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

Mapping Injection Order Messages to Health Level 7 Fast Healthcare Interoperability Resources to Collate Infusion Pump Data

BACKGROUND

When administering an infusion to a patient, it is necessary to verify that the infusion pump settings are in accordance with the injection orders provided by the physician. However, the infusion rate entered into the infusion pump by the health care provider cannot be automatically reconciled with the injection order information entered into the electronic medical records (EMRs). This is because of the difficulty in linking the infusion rate entered into the infusion pump by the health care provider with the injection order information entered into the EMRs.

OBJECTIVES

This study investigated a data linkage method for reconciling infusion pump settings with injection orders in the EMRs.

METHODS

We devised and implemented a mechanism to convert injection order information into the Health Level 7 Fast Healthcare Interoperability Resources (FHIR), a new health information exchange standard, and match it with an infusion pump management system in a standard and simple manner using a REpresentational State Transfer (REST) application programming interface (API). The injection order information was extracted from Standardized Structured Medical Record Information Exchange version 2 International Organization for Standardization/technical specification 24289:2021 and was converted to the FHIR format using a commercially supplied FHIR conversion module and our own mapping definition. Data were also sent to the infusion pump management system using the REST Web API.

RESULTS

Information necessary for injection implementation in hospital wards can be transferred to FHIR and linked. The infusion pump management system application screen allowed the confirmation that the two pieces of information matched, and it displayed an error message if they did not.

CONCLUSION

Using FHIR, the data linkage between EMRs and infusion pump management systems can be smoothly implemented. We plan to develop a new mechanism that contributes to medical safety through the actual implementation and verification of this matching system.

Attributes of errors, facilitators, and barriers related to rate control of IV medications: a scoping review

BACKGROUND

Intravenous (IV) medication is commonly administered and closely associated with patient safety. Although nurses dedicate considerable time and effort to rate the control of IV medications, many medication errors have been linked to the wrong rate of IV medication. Further, there is a lack of comprehensive studies examining the literature on rate control of IV medications. This study aimed to identify the attributes of errors, facilitators, and barriers related to rate control of IV medications by summarizing and synthesizing the existing literature.

METHODS

This scoping review was conducted using the framework proposed by Arksey and O'Malley and PRISMA-ScR. Overall, four databases-PubMed, Web of Science, EMBASE, and CINAHL-were employed to search for studies published in English before January 2023. We also manually searched reference lists, related journals, and Google Scholar.

RESULTS

A total of 1211 studies were retrieved from the database searches and 23 studies were identified from manual searches, after which 22 studies were selected for the analysis. Among the nine project or experiment studies, two interventions were effective in decreasing errors related to rate control of IV medications. One of them was prospective, continuous incident reporting followed by prevention strategies, and the other encompassed six interventions to mitigate interruptions in medication verification and administration. Facilitators and barriers related to rate control of IV medications were classified as human, design, and system-related contributing factors. The sub-categories of human factors were classified as knowledge deficit, performance deficit, and incorrect dosage or infusion rate. The sub-category of design factor was device. The system-related contributing factors were classified as frequent interruptions and distractions, training, assignment or placement of healthcare providers (HCPs) or inexperienced personnel, policies and procedures, and communication systems between HCPs.

CONCLUSIONS

Further research is needed to develop effective interventions to improve IV rate control. Considering the rapid growth of technology in medical settings, interventions and policy changes regarding education and the work environment are necessary. Additionally, each key group such as HCPs, healthcare administrators, and engineers specializing in IV medication infusion devices should perform its role and cooperate for appropriate IV rate control within a structured system.

Automation in health care: the need for an ergonomics-based approach

Healthcare quality and efficiency challenges degrade outcomes and burden multiple stakeholders. Workforce shortage, burnout, and complexity of workflows necessitate effective support for patients and providers. There is interest in employing automation, or the use of 'computer[s] [to] carry out… functions that the human operator would normally perform', in health care to improve delivery of services. However, unique aspects of health care require analysis of workflows across several domains and an understanding of the ways work system factors interact to shape those workflows. Ergonomics has identified key work system issues relevant to effective automation in other industries. Understanding these issues in health care can direct opportunities for the effective use of automation in health care. This article illustrates work system considerations using two example workflows; discusses how those considerations may inform solution design, implementation, and use; and provides future directions to advance the essential role of ergonomics in healthcare automation.

Determinants of Intravenous Infusion Longevity and Infusion Failure via a Nonlinear Model Analysis of Smart Pump Event Logs: Retrospective Study

BACKGROUND

Infusion failure may have severe consequences for patients receiving critical, short-half-life infusions. Continued interruptions to infusions can lead to subtherapeutic therapy.

OBJECTIVE

This study aims to identify and rank determinants of the longevity of continuous infusions administered through syringe drivers, using nonlinear predictive models. Additionally, this study aims to evaluate key factors influencing infusion longevity and develop and test a model for predicting the likelihood of achieving successful infusion longevity.

METHODS

Data were extracted from the event logs of smart pumps containing information on care profiles, medication types and concentrations, occlusion alarm settings, and the final infusion cessation cause. These data were then used to fit 5 nonlinear models and evaluate the best explanatory model.

RESULTS

Random forest was the best-fit predictor, with an F1-score of 80.42, compared to 5 other models (mean F1-score 75.06; range 67.48-79.63). When applied to infusion data in an individual syringe driver data set, the predictor model found that the final medication concentration and medication type were of less significance to infusion longevity compared to the rate and care unit. For low-rate infusions, rates ranging from 2 to 2.8 mL/hr performed best for achieving a balance between infusion longevity and fluid load per infusion, with an occlusion versus no-occlusion ratio of 0.553. Rates between 0.8 and 1.2 mL/hr exhibited the poorest performance with a ratio of 1.604. Higher rates, up to 4 mL/hr, performed better in terms of occlusion versus no-occlusion ratios.

CONCLUSIONS

This study provides clinicians with insights into the specific types of infusion that warrant more intense observation or proactive management of intravenous access; additionally, it can offer valuable information regarding the average duration of uninterrupted infusions that can be expected in these care areas. Optimizing rate settings to improve infusion longevity for continuous infusions, achieved through compounding to create customized concentrations for individual patients, may be possible in light of the study's outcomes. The study also highlights the potential of machine learning nonlinear models in predicting outcomes and life spans of specific therapies delivered via medical devices.

Uncovering Discrepancies in IV Vancomycin Infusion Records between Pump Logs and EHR Documentation

BACKGROUND

Infusion start time, completion time, and interruptions are the key data points needed in both area under the concentration-time curve (AUC)- and trough-based vancomycin therapeutic drug monitoring (TDM). However, little is known about the accuracy of documented times of drug infusions compared with automated recorded events in the infusion pump system. A traditional approach of direct observations of infusion practice is resource intensive and impractical to scale. We need a new methodology to leverage the infusion pump event logs to understand the prevalence of timestamp discrepancies as documented in the electronic health records (EHRs).

OBJECTIVES

We aimed to analyze timestamp discrepancies between EHR documentation (the information used for clinical decision making) and pump event logs (actual administration process) for vancomycin treatment as it may lead to suboptimal data used for therapeutic decisions.

METHODS

We used process mining to study the conformance between pump event logs and EHR data for a single hospital in the United States from July to December 2016. An algorithm was developed to link records belonging to the same infusions. We analyzed discrepancies in infusion start time, completion time, and interruptions.

RESULTS

Of the 1,858 infusions, 19.1% had infusion start time discrepancy more than ± 10 minutes. Of the 487 infusion interruptions, 2.5% lasted for more than 20 minutes before the infusion resumed. 24.2% (312 of 1,287) of 1-hour infusions and 32% (114 of 359) of 2-hour infusions had over 10-minute completion time discrepancy. We believe those discrepancies are inherent part of the current EHR documentation process commonly found in hospitals, not unique to the care facility under study.

CONCLUSION

We demonstrated pump event logs and EHR data can be utilized to study time discrepancies in infusion administration at scale. Such discrepancy should be further investigated at different hospitals to address the prevalence of the problem and improvement effort.

Clinical Decision Support Stewardship: Best Practices and Techniques to Monitor and Improve Interruptive Alerts

Interruptive clinical decision support systems, both within and outside of electronic health records, are a resource that should be used sparingly and monitored closely. Excessive use of interruptive alerting can quickly lead to alert fatigue and decreased effectiveness and ignoring of alerts. In this review, we discuss the evidence for effective alert stewardship as well as practices and methods we have found useful to assess interruptive alert burden, reduce excessive firings, optimize alert effectiveness, and establish quality governance at our institutions. We also discuss the importance of a holistic view of the alerting ecosystem beyond the electronic health record.

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.

Principles for Designing and Developing a Workflow Monitoring Tool to Enable and Enhance Clinical Workflow Automation

BACKGROUND

Automation of health care workflows has recently become a priority. This can be enabled and enhanced by a workflow monitoring tool (WMOT).

OBJECTIVES

We shared our experience in clinical workflow analysis via three cases studies in health care and summarized principles to design and develop such a WMOT.

METHODS

The case studies were conducted in different clinical settings with distinct goals. Each study used at least two types of workflow data to create a more comprehensive picture of work processes and identify bottlenecks, as well as quantify them. The case studies were synthesized using a data science process model with focuses on data input, analysis methods, and findings.

RESULTS

Three case studies were presented and synthesized to generate a system structure of a WMOT. When developing a WMOT, one needs to consider the following four aspects: (1) goal orientation, (2) comprehensive and resilient data collection, (3) integrated and extensible analysis, and (4) domain experts.

DISCUSSION

We encourage researchers to investigate the design and implementation of WMOTs and use the tools to create best practices to enable workflow automation and improve workflow efficiency and care quality.

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.