Emergency Severity Index Research Articles

Analysis of the Application of Emergency Severity Index Toward Patient Triage Response Time In the Emergency Department: Literature Review

Introduction: Triage is the process of determining the condition of patients at risk of death, potential disability, and worsening patient conditions. Response time is crucial in treating patients quickly and accurately. Objective This literature study aims to analyze the application of the Emergency Severity Index (ESI) Triage to the Triage Response Time in the Emergency Department. Methods: This research employs a literature review study with an analytic method approach that meets the inclusion and exclusion criteria. The search for articles or journals utilizes keywords and Boolean operators to refine the search and select relevant literature. The keywords used include ESI, Triage, Response time," and "Emergency Department. Results: The study reveals the effectiveness of utilizing the Emergency Severity Index (ESI) Triage on Triage Response Time in the Emergency Department. . Conclusion The speed at which nurses respond and determine patient triage can be enhanced through the use of ESI (Emergency Severity Index). To support this, nurses must possess knowledge and skills related to the ESI to minimize the mortality rate of patients.

Psychosocial Dimensions of Pain Disparities in Youth Diagnosed with Unspecified Abdominal Pain in an Emergency Department

Diagnosis ambiguity, paired with pain care inequities experienced by marginalized groups may increase risk for ongoing pain and impairment in children diagnosed with unspecified abdominal pain in the pediatric emergency department (PED). This cross-sectional study examined psychological, caregiver, cultural, and socio-ecological factors associated with pain-related impairment in an ethnically and socioeconomically diverse population diagnosed with unspecified abdominal pain in a PED. The sample included 111 children 8-17 years old (59.8% female, 72.7% Latinx) and their caregivers. Exclusion criteria included an Emergency Severity Index <2, psychiatric complaint, or not fluent in English or Spanish. Children completed the PROMIS Pain Interference Scale and reported pain intensity, pain duration, and pain catastrophizing. Caregivers reported language preference, pain catastrophizing, trait anxiety, and child internalizing symptoms. Area Deprivation Index quantified socio-ecological deprivation according to 9-digit zip code. Multivariable logistic regression analyses identified independent associations with likelihood of reporting severe levels of pain-related impairment. Approximately 35.3% of children reported severe levels of pain-related impairment. In logistic regression analysis, an increased likelihood of endorsing severe pain-related impairment was associated with pain for >1 month (OR=9.19, p=.044), higher child pain catastrophizing (OR=1.23, p<.001), caregiver Spanish language (OR=11.11, p=.044), and clinically significant caregiver trait anxiety (OR=58.16, p=.004). Results highlight the incidence of severe pain-related impairment in children diagnosed with unspecified abdominal pain in a PED. Moreover, findings underscore the importance of screening pain-related impairment and caregiver anxiety, and addressing language barriers in this PED population. PerspectiveThis paper highlights the incidence of severe pain-related impairment among pediatric patients diagnosed with unspecified abdominal pain in a pediatric emergency department (PED). Results could inform early assessment and targeted interventions in the PED to prevent outcome disparities.

Early prediction of sepsis in emergency department patients using various methods and scoring systems.

Early recognition of sepsis, a common life-threatening condition in intensive care units (ICUs), is beneficial for improving patient outcomes. However, most sepsis prediction models were trained and assessed in the ICU, which might not apply to emergency department (ED) settings. To establish an early predictive model based on basic but essential information collected upon ED presentation for the follow-up diagnosis of sepsis observed in the ICU. This study developed and validated a reliable model of sepsis prediction among ED patients by comparing 10 different methods based on retrospective electronic health record data from the MIMIC-IV database. In-ICU sepsis was identified as the primary outcome. The potential predictors encompassed baseline demographics, vital signs, pain scale, chief complaints and Emergency Severity Index (ESI). 80% and 20% of the total of 425 737 ED visit records were randomly selected for the train set and the test set for model development and validation, respectively. Among the methods evaluated, XGBoost demonstrated an optimal predictive performance with an area under the curve (AUC) of 0.90 (95% CI: 0.90-0.91). Logistic regression exhibited a comparable predictive ability to XGBoost, with an AUC of 0.89 (95% CI: 0.89-0.90), along with a sensitivity and specificity of 85% (95% CI: 0.83-0.86) and 78% (95% CI: 0.77-0.80), respectively. Neither of the five commonly used severity scoring systems demonstrated satisfactory performance for sepsis prediction. The predictive ability of using ESI as the sole predictor (AUC: 0.79, 95% CI: 0.78-0.80) was also inferior to the model integrating ESI and other basic information. The use of ESI combined with basic clinical information upon ED presentation accurately predicted sepsis among ED patients, strengthening its application in ED. The proposed model may assist nurses in risk stratification management and prioritize interventions for potential sepsis patients, even in low-resource settings.

Effectiveness of the Emergency Severity Index in Early Identification of Sepsis in a Tertiary Care Hospital of a Low-resource Setting

Background: Sepsis is life-threatening organ dysfunction, and a global issue, with incidence and mortality are around 49 M cases in 2017. Annual sepsis incidence from Pakistan is around 1600-2500 per 100, 000 cases with high mortality of approx. 30%. This huge mortality among sepsis and septic shock cases from low-income settings it is necessary to implement a focused and effective triage tool to prioritize and categorize the sick patients from huge bulk of sick patients who attend the ED and to identify those who need to be seen and manage accordingly. Emergency severity index (ESI), a widely acceptable tool that was developed to assess illness severity, hence able to identify the sick patients that need immediate attention at triage. Objectives: Our objective is to determine the effectiveness of ESI triage acuity in early recognition of patient with sepsis and septic shock at triage in a busy ED. Methods: This was a single center, cross sectional and observational study, we include 240 cases who present to the ED of a tertiary care hospital. We follow ESI v. 4.0. All cases presenting to the ED with suspected diagnosis of sepsis or septic shock were included. Data was collected on a predesigned data sheet. Participants were followed by their electronic medical record number through patient care information software where patient’s medical diagnosis and discharge diagnosis are present. Results: Total of 240 patients in the inclusion criteria, area under curve (AUC) for the ESI score I, for septic shock was 0.943 [0.921 - 0.964] with the optimal cutoff value of 2.0, sensitivity of 88.5% and specificity 100%. Similarly, the receiver operating curve (ROC) with area under the curve of ESI Score II for sepsis is found 0.2 [0.129-0.270] with sensitivity 23% and specificity was 12.7%. Conclusion: ESI is a reliable tool which is both sensitive and specific in screening patients with septic shock in a busy emergency department.

Prediction of 90-day mortality risk after unplanned emergency department visits of advanced stage cancer patients

PurposeCancer represents the leading cause of mortality in high-income countries. In the last years, the rate of emergency department (ED) visits by cancer patients has increased 5.5-fold. These ED visits impose a significant economic burden and may indicate the progression of the oncologic disease. The goal of this retrospective study was to identify patient-derived risk factors, especially focusing on serum albumin and body mass index (BMI) for 90-day mortality following unplanned ED visits by cancer patients.MethodsA retrospective chart review of all patients with an ICD-10 diagnosis for cancer undergoing palliative treatment presenting at the ED between 2016 and 2018 at the General Hospital of Vienna was performed. Laboratory values, emergency severity index (ESI), and BMI were collected at the ED presentation. 90-day mortality (90MM) was calculated from the ED presentation.ResultsA total of 448 cancer patients were included. Lung cancer (19.2%) and pancreaticobiliary cancer (15.6%) were the most frequent diagnoses. The main reasons for ED visits were pain (20.5%) and fever (17.4%). Sixty-nine percent of patients had to be admitted and 17.5% of patients died during hospitalization. 90MM was highest for patients with low albumin (< 35 g/L vs. > 35 g/L: 60.4% vs. 31.4%; p < .0001). When incorporating albumin levels and BMI, patients with both values below the cutoff had the highest risk for death (HR 4.01, 95% CI 2.30–7.02).ConclusionCancer patients face a high risk for hospitalization when presenting at the ED. The 90MM rate is highest in patients with low BMI and albumin levels. This highlights an especially vulnerable cohort of cancer patients for whom supportive care and palliative care have to be optimized.

Construction and validation of the Emergency Oncology Scale (EMOnco), a risk rating protocol for the triage of cancer patients in acute care settings.

Manfredini et al. demonstrate that the new rating protocol, EMOnco, can triage of cancer patients in acute care settings safely, considering their cancer type, stage and treatment histories and oncological emergencies, enabling the appropriate classification from high-risk patients to non-urgent patients. ■ EMOnco considers variables related to the cancer history and treatment. ■ Triages patients in the emergency care in less than three minutes. ■ Cancer patients need priority care regarding infection, and this protocol consider it. ■ EMOnco has shown to be a valid and reliable scale for the triage of oncological patients in the emergency room or acute care clinics. To validate a risk rating scale for triaging of cancer patients in emergency rooms that can identify individuals needing urgent care or in imminent worsening of the clinical condition. This is a health instrument validation study developed in the emergency care ward of a Brazilian hospital, a referral center for cancer and hematological diseases. We built the Emergency Oncology Scale (EMOnco) based on literature review and a Delphi survey with 20 experienced oncologists (physicians and nurses). We validated the scale by assessing its construct validity, interobserver agreement and reliability after applying them in a convenience sample of all consecutive patients with cancer who visited the ward between August 2017 and January 2018. We compared the EMOnco Scores with those from other scales, used by six trained nurses: the Emergency Severity Index, the Manchester Triage System, and the Karnofsky Performance Status. We also recorded socio-demographic and clinical features and the Sequential Organ Failure Assessment (SOFA) results in the intensive care unit. We included 250 patients with locally advanced or recurrent disease and undergoing chemotherapy. EMOnco screening took 2.24 (± 2.9) minutes in average. The interobserver correlation coefficient was 0.9. EMOnco was highly correlated with Emergency Severity Index (r=0.617) and also correlated with Karnofsky Performance Status (0.420) Manchester Triage System (0.491; p<0.001 for all). EMOnco in Portuguese considers variables related to the cancer history and treatment and has proven to be a valid and reliable for the risk classification of oncological patients in emergency care services.

Comparative analysis of Glasgow Coma Scale, quick Sepsis-related Organ Failure Assessment, base excess, and lactate for mortality prediction in critically ill emergency department patients

Abstract OBJECTIVES: It is crucial to promptly identify high-mortality patients in emergency departments and initiate their treatment as soon as possible. Although many parameters have been studied to select patients with high mortality, no comprehensive evaluation exists in previous literature on these parameters in critically ill patients, regardless of patient groups. The aim of this study is to evaluate the Glasgow Coma Scale (GCS), quick Sepsis-related Organ Failure Assessment (qSOFA), blood gas base excess (BE), and blood gas lactate in predicting mortality in critically ill patients admitted to the emergency department. METHODS: This prospective observational cohort study included adult patients with Emergency Severity Index 1–2 (critically ill) admitted to the emergency department. All patients were evaluated by the physician within 10 min, and blood gas samples were taken. The data collection forms recorded the patients’ GCS and qSOFA scores at the time of first evaluation by the physician. The qSOFA score assessment was performed in all patients with ESI levels 1 and 2, regardless of whether infective pathology was suspected. Blood gas BE and lactate values were also from laboratory test results. Patients or their relatives were contacted by phone at the end of the 1st month to obtain information about the clinical condition (survival or mortality). RESULTS: A total of 868 patients were included, with 163 deaths observed within 30 days. GCS score, qSOFA score, and lactate value were significant in predicting mortality within 30 days. While the BE value was significant for predicting 30-day mortality for values equal to or below the lower limit of −1.5 (P &lt; 0.001), it was not significant for values equal to or above the upper limit of +3 (P &gt; 0.05). The most successful prediction model for predicting 30-day mortality was found to be qSOFA with a cutoff value of ≥1. CONCLUSION: In emergency departments, each of the GCS, qSOFA scores, BE, and lactate values can be used independently as a practical mortality prediction model in critically ill patients. Among these four models, qSOFA is the most successful practical mortality prediction model in critically ill patients.

Emergency department triaging using ChatGPT based on emergency severity index principles: a cross-sectional study

Erroneous and delayed triage in an increasingly crowded emergency department (ED). ChatGPT is an artificial intelligence model developed by OpenAI® and is being trained for use in natural language processing tasks. Our study aims to determine the accuracy of patient triage using ChatGPT according to the emergency severity index (ESI) for triage in EDs. In our cross-sectional study, 18 years and over patients who consecutively presented to our ED within 24 h were included. Age, gender, admission method, chief complaint, state of consciousness, and comorbidities were recorded on the case form, and the vital signs were detected at the triage desk. A five-member expert committee (EC) was formed from the fourth-year resident physicians. The investigators converted real-time patient information into a standardized case format. The urgency status of the patients was evaluated simultaneously by EC and ChatGPT according to ESI criteria. The median value of the EC decision was accepted as the gold standard. There was a statistically significant moderate agreement between EC and ChatGPT assessments regarding urgency status (Cohen’s Kappa = 0.659; P < 0.001). The accuracy between these two assessments was calculated as 76.6%. There was a high degree of agreement between EC and ChatGPT for the prediction of ESI-1 and 2, indicating high acuity (Cohen’s Kappa = 0.828). The diagnostic specificity, NPV, and accuracy of ChatGPT were determined as 95.63, 98.17 and 94.90%, respectively, for ESI high acuity categories. Our study shows that ChatGPT can successfully differentiate patients with high urgency. The findings are promising for integrating artificial intelligence-based applications such as ChatGPT into triage processes in EDs.

Evaluation Of the Emergency Severity Index Triage For Improving Emergency Room Services Quality In Indonesian Hospital

Background:Effective prioritization is crucial in delivering high-quality emergency medical services, although it is often lacking. Objective:This study aims to evaluate the application of triage based on the Emergency Severity Index (ESI) in improving the quality of emergency departments in hospitals in Indonesia. Methods:An accidental sample of 100 emergency room patients was selected. Patient satisfaction was measured using questionnaires, while infrastructure, support facilities, and the triage process were assessed through direct observation. Results:The evaluation found that nurse staffing levels did not meet standards and were inadequate to meet patient needs. Triage facilities and infrastructure at both hospitals met established standards. The triage process, conducted on 100 respondents by 30 nurses, revealed that the majority of patients were categorized as ESI level 3, comprising 82 (82%) respondents. There was an excessive use of triage resources beyond projections, and patient placement did not always adhere to standards, although the average response time was within the accepted range. Patient satisfaction was evaluated across five dimensions; the most satisfying dimensions were assurance and empathy, with 90% of respondents rating thempositively, while the tangibles dimension received the lowest satisfaction rating at 84%. The correlation between the application of ESI triage and patient satisfaction was not statistically significant (p-value = 0.101, higher than 0.05). Conclusion:It is recommended that hospitals increase the number and competence of nursing staff in emergency departments to ensure response times and patient placement align with ESI triage standards, thereby enhancing the effectiveness of triage implementation. Keywords:emergency severity index triage; quality of care; emergency department

Languages other than English in a pediatric ED: Documentation, extended stays, and revisits

ObjectiveGiven the increasing proportion of patients and caregivers who use languages other than English (LOE) at our institution and across the U.S, we evaluated key workflow and outcome measures in our emergency department (ED) for patients and caregivers who use LOE. MethodsThis was a retrospective, cross-sectional study of patients and caregivers who presented to a free-standing urban pediatric facility. We used electronic health record data (EHR) and interpreter usage log data for our analysis of language documentation, length of stay, and ED revisits. We assessed ED revisits within 72-h using a multivariable logistic regression model adjusting for whether a primary care provider (PCP) was listed in the EHR, whether discharge was close to or on the weekend, and insurance status. We restricted our analysis to low-acuity patient encounters (Emergency Severity Index (ESI) scores of 4 and 5) to limit confounding factors related to higher ESI scores. ResultsWe found that one in five patients and caregivers who use LOE had incorrect documentation of their language needs in the EHR. Using interpreter usage data to most accurately capture encounters using LOE, we found that patient encounters using LOE had a 38-min longer length of stay (LOS) and twice the odds of a 72-h ED revisit compared to encounters using English. ConclusionThese results highlight the need for better language documentation and understanding of factors contributing to extended stays and increased revisits for pediatric patients and caregivers who use LOE.

Validation of Emergency Nurse Practitioner Competencies: Patient Complexity and Clinical Decision Making

IntroductionOver the past 15 years, the emergency nurse practitioner has been recognized as a nursing specialty role with dedicated scope and standards of practice. However, a paucity of objective data exists to validate the actual practice of emergency nurse practitioners in the emergency care setting. The purpose of this pilot study was to describe the initial acuity of patients assigned to emergency nurse practitioners, actions, decisional complexity, and disposition decisions of advanced practice nurses as they function in emergency departments in a single system. MethodsThis descriptive exploratory study used retrospective chart data to gain understanding. ResultsThe most common Emergency Severity Index level seen by emergency nurse practitioners was Emergency Severity Index 3. Of 8513 encounters with Emergency Severity Index level data, 21% were triaged at Emergency Severity Index 2, 56% at Emergency Severity Index 3, 21% at Emergency Severity Index 4, and only 2% at Emergency Severity Index 5. Half of encounters resulted in a Current Procedural Terminology code of 99825 or higher and 94% were coded at 99824 or higher. There were a high percentage of admissions including intensive care unit admissions. DiscussionAlthough descriptive, this study is highly illustrative of the broad scope of complex skills and clinical decision making required to perform as an advanced practice nurse in the emergency department. Further examination of education and training is warranted.

Association of Emergency Department Length of Stay and Hospital Mortality in Patients Under Investigation for COVID-19.

We aimed to determine the association between hospital mortality of patients under investigation (PUI) for COVID-19 and emergency department length of stay (EDLOS). A retrospective study was conducted from April 3, 2020 to April 2,2022. Adult PUI who presented with both clinical and epidemiological risk factors for COVID-19 disease and underwent sample collection with nasal swab for reverse transcription polymerase chain reaction were included in the study. The factors associated with EDLOS and hospital mortality were investigated using univariate logistic regression and multivariate logistic regression analyses. A total of 961 PUI were enrolled that included 836 (87%) non-COVID-19 patients. The median (interquartile range [IQR]) EDLOS durations for 7-day and 30-day mortality of all PUI were 3.1hours (2.1,4.3, P = 0.231) and 3.2hours (2.1,4.3, P = 0.653). Multivariate logistic regression analysis revealed that the significant factors associated with EDLOS longer than 4hours were consultation of three departments (adjusted odds ratio (aOR) 27.3, 95% CI 2.42-309.71, P = 0.007), emergency severity index (ESI) level 3 (aOR 2.31, 95% CI 1.37-3.9), investigations >2 (aOR 2.62, 95% CI 1.62-4.25), nebulization (aOR 2.34, 95% CI 1.39-3.96), administration of intravenous fluid (aOR 2.62, 95% CI 1.59-4.33), performing ≥1 procedure (aOR 3.35, 95% CI 1.51-7.43), and discharged patients (aOR 2.13, 95% CI 1.02-4.48). The significant factors associated with prolonged EDLOS in PUI included consultation of three departments, ESI level 3, investigations >2, ED treatment, ED procedures, and discharged patients. The median times of EDLOS and hospital LOS were 3.2hours and 5.7 days. The EDLOS had no significant association with short-term mortality.

Emergency Severity Index Version 4 and Triage of Pediatric Emergency Department Patients

Most emergency departments (EDs) across the US use the Emergency Severity Index (ESI) to predict acuity and resource needs. A comprehensive assessment of ESI accuracy among pediatric patients is lacking. To assess the frequency of mistriage using ESI (version 4) among pediatric ED visits using automated measures of mistriage and identify characteristics associated with mistriage. This cohort study used operational measures for each ESI level to classify encounters as undertriaged, overtriaged, or correctly triaged to assess the accuracy of the ESI and identify characteristics of mistriage. Participants were pediatric patients at 21 EDs within Kaiser Permanente Northern California from January 1, 2016, to December 31, 2020. During that time, version 4 of the ESI was in use by these EDs. Visits with missing ESI, incomplete ED time variables, patients transferred from another ED, and those who left against medical advice or without being seen were excluded. Data were analyzed between January 2022 and June 2023. Assigned ESI level. Rates of undertriage and overtriage by assigned ESI level based on mistriage algorithm, patient and visit characteristics associated with undertriage and overtriage. This study included 1 016 816 pediatric ED visits; the mean (SD) age of patients was 7.3 (5.6) years, 479 610 (47.2%) were female, and 537 206 (52.8%) were male. Correct triage occurred in 346 918 visits (34.1%; 95% CI, 34.0%-34.2%), while overtriage and undertriage occurred in 594 485 visits (58.5%; 95% CI, 58.4%-58.6%) and 75 413 visits (7.4%; 95% CI, 7.4%-7.5%), respectively. In adjusted analyses, undertriage was more common among children at least 6 years old compared with those younger 6 years; male patients compared with female patients; patients with Asian, Black, or Hispanic or other races or ethnicities compared with White patients; patients with comorbid illnesses compared with those without; and patients who arrived by ambulance compared with nonambulance patients. This multicenter retrospective study found that mistriage with ESI version 4 was common in pediatric ED visits. There is an opportunity to improve pediatric ED triage, both in early identification of critically ill patients (limit undertriage) and in more accurate identification of low-acuity patients with low resource needs (limit overtriage). Future research should include assessments based on version 5 of the ESI, which was released after this study was completed.

The accuracy of The Emergency Severity Index Triage Score in Predicting Cardiac Events among Suspected Acute Coronary Syndrome Patients in The Emergency Department

The accuracy of The Emergency Severity Index Triage Score in Predicting Cardiac Events among Suspected Acute Coronary Syndrome Patients in The Emergency Department

Does Practice Match Protocol? A Comparison of Triage-to-Provider Time Among More vs. Less Acute Emergency Department Patients.

Introduction The Emergency Severity Index (ESI) stratifies emergency department (ED) patients for triage, from "most acute" (level 1) to "least acute" (level 5). Many EDs have a split flow model where less acute (ESI 4 and 5) are seen in a fast track, while more acute (ESI 1, 2, and 3) are seen in the acute care area. A core principle of emergency medicine (EM) is to attend to more acute patients first. Deliberately designating an area for less acute patients to be initially assessed quickly by a first provider might result in them being seen before more acute patients. This study aims to determine the percentage of less acute patients seen by a provider sooner after triage than more acute patients who arrived within 10 minutes of one another. Additionally, this study compares the fast track and acute care areas to see if location affects triage-to-provider time. Methods A random convenience sample of 252 ED patients aged ≥18 was taken. Patients were included if their ESI was available for the provider during sign-up. Patients were excluded if they were directly sent to the ED psychiatric area or attended to by the author. We collected data on the ESI level, time stamps for triage and first provider sign-up, and the location to which the patient was triaged (fast track vs. acute care). Paired patients' ESI levels, locations, and triage and first provider sign-up times were compared. Results The study included 126 pairs of patients. There was a statistically significant difference in triage-to-provider times for paired ESI 2 vs. 3 patients (60.5 vs. 35.5 minutes, p = 0.0007) and overall paired high- vs. low-acuity patients (55 vs. 39.5 minutes, p = 0.004). However, in 34.8% of paired ESI 2 vs. 3 patients, the ESI 3 patient was seen prior to the paired ESI 2 patient, and in 39.4% of overall paired high vs. low acuity patients, the less acute patient was seen before the more acute patient. Additionally, patients in the acute care area had significantly shorter median triage-to-provider times (~40 minutes) compared to those in the fast track area for ESI 2 (acute care) vs. ESI 3 (fast track) and overall high acuity (acute care) vs. low acuity (fast track). Nonetheless, approximately one-third of ESI 3 patients triaged to fast track were seen before ESI 2 patients triaged to the acute care area. Conclusion The split flow model reduces overall ED length of stay, improving flow volume, revenue, and patient satisfaction. However, it comes at the expense of the fundamental ethos of EM and potentially subverts the intended triage process. Although most more acute patients are seen by a provider sooner after triage than less acute patients, a substantial number are seen later, which could delay urgent medical needs and negatively impact patients' outcomes. Furthermore, patients triaged to acute care are, in general, seen sooner post-triage than identical-ESI-level fast track patients, suggesting fast track might not function as intended (for low-acuity patients to be quickly assessed and initiate diagnostic and treatment plans). We intend to follow this exploratory study with a more comprehensive, multivariate analysis that will consider confounding variables such as initial vital signs, how busy a provider was that day, etc. The future study will also examine patient outcomes to determine the impact on more acute patients of the split flow model and, in particular, on less acute patients being seen sooner by a first provider.

Does initiating care in alternate care sites decrease time to disposition in the emergency department?

During the coronavirus disease 2019 (COVID-19) pandemic surge, alternate care sites (ACS) such as the waiting room or hospital lobby were created amongst hospitals nationwide to help alleviate emergency department (ED) overflow. Despite the end of the pandemic surge, many of these ACS remain functional given the burden of prolonged ED wait times, with providers now utilizing the waiting room or ACS to initiate care. Therefore, the objective of this study is to evaluate if initiating patient care in ACS helps to decrease time to disposition. Retrospective data were collected on 61,869 patient encounters presenting to an academic medical center ED. Patients with an emergency severity index (ESI) of 1 were excluded. The "pre-ACS" or control data consisted of 38,625 patient encounters from September 30, 2018 to October 1, 2019, prior to the development of ACS, in which the patient was seen by a physician after they were brought to an assigned ED room. The "post-ACS" study cohort consisted of 23,244 patient encounters from September 30, 2022 to October 1, 2023, after the initiation of ACS, during which patients were initially seen by a provider in an ACS. ACS at this institution included the three following areas: waiting room, ambulance waiting area, and a newly constructed ACS that was built next to the ED entrance on the first floor of the hospital. The newly constructed ACS consisted of 16 care spaces each containing an upright exam chair with dividers between each care space. Door-to-disposition time (DTD) was calculated by identifying the time when the patient entered the ED and the time when disposition was decided (admission requested or patient discharged). Using regression analysis, we compared the two data sets to determine significant differences among DTD time. The largest proportion of encounters were among ESI 3 patients, that is, 56.1%. There was a significant increase in median DTD for ESI 2 and 3 patients who were seen initially in an ACS compared to those who were not seen until they were in an assigned ER room. Specifically, there was a median increase of 40.9 min for ESI 2 patients and 18.8 min for ESI 3 patients who were seen initially in an ACS (p<0.001). There was a 29-min decrease in median DTD for ESI 5 patients who were seen in ACS (p=0.09). Initiating patient care earlier in ACS did not appear to decrease DTD time for patients in the ED. Overall, the benefits of early initiation of care likely lie elsewhere within patient care and the ED throughput process.

Nonresponse Bias against the Underserved in Emergency Department Patient Experience Surveys: A Cross-Sectional Analysis: Nonresponse to Patient Surveys

BackgroundPatient experience scores are used to assess emergency physicians, but only a small fraction of patients actually respond to patient experience surveys. No prior studies have determined patient characteristics that predict emergency department (ED) patient experience survey response. ObjectivesTo determine which patient characteristics are associated with ED patient experience survey response. MethodsThis was a cross-sectional analysis of a random sample of 2500 patients from our hospital system who were discharged from an ED between January 1 and June 30, 2022. Our hospital system has one hospital-based ED and two freestanding EDs, one of which is in an economically disadvantaged area. For each randomly selected patient, we used chart review to gather the following data points: ED visited, patient age, gender, race, preferred language, primary residence, time of arrival, marital status, health insurance, and emergency severity index for that visit. We used multivariable logistic regression to determine which of those variables were associated with patient experience survey response. ResultsAmong 2500 patients sent surveys, 207 (8.3%) responded. Those with the lowest response rates were from a freestanding ED in an economically disadvantaged area (5.9%), were uninsured (4.9%), or were homeless (2.1%). On multivariable analysis, adjusted odds ratios for survey response for those characteristics were as follows: 0.54 (95% CI 0.33-0.84), 0.56 (95% CI 0.34-0.90), and 0.30 (95% CI 0.02-1.44), respectively. ConclusionMultiple markers of lower socioeconomic status were associated with decreased patient experience survey response rates, suggestive of nonresponse bias against these individuals.

Development and validation of a machine learning framework for improved resource allocation in the emergency department

ObjectiveThe Emergency Severity Index (ESI) is the most commonly used system in over 70% of all U.S. emergency departments (ED) that uses predicted resource utilization as a means to triage [1], Mistriage, which includes both undertriage and overtriage has been a persistent issue, affecting 32.2% of total ED visits [2]. Our goal is to develop a machine learning framework that predicts patients' resource needs, thereby improving resource allocation during triage. MethodsThis retrospective study analyzed ED visits from the Medical Information Mart for Intensive Care IV, dividing the data into training (80%) and testing (20%) cohorts. We utilized data available during triage, including patient vital signs, age, gender, mode of arrival, medication history, and chief complaint. Azure AutoML was used to create different machine learning models trained to predict the 144 target columns including laboratory panels and imaging modalities as well as medications required during patients' ED visits. The 144 models' performance was evaluated using the area under the receiver operating characteristic curve (AUROC), F1 score, accuracy, precision and recall. ResultsA total of 391,472 ED visits were analyzed. 144 Voting ensemble models were created for each target. All frameworks achieved on average an AUC score of 0.82 and accuracy of 0.76. We gathered the feature importance for each target and observed that ‘chief complaint’, among others, had a high aggregate feature importance across different targets. ConclusionThis study shows the high accuracy in predicting resource needs for patients in the ED using a machine learning model. This can greatly improve patient flow and resource allocation in already resource limited emergency departments.

Assessing the Validity of the Centers for Medicare & Medicaid Services Measure in Identifying Potentially Preventable Emergency Department Visits by Patients With Cancer.

The Centers for Medicare & Medicaid Services (CMS) implemented chemotherapy measures (OP-35) to reduce potentially preventable emergency department visits (PPEDVs) and hospitalizations. This study evaluated the validity of the OP-35 measure in identifying PPEDVs among patients with cancer. This is a cross-sectional study, which used data from the 2012-2022 National Hospital Ambulatory Medical Care Survey. ED visits are assessed and compared on the basis of three measures: immediacy using Emergency Severity Index (ESI), disposition (discharge v hospitalization), and OP-35 criteria. Between 2012 and 2022, a weighted sample of 46,723,524 ED visits were made by patients with cancer. Among reported ESI cases, 25.2% (8,346,443) was high urgency. In addition, 30.3% (14,135,496) of ED visits among patients with cancer led to hospitalizations. Using the OP-35 measure, it was found that 20.85% (9,743,977) was PPEDVs. A 21.9% (10,232,102) discrepancy between discharge diagnosis (CMS billing codes) and chief complaints was identified. Further analysis showed that 19.2% (1,872,556) of potentially preventable ED visits (CMS OP-35) were high urgency and 32.6% (3,181,280) resulted in hospitalization. The CMS approach to identifying PPEDVs has limitations. First, it may overcount preventable visits by including high-urgency or hospitalization-requiring cases. Second, relying on final diagnoses for retrospective preventability judgment can be misleading as they may not reflect the initial reason for the visit. In addition, differentiating causes for ED visits in patients with cancer undergoing various treatments is challenging as the approach does not distinguish between chemotherapy-related complications and others. Identification inconsistencies arise because of varying coding practices and chosen preventable conditions, lacking consensus and alignment with specific hospital or patient needs. Finally, the model fails to consider crucial nonclinical factors like social support, economic barriers, and alternative care access, potentially unfairly penalizing hospitals serving underserved populations.

Evaluation of compliance of emergency severity index triage levels and demographic characteristics with the outcome of patients referred to the emergency department: A retrospective study

This research sought to examine the relationship between emergency severity index (ESI) triage levels, demographic factors, and patient outcomes. A total of 350 patient cases were analyzed from the emergency department (ED) of Khoy University of Medical Sciences and Health Services Hospital, where ESI triage was systematically implemented. A simple random sampling method was employed for selection. The study identified key variables including age, gender, outcomes, and triage levels, categorizing levels 1 and 2 as high-priority triage and levels 3, 4, and 5 as low priority. Data were retrospectively gathered from the medical records of all patients involved. The outcomes assessed included hospitalization, discharge from the hospital, patient satisfaction, and mortality. Statistical analysis was conducted using SPSS software version 20, employing non-parametric chi-square tests, analysis of variance (ANOVA), and multiple regression, with a significance threshold set at 0.05. The findings revealed a statistically significant correlation between ESI triage levels, demographic characteristics, and the outcomes of patients presenting to the ED (P&lt;0.0001). The results indicate that the ESI triage system demonstrates high accuracy in prioritizing patients and effectively predicting outcomes, thereby serving as a valuable tool in hospital triage processes.