Mapping out the emergency department disposition decision for high-acuity patients

Factors contributing to patient safety during triage process in the emergency department: A systematic review

BACKGROUND

Triage is a dynamic environment in which large numbers of people can present. It presents a vulnerable assessment point, as a triage nurse must assess a patient's urgency level and analyse their health status and expected resource needs. Given the critical nature of triage, it is necessary to understand the factors contributing to patient safety.

OBJECTIVES

To identify and examine the factors contributing to patient safety during the triage process.

METHODS

A systematic review of the literature was undertaken, and a thematic analysis of the factors contributing to patient safety during the triage process. PubMed, CINAHL, Web of Sciences, Science Direct, SAGE, EMBASE and reference lists of relevant studies published in English until March 2022 were searched for relevant studies. The search protocol has been registered at the PROSPERO (CRD42019146616), and the review was conducted using the PRISMA criteria.

RESULTS

Out of 5366 records, we included 11 papers for thematic synthesis. Identified factors contributing to patient safety in triage are related to the emergency's work environment, such as patient assessment, high workload, frequent interruptions and staffing, and personal factors such as nurse traits, experience, knowledge, triage fatigue and work schedule.

CONCLUSIONS

This review shows that patient safety is influenced by the attitude, capabilities and experiences of triage nurses, the time when nurses can dedicate themselves to the patient and triage the patient without disruption. It is necessary to raise awareness among nursing administrators and healthcare professionals to provide a safe triage environment for patients.

RELEVANCE TO CLINICAL PRACTICE

This review highlights the evidence on the factors contributing to patient safety in the triage process. Further research is needed for this cohort of triage nurses in the emergency department concerning ensuring patient safety.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution was required to design or undertake this review.

Evaluation of the Emergency Severity Index in US Emergency Departments for the Rate of Mistriage

Importance

Accurate emergency department (ED) triage is essential to prioritize the most critically ill patients and distribute resources appropriately. The most used triage system in the US is the Emergency Severity Index (ESI).

Objectives

To derive and validate an algorithm to assess the rate of mistriage and to identify characteristics associated with mistriage.

Design, Setting, and Participants

This retrospective cohort study created operational definitions for each ESI level that use ED visit electronic health record data to classify encounters as undertriaged, overtriaged, or correctly triaged. These definitions were applied to a retrospective cohort to assess variation in triage accuracy by facility and patient characteristics in 21 EDs within the Kaiser Permanente Northern California (KPNC) health care system. All ED encounters by patients 18 years and older between January 1, 2016, and December 31, 2020, were assessed for eligibility. Encounters with missing ESI or incomplete ED time variables and patients who left against medical advice or without being seen were excluded. Data were analyzed between January 1, 2021, and November 30, 2022.

Exposures

Assigned ESI level.

Main Outcomes and Measures

Rate of undertriage and overtriage by assigned ESI level based on a mistriage algorithm and patient and visit characteristics associated with undertriage and overtriage.

Results

A total of 5 315 176 ED encounters were included. The mean (SD) patient age was 52 (21) years; 44.3% of patients were men and 55.7% were women. In terms of race and ethnicity, 11.1% of participants were Asian, 15.1% were Black, 21.4% were Hispanic, 44.0% were non-Hispanic White, and 8.5% were of other (includes American Indian or Alaska Native, Native Hawaiian or other Pacific Islander, and multiple races or ethnicities), unknown, or missing race or ethnicity. Mistriage occurred in 1 713 260 encounters (32.2%), of which 176 131 (3.3%) were undertriaged and 1 537 129 (28.9%) were overtriaged. The sensitivity of ESI to identify a patient with high-acuity illness (correctly assigning ESI I or II among patients who had a life-stabilizing intervention) was 65.9%. In adjusted analyses, Black patients had a 4.6% (95% CI, 4.3%-4.9%) greater relative risk of overtriage and an 18.5% (95% CI, 16.9%-20.0%) greater relative risk of undertriage compared with White patients, while Black male patients had a 9.9% (95% CI, 9.8%-10.0%) greater relative risk of overtriage and a 41.0% (95% CI, 40.0%-41.9%) greater relative risk of undertriage compared with White female patients. High relative risk of undertriage was found among patients taking high-risk medications (30.3% [95% CI, 28.3%-32.4%]) and those with a greater comorbidity burden (22.4% [95% CI, 20.1%-24.4%]) and recent intensive care unit utilization (36.7% [95% CI, 30.5%-41.4%]).

Conclusions and Relevance

In this retrospective cohort study of over 5 million ED encounters, mistriage with ESI was common. Quality improvement should focus on limiting critical undertriage, optimizing resource allocation by patient need, and promoting equity.

Developing and Validating an Emergency Triage Model Using Machine Learning Algorithms with Medical Big Data

Objective

To establish an emergency triage model through the statistical analysis of big data during a particular time period from a hospital information system to improve the accuracy of triage in emergency department (ED).

Methods

A total of 276,164 patients who visited the Emergency Medicine Department of Beijing Chao-Yang Hospital from 2017 to 2020 were included in this study, including 123,392 men and 152,772 women aged from 14 to 112 years. The baseline characteristics (age and gender) and medical records (patient's condition, body temperature, heart rate, breathing, blood pressure, consciousness, and oxygen saturation) of the patients was collected. The data samples were randomly allocated, with 80% as the training set and 20% as the testing set. The patients were divided into levels I, II, III, and IV in accordance with a four-level triage standard. We selected the effective Extreme Gradient Boosting (XGBoost) algorithm as our emergency classification prediction model. The XGBoost model was applied to simulate the thinking process of triage nurses, and the De Long's test was used to compare the receiver operating characteristic (ROC) curve of different models. The P value was obtained by calculating the variance and covariance of area under the curve (AUC) values of different ROC curves.

Results

Level I had 4960 (1.8%) patients, level II had 25,646 (9.29%), level III had 130,664 (47.31%), and level IV had 114,894 (41.6%). The XGBoost model was built following a logic exercise based on the traditional manual pre-inspection and triage results. After verification, the prediction accuracy was 82.57%. The AUC of each disease severity level (levels I, II, III, and IV) was 0.9629, 0.9554, 0.9120, and 0.9296, respectively.

Conclusion

The emergency triage prediction model, which achieved a relatively strong accuracy rate, can reduce the work intensity of medical workers and improve their working efficiency.

The Admission Checklist: The key steps and responsibilities for the admitting resident

The process of admitting patients from the emergency department to the general medicine floor is foundational to the medical training process and medical practice more generally. Yet this process is rife with potential error if not approached systematically, and residents rarely receive explicit teaching in this area. The creation of an “Admission Checklist” proposed by the authors could serve the function of reducing error and enhancing inter-provider communication throughout this process. Such a checklist could improve trainee experience and education, and ultimately allow for improved outcomes for patients during transitions of care.

An Artificial Intelligence-Based Alarm Strategy Facilitates Management of Acute Myocardial Infarction

(1) Background: While an artificial intelligence (AI)-based, cardiologist-level, deep-learning model for detecting acute myocardial infarction (AMI), based on a 12-lead electrocardiogram (ECG), has been established to have extraordinary capabilities, its real-world performance and clinical applications are currently unknown. (2) Methods and Results: To set up an artificial intelligence-based alarm strategy (AI-S) for detecting AMI, we assembled a strategy development cohort including 25,002 visits from August 2019 to April 2020 and a prospective validation cohort including 14,296 visits from May to August 2020 at an emergency department. The components of AI-S consisted of chest pain symptoms, a 12-lead ECG, and high-sensitivity troponin I. The primary endpoint was to assess the performance of AI-S in the prospective validation cohort by evaluating F-measure, precision, and recall. The secondary endpoint was to evaluate the impact on door-to-balloon (DtoB) time before and after AI-S implementation in STEMI patients treated with primary percutaneous coronary intervention (PPCI). Patients with STEMI were alerted precisely by AI-S (F-measure = 0.932, precision of 93.2%, recall of 93.2%). Strikingly, in comparison with pre-AI-S (N = 57) and post-AI-S (N = 32) implantation in STEMI protocol, the median ECG-to-cardiac catheterization laboratory activation (EtoCCLA) time was significantly reduced from 6.0 (IQR, 5.0–8.0 min) to 4.0 min (IQR, 3.0–5.0 min) (p < 0.01). The median DtoB time was shortened from 69 (IQR, 61.0–82.0 min) to 61 min (IQR, 56.8–73.2 min) (p = 0.037). (3) Conclusions: AI-S offers front-line physicians a timely and reliable diagnostic decision-support system, thereby significantly reducing EtoCCLA and DtoB time, and facilitating the PPCI process. Nevertheless, large-scale, multi-institute, prospective, or randomized control studies are necessary to further confirm its real-world performance.

An interrater reliability study on the Gothenburg obstetric triage system- a new obstetric triage system

BACKGROUND

Triage, identifying patients with critical and time-sensitive disorders, is an integrated process in general emergency medicine. Obstetric triage is more specialised, requiring assessment of both woman, fetus and labour status. Failure to identify severely ill obstetric patients has repeatedly led to maternal morbidity and mortality. Reliable triage systems, adapted to obstetric patients as well as local conditions, are thus essential. The study aims to assess the interrater reliability (IRR) of the Gothenburg Obstetric Triage System (GOTS).

METHODS

Midwives (n = 6) and registered nurses with no experience in managing obstetric patients (n = 7), assessed 30 paper cases based on actual real-life cases, using the GOTS. Furthermore, a reference group consisting of two midwives and two obstetricians, with extensive experience in obstetric care, determined the correct triage level in order to enable analysis of over- and undertriage. IRR was assessed, both with percentage of absolute agreement and with intra-class correlation coefficients (ICC) with 95% confidence intervals (CI).

RESULTS

A total of 388 assessments were performed, comprising all five levels of acuity in the GOTS. Absolute agreement was found in 69.6% of the assessments. The overall IRR was good, with a Kappa value of 0.78 (0.69-0.87, 95% CI) for final triage level. Comparison with reference group assessments established that over- and undertriage had occurred in 9% and 21% of the cases, respectively. The main reasons for undertriage were "not acknowledging abnormal vital sign parameters" and "limitations in study design".

CONCLUSION

The GOTS is a reliable tool for triaging obstetric patients. It enables a standardized triage process unrelated to the assessors' level of experience in assessing and managing obstetric patients and is applicable for triaging obstetric patients presenting for emergency care at obstetric or emergency units.

Prevalence, determinants and practical implications of inappropriate hospitalizations in older subjects: A prospective observational study

In a context of high demand for hospital services among older people, we aimed to assess the rate and determinants of inappropriate hospitalizations of older patients, and to what extent they were associated with inappropriate hospital stay. This prospective observational multicentre study evaluated a random sample of consecutive patients aged ≥ 70 years accessing the Emergency Department (ED) of two Italian tertiary hospitals. A standardized comprehensive geriatric assessment was carried out in each patient, including the Blaylock Risk Assessment Screen Scale (BRASS) for identification of patients at risk of difficult discharge. Inappropriate hospitalization was defined by the ED physician when patients did not necessitate hospital-provided procedures but was due to social reasons or lack of an alternative care-setting. Among 1877 patients (median age 80.7 years, 50.1% male), with a high prevalence of functional dependence and social isolation (around 30% and 25%, respectively), 767 (40.9%) were hospitalized. Incidence of inappropriate hospitalization was 14.6% (95% CI 12.1%-17.1%) and was associated with moderate-high risk of difficult discharge at BRASS (OR = 1.98, 95% CI 1.16-3.39, p = 0.013) and the presence of dementia with behavioural disorders (OR = 1.79, 95% CI 1.10-2.91, p = 0.020). Compared with patients appropriately admitted, inappropriate hospitalizations had shorter length of hospital stay but accounted for 1059/9154 days of stay (11.6%). Inappropriate hospitalizations occurred in less than 15% of cases, mainly accounted for by patients no longer manageable at home, but contributed to the greatest proportion of inappropriate hospital stay. These findings highlight the need of implementing appropriate home-care services and ensuring rapid access to suitable care-facilities for community-dwelling frail older patients.

Intuitive and deliberative approaches for diagnosing 'well' versus 'unwell': evidence from eye tracking, and potential implications for training

Rapidly assessing how ill a patient is based on their immediate presentation-colloquially termed 'eyeballing' in practice-serves a vital role in acute care settings. Yet surprisingly little is known about how this diagnostic skill is learned or how it should be taught. Some authors have pointed to a dual-process model, suggesting that assessments of illness severity are driven by two distinct types of processing: an intuitive, fast, pattern recognition-like process (Type 1) that depends on many prior patient encounters and outcomes being stored in memory; and a deliberate, slow, analytic process (Type 2) characterized by additional data gathering, data scrutiny, or recollection of rules. But prior studies have supported a dual-process model for the assessment of illness severity only insofar as experienced clinicians chiefly displayed what was presumed to be Type 1 processing. Here we further explored a dual-process model by examining whether less experienced clinicians displayed both types of processing when assessing illness severity across a series of cases. Consistent with the model, a dissociation between Type 1 and Type 2 processing was observed through resident reports of deliberation, response times, and three eye tracking metrics associated with diagnostic expertise. We conclude by discussing potential implications for the training of this enigmatic diagnostic skill.

Qualitative analysis of disposition decision making for patients referred for admission from the emergency department without definite medical acuity

OBJECTIVE

To map the physician approach when determining disposition for a patient who presents without the level of definite medical acuity that would generally warrant hospitalisation.

DATA SOURCES/STUDY SETTING

Since 2018, our US academic county hospital/trauma centre has maintained a database in which hospitalists ('triage physicians') document the rationale and outcomes of requests for admission to the acute care medical ward during each shift.

STUDY DESIGN

Narrative text from the database was analysed using a grounded theory approach to identify major themes and subthemes, and a conceptual model of the admission decision-making process was constructed.

PARTICIPANTS

Database entries were included (n=300) if the admission call originated from the emergency department and if the triage physician characterised the request as potentially inappropriate because the patient did not have definite medical acuity.

RESULTS

Admission decision making occurs in three main phases: evaluation of unmet needs, assessment of risk and re-evaluation. Importantly, admission decision making is not solely based on medical acuity or clinical algorithms, and patients without a definite medical need for admission are hospitalised when physicians believe a potential issue exists if discharged. In this way, factors such as homelessness, substance use disorder, frailty, etc, contribute to admission because they raise concern about patient safety and/or barriers to appropriate treatment. Physician decision making can be altered by activities such as care coordination, advocacy by the patient or surrogate, interactions with other physicians or a change in clinical trajectory.

CONCLUSIONS

The decision to admit ultimately remains a clinical determination constructed between physician and patient. Physicians use a holistic process that incorporates broad consideration of the patient's medical and social needs with emphasis on risk assessment; thus, any analysis of hospitalisation trends or efforts to impact such should seek to understand this individual-level decision making.

Process mapping in healthcare: a systematic review

INTRODUCTION

Process mapping (PM) supports better understanding of complex systems and adaptation of improvement interventions to their local context. However, there is little research on its use in healthcare. This study (i) proposes a conceptual framework outlining quality criteria to guide the effective implementation, evaluation and reporting of PM in healthcare; (ii) reviews published PM cases to identify context and quality of PM application, and the reported benefits of using PM in healthcare.

METHODS

We developed the conceptual framework by reviewing methodological guidance on PM and empirical literature on its use in healthcare improvement interventions. We conducted a systematic review of empirical literature using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. Inclusion criteria were: full text empirical study; describing the process through which PM has been applied in a healthcare setting; published in English. Databases searched are: Medline, Embase, HMIC-Health Management Information Consortium, CINAHL-Cumulative Index to Nursing and Allied Health Literature, Scopus. Two independent reviewers extracted and analysed data. Each manuscript underwent line by line coding. The conceptual framework was used to evaluate adherence of empirical studies to the identified PM quality criteria. Context in which PM is used and benefits of using PM were coded using an inductive thematic analysis approach.

RESULTS

The framework outlines quality criteria for each PM phase: (i) preparation, planning and process identification, (ii) data and information gathering, (iii) process map generation, (iv) analysis, (v) taking it forward. PM is used in a variety of settings and approaches to improvement. None of the reviewed studies (N = 105) met all ten quality criteria; 7% were compliant with 8/10 or 9/10 criteria. 45% of studies reported that PM was generated through multi-professional meetings and 15% reported patient involvement. Studies highlighted the value of PM in navigating the complexity characterising healthcare improvement interventions.

CONCLUSION

The full potential of PM is inhibited by variance in reporting and poor adherence to underpinning principles. Greater rigour in the application of the method is required. We encourage the use and further development of the proposed framework to support training, application and reporting of PM.

TRIAL REGISTRATION

Prospero ID: CRD42017082140.

Physician Perceptions of Disposition Decision-making for Older Adults in the Emergency Department: A Preliminary Analysis

Disposition decision-making in the emergency department (ED) is critical to patient safety and quality of care. Disposition decision-making has particularly important implications for older adults who comprise a significant portion of ED visits annually and are vulnerable to suboptimal outcomes throughout ED care transitions. We conducted a secondary inductive content analysis of interviews with ED physicians (N= 11) to explore their perceptions of who they involve in disposition decision-making and what information they use to make disposition decisions for older adults. ED physicians cited 7 roles (5 types of clinicians, patients and families) and 11 information types, both clinical (e.g. test/lab results) and non-clinical (e.g. family's preference). Our preliminary findings represent a key first step toward the development of interventions that promote patient safety and quality of care for older adults in the ED by supporting the cognitive and communicative aspects of disposition decision-making.

Consistency of triage scores by presenting complaint pre- and post-implementation of a real-time electronic triage decision support tool

OBJECTIVE

eCTAS is a real-time electronic decision-support tool designed to standardize the application of the Canadian Triage and Acuity Scale (CTAS). This study addresses the variability of CTAS score distributions across institutions pre- and post-eCTAS implementation.

METHODS

We used population-based administrative data from 2016-2018 from all emergency departments (EDs) that had implemented eCTAS for 9 months. Following a 3-month stabilization period, we compared 6 months post-eCTAS data to the same 6 months the previous year (pre-eCTAS). We included triage encounters of adult (≥17 years) patients who presented with 1 of 16 pre-specified, high-volume complaints. For each ED, consistency was calculated as the absolute difference in CTAS distribution compared to the average of all included EDs for each presenting complaint. Pre-eCTAS and post-eCTAS change scores were compared using a paired-samples t-test. We also assessed if eCTAS modifiers were associated with triage consistency.

RESULTS

There were 363,214 (183,231 pre-eCTAS, 179,983 post-eCTAS) triage encounters included from 35 EDs. Triage scores were more consistent (P < 0.05) post-eCTAS for 6 (37.5%) presenting complaints: chest pain (cardiac features), extremity weakness/symptoms of cerebrovascular accident, fever, shortness of breath, syncope, and hyperglycemia. Triage consistency was similar pre- and post-eCTAS for altered level of consciousness, anxiety/situational crisis, confusion, depression/suicidal/deliberate self-harm, general weakness, head injury, palpitations, seizure, substance misuse/intoxication, and vertigo. Use of eCTAS modifiers was associated with increased triage consistency.

CONCLUSIONS

eCTAS increased triage consistency across many, but not all, high-volume presenting complaints. Modifier use was associated with increased triage consistency, particularly for non-specific complaints such as fever and general weakness.

Factors associated with low-acuity hospital admissions in a public safety-net setting: a cross-sectional study

Background

Given system-level focus on avoidance of unnecessary hospitalizations, better understanding admission decision-making is of utility. Our study sought to identify factors associated with hospital admission versus discharge from the Emergency Department (ED) for a population of patients who were assessed as having low medical acuity at time of decision.

Methods

Using an institutional database, we identified ED admission requests received from March 1, 2018 to Feb 28, 2019 that were assessed by a physician at the time of request as potentially inappropriate based on lack of medical acuity. Focused chart review was performed to extract data related to patient demographics, socioeconomic information, measures of illness, and system-level factors such as previous healthcare utilization and day/time of presentation. A binary logistic regression model was constructed to correlate patient and system factors with disposition outcome of admission to the hospital versus discharge from the ED. Physician-reported contributors to admission decision-making and chief complaint/reason for admission were summarized.

Results

A total of 349 (77.2%) of 452 calls resulted in admission to the hospital and 103 (22.8%) resulted in discharge from the ED. Predictors of admission included age over 65 (OR 3.5 [95%CI 1.1–11.6], p = 0.039), homelessness (OR 3.3 [95% CI 1.7–6.4], p=0.001), and night/weekend presentation (OR 2.0 [95%CI 1.1–3.5], p = 0.020). The most common contributing factors to the decision to admit reported by the responding physician included: lack of outpatient social support (35.8% of admissions), homelessness (33.0% of admissions), and substance use disorder (23.5% of admissions).

Conclusions

Physician medical decision-making regarding the need for hospitalization incorporates consideration of individual patient characteristics, social setting, and system-level barriers. Interventions aimed at reducing unnecessary hospitalizations, especially those involving patients with low medical acuity, should focus on underlying unmet needs and involve a broad set of perspectives.

Influence of Overcrowding in the Emergency Department on Return Visit within 72 Hours

This study was conducted to determine whether overcrowding in the emergency department (ED) affects the occurrence of a return visit (RV) within 72 h. The crowding indicator of index visit was the average number of total patients, patients under observation, and boarding patients during the first 1 and 4 h from ED arrival time and the last 1 h before ED departure. Logistic regression analysis was conducted to determine whether each indicator affects the occurrence of RV and post-RV admission. Of the 87,360 discharged patients, 3743 (4.3%) returned to the ED within 72 h. Of the crowding indicators pertaining to total patients, the last 1 h significantly affected decrease in RV (p = 0.0046). Boarding patients were found to increase RV occurrence during the first 1 h (p = 0.0146) and 4 h (p = 0.0326). Crowding indicators that increased the likelihood of admission post-RV were total number of patients during the first 1 h (p = 0.0166) and 4 h (p = 0.0335) and evaluating patients during the first 1 h (p = 0.0059). Overcrowding in the ED increased the incidence of RV and likelihood of post-RV admission. However, overcrowding at the time of ED departure was related to reduced RV.

Work system barriers and facilitators in inpatient care transitions of pediatric trauma patients

Hospital-based care of pediatric trauma patients includes transitions between units that are critical for quality of care and patient safety. Using a macroergonomics approach, we identify work system barriers and facilitators in care transitions. We interviewed eighteen healthcare professionals involved in transitions from emergency department (ED) to operating room (OR), OR to pediatric intensive care unit (PICU) and ED to PICU. We applied the Systems Engineering Initiative for Patient Safety (SEIPS) process modeling method and identified nine dimensions of barriers and facilitators - anticipation, ED decision making, interacting with family, physical environment, role ambiguity, staffing/resources, team cognition, technology and characteristic of trauma care. For example, handoffs involving all healthcare professionals in the OR to PICU transition created a shared understanding of the patient, but sometimes included distractions. Understanding barriers and facilitators can guide future improvements, e.g., designing a team display to support team cognition of healthcare professionals in the care transitions.

Emergency department disposition prediction using a deep neural network with integrated clinical narratives and structured data

BACKGROUND

Emergency department (ED) overcrowding has been a serious issue and demands effective clinical decision-making of patient disposition. In previous studies, emergency clinical narratives provide a rich context for clinical decisions. We aimed to develop the disposition prediction model using deep learning modeling strategy with the heterogeneous data, including the physicians' narratives.

METHODS

We constructed a retrospective cohort of all 104,083 ED visits of non-trauma adults during 2017-18 from an academically affiliated ED in Taiwan. 18,308 visits were excluded based on the completeness of each record and the unpredictable dispositions, such as out-of-hospital cardiac arrest, against-advice discharge, and escapes. We integrated subjective section of the first physicians' clinical narratives and structured data (e.g., demographics, triage vital signs, etc.) as available predictors at the first physician-patient encounter. To predict final patient disposition (i.e., hospitalization or discharge), a deep neural network (DNN) model was developed with word embedding, a common natural language processing method. We compared the proposed model to a reference model using the Rapid Emergency Medicine Score, a logistic regression model with structured data, and a DNN model with paragraph vectors. F1 score was used to measure the predictive performance for each model.

RESULTS

The F1 score (with 95 % CI) for the proposed model, the reference model, the logistic regression model with structured data, and the DNN model with paragraph vectors were 0.674 (0.669-0.679), 0.474 (0.469-0.479), 0.547 (0.543-0.551), and 0.602 (0.596-0.607), respectively. While analyzing the relationship between context length and predictive performance under the proposed model, the F1 score at 95th percentile of the word counts was higher than that at 25th percentile of the word counts in chief complaint [0.634 (0.629-0.640) vs. 0.624 (0.620-0.628)] and in present illness [0.671 (0.667-0.674) vs. 0.654 (0.651-0.658)], but not in past medical history [0.674 (0.669-0.679) vs. 0.673 (0.666-0.679)].

CONCLUSIONS

The proposed deep learning model with the usage of the first physicians' clinical narratives and structured data based on natural language processing outperformed the commonly used ones in terms of F1 score. It also evidenced the importance of the subjective section of clinical narratives, which serve as vital predictors for ED clinical decision-making.

Lean management in a liaison psychiatry department: implementation, benefits and pitfalls

AIMS AND METHOD

To apply process mapping, a component of lean management, to a liaison psychiatry service of an emergency department. Lean management is a strategy that has been adapted to healthcare from business and production industries and aims to improve efficiency of a process. The process consisted of four stages: individual interviews with stakeholders, generation of process maps, allocation of goals and assessment of outcomes.

RESULTS

There was a significant reduction in length of stay of psychiatric patients in the emergency department (median difference: 1 h; P = 0.015). Five of the six goals were met successfully.

CLINICAL IMPLICATIONS

This article demonstrates a management intervention that successfully reduced length of stay in an emergency department. Further to the improvements in tangible (quantitative) outcomes, process mapping improved interpersonal relations between different disciplines. This paper may be used to guide similar quality improvement exercises in other areas of healthcare.

A latent variable approach to potential outcomes for emergency department admission decisions

In emergency departments (EDs), care providers continuously weigh admissions against continued monitoring and treatment often without knowing their condition and health needs. To understand the decision process and its causal effect on outcomes, an observational study must contend with unobserved/missing information and a lack of exchangeability between admitted and discharged patients. Our goal was to provide a general framework to evaluate admission decisions from electronic healthcare records (EHRs). We describe admission decisions as a decision-making process in which the patient's health needs is a binary latent variable. We estimate latent health needs from EHR with only partial knowledge of the decision process (ie, initial evaluation, admission decision, length of stay). Estimated latent health needs are then used to understand the admission decision and the decision's causal impact on outcomes. For the latter, we assume potential outcomes are stochastically independent from the admission decision conditional on latent health needs. As a case study, we apply our approach to over 150 000 patient encounters with the ED from the University of Michigan Health System collected from August 2012 through July 2015. We estimate that while admitting a patient with higher latent needs reduces the 30-day risk of revisiting the ED or later being admitted through the ED by over 79%, admitting a patient with lower latent needs actually increases these 30-day risks by 3.0% and 7.6%, respectively.

Evaluating the impact of emergency department crowding on disposition patterns and outcomes of discharged patients

BACKGROUND

Crowding is a major challenge faced by EDs and is associated with poor outcomes.

OBJECTIVES

Determine the effect of high ED occupancy on disposition decisions, return ED visits, and hospitalizations.

METHODS

We conducted a retrospective analysis of electronic health records of patients evaluated at an adult, urban, and academic ED over 20 months between the years 2012 and 2014. Using a logistic regression model predicting admission, we obtained estimates of the effect of high occupancy on admission disposition, adjusted for key covariates. We then stratified the analysis based on the presence or absence of high boarder patient counts.

RESULTS

Disposition decisions during a high occupancy hour decreased the odds of admission (OR = 0.93, 95% CI: [0.89, 0.98]). Among those who were not admitted, high occupancy was not associated with increased odds of return in the combined (OR = 0.94, 95% CI: [0.87, 1.02]), with-boarders (OR = 0.96, 95% CI: [0.86, 1.09]), and no-boarders samples (OR = 0.93, 95% CI: [0.83, 1.04]). Among those who were not admitted and who did return within 14 days, disposition during a high occupancy hour on the initial ED visit was not associated with a significant increased odds of hospitalization in the combined (OR = 1.04, 95% CI: [0.87, 1.24]), the with-boarders (OR = 1.12, 95% CI: [0.87, 1.44]), and the no-boarders samples (OR = 0.98, 95% CI: [0.77, 1.24]).

CONCLUSION

ED crowding was associated with reduced likelihood of hospitalization without increased likelihood of 2-week return ED visit or hospitalization. Furthermore, high occupancy disposition hours with high boarder patient counts were associated with decreased likelihood of hospitalization.

Emergency Department Computed Tomography Use for Non-traumatic Abdominal Pain: Minimal Variability

Introduction

Variability in the use of computed tomography (CT) between providers in the emergency department (ED) suggests that CT is ordered on a provider rather than a patient level. We aimed to evaluate the variability of CT ordering practices for non-traumatic abdominal pain (NTAP) across physicians in the ED using patient-visit and physician-level factors.

Methods

We conducted a retrospective study among 6,409 ED visits for NTAP from January 1 to December 31, 2012, at a large, urban, academic, tertiary-care hospital. We used a two-level hierarchical logistic regression model to estimate inter-physician variation. Intraclass correlation coefficient (ICC) was calculated.

Results

The hierarchical logistic regression analyses showed that patient-visit factors including younger age, arrival mode by ambulance, prior CT, >79 ED arrivals in the previous four hours, and ultrasound had statistically significant negative associations with physician CT ordering, while surgical team admission and white blood count (WBC) >12.5 K/millimeter cubed (mm³) had statistically significant positive associations with physician CT ordering. With physician-level factors, only physicians with >21 years experience after medical school graduation showed statistical significance negatively associated with physician CT ordering. Our data demonstrated increased CT ordering from the mean in only one out of 43 providers (2.3%), which indicated limited variation across physicians to order CT. After adjusting for patient-visit and physician-level factors, the calculated ICC was 1.46%.

Conclusion

We found minimal physician variability in CT ordering practices for NTAP. Patient-visit factors such as age, arrival mode, admission team, prior CT, ED arrivals in previous four hours, ultrasound, and WBC count were found to largely influence CT ordering practices.

A clinical decision instrument to predict 30-day death and cardiovascular hospitalizations after an emergency department visit for atrial fibrillation: The Atrial Fibrillation in the Emergency Room, Part 2 (AFTER2) study

BACKGROUND

In previous work, we derived and validated a tool that predicts 30-day mortality in emergency department atrial fibrillation (AF) patients. The objective of this study was to derive and validate a tool that predicts a composite of 30-day mortality and return cardiovascular hospitalizations.

METHODS

This retrospective cohort study at 24 emergency departments in Ontario, Canada, included patients with a primary diagnosis of AF who were seen between April 2008 and March 2009. We assessed a composite outcome of 30-day mortality and subsequent hospitalizations for a cardiovascular reason, including stroke.

RESULTS

Of 3,510 patients, 2,343 were randomly selected for the derivation cohort, leaving 1,167 in the validation cohort. The composite outcome occurred in 227 (9.7%) and 125 (10.7%) patients in the derivation and validation cohorts, respectively. Eleven variables were independently associated with the outcome: older age, not taking anticoagulation, HAS-BLED score of ≥3, 3 laboratory results (positive troponin, supratherapeutic international normalized ratio, and elevated creatinine), emergency department administration of furosemide, and 4 patient comorbidities (heart failure, chronic obstructive lung disease, cancer, dementia). In the validation cohort, the observed 30-day outcomes in the 5 risk strata that were defined using the derivation cohort were 2.0%, 6.6%, 10.7%, 12.5%, and 20.0%. The c statistic was 0.73 and 0.69 in the derivation and validation cohort, respectively.

CONCLUSIONS

Using a population-based sample, we derived and validated a tool that predicts the risk of early death and rehospitalization for a cardiovascular reason in emergency department AF patients. The tool can offer information to managing physicians about the risk of death and rehospitalization for AF patients seen in the in emergency department, as well as identify patient groups for future targeted interventions aimed at preventing these outcomes.

Managing Multiplicity: Conceptualizing Physician Cognition in Multipatient Environments

PURPOSE

Emergency physicians (EPs) regularly manage multiple patients simultaneously, often making time-sensitive decisions around priorities for multiple patients. Few studies have explored physician cognition in multipatient scenarios. The authors sought to develop a conceptual framework to describe how EPs think in busy, multipatient environments.

METHOD

From July 2014 to May 2015, a qualitative study was conducted at McMaster University, using a think-aloud protocol to examine how 10 attending EPs and 10 junior residents made decisions in multipatient environments. Participants engaged in the think-aloud exercise for five different simulated multipatient scenarios. Transcripts from recorded interviews were analyzed inductively, with an iterative process involving two independent coders, and compared between attendings and residents.

RESULTS

The attending EPs and junior residents used similar processes to prioritize patients in these multipatient scenarios. The think-aloud processes demonstrated a similar process used by almost all participants. The cognitive task of patient prioritization consisted of three components: a brief overview of the entire cohort of patients to determine a general strategy; an individual chart review, whereby the participant created a functional patient story from information available in a file (i.e., vitals, brief clinical history); and creation of a relative priority list. Compared with residents, the attendings were better able to construct deeper and more complex patient stories.

CONCLUSIONS

The authors propose a conceptual framework for how EPs prioritize care for multiple patients in complex environments. This study may be useful to teachers who train physicians to function more efficiently in busy clinical environments.

Characterizing Potentially Preventable Admissions: A Mixed Methods Study of Rates, Associated Factors, Outcomes, and Physician Decision-Making

BACKGROUND

Potentially preventable admissions are a target for healthcare cost containment.

OBJECTIVE

To identify rates of, characterize associations with, and explore physician decision-making around potentially preventable admissions.

DESIGN

A comparative cohort study was used to determine rates of potentially preventable admissions and to identify associated factors and patient outcomes. A qualitative case study was used to explore physicians' clinical decision-making.

PARTICIPANTS

Patients admitted from the emergency department (ED) to the general medicine (GM) service over a total of 4 weeks were included as cases (N = 401). Physicians from both emergency medicine (EM) and GM that were involved in the cases were included (N = 82).

APPROACH

Physicians categorized admissions as potentially preventable. We examined differences in patient characteristics, admission characteristics, and patient outcomes between potentially preventable and control admissions. Interviews with participating physicians were conducted and transcribed. Transcriptions were systematically analyzed for key concepts regarding potentially preventable admissions.

KEY RESULTS

EM and GM physicians categorized 22.2% (90/401) of admissions as potentially preventable. There were no significant differences between potentially preventable and control admissions in patient or admission characteristics. Potentially preventable admissions had shorter length of stay (2.1 vs. 3.6 days, p < 0.001). There was no difference in other patient outcomes. Physicians discussed several provider, system, and patient factors that affected clinical decision-making around potentially preventable admissions, particularly in the "gray zone," including risk of deterioration at home, the risk of hospitalization, the cost to the patient, and the presence of outpatient resources. Differences in provider training, risk assessment, and provider understanding of outpatient access accounted for differences in decisions between EM and GM physicians.

CONCLUSIONS

Collaboration between EM and GM physicians around patients in the gray zone, focusing on patient risk, cost, and outpatient resources, may provide an avenues for reducing potentially preventable admissions and lowering healthcare spending.

Evidence of Racial and Geographic Disparities in the Use of Medicare Observation Stays and Subsequent Patient Outcomes Relative to Short-Stay Hospitalizations

Purpose: To examine racial and geographic disparities in the use of—and outcomes associated with—Medicare observation stays versus short-stay hospitalizations.

Methods: We used 2007–2010 fee-for-service Medicare claims, including 3,555,994 observation and short-stay hospitalizations for individuals over age 65. We estimated linear probability models with hospital fixed effects to identify within-facility disparities in observation stay use, estimated in-hospital mortality, 30- and 90-day postdischarge mortality, return emergency department (ED) visits, and hospital readmissions as a function of placement in observation using linear probability models, propensity-score matching, and interaction terms.

Results: We identified racial and geographic disparities in the likelihood of observation stay use within hospitals (blacks 3.9% points more likely than whites, rural 5.4% points less likely than urban). Observation is associated with an increased likelihood of returning to the ED within 30 or 90 days and a decreased likelihood of readmission or mortality, but there are racial and geographic disparities in these outcomes.

Conclusion: While observation generally results in improved outcomes, disparities in these outcomes and the use of observation stays within hospitals are concerning and may be driven by clinical and nonclinical factors.

Accuracy of emergency department triage using the Emergency Severity Index and independent predictors of under-triage and over-triage in Brazil: a retrospective cohort analysis

Background

Emergency department (ED) triage is performed to prioritize care for patients with critical and time-sensitive illness. Triage errors create opportunity for increased morbidity and mortality. Here, we sought to measure the frequency of under- and over-triage of patients by nurses using the Emergency Severity Index (ESI) in Brazil and to identify factors independently associated with each.

Methods

This was a single-center retrospective cohort study. The accuracy of initial ESI score assignment was determined by comparison with a score entered at the close of each ED encounter by treating physicians with full knowledge of actual resource utilization, disposition, and acute outcomes. Chi-square analysis was used to validate this surrogate gold standard, via comparison of associations with disposition and clinical outcomes. Independent predictors of under- and over-triage were identified by multivariate logistic regression.

Results

Initial ESI-determined triage score was classified as inaccurate for 16,426 of 96,071 patient encounters. Under-triage was associated with a significantly higher rate of admission and critical outcome, while over-triage was associated with a lower rate of both. A number of factors identifiable at time of presentation including advanced age, bradycardia, tachycardia, hypoxia, hyperthermia, and several specific chief complaints (i.e., neurologic complaints, chest pain, shortness of breath) were identified as independent predictors of under-triage, while other chief complaints (i.e., hypertension and allergic complaints) were independent predictors of over-triage.

Conclusions

Despite rigorous and ongoing training of ESI users, a large number of patients in this cohort were under- or over-triaged. Advanced age, vital sign derangements, and specific chief complaints—all subject to limited guidance by the ESI algorithm—were particularly under-appreciated.

Failure to flow: An exploration of learning and teaching in busy, multi-patient environments using an interpretive description method

INTRODUCTION

As patient volumes continue to increase, more attention must be paid to skills that foster efficiency without sacrificing patient safety. The emergency department is a fertile ground for examining leadership and management skills, especially those that concern prioritization in multi-patient environments. We sought to understand the needs of emergency physicians (EPs) and emergency medicine junior trainees with regards to teaching and learning about how best to handle busy, multi-patient environments.

METHOD

A cognitive task analysis was undertaken, using a qualitative approach to elicit knowledge of EPs and residents about handling busy emergency department situations. Ten experienced EPs and 10 junior emergency medicine residents were interviewed about their experiences in busy emergency departments. Transcripts of the interviews were analyzed inductively and iteratively by two independent coders using an interpretive description technique.

RESULTS

EP teachers and junior residents differed in their perceptions of what makes an emergency department busy. Moreover, they focused on different aspects of patient care that contributed to their busyness: EP teachers tended to focus on volume of patients, junior residents tended to focus on the complexity of certain cases. The most important barrier to effective teaching and learning of managerial skills was thought to be the lack of faculty development in this skill set.

CONCLUSIONS

This study presents qualitative data that helps us elucidate how patient volumes affect our learning environments, and how clinical teachers and residents operate within these environments.

We all make choices: A decision analysis framework for disposition decision in the ED

BACKGROUND

Emergency Department (ED) providers' disposition decision impacts patient care and safety. The objective of this brief report is to gain a better understanding of ED providers' disposition decision and risk tolerance of associated outcomes.

METHODS

We synthesized qualitative and quantitative methods including decision mapping, survey research, statistical analysis, and word clouds. Between July 2017 and August 2017, a 10-item survey was developed and conducted at the study hospital. Descriptive and statistical analyses were used to assess the relationship between the participant characteristics (age, gender, years of experience in the ED, and level of expertise) and risk tolerance of outcomes (72-h return and negative outcome) associated with disposition decision. Word clouds facilitated prioritization of qualitative responses regarding information impacting and supporting the disposition decision.

RESULTS

Total of 46 participants completed the survey. The mean age was 39.5 (standard deviation (SD) 10years), and mean years of experience was 9.6years (SD 8.7years). Decision map highlighted the connections between patient-, provider-, and system-related factors. Survey results showed that negative outcome resulted in less risk tolerance compared to 72-h return. Chi-square tests did not provide sufficient evidence to indicate that the responses are independent of participants characteristics - except age and the risk of 72-h return (p=0.046).

CONCLUSION

Discharge decision making in the ED is complex as it involves interconnected patient, provider, and system factors. Synthesizing qualitative and quantitative methods promise enhanced understanding of how providers arrive to disposition decision, as well as safety and quality of care in the ED.

Eyeballing: the use of visual appearance to diagnose 'sick'

CONTEXT

Prior studies suggest that clinicians can categorise patients in an emergency room as 'sick' or 'not sick' using rapid visual assessment. The rapid nature of these decisions suggests clinicians are relying on pattern recognition or System 1 processing; however, this has not been studied experimentally. In this study, we explore the accuracy of these decisions using patient disposition (discharge, admission to ward or admission to critical care) as an objective outcome, and collect evidence to argue for the use of System 1 processing in the 'sick' or 'not sick' decision process.

METHODS

Fourteen practising emergency physicians reviewed 25 videos of patients presenting to the emergency room. They were asked to predict patient disposition (discharge, admission to ward or admission to critical care) and estimate whether they were 'sick' or 'not sick' using a continuous slider on a 'sick' scale from 'not sick' (0) to 'sick' (100). We collected decision time and asked physicians to identify how they came to the decision using a continuous slider on a 'system processing' scale from 'knew immediately' (0) to 'deliberated intently' (1).

RESULTS

Inter-rater reliability judging 'sick' was computed as an intraclass correlation coefficient (ICC) of 0.54. Agreement among physicians in predicting disposition was 68% with ICC of 0.44, and accuracy at predicting disposition was 55%. Physicians made their decision in an average of 10 - 11 seconds and rated 70% of their decisions as < 0.5 on the scale from 'knew immediately' (0) to 'deliberated intently' (1).

CONCLUSIONS

Experienced emergency physicians are able to visually assess patients rapidly and predict disposition in a very short time, albeit with fair reliability and lower accuracy than reported previously. Subjectively, they reported that the majority of decisions were on the side of 'knew immediately', consistent with the application of System 1 processing.

A qualitative study exploring the factors influencing admission to hospital from the emergency department

OBJECTIVE

The number of emergency admissions to hospital in England and Wales has risen sharply in recent years and is a matter of concern to clinicians, policy makers and patients alike. However, the factors that influence this decision are poorly understood. We aimed to ascertain how non-clinical factors can affect hospital admission rates.

METHOD

We conducted semistructured interviews with 21 participants from three acute hospital trusts. Participants included 11 emergency department (ED) doctors, 3 ED nurses, 3 managers and 4 inpatient doctors. A range of seniority was represented among these roles. Interview questions were developed from key themes identified in a theoretical framework developed by the authors to explain admission decision-making. Interviews were recorded, transcribed and analysed by two independent researchers using framework analysis.

FINDINGS

Departmental factors such as busyness, time of day and levels of senior support were identified as non-clinical influences on a decision to admit rather than discharge patients. The 4-hour waiting time target, while overall seen as positive, was described as influencing decisions around patient admission, independent of clinical need. Factors external to the hospital such as a patient's social support and community follow-up were universally considered powerful influences on admission. Lastly, the culture within the ED was described as having a strong influence (either negatively or positively) on the decision to admit patients.

CONCLUSION

Multiple factors were identified which go some way to explaining marked variation in admission rates observed between different EDs. Many of these factors require further inquiry through quantitative research in order to understand their influence further.

Risk factors for admission at three urban emergency departments in England: a cross-sectional analysis of attendances over 1 month

OBJECTIVE

To investigate factors associated with unscheduled admission following presentation to emergency departments (EDs) at three hospitals in England.

DESIGN AND SETTING

Cross-sectional analysis of attendance data for patients from three urban EDs in England: a large teaching hospital and major trauma centre (site 1) and two district general hospitals (sites 2 and 3). Variables included patient age, gender, ethnicity, deprivation score, arrival date and time, arrival by ambulance or otherwise, a variety of ED workload measures, inpatient bed occupancy rates and admission outcome. Coding inconsistencies in routine ED data used for this study meant that diagnosis could not be included.

OUTCOME MEASURE

The primary outcome for the study was unscheduled admission.

PARTICIPANTS

All adults aged 16 and older attending the three inner London EDs in December 2013. Data on 19 734 unique patient attendances were gathered.

RESULTS

Outcome data were available for 19 721 attendances (>99%), of whom 6263 (32%) were admitted to hospital. Site 1 was set as the baseline site for analysis of admission risk. Risk of admission was significantly greater at sites 2 and 3 (adjusted OR (AOR) relative to site 1 for site 2 was 1.89, 95% CI 1.74 to 2.05, p<0.001) and for patients of black or black British ethnicity (AOR 1.29, 1.16 to 1.44, p<0.001). Deprivation was strongly associated with admission. Analysis of departmental and hospital-wide workload pressures gave conflicting results, but proximity to the "4-hour target" (a rule that limits patient stays in EDs to 4 hours in the National Health Service in England) emerged as a strong driver for admission in this analysis (AOR 3.61, 95% CI 3.30 to 3.95, p<0.001).

CONCLUSION

This study found statistically significant variations in odds of admission between hospital sites when adjusting for various patient demographic and presentation factors, suggesting important variations in ED-level and clinician-level behaviour relating to admission decisions. The 4-hour target is a strong driver for emergency admission.

The Impact of Emergency Department Census on the Decision to Admit

OBJECTIVE

We evaluated the effect of emergency department (ED) census on disposition decisions made by ED physicians.

METHODS

We performed a retrospective analysis using 18 months of all adult patient encounters seen in the main ED at an academic tertiary care center. Patient census information was calculated at the time of physician assignment for each individual patient and included the number of patients in the waiting room (waiting room census) and number of patients being managed by the patient's attending (physician load census). A multiple logistic regression model was created to assess the association between these census variables and the disposition decision, controlling for potential confounders including Emergency Severity Index acuity, patient demographics, arrival hour, arrival mode, and chief complaint.

RESULTS

A total of 49,487 patient visits were included in this analysis, of whom 37% were admitted to the hospital. Both census measures were significantly associated with increased chance of admission; the odds ratio (OR) per patient increase for waiting room census was 1.011 (95% confidence interval [CI] = 1.001 to 1.020), and the OR for physician load census was 1.010 (95% CI = 1.002 to 1.019). To put this in practical terms, this translated to a modeled rise from 35.3% to 40.1% when shifting from an empty waiting room and zero patient load to a 12-patient wait and 16-patient load for a given physician.

CONCLUSION

Waiting room census and physician load census at time of physician assignment were positively associated with the likelihood that a patient would be admitted, controlling for potential confounders. Our data suggest that disposition decisions in the ED are influenced not only by objective measures of a patient's disease state, but also by workflow-related concerns.

Reducing variation in hospital admissions from the emergency department for low-mortality conditions may produce savings

The emergency department (ED) is now the primary source for hospitalizations in the United States, and admission rates for all causes differ widely between EDs. In this study we used a national sample of ED visits to examine variation in risk-standardized hospital admission rates from EDs and the relationship of this variation to inpatient mortality for the fifteen most commonly admitted medical and surgical conditions. We then estimated the impact of variation on national health expenditures under different utilization scenarios. Risk-standardized admission rates differed substantially across EDs, ranging from 1.03-fold for sepsis to 6.55-fold for chest pain between the twenty-fifth and seventy-fifth percentiles of the visits. Conditions such as chest pain, soft tissue infection, asthma, chronic obstructive pulmonary disease, and urinary tract infection were low-mortality conditions that showed the greatest variation. This suggests that some of these admissions might not be necessary, thus representing opportunities to improve efficiency and reduce health spending. Our data indicate that there may be sizeable savings to US payers if differences in ED hospitalization practices could be narrowed among a few of these high-variation, low-mortality conditions.

Qualitative Analysis of Surveyed Emergency Responders and the Identified Factors That Affect First Stage of Primary Triage Decision-Making of Mass Casualty Incidents

Introduction: After all large-scale disasters multiple papers are published describing the shortcomings of the triage methods utilized. This paper uses medical provider input to help describe attributes and patient characteristics that impact triage decisions.

Methods: A survey distributed electronically to medical providers with and without disaster experience. Questions asked included what disaster experiences they had, and to rank six attributes in order of importance regarding triage.

Results: 403 unique completed surveys were analyzed. 92% practiced a structural triage approach with the rest reporting they used “gestalt”.(gut feeling) Twelve per cent were identified as having placed patients in an expectant category during triage. Respiratory status, ability to speak, perfusion/pulse were all ranked in the top three. Gut feeling regardless of statistical analysis was fourth. Supplies were ranked in the top four when analyzed for those who had placed patients in the expectant category.

Conclusion: Primary triage decisions in a mass casualty scenario are multifactorial and encompass patient mobility, life saving interventions, situational instincts, and logistics.

Factors Influencing Hospital Admission of Non-critically Ill Patients Presenting to the Emergency Department: a Cross-sectional Study

BACKGROUND

Little is known about the factors that influence physicians' admission decisions, especially among lower acuity patients. For the purpose of our study, non-medical refers to all of the factors-other than the patient's clinical condition-that could potentially influence admission decisions.

OBJECTIVE

To describe the influence of non-medical factors on physicians' decisions to admit non-critically ill patients presenting to the ED.

DESIGN

Cross-sectional study of hospital admissions at a single academic medical center.

PARTICIPANTS

Non-critically ill adult patients admitted to the hospital (n = 297) and the admitting emergency medicine physicians (n = 34).

MAIN MEASURES

A patient survey assessed non-medical factors, including primary care access and utilization. A physician survey assessed clinical and non-medical factors influencing the decision to admit. Based on physician responses, admissions were characterized as "strongly acuity-driven," "moderately acuity-driven," or "weakly acuity-driven." Among these admission types, we compared length of stay, cost, and readmission within 30 days to the hospital or ED.

KEY RESULTS

Based on the admitting physician's assessment, we categorized the motivation for admission as strongly acuity-driven in 185 (62 %) admissions, moderately acuity-driven in 92 (31 %), and weakly acuity-driven in 20 (7 %). Per the physician surveys, 51 % of hospitalizations were strongly or moderately influenced by one or more non-medical factors, including lack of information about baseline conditions (23 %); inadequate access to outpatient specialty care (14 %); need for a diagnostic testing or procedure (12 %); a recent ED visit (11 %); and inadequate access to primary care (10 %). Compared with strongly-acuity driven admissions, admissions that were moderately or weakly acuity-driven were shorter and less costly but were associated with similar rates of ED (35 %) and hospital (27 %) readmission.

CONCLUSIONS

Non-medical factors are influential in the admission decisions for many patients presenting to the emergency department. Moderately and weakly acuity-driven admissions may represent a feasible target for alternative care pathways.

Who Am I to Decide Whether This Person Is to Die Today? Physicians' Life-or-Death Decisions for Elderly Critically Ill Patients at the Emergency Department-ICU Interface: A Qualitative Study

STUDY OBJECTIVE

We explored physicians' perceptions of and attitudes toward triage and end-of-life decisions for elderly critically ill patients at the emergency department (ED)-ICU interface.

METHODS

This was a qualitative study with thematic analysis of data collected through semistructured interviews (15 emergency physicians and 9 ICU physicians) and nonparticipant observations (324 hours, 8 units, in 2 hospitals in France).

RESULTS

Six themes emerged: (1) Physicians revealed a representation of elderly patients that comprised both negative and positive stereotypes, and expressed the concept of physiologic age. (2) These age-related factors influenced physicians' decisionmaking in resuscitate/not resuscitate situations. (3) Three main communication patterns framed the decisions: interdisciplinary decisions, decisions by 2 physicians on their own, and unilateral decisions by 1 physician; however, some physicians avoided decisions, facing uncertainty and conflicts. (4) Conflicts and communication gaps occurred at the ED-ICU interface and upstream of the ED-ICU interface. (5) End-of-life decisions were perceived as more complex in the ED, in the absence of family or of information about elderly patients' end-of-life preferences, and when there was conflict with relatives, time pressure, and a lack of training in end-of-life decisionmaking. (6) During decisionmaking, patients' safety and quality of care were potentially compromised by delayed or denied intensive care and lack of palliative care.

CONCLUSION

These qualitative findings highlight the cognitive heuristics and biases, interphysician conflicts, and communication gaps influencing physicians' triage and end-of-life decisions for elderly critically ill patients at the ED-ICU interface and suggest strategies to improve these decisions.

Accuracy of 'My Gut Feeling:' Comparing System 1 to System 2 Decision-Making for Acuity Prediction, Disposition and Diagnosis in an Academic Emergency Department

Introduction

Current cognitive sciences describe decision-making using the dual-process theory, where a System 1 is intuitive and a System 2 decision is hypothetico-deductive. We aim to compare the performance of these systems in determining patient acuity, disposition and diagnosis.

Methods

Prospective observational study of emergency physicians assessing patients in the emergency department of an academic center. Physicians were provided the patient’s chief complaint and vital signs and allowed to observe the patient briefly. They were then asked to predict acuity, final disposition (home, intensive care unit (ICU), non-ICU bed) and diagnosis. A patient was classified as sick by the investigators using previously published objective criteria.

Results

We obtained 662 observations from 289 patients. For acuity, the observers had a sensitivity of 73.9% (95% CI [67.7–79.5%]), specificity 83.3% (95% CI [79.5–86.7%]), positive predictive value 70.3% (95% CI [64.1–75.9%]) and negative predictive value 85.7% (95% CI [82.0–88.9%]). For final disposition, the observers made a correct prediction in 80.8% (95% CI [76.1–85.0%]) of the cases. For ICU admission, emergency physicians had a sensitivity of 33.9% (95% CI [22.1–47.4%]) and a specificity of 96.9% (95% CI [94.0–98.7%]). The correct diagnosis was made 54% of the time with the limited data available.

Conclusion

System 1 decision-making based on limited information had a sensitivity close to 80% for acuity and disposition prediction, but the performance was lower for predicting ICU admission and diagnosis. System 1 decision-making appears insufficient for final decisions in these domains but likely provides a cognitive framework for System 2 decision-making.

A Clinical Decision Instrument for 30-Day Death After an Emergency Department Visit for Atrial Fibrillation: The Atrial Fibrillation in the Emergency Room (AFTER) Study

STUDY OBJECTIVE

The high volume of patients treated in an emergency department (ED) for atrial fibrillation is predicted to increase significantly in the next few decades. Currently, 11% of these patients die within a year. We sought to derive and validate a complex model and a simplified model that predicts mortality in ED patients with atrial fibrillation.

METHODS

This population-based, retrospective cohort study included 3,510 adult patients with a primary diagnosis of atrial fibrillation who were treated at 24 hospital EDs in Ontario, Canada, between April 2008 and March 2009. The main outcome was 30-day all-cause mortality.

RESULTS

In the derivation cohort (n=2,343; mean age 68.8 years), 2.6% of patients died within 30 days of the ED visit versus 2.7% in the validation cohort (n=1,167; mean age 68.3 years). Variables associated with mortality in the complex model included age, presenting pulse rate and systolic blood pressure, presence of chest pain, 2 laboratory results (positive troponin result and creatinine level greater than 200 μmol [2.26 mg/dL]), 4 comorbidities (smoking, chronic obstructive pulmonary disease, cancer, and dementia), an increased bleeding risk, and a second acute ED diagnosis (in addition to atrial fibrillation). Observed 30-day mortality in the 5 risk strata that were defined by the predicted probability of death were 0.44%, 0.41%, 0.23%, 1.61%, and 10.3%. The c statistics were 0.88 and 0.87 in the derivation and validation cohorts, respectively. The a priori-selected 6-variable model, TrOPs-BAC, included a positive Troponin result, Other acute ED diagnosis, Pulmonary disease (chronic obstructive pulmonary disease), Bleeding risk, Aged 75 years or older, and Congestive heart failure. The c statistic for the simplified model was 0.81 in both the derivation and validation cohorts.

CONCLUSION

Using a population-based sample, we derived and validated both a complex and a simplified instrument that predicts mortality after an emergency visit for atrial fibrillation. These may aid clinicians in identifying high-risk patients for hospitalization while safely discharging more patients home.

Exploring the Potential of Predictive Analytics and Big Data in Emergency Care

Clinical research often focuses on resource-intensive causal inference, whereas the potential of predictive analytics with constantly increasing big data sources remains largely unexplored. Basic prediction, divorced from causal inference, is much easier with big data. Emergency care may benefit from this simpler application of big data. Historically, predictive analytics have played an important role in emergency care as simple heuristics for risk stratification. These tools generally follow a standard approach: parsimonious criteria, easy computability, and independent validation with distinct populations. Simplicity in a prediction tool is valuable, but technological advances make it no longer a necessity. Emergency care could benefit from clinical predictions built using data science tools with abundant potential input variables available in electronic medical records. Patients' risks could be stratified more precisely with large pools of data and lower resource requirements for comparing each clinical encounter to those that came before it, benefiting clinical decisionmaking and health systems operations. The largest value of predictive analytics comes early in the clinical encounter, in which diagnostic and prognostic uncertainty are high and resource-committing decisions need to be made. We propose an agenda for widening the application of predictive analytics in emergency care. Throughout, we express cautious optimism because there are myriad challenges related to database infrastructure, practitioner uptake, and patient acceptance. The quality of routinely compiled clinical data will remain an important limitation. Complementing big data sources with prospective data may be necessary if predictive analytics are to achieve their full potential to improve care quality in the emergency department.

Patient flow in the emergency department: a classification and analysis of admission process policies

STUDY OBJECTIVE

We investigate the effect of admission process policies on patient flow in the emergency department (ED).

METHODS

We surveyed an advisory panel group to determine approaches to admission process policies and classified them as admission decision is made by the team of providers (attending physicians, residents, physician extenders) (type 1) or attending physicians (type 2) on the admitting service, team of providers (type 3), or attending physicians (type 4) in the ED. We developed discrete-event simulation models of patient flow to evaluate the potential effect of the 4 basic policy types and 2 hybrid types, referred to as triage attending physician consultation and remote collaborative consultation on key performance measures.

RESULTS

Compared with the current admission process policy (type 1), the alternatives were all effective in reducing the length of stay of admitted patients by 14% to 26%. In other words, patients may spend 1.4 to 2.5 hours fewer on average in the ED before being admitted to internal medicine under a new admission process policy. The improved flow of admitted patients decreased both the ED length of stay of discharged patients and the overall length of stay by up to 5% and 6.4%, respectively. These results are framed in context of teaching mission and physician experience.

CONCLUSION

An efficient admission process can reduce waiting times for both admitted and discharged ED patients. This study contributed to demonstrating the potential value of leveraging admission process policies and developing a framework for pursuing these policies.

How do emergency physicians make discharge decisions?

Background

One of the most important decisions that emergency department (ED) physicians make is patient disposition (admission vs discharge).

Objectives

To determine how ED physicians perceive their discharge decisions for high-acuity patients and the impact on adverse events (adverse outcomes associated with healthcare management).

Methods

We conducted a real-time survey of staff ED physicians discharging consecutive patients from high-acuity areas of a tertiary care ED. We asked open-ended questions about rationale for discharge decisions and use of clinical judgement versus evidence. We searched for 30-day flagged outcomes (deaths, unscheduled admissions, ED or clinic visits). Three trained blinded ED physicians independently reviewed these for adverse events and preventability. We resolved disagreements by consensus. We used descriptive statistics and 95% CIs.

Results

We interviewed 88.9% (32/36) of possible ED physicians for 366 discharge decisions. Respondents were mostly male (71.9%) and experienced (53.1% >10 years). ED physicians stated they used clinical judgement in 87.6% of decisions and evidence in 12.4%. There were 69 flagged outcomes (18.8%) and 10 adverse events (2.7%, 95% CI 1.1 to 4.5%). All adverse events were preventable (1 death, 4 admissions, 5 return ED visits). No significant associations occurred between decision-making rationale and adverse events.

Conclusions

Experienced ED physicians most often relied on clinical acumen rather than evidence-based guidelines when discharging patients from ED high-acuity areas. Neither approach was associated with adverse events. In order to improve the safety of discharge decisions, further research should focus on decision support solutions and feedback interventions.