Advanced diagnostic imaging utilization during emergency department visits in the United States: A predictive modeling study for emergency department triage

Qualitative and quantitative spermatic cord abnormalities at CT predict symptomatic scrotal pathology

PURPOSE

To evaluate quantitative and qualitative spermatic cord CT abnormalities and presence of unilateral or bilateral symptomatic scrotal pathology (SSP) at ultrasound.

METHODS

This retrospective study included 122 male patients (mean age 47.8 years) undergoing scrotal ultrasound within 24 h of contrast-enhanced CT (n = 85), non-contrast CT (NECT, n = 32) or CT-Urogram (n = 5). CECT quantitative analysis assessed differential cord enhancement using maximum Hounsfield unit measurements. Three fellowship trained body radiologists independently assessed qualitative cord abnormalities for both CECT and NECT. Qualitative and quantitative findings were compared with the presence of SSP. Reader performance, interobserver agreement and reader confidence were assessed for NECT and CECT. Quantitative cutoff points were identified which maximized accuracy, specificity, negative predictive value, and other measures.

RESULTS

SSP was present in 36/122 patients (29.5%). Positive cases were unilateral in 30 (83.3%) and bilateral in 6 (16.6%). At quantitative assessment, 25% differential cord enhancement had the highest diagnostic accuracy (88.9%), with 90.5% positive predictive value, 88.4% negative predictive value, 96.8% specificity, and 70.4% sensitivity. At qualitative evaluation, CECT reader performance was excellent (aggregate AUC = 0.86; P < .001); NECT was poorly discriminatory, although remained significant (aggregate AUC = 0.67; P = .002). Readers had significantly higher confidence levels with CECT (P < .001). Qualitative inter-observer agreement was high for both CECT and NECT (ICC = 0.981 and 0.963, respectively).

CONCLUSION

Simple quantitative assessment of differential cord enhancement is highly accurate and specific for SSP at CECT. Qualitative abnormalities at CECT and NECT are also both predictors of SSP, however, CECT significantly out-performs non-contrast exams.

Leveraging graph neural networks for supporting automatic triage of patients

Patient triage is crucial in emergency departments, ensuring timely and appropriate care based on correctly evaluating the emergency grade of patient conditions. Triage methods are generally performed by human operator based on her own experience and information that are gathered from the patient management process. Thus, it is a process that can generate errors in emergency-level associations. Recently, Traditional triage methods heavily rely on human decisions, which can be subjective and prone to errors. A growing interest has recently been focused on leveraging artificial intelligence (AI) to develop algorithms to maximize information gathering and minimize errors in patient triage processing. We define and implement an AI-based module to manage patients' emergency code assignments in emergency departments. It uses historical data from the emergency department to train the medical decision-making process. Data containing relevant patient information, such as vital signs, symptoms, and medical history, accurately classify patients into triage categories. Experimental results demonstrate that the proposed algorithm achieved high accuracy outperforming traditional triage methods. By using the proposed method, we claim that healthcare professionals can predict severity index to guide patient management processing and resource allocation.

Applications of natural language processing at emergency department triage: A narrative review

INTRODUCTION

Natural language processing (NLP) uses various computational methods to analyse and understand human language, and has been applied to data acquired at Emergency Department (ED) triage to predict various outcomes. The objective of this scoping review is to evaluate how NLP has been applied to data acquired at ED triage, assess if NLP based models outperform humans or current risk stratification techniques when predicting outcomes, and assess if incorporating free-text improve predictive performance of models when compared to predictive models that use only structured data.

METHODS

All English language peer-reviewed research that applied an NLP technique to free-text obtained at ED triage was eligible for inclusion. We excluded studies focusing solely on disease surveillance, and studies that used information obtained after triage. We searched the electronic databases MEDLINE, Embase, Cochrane Database of Systematic Reviews, Web of Science, and Scopus for medical subject headings and text keywords related to NLP and triage. Databases were last searched on 01/01/2022. Risk of bias in studies was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST). Due to the high level of heterogeneity between studies and high risk of bias, a metanalysis was not conducted. Instead, a narrative synthesis is provided.

RESULTS

In total, 3730 studies were screened, and 20 studies were included. The population size varied greatly between studies ranging from 1.8 million patients to 598 triage notes. The most common outcomes assessed were prediction of triage score, prediction of admission, and prediction of critical illness. NLP models achieved high accuracy in predicting need for admission, triage score, critical illness, and mapping free-text chief complaints to structured fields. Incorporating both structured data and free-text data improved results when compared to models that used only structured data. However, the majority of studies (80%) were assessed to have a high risk of bias, and only one study reported the deployment of an NLP model into clinical practice.

CONCLUSION

Unstructured free-text triage notes have been used by NLP models to predict clinically relevant outcomes. However, the majority of studies have a high risk of bias, most research is retrospective, and there are few examples of implementation into clinical practice. Future work is needed to prospectively assess if applying NLP to data acquired at ED triage improves ED outcomes when compared to usual clinical practice.

Utilizing Triage Data for Medical Imaging Studies in the Emergency Department

The use of radiological images is widespread in the emergency department (ED) as physicians commonly rely on them during initial evaluations to confirm diagnoses, contributing to prolonged waiting times. This study aimed to determine the relationship between commonly gathered triage data and the need for radiological imaging. Data were collected from electronic charts that contained routinely collected hospital data at the time of triage in the King Abdulaziz Medical City (KAMC) in Riyadh ED. The binary logistic regression results demonstrated a statistically significant relationship between age and radiological imaging ordered in the ED. Each one-unit increase in age corresponded to a 0.983-fold increase in the likelihood of ordering radiological imaging (odds ratio: 0.983, 95% confidence interval: 0.972-0.995, p = 0.004). In contrast, hypertension, diabetes, and heart failure were independent predictors of the need for radiological imaging in the ED (p >0.05). Patient data that are immediately available during ED triage can be used to predict the need for radiological imaging during ED visits. Such models can identify patients who may require radiological imaging during ED visits and expedite patient disposition.

The Use and Structure of Emergency Nurses' Triage Narrative Data: Scoping Review

BACKGROUND

Emergency departments use triage to ensure that patients with the highest level of acuity receive care quickly and safely. Triage is typically a nursing process that is documented as structured and unstructured (free text) data. Free-text triage narratives have been studied for specific conditions but never reviewed in a comprehensive manner.

OBJECTIVE

The objective of this paper was to identify and map the academic literature that examines triage narratives. The paper described the types of research conducted, identified gaps in the research, and determined where additional review may be warranted.

METHODS

We conducted a scoping review of unstructured triage narratives. We mapped the literature, described the use of triage narrative data, examined the information available on the form and structure of narratives, highlighted similarities among publications, and identified opportunities for future research.

RESULTS

We screened 18,074 studies published between 1990 and 2022 in CINAHL, MEDLINE, Embase, Cochrane, and ProQuest Central. We identified 0.53% (96/18,074) of studies that directly examined the use of triage nurses' narratives. More than 12 million visits were made to 2438 emergency departments included in the review. In total, 82% (79/96) of these studies were conducted in the United States (43/96, 45%), Australia (31/96, 32%), or Canada (5/96, 5%). Triage narratives were used for research and case identification, as input variables for predictive modeling, and for quality improvement. Overall, 31% (30/96) of the studies offered a description of the triage narrative, including a list of the keywords used (27/96, 28%) or more fulsome descriptions (such as word counts, character counts, abbreviation, etc; 7/96, 7%). We found limited use of reporting guidelines (8/96, 8%).

CONCLUSIONS

The breadth of the identified studies suggests that there is widespread routine collection and research use of triage narrative data. Despite the use of triage narratives as a source of data in studies, the narratives and nurses who generate them are poorly described in the literature, and data reporting is inconsistent. Additional research is needed to describe the structure of triage narratives, determine the best use of triage narratives, and improve the consistent use of triage-specific data reporting guidelines.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1136/bmjopen-2021-055132.

Limited added value of Doppler ultrasound of the liver after recent contrast-enhanced computed tomography

PURPOSE

The aim of this study is to assess the added diagnostic value of Doppler ultrasound of the liver (DUL) performed within 3 days of contrast-enhanced CT (CECT) for the diagnosis of portal vein (PV) or hepatic vein (HV) thrombosis.

METHODS

Adult patients were included if they underwent DUL within three days after a CECT of the abdomen in the emergency or inpatient setting. Retrospective review of clinical data and imaging reports was performed. In patients with discrepant or positive findings on CECT and/or DUL with respect to PV or HV thrombosis, image review was performed by three fellowship-trained abdominal radiologists in consensus.

RESULTS

The final cohort consisted of 468 patients. Of these, 26 (5.6%) patients had equivocal findings for thrombosis on CECT, and DUL could make a confident diagnosis of positive or negative in 18 (69%) patients. Additionally, there were 2 (0.4%) patients with PV or HV thrombosis on DUL following a limited CECT, and 2 (0.4%) patients who developed interval PV thrombosis between CECT and DUL.

CONCLUSION

DUL after CECT added diagnostic value for PV and/or HV thrombosis in less than 5% of patients. The patency of PV and HV is often not explicitly mentioned in CECT reports at our institution, which may lead to uncertainty for the referring provider as to whether the PV and HV were adequately evaluated. Few CECT have false positive or missed or underreported findings, and a careful review of the original CECT should be performed if DUL is requested.

Role of MRI in the Evaluation of Thoracoabdominal Emergencies

Thoracic and abdominal pathology are common in the emergency setting. Although computed tomography is preferred in many clinical situations, magnetic resonance imaging (MRI) and magnetic resonance angiography (MRA) have emerged as powerful techniques that often play a complementary role to computed tomography or may have a primary role in selected patient populations in which radiation is of specific concern or intravenous iodinated contrast is contraindicated. This review will highlight the role of MRI and MRA in the emergent imaging of thoracoabdominal pathology, specifically covering acute aortic pathology (acute aortic syndrome, aortic aneurysm, and aortitis), pulmonary embolism, gastrointestinal conditions such as appendicitis and Crohn disease, pancreatic and hepatobiliary disease (pancreatitis, choledocholithiasis, cholecystitis, and liver abscess), and genitourinary pathology (urolithiasis and pyelonephritis). In each section, we will highlight the specific role for MRI, discuss basic imaging protocols, and illustrate the MRI features of commonly encountered thoracoabdominal pathology.

The development and validation of a resource consumption score of an emergency department consultation

Background

Emergency Department (ED) visits and health care costs are increasing globally, but little is known about contributing factors of ED resource consumption. This study aims to analyse and to predict the total ED resource consumption out of the patient and consultation characteristics in order to execute performance analysis and evaluate quality improvements.

Methods

Characteristics of ED visits of a large Swiss university hospital were summarized according to acute patient condition factors (e.g. chief complaint, resuscitation bay use, vital parameter deviations), chronic patient conditions (e.g. age, comorbidities, drug intake), and contextual factors (e.g. night-time admission). Univariable and multivariable linear regression analyses were conducted with the total ED resource consumption as the dependent variable.

Results

In total, 164,729 visits were included in the analysis. Physician resources accounted for the largest proportion (54.8%), followed by radiology (19.2%), and laboratory work-up (16.2%). In the multivariable final model, chief complaint had the highest impact on the total ED resource consumption, followed by resuscitation bay use and admission by ambulance. The impact of age group was small. The multivariable final model was validated (R² of 0.54) and a scoring system was derived out of the predictors.

Conclusions

More than half of the variation in total ED resource consumption can be predicted by our suggested model in the internal validation, but further studies are needed for external validation. The score developed can be used to calculate benchmarks of an ED and provides leaders in emergency care with a tool that allows them to evaluate resource decisions and to estimate effects of organizational changes.

Two-step predictive model for early detection of emergency department patients with prolonged stay and its management implications

OBJECTIVE

To develop a novel model for predicting Emergency Department (ED) prolonged length of stay (LOS) patients upon triage completion, and further investigate the benefit of a targeted intervention for patients with prolonged ED LOS.

MATERIALS AND METHODS

A two-step model to predict patients with prolonged ED LOS (>16 h) was constructed. This model was initially used to predict ED resource usage and was subsequently adapted to predict patient ED LOS based on the number of ED resources using binary logistic regressions and was validated internally with accuracy. Finally, a discrete event simulation was used to move patients with predicted prolonged ED LOS directly to a virtual Clinical Decision Unit (CDU). The changes of ED crowding status (Overcrowding, Crowding, and Not-Crowding) and savings of ED bed-hour equivalents were estimated as the measures of the efficacy of this intervention.

RESULTS

We screened a total of 123,975 patient visits with final enrollment of 110,471 patient visits. The overall accuracy of the final model predicting prolonged patient LOS was 67.8%. The C-index of this model ranges from 0.72 to 0.82. By implementing the proposed intervention, the simulation showed a 12% (1044/8760) reduction of ED overcrowded status - an equivalent savings of 129.3 ED bed-hours per day.

CONCLUSIONS

Early prediction of prolonged ED LOS patients and subsequent (simulated) early CDU transfer could lead to more efficiently utilization of ED resources and improved efficacy of ED operations. This study provides evidence to support the implementation of this novel intervention into real healthcare practice.

Use of natural language processing to improve predictive models for imaging utilization in children presenting to the emergency department

OBJECTIVE

To examine the association between the medical imaging utilization and information related to patients' socioeconomic, demographic and clinical factors during the patients' ED visits; and to develop predictive models using these associated factors including natural language elements to predict the medical imaging utilization at pediatric ED.

METHODS

Pediatric patients' data from the 2012-2016 United States National Hospital Ambulatory Medical Care Survey was included to build the models to predict the use of imaging in children presenting to the ED. Multivariable logistic regression models were built with structured variables such as temperature, heart rate, age, and unstructured variables such as reason for visit, free text nursing notes and combined data available at triage. NLP techniques were used to extract information from the unstructured data.

RESULTS

Of the 27,665 pediatric ED visits included in the study, 8394 (30.3%) received medical imaging in the ED, including 6922 (25.0%) who had an X-ray and 1367 (4.9%) who had a computed tomography (CT) scan. In the predictive model including only structured variables, the c-statistic was 0.71 (95% CI: 0.70-0.71) for any imaging use, 0.69 (95% CI: 0.68-0.70) for X-ray, and 0.77 (95% CI: 0.76-0.78) for CT. Models including only unstructured information had c-statistics of 0.81 (95% CI: 0.81-0.82) for any imaging use, 0.82 (95% CI: 0.82-0.83) for X-ray, and 0.85 (95% CI: 0.83-0.86) for CT scans. When both structured variables and free text variables were included, the c-statistics reached 0.82 (95% CI: 0.82-0.83) for any imaging use, 0.83 (95% CI: 0.83-0.84) for X-ray, and 0.87 (95% CI: 0.86-0.88) for CT.

CONCLUSIONS

Both CT and X-rays are commonly used in the pediatric ED with one third of the visits receiving at least one. Patients' socioeconomic, demographic and clinical factors presented at ED triage period were associated with the medical imaging utilization. Predictive models combining structured and unstructured variables available at triage performed better than models using structured or unstructured variables alone, suggesting the potential for use of NLP in determining resource utilization.