The effectiveness of a noninterruptive alert to increase prescription of take-home naloxone in emergency departments

The role of information systems in emergency department decision-making-a literature review

OBJECTIVES

Healthcare providers employ heuristic and analytical decision-making to navigate the high-stakes environment of the emergency department (ED). Despite the increasing integration of information systems (ISs), research on their efficacy is conflicting. Drawing on related fields, we investigate how timing and mode of delivery influence IS effectiveness. Our objective is to reconcile previous contradictory findings, shedding light on optimal IS design in the ED.

MATERIALS AND METHODS

We conducted a systematic review following PRISMA across PubMed, Scopus, and Web of Science. We coded the ISs' timing as heuristic or analytical, their mode of delivery as active for automatic alerts and passive when requiring user-initiated information retrieval, and their effect on process, economic, and clinical outcomes.

RESULTS

Our analysis included 83 studies. During early heuristic decision-making, most active interventions were ineffective, while passive interventions generally improved outcomes. In the analytical phase, the effects were reversed. Passive interventions that facilitate information extraction consistently improved outcomes.

DISCUSSION

Our findings suggest that the effectiveness of active interventions negatively correlates with the amount of information received during delivery. During early heuristic decision-making, when information overload is high, physicians are unresponsive to alerts and proactively consult passive resources. In the later analytical phases, physicians show increased receptivity to alerts due to decreased diagnostic uncertainty and information quantity. Interventions that limit information lead to positive outcomes, supporting our interpretation.

CONCLUSION

We synthesize our findings into an integrated model that reveals the underlying reasons for conflicting findings from previous reviews and can guide practitioners in designing ISs in the ED.

Enhanced phenotypes for identifying opioid overdose in emergency department visit electronic health record data

Background

Accurate identification of opioid overdose (OOD) cases in electronic healthcare record (EHR) data is an important element in surveillance, empirical research, and clinical intervention. We sought to improve existing OOD electronic phenotypes by incorporating new data types beyond diagnostic codes and by applying several statistical and machine learning methods.

Materials and Methods

We developed an EHR dataset of emergency department visits involving OOD cases or patients considered at risk for an OOD and ascertained true OOD status through manual chart reviews. We developed and validated prediction models using Random Forest, Extreme Gradient Boost, and Elastic Net models that incorporated 717 features involving primary and second diagnoses, chief complaints, medications prescribed, vital signs, laboratory results, and procedural codes. We also developed models limited to single data types.

Results

A total of 1718 records involving 1485 patients were manually reviewed; 541 (36.4%) patients had one or more OOD. Prediction performance was similar for all models; sensitivity varied from 94% to 97%; and area under the receiver operating characteristic curve (AUC) was 98% for all methods. The primary diagnosis and chief complaint were the most important contributors to AUC performance; primary diagnoses and medication class contributed most to sensitivity; chief complaint, primary diagnosis, and vital signs were most important for specificity. Models limited to decision support data types available in real time demonstrated robust prediction performance.

Conclusions

Substantial prediction performance improvements were demonstrated for identifying OODs in EHR data. Our e-phenotypes could be applied in surveillance, retrospective empirical applications, or clinical decision support systems.

Opioid Prescribing for Chronic Musculoskeletal Conditions: Trends over Time and Implementation of Safe Opioid-Prescribing Practices

OBJECTIVES

This study aimed (1) to determine the impact of a clinical decision support (CDS) tool on rate of opioid prescribing and opioid dose for patients with chronic musculoskeletal conditions and (2) to identify prescriber and facility characteristics associated with adherence to the Centers for Disease Control and Prevention guideline for prescribing opioids for chronic pain in this population.We conducted an interrupted time series analysis to assess trends in percentage of patients from 2016 to 2020, receiving an opioid and the average opioid dose, as well as the change associated with implementation of the CDS toolkit. We conducted a retrospective cohort study to assess the association between prescriber and facility characteristics and safe opioid-prescribing practices.

METHODS

We assessed the impact of the CDS intervention on percent of patients receiving an opioid and average opioid dose (morphine milligram equivalents). We operationalized safe opioid prescribing as a composite score of several behaviors (i.e., prescribing naloxone, initiating a pain agreement, prescribing <90 MME, avoiding extended-release prescriptions for opioid-naïve patients, and avoiding coprescribing opioids and benzodiazepines) and used a hierarchical linear regression model to assess associations between prescriber and facility characteristics and safe opioid prescribing.

RESULTS

This CDS intervention had a modest but statistically significant 1.6% reduction on the percent of patients (n = 1,290,746) receiving an opioid (mean: 15% preintervention; 10% postintervention). The average dose of opioid prescriptions did not significantly change. Advanced practice providers and prescribers with higher percentages of patients aged 18 to 64 exhibited safer opioid prescribing, while prescribers with higher percentages of white patients and larger numbers of patients on opioids exhibited less safe opioid prescribing.

CONCLUSION

A CDS intervention was associated with a small improvement in percent of patients receiving an opioid, but not on average dose. Clinicians are not prescribing opioids for chronic musculoskeletal conditions frequently, when they do, they are generally adhering to guidelines.