Evolution of Intravenous Medication Errors and Preventive Systemic Defenses in Hospital Settings-A Narrative Review of Recent Evidence

OBJECTIVES

Intravenous drug administration has been associated with severe medication errors in hospitals. The present narrative review is based on a systematic literature search, and aimed to describe the recent evolution in research on systemic causes and defenses in intravenous medication errors in hospitals.

METHODS

This narrative review was based on Reason's theory of systems-based risk management. A systematic literature search covering the period from June 2016 to October 2021 was conducted on Medline (Ovid). We used the search strategy and selection criteria developed for our previous systematic reviews. The included articles were analyzed and compared to our previous reviews.

RESULTS

The updated search found 435 articles. Of the 63 included articles, 16 focused on systemic causes of intravenous medication errors, and 47 on systemic defenses. A high proportion (n = 24, 38%) of the studies were conducted in the United States or Canada. Most of the studies focused on drug administration (n = 21/63, 33%) and preparation (n = 19/63, 30%). Compared to our previous review of error causes, more studies (n = 5/16, 31%) utilized research designs with a prospective risk management approach. Within articles related to systemic defenses, smart infusion pumps remained most widely studied (n = 10/47, 21%), while those related to preparation technologies (n = 7/47, 15%) had increased.

CONCLUSIONS

This narrative review demonstrates a growing interest in systems-based risk management for intravenous drug therapy and in introducing new technology, particularly smart infusion pumps and preparation systems, as systemic defenses. When introducing new technologies, prospective assessment and continuous monitoring of emerging safety risks should be conducted.

A Data-Driven Approach to Evaluate Barcode-Assisted Medication Preparation Alerts at a Large Academic Medical Center

BACKGROUND

The purpose of this study was to develop a data-driven process to analyze barcode-assisted medication preparation alert data with a goal of minimizing inaccurate alerts.

METHODS

Medication preparation data for the prior three-month period was obtained from an electronic health record system. A dashboard was developed to identify recurrent, high-volume alerts and associated medication records. A randomization tool was used to obtain a prespecified proportion of the alerts to review for appropriateness. Alert root causes were identified by chart review. Depending on the alert's cause(s), targeted informatics build changes, workflow and purchasing changes, and/or staff education were implemented. The rate of alerts was measured postintervention for select drugs.

RESULTS

The institution averaged 31,000 medication preparation alerts per month. The "barcode not recognized" alert (13,000) was the highest volume over the study period. Eighty-five medication records were identified as contributing to a high volume of alerts (5,200/31,000), representing 49 unique drugs. Of the 85 medication records triggering alerts, 36 required staff education, 22 required informatics build changes, and 8 required workflow changes. Targeted interventions for 2 medications, resulted in reducing the rate of the "barcode not recognized" alert from 26.6% to 1.3% for polyethylene glycol and from 48.7% to 0% for cyproheptadine.

CONCLUSION

This quality improvement project highlighted opportunities to improve medication purchasing, storage, and preparation through development of a standard process to evaluate barcode-assisted medication preparation alert data. A data-driven approach can help identify and minimize inaccurate alerts ("noise") and promote medication safety.

Meaningful time-related aspects of alerts in Clinical Decision Support Systems. A unified framework

Alerts are a common functionality of clinical decision support systems (CDSSs). Although they have proven to be useful in clinical practice, the alert burden can lead to alert fatigue and significantly reduce their usability and acceptance. Based on a literature review, we propose a unified framework consisting of a set of meaningful timestamps that allows the use of state-of-the-art measures for alert burden, such as alert dwell time, alert think time, and response time. In addition, it can be used to investigate other measures that could be relevant as regards dealing with this problem. Furthermore, we provide a case study concerning three different types of alerts to which the framework was successfully applied. We consider that our framework can easily be adapted to other CDSSs and that it could be useful for dealing with alert burden measurement thus contributing to its appropriate management.

The Impact of Smart Pump Technology in the Healthcare System: A Scope Review

Smart infusion pump technology prevents errors caused by parenteral therapy. This paper aims to review the recent literature about smart pump uses, cases and adverse events, and strategies to minimize these disadvantages. Literature was explored from January 2000 to November 2021 using Google Scholar, PubMed, and ScienceDirect. There were assessments of the advantages and adverse effects of using smart pumps and strategies to overcome the adverse effects of smart pumps. The advantage of using smart pumps is that they decrease errors like incorrect rate and dose. Other benefits include a decrease in medication event rates and the ability to connect smart pumps to home health providers. However, compliance rates were negatively influenced by improper smart pumps and the overriding of soft alerts, which can cause alert fatigue and drug library update delays. Recent studies have tried to address the negative issues by improving drug library compliance and decreasing alerts to avoid alert desensitization. The investigations revealed that the smart pumps reduced errors but would only prevent some programming errors. Compliance with utilizing smart pump technology is critical in stopping medication errors. Opportunities for future improvement are broad, including integrating a smart pump infusion with the hospital system, implementing auto programming, and designing smart pump devices to be lighter, smaller, and more portable instead of the heavy, large smart pump used by most hospitals today.

Impact of a Review of a Smart Infusion Pump Library in Hematology/Oncology: Tailoring Content to Meet Specialty Needs

OBJECTIVES

Errors involving chemotherapy or intravenous medications may cause serious patient harm. Dose error reduction software (DERS) for "smart" infusion pumps offers additional safety protection for their administration. Our institution uses DERS software hospital-wide; however, the hematology/oncology areas were noted to have reduced compliance with DERS recommendations. In response, we sought to review the DERS content and survey hematology/oncology end users' satisfaction with the software.

METHODS

A multidisciplinary working group was formed to review the current DERS entries for medications, fluids, and blood products. The review included details such as dose, rate, and concentrations. Dose error reduction software compliance was determined using vendor-supplied Continuous Quality Improvement software. An electronic survey assessing clinicians' satisfaction with the current DERS library and any challenges in its use was conducted before and after the review.

RESULTS

Ninety-one changes were made to 71 medications by the working group. Compliance with the DERS library went from 81.5% before the review to 87.3% after the review (P = 0.257). Fifty-eight survey responses were received (30 prereview and 28 postreview) with improvements in staff satisfaction with the DERS library (83.3%-92.8%, P < 0.05). Near-miss events changed after the review from 2 to 0, and the number of alerts caused by values outside the DERS library reduced from 8788 before the review to 3383 after the review (P ≤ 0.05).

CONCLUSIONS

Review of a hematology/oncology DERS library found improvements that better met the needs of end users. Engagement with relevant stakeholders, in conjunction with ongoing communication and review, is required to improve compliance and satisfaction with DERS.

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

Background

Smart infusion pumps with dose error reduction software can be used to prevent harmful medication errors. The aim of this study was to develop a method for defining and assessing optimal dosing limits in a neonatal intensive care unit’s smart infusion pump drug library by using simulation-type test cases developed based on medication error reports.

Methods

This mixed-methods study applied both qualitative and quantitative methods. First, wrong infusion rate-related medication errors reported in the neonatal intensive care unit during 2018–2019 were explored by quantitative descriptive analysis and qualitative content analysis to identify the error mechanisms. The researchers developed simulation-type test cases with potential errors, and a literature-based calculation formula was used to set upper soft limits to the drug library. The limits were evaluated by conducting programming of pumps without errors and with potential errors for two imaginary test patients (1 kg and 3.5 kg).

Results

Of all medication errors reported in the neonatal intensive care unit, 3.5% (n = 21/601) involved an error or near-miss related to wrong infusion rate. Based on the identified error mechanisms, 2-, 5-, and 10-fold infusion rates, as well as mix-ups between infusion rates of different drugs, were established as test cases. When conducting the pump programming for the test cases (n = 226), no alerts were triggered with infusion rates responding to the usual dosages (n = 32). 73% (n = 70/96) of the erroneous 2-, 5-, and 10-fold infusion rates caused an alert. Mix-ups between infusion rates triggered an alert only in 24% (n = 24/98) of the test cases.

Conclusions

Simulation-type test cases can be applied to assess the appropriateness of dosing limits within the neonatal intensive care unit’s drug library. In developing the test cases, combining hospital’s medication error data to other prospective data collection methods is recommended to gain a comprehensive understanding on mechanisms of wrong infusion rate errors. After drug library implementation, the alert log data and drug library compliance should be studied to verify suitability of dosing limits.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12887-022-03183-8.

Human-Based Errors Involving Smart Infusion Pumps: A Catalog of Error Types and Prevention Strategies

Over 4000 preventable injuries due to medication errors occur each year in any given hospital. Smart pumps have been widely introduced as one means to prevent these errors. Although smart pumps have been implemented to prevent errors, they fail to prevent specific types of errors in the medication administration process and may introduce new errors themselves. As a result, unique prevention strategies have been implemented by providers. No catalog of smart pump error types and prevention strategies currently exists. The aim of this study is to review and catalog the types of human-based errors related to smart pump use identified in the literature and to summarize the associated error-prevention strategies. We searched MEDLINE, PubMed, PubMed Central, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) for literature pertaining to human-based errors associated with smart pumps. Studies related to smart pump implementation, other types of pumps, and mechanical failures were excluded. Final selections were mapped for error types and associated prevention strategies. A total of 1177 articles were initially identified, and 105 articles were included in the final review. Extraction of error types and prevention strategies resulted in the identification of 18 error types and ten prevention strategies. Through a comprehensive literature review, we compiled a catalog of smart pump-related errors and associated prevention strategies. Strategies were mapped to error types to provide an initial framework for others to use as a resource in their error reviews and improvement work. Future research should assess the application of the resources provided by this review.

Early mobilisation algorithm for the critical patient. Expert recommendations

INTRODUCTION

Intensive care unit (ICU)-acquired weakness is developed by 40%-46% of patients admitted to ICU. Different studies have shown that Early Mobilisation (EM) is safe, feasible, cost-effective and improves patient outcomes in the short and long term.

OBJECTIVE

To design an EM algorithm for the critical patient in general and to list recommendations for EM in specific subpopulations of the critical patient most at risk for mobilisation: neurocritical, traumatic, undergoing continuous renal replacement therapy (CRRT) and with ventricular assist devices (VAD) or extracorporeal membrane oxygenation (ECMO).

METHODOLOGY

Review undertaken in the Medline, CINAHL, Cochrane and PEDro databases of studies published in the last 10 years, providing EM protocols/interventions.

RESULTS

30 articles were included. Of these, 21 were on guiding EM in critical patients in general, 7 in neurocritical and/or traumatic patients, 1 on patients undergoing CRRT and 1 on patients with ECMO and/or VAD. Two figures were designed: one for decision-making, taking the ABCDEF bundle into account and the other with the safety criteria and mobility objective for each.

CONCLUSIONS

The EM algorithms provided can promote early mobilisation (between the 1st and 5th day from admission to ICU), along with aspects to consider before mobilisation and safety criteria for discontinuing it.

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

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.

Intravenous Smart Pumps During Actual Clinical Use: A Descriptive Comparison of Primary and Secondary Infusion Practices

This descriptive observational study was conducted to increase understanding of medication administration practices during actual clinical use between 2 commonly used, different types of intravenous (IV) smart pumps. Compliance with manufacturer-recommended setup requirements for both primary and secondary infusions and secondary medication administration delay was compared between a head-height differential system and a cassette system. A total of 301 medication administration observations were included in this study: 102 (34%) for the linear peristaltic IV smart pump (medical-surgical: N = 51; critical care: N = 51) and 199 (66%) for the cassette pump (medical-surgical: N = 88; critical care: N = 111). Results found a 0% compliance for primary line setup and 84% compliance for secondary line setup and 1 omitted medication due to a closed clamp with the linear peristaltic system. For the cassette system, there are no head-height requirements. Two roller clamps were found to be in the closed position on initiation of the secondary infusion, but the clinician was alerted by an alarm, so no medication delays occurred. These findings support that the current system requirements for flow rate accuracy using head-height differential systems are difficult to achieve consistently at the point of care. There is a need for additional human factor designed technology to replace manual actions to improve the process of care for nurses and the safety of care for patients.

Early mobilisation algorithm for the critical patient. Expert recommendations

INTRODUCTION

Intensive care unit (ICU)-acquired weakness is developed by 40%-46% of patients admitted to ICU. Different studies have shown that Early Mobilisation (EM) is safe, feasible, cost-effective and improves patient outcomes in the short and long term.

OBJECTIVE

To design an EM algorithm for the critical patient in general and to list recommendations for EM in specific subpopulations of the critical patient most at risk for mobilisation: neurocritical, traumatic, undergoing continuous renal replacement therapy (CRRT) and with ventricular assist devices (VAD) or extracorporeal membrane oxygenation (ECMO).

METHODOLOGY

Review undertaken in the Medline, CINAHL, Cochrane and PEDro databases of studies published in the last 10 years, providing EM protocols/interventions.

RESULTS

30 articles were included. Of these, 21 were on guiding EM in critical patients in general, 7 in neurocritical and/or traumatic patients, 1 on patients undergoing CRRT and 1 on patients with ECMO and/or VAD. Two figures were designed: one for decision-making, taking the ABCDEF bundle into account and the other with the safety criteria and mobility objective for each.

CONCLUSIONS

The EM algorithms provided can promote early mobilisation (between the 1^st and 5^th day from admission to ICU), along with aspects to consider before mobilisation and safety criteria for discontinuing it.

The Generalizability of a Medication Administration Discrepancy Detection System: Quantitative Comparative Analysis

Background

As a result of the overwhelming proportion of medication errors occurring each year, there has been an increased focus on developing medication error prevention strategies. Recent advances in electronic health record (EHR) technologies allow institutions the opportunity to identify medication administration error events in real time through computerized algorithms. MED.Safe, a software package comprising medication discrepancy detection algorithms, was developed to meet this need by performing an automated comparison of medication orders to medication administration records (MARs). In order to demonstrate generalizability in other care settings, software such as this must be tested and validated in settings distinct from the development site.

Objective

The purpose of this study is to determine the portability and generalizability of the MED.Safe software at a second site by assessing the performance and fit of the algorithms through comparison of discrepancy rates and other metrics across institutions.

Methods

The MED.Safe software package was executed on medication use data from the implementation site to generate prescribing ratios and discrepancy rates. A retrospective analysis of medication prescribing and documentation patterns was then performed on the results and compared to those from the development site to determine the algorithmic performance and fit. Variance in performance from the development site was further explored and characterized.

Results

Compared to the development site, the implementation site had lower audit/order ratios and higher MAR/(order + audit) ratios. The discrepancy rates on the implementation site were consistently higher than those from the development site. Three drivers for the higher discrepancy rates were alternative clinical workflow using orders with dosing ranges; a data extract, transfer, and load issue causing modified order data to overwrite original order values in the EHRs; and delayed EHR documentation of verbal orders. Opportunities for improvement were identified and applied using a software update, which decreased false-positive discrepancies and improved overall fit.

Conclusions

The execution of MED.Safe at a second site was feasible and effective in the detection of medication administration discrepancies. A comparison of medication ordering, administration, and discrepancy rates identified areas where MED.Safe could be improved through customization. One modification of MED.Safe through deployment of a software update improved the overall algorithmic fit at the implementation site. More flexible customizations to accommodate different clinical practice patterns could improve MED.Safe’s fit at new sites.

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

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 incorporated with prescribing data from EHRs, smart pump records (SPRs) are capable of shedding light on actions that take place during the medication use process. However, harmoniz-ing the 2 sources is hindered by multiple technical challenges, and the data quality and utility of SPRs have not been fully realized.

Objective

This study aims to evaluate the quality and utility of SPRs incorporated with EHR data in detecting medication administration errors. Our overarching hypothesis is that SPRs would contribute unique information in the med-ication use process, enabling more comprehensive detection of discrepancies and potential errors in medication administration.

Methods

We evaluated the medication use process of 9 high-risk medications for patients admitted to the neonatal inten-sive care unit during a 1-year period. An automated algorithm was developed to align SPRs with their medica-tion orders in the EHRs using patient ID, medication name, and timestamp. The aligned data were manually re-viewed by a clinical research coordinator and 2 pediatric physicians to identify discrepancies in medication ad-ministration. The data quality of SPRs was assessed with the proportion of information that was 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 understand the detec-tion power and capabilities of SPR and EHR data.

Results

Approximately 70% of the SPRs contained valid patient IDs and medication names, making them feasible for data integration. After combining the 2 sources, the investigative team reviewed 2307 medication orders with 10,575 medication administration records (MARs) and 23,397 SPRs. A total of 321 MAR and 682 SPR dis-crepancies were identified, with vasopressors showing the highest discrepancy rates, followed by narcotics and total parenteral nutrition. Compared with EHR MARs, substantial dosing discrepancies were more commonly detectable using the SPRs. The concordance analysis showed little overlap between MAR and SPR discrepan-cies, with most discrepancies captured by the SPR data.

Conclusions

We integrated smart infusion pump information with EHR data to analyze the most error-prone phases of the medication lifecycle. The findings suggested that SPRs could be a more reliable 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.

Alert Types and Frequencies During Bar Code-Assisted Medication Administration: A Systematic Review

BACKGROUND

Existing literature explores the effectiveness of bar code-assisted medication administration (BCMA) on the reduction of medication administration error as well as on nurse workarounds during BCMA. However, there is no review that comprehensively explores types and frequencies of alerts generated by nurses during BCMA.

PURPOSE

The purpose was to describe alert generation type and frequency during BCMA.

METHODS

A systematic review of the literature using PRISMA guidelines was conducted using CINAHL, PubMed, EMBASE, and Ovid Medline databases.

RESULTS

After screening for inclusion and exclusion criteria, a total of 8 articles were identified and included in the review. Alert types included patient mismatch, wrong medication, and wrong dose, though other alert types were also reported. The frequency of alert generation varied across studies, from 0.18% to 42%, and not all alerts were clinically meaningful.

CONCLUSIONS

This systematic review synthesized literature related to alert type and frequency during BCMA. However, further studies are needed to better describe alert generation patterns as well as factors that influence alert generation.

Practice-enhancing publications about the medication-use process in 2018

PURPOSE

This article identifies, prioritizes, and summarizes published literature on the medication-use process (MUP) from calendar year 2018 that can impact health-system pharmacy daily practice. The MUP is the foundational system that provides the framework for safe medication utilization within the healthcare environment. The MUP is defined in this article as having the following steps: prescribing/transcribing, dispensing, administration, and monitoring. Articles that evaluated one of the steps were gauged for their usefulness toward daily practice change.

SUMMARY

A PubMed search was conducted in February 2019 for articles published in calendar year 2018 using targeted Medical Subject Headings (MeSH) keywords, targeted non-MeSH keywords, and the table of contents of selected pharmacy journals, providing a total of 43,977 articles. A thorough review identified 62 potentially significant articles: 9 for prescribing/transcribing, 12 for dispensing, 13 for administration, and 28 for monitoring. Ranking of the articles for importance by peers led to the selection of key articles from each category. The highest-ranked articles are briefly summarized, with a mention of why they are important within health-system pharmacy. The other articles are listed for further review and evaluation.

CONCLUSION

It is important to routinely review the published literature and to incorporate significant findings into daily practice. This article assists in identifying and summarizing recent impactful contributions to the MUP literature. Health-system pharmacists have an active role in improving the MUP in their institution, and awareness of significant published studies can assist in changing practice at the institutional level.

Unintended Patient Safety Risks Due to Wireless Smart Infusion Pump Library Update Delays

OBJECTIVE

Our previous study showed that the issue of drug library update delays on wireless intravenous (IV) infusion pumps of one major vendor was widespread and significant. However, the impact of such a delay was unclear. The objective of this study was to quantify the impact of pump library update delays on patient safety in terms of missed and false infusion programming alerts.

METHODS

The study data sets included infusion logs and drug libraries from three hospitals of one health system from January 2015 to December 2016. We identified limit setting changes of any two consecutive drug library versions. We quantified the impact of using outdated drug limit settings by missed and false infusion programming alerts.

RESULTS

Twenty-five updates of the drug library were released within the health system during the 2-year period with an average interval of 28.8 days. After a new library version was issued, it took at least 6 days for 50% of all pumps to become up-to-date and 15 days or more to reach 80%. All three hospitals had at least 16% of all IV infusions programmed with outdated libraries. This resulted in 18%, 24.4%, and 27% of false alerts in the three hospitals, respectively. We identified two cases of missed alert infusions of high-risk medications, propofol, and potassium chloride, which could have negatively impacted patient safety.

CONCLUSIONS

These findings support our assumption that potential serious harm can happen when IV infusions are administered with outdated drug limit settings due to delays in drug library updates on the pump.