreed on 36%. Each algorithm detected one specific anomaly that the mr-chart did not detect. The anomaly represented a day with 71 propofol alerts (half of which were overridden) generated at an average rate of 1.06 per infusion, whereas the moving alert rate for the week was 0.35 per infusion.

DISCUSSION

These findings show that ML-based algorithms are more robust than control charts in detecting unusual alerting patterns. However, we recommend using a combination of algorithms, as multiple algorithms serve a benchmarking function and allow researchers to focus on data points with the highest algorithm agreeability.

CONCLUSION

Unsupervised ML algorithms can assist clinicians in identifying unusual alert patterns as a first step toward achieving safer infusion practices.