Triage Category Research Articles

Misdiagnosis of type A acute aortic dissection in the emergency department: 10 Year retrospective cohort study

AbstractBackgroundType A acute aortic dissection (TAAAD) is a serious cardiovascular disease with a high mortality rate and prompt diagnosis is the key to survival. However, misdiagnosis is common in the Emergency Department (ED). The aim of this study is to evaluate the rate of misdiagnosis, factors associated with misdiagnosis and patient outcomes.MethodsThis retrospective cohort study recruited 91 patients with TAAAD at the ED of a tertiary university hospital in Hong Kong from 2013 to 2022. Patients were divided into the following two groups: correct diagnosis (60 patients) and misdiagnosis (31 patients) of TAAAD. Clinical symptoms, signs, and investigations were analyzed as factors associated with misdiagnosis by multivariate analysis. In‐patient mortality, 30‐day mortality, 1‐year mortality, and hospital length of stay (LOS) were compared between the two groups.ResultsThe misdiagnosis rate of TAAAD was 34.1% (31/91). Absence of severe intensity pain (OR = 6.01, 95% confidence interval [CI] = 1.41–25.63, p = 0.015), less urgent triage category (OR = 4.46, 95% CI = 1.77–11.22, p = 0.002), and absence of point‐of‐care ultrasound exam (POCUS) (OR = 13.00, 95% CI = 3.05–55.40, p = 0.001) were associated with misdiagnosis. There were no statistically significant differences between correct diagnosis and misdiagnosis groups in patient outcomes including in‐hospital mortality (28.3% vs. 38.7%), 30‐day mortality (28.3% vs. 38.7%), 1‐year mortality (30.0% vs. 41.9%), or hospital LOS (15.5 ± 15.6 days vs. 14.7 ± 15.5 days).ConclusionsOne‐third of cases in our study were misdiagnosed. The absence of severe intensity pain, less urgent triage category, and absence of POCUS ultrasound exam were independent factors associated with misdiagnosis. More widespread use of POCUS of the aorta and heart may help to improve the diagnosis of TAAAD in the ED.

Assessing AI chatbots efficacy in ophthalmic triage and referrals: A comparative study

To evaluate the efficacy of AI chatbots (OpenAI ChatGPT GPT-3.5, Google Bard, and WebMD) compared to human ophthalmology trainees in triage and referrals for common ophthalmic conditions. A single-center study was conducted at MKCG Medical College, Berhampur, Odisha, involving six ophthalmology trainees. The performance of AI chatbots was assessed based on diagnostic accuracy and triage categorization. Key performance indicators included the accuracy of the top three suggested diagnoses and concordance in recommendations for investigations and referrals. Physician respondents identified the correct diagnosis among the top three suggestions in 95% of cases. Google Bard achieved 90% accuracy, ChatGPT 85%, and WebMD 20%. High concordance was observed between physician and AI recommendations for investigations and referrals. AI chatbots demonstrate promising potential in supporting triage and referral decisions for ophthalmic conditions. While human expertise remains crucial, AI tools can augment diagnostic accuracy, improve efficiency, and enhance patient care. Future research should focus on refining AI algorithms, integrating clinical data, and exploring real-world implementation strategies.

Consideration and implementation of the elements of hospital mass casualty planning in the hospitals of the TraumaNetworks DGU® : An evaluation within the framework of the development process of the guidelines for clinical disaster medicine in Germany (LeiKliKatMeD) by the EKTC, NIS, AKUT, AUC

The protection and maintenance of hospital functionality and treatment capacity require preventative planning and preparation for amass casualty incident with respect to the scenarios, disasters or catastrophes to be expected. The hospital alarm and operations (KAEP) or stockpiling plan should include and organize the procedures and measures in the respective clinics and hospitals. The aim of the present study was to evaluate the hospitals of the Trauma Networks of the German Society for Trauma Surgery® (TNW DGU®) with respect to the established organizational structures and contents of the KAEP. In this study 622 hospitals from the TNW DGU® were surveyed to determine current treatment capacities depending on the principles and standards of care. This was done via the DGU Academy of Trauma Surgery (AUC) and an online-based survey with voluntary participation via aweb-based platform (SurveyMonkey Europe UC, Dublin, Ireland). The data presented here represent an excerpt of the overall data focused on the topic of this paper. Of the 622 certified clinics 252 (40%) took part in the survey in December 2022 and 250 data sets could be evaluated. LevelI,II,III trauma centers (LTZ, RTZ, ÜRTZ) took part in equal numbers. Of the participating clinics 90% have aKAEP that has been updated in the last 3 years. The manual of the Federal Office of Civil Protection and Disaster Assistance (BBK) was known in two thirds of the clinics surveyed. Only 30% of the clinics adapted their own KAEP after it was published and only 40% exercised it in the last 3 years. Elements for the care of those affected and injured (psychosocial network, end-of-life care, care of relatives) were included in 25-44% of hospitals. Regardless of the certification, it became obvious that around 80% of the hospitals rely exclusively on one alarm procedure and only 20% use aredundant system with 2 or more notification procedures. The survey showed that more than 75% of the hospitals already have the option of selective or partial alerting. Depending on the triage category, the LTZs were able to initially admit an average of 2SKI and 4SKII patients. These capacities were almost doubled by the RTZ and tripled by the ÜTZ. The DGU Trauma Networks represent an essential part for the care in disaster and major disaster situations. The KAEP has been implemented in the majority of hospitals. Updating the KAEPs to current content, practicing and digital, selective and redundant systems for alerting represent achallenge. In addition, the harmonization of terms and organizational elements but also the establishment of care for relatives and the care and psychosocial emergency treatment (PSNV) have not yet been fully implemented. Ultimately, aKAEP must be implemented to make hospitals and the healthcare system more resilient to disasters and major disasters. It is important to support the hospitals and ensure that costs for exercises and training course formats are covered.

Impact of the Astra Zeneca COVID-19 vaccine on an emergency department.

To assess the impact of the AstraZeneca (AZ) vaccine roll-out on an ED. Primary outcomes are ED length of stay (LOS), investigation ordering and costs. Secondary measures are compliance with the Thrombosis and Haemostasis Society of Australia and New Zealand (THANZ) Vaccine-Induced Thrombotic Thrombocytopaenia Syndrome (VITT) guidelines. This retrospective observational study examined a level 5 ED in Sydney, Australia. Triages mentioning the AZ vaccine between 7 July 2021 and 8 November 2021 were reviewed. Cases were limited to a single day of the week. Researchers reviewed clinical notes to identify patients that presented due to AZ vaccine concern and abstracted relevant data. Costings were calculated using an ED activity-based funding algorithm. One thousand three hundred and fifty cases were identified, with 167 analysed (12%) and 97 presented (7%). Median LOS was 195 min (IQR: 152-232 min, 95% CI [184, 217]). Median age was 43 years (IQR: 35-65 years, 95% CI [44, 51]). Median Australian Triage Category was 4. Ninety-eight percent were discharged directly home. Ninety-four percent underwent pathology and 41% radiology. Compliance with the THANZ guidelines was 16%. No findings were related to VITT. AZ vaccine concern contributed 12% of August ED caseload. Over the 16-week study period a cost of AUD$486 747.99 was extrapolated. A high number of young, low acuity patients presented to the ED with AZ vaccine concerns and were associated with financial and workload implications. The quantity of ED presentations appears to be associated with vaccine administration rates. There was poor compliance with the THANZ guidelines, and they appear to have contributed to the high volume of investigations.

Implementing Person-Centred Lean Six Sigma to Transform Dermatology Waiting Lists: A Case Study from a Major Teaching Hospital in Dublin, Ireland

The study site, a major teaching hospital in Dublin, Ireland, addressed significant challenges within its dermatology service through a comprehensive improvement initiative using a person-centred Lean Six Sigma methodology. Initially, the hospital’s dermatology department faced excessive outpatient waiting times, with 3736 patients awaiting appointments, and 1615 waiting over 12 months. The person-centred Lean Six Sigma approach, which combines Lean techniques to reduce non-value add and Six Sigma methods to eliminate variation through a person-centred lens, was applied to overhaul the referral, triage, and scheduling processes. Key interventions included standardising triage categories, centralising the triage process, and redistributing referrals equitably among consultants. A new centralised triage system was established, leading to a more efficient allocation of appointments and better management of urgent cases. Post-implementation data showed a 40% reduction in the overall waiting list and a 60% reduction in the number of patients waiting over 12 months. The initiative significantly decreased the wait times across all urgency categories, with the most notable improvements in soon and urgent referrals. These changes were also the impetus for a follow-up design-led innovation phase, where the team worked with partners across the educational and healthcare system to enable disruptive change. The success of this project provides a scalable model for improvements in similar healthcare settings.

Integrating an AI-powered symptom assessment application (SAA) in a hospital network in Portugal

Abstract Introduction Patients often struggle to understand medical information and healthcare worker shortage strains health systems. Integrating SAAs that guide patients and provide diagnostic decision support (DDS) to doctors can help. This study evaluates the impact of the SAA ‘Ada’, a class IIa medical device, after its integration into the patient portal of a Portuguese hospital network, CUF, on clinical workflow and patient health. Methods This on-going post-market quality improvement study, started in Nov 2023 and has enrolled 664 participants. Participating doctors complete a baseline survey. Consenting patients conduct symptom assessments and answer survey questions within the app. The patient’s symptom report is shared with their physician, who then completes a post-consultation survey. Results After using Ada, 86% of patients could enter all their symptoms, 79% felt more prepared for their consultation, and 39% felt less health anxiety (N = 664). In the baseline survey, most doctors utilize online resources (96%) in the absence of a symptom checker, followed by discussions with colleagues (65%) for challenging cases (N = 26). In the post-consultation survey, 95% of doctors found Ada’s condition suggestions reasonable, with only 10% disagreeing with its triage classification. Doctors reported time saving and increased consultation efficiency (64%), and 59% felt more confidence in their diagnosis. A majority said that Ada provided them with additional information about patients (77%). Ada also improved consultation efficiency (74%), preparation (78%), patient engagement (82%), and health knowledge (77%; N = 22). Conclusions By providing personalized health information, Ada prepares patients for their consultation, resulting in improved patient engagement and facilitation of clinical conversation. Incorporating a SAA into routine patient care enhances quality of consultation, reduces consultation time, increases efficiency, and provides DDS to doctors. Key messages • Digital health enhances patient engagement, clinical efficiency, and diagnostic decision support for doctors in healthcare settings. • Ada’s personalized health information empowers patients, improves consultations, and reduces health-related anxiety.

Exploring the potential of artificial intelligence models for triage in the emergency department

ABSTRACT Objective To perform a comparative analysis of the three-level triage protocol conducted by triage nurses and emergency medicine doctors with the use of ChatGPT, Gemini, and Pi, which are recognized artificial intelligence (AI) models widely used in the daily life Materials and methods The study was prospectively conducted with patients presenting to the emergency department of a tertiary care hospital from 1 April 2024, to 7 April 2024. Among the patients who presented to the emergency department over this period, data pertaining to their primary complaints, arterial blood pressure values, heart rates, peripheral oxygen saturation values measured by pulse oximetry, body temperature values, age, and gender characteristics were analyzed. The triage categories determined by triage nurses, the abovementioned AI chatbots, and emergency medicine doctors were compared. Results The study included 500 patients, of whom 23.8% were categorized identically by all triage evaluators. Compared to the triage conducted by emergency medicine doctors, triage nurses overtriaged 6.4% of the patients and undertriaged 3.1% of the yellow-coded patients and 3.4% of the red-coded patients. Of the AI chatbots, ChatGPT exhibited the closest triage approximation to that of emergency medicine doctors; however, its undertriage rates were 26.5% for yellow-coded patients and 42.6% for red-coded patients. Conclusion The undertriage rates observed in AI models were considerably high. Hence, it does not yet seem appropriate to solely rely on the specified AI models for triage purposes in the emergency department.

Benefits of emergency department routine blood test performance on patients whose allocated triage category is not time critical: a retrospective study

IntroductionEmergency department clinicians, and particularly nurses in triage, frequently perform routine blood tests on patients whose allocated triage category is not time critical (triage categories 3, 4 and 5 of the Australasian Triage Scale). Some observers have questioned the utility of routine blood testing in these acute healthcare settings given the cost and workload implications.MethodsA quantitative method using retrospective observational design was utilised guided by STROBE checklist. Electronic medical records of patient data collected at a quaternary Australian metropolitan hospital emergency department were reviewed.Results and discussionA total of 74,878 adult patients attended the emergency department between 1st January and 31st December 2021 and a sample of 383 were randomly allocated for this study. Of the 383 patients included, 51% were female, age ranges were 18–99 years (mean 51.6). The majority were Australasian Triage scale (ATS) triage category 3 (55%) and 62% had blood tests performed. Blood test performance was found to be associated with advancing age (p < 0.001) but not with department occupancy as determined by the national emergency department overcrowding scale (p = 0.230).ConclusionBlood testing in the emergency department in triaged non-time critical patients was found to be frequent thereby affecting nurses’ already stretched time resource. Older patients were found to be more likely to have a blood test. There is a positive correlation between blood test performance and length of stay in the emergency department.

Automated Unmanned Aerial System for Camera-Based Semi-Automatic Triage Categorization in Mass Casualty Incidents

Using drones to obtain vital signs during mass-casualty incidents can be extremely helpful for first responders. Thanks to technological advancements, vital parameters can now be remotely assessed rapidly and robustly. This motivates the development of an automated unmanned aerial system (UAS) for patient triage, combining methods for the automated detection of respiratory-related movements and automatic classification of body movements and body poses with an already published algorithm for drone-based heart rate estimation. A novel UAS-based triage algorithm using UAS-assessed vital parameters is proposed alongside a robust UAS-based respiratory rate assessment and pose classification algorithm. A pilot concept study involving 15 subjects and 30 vital sign measurements under outdoor conditions shows that with our approach, an overall triage classification accuracy of 89% and an F1 score of 0.94 can be achieved, demonstrating its basic feasibility.

Implementation of an early attention strategy to reduce emergency room overcrowding in an academic institution in Colombia, a pilot study

Overcrowding is a worldwide problem, and long waiting times are associated with increased morbidity and even mortality of patients regardless of triage classification. Although there are many tools published in the literature that contribute to the reduction of overcrowding, for the Colombian population there are not many tools evaluated to reduce the length of stay of patients in the emergency department. This is a retrospective analytical study that compared whether there was a difference in patient definition time and ED length of stay between a group attended under an early care protocol (PAT) versus the usual protocol. Of the total of 969 patients included it was found that the group attended under the PAT protocol had a shorter definition time than the usual protocol, also the Emergency department length of stay (EDLOS) was significantly lower in the PAT group compared to the usual protocol. The implementation of the PAT protocol performed by emergency physicians allows a faster contact with the patient by the physician, and leads to a significant reduction of EDLOS, contributing to the reduction of overcrowding in the emergency department.

Evaluation of nurse practitioners' extended scope of practice in a regional hospital emergency department in tropical Australia.

There is very little literature examining the workload and impact of nurse practitioners (NPs) working in emergency departments (ED) in regional and rural Australia. The aim of this paper was to review the ED NPs scope of practice in the ED discharge stream and patient outcomes at Cairns Hospital over a 7-month period. This retrospective study examined the ED electronic medical record between 14 May 2019 and 31 December 2019. Cases managed by ED NPs, referrals, procedures performed, representations and disposition data were collected. Adverse events were sought from the Qld Health adverse events register (Riskman), the department complaints register and the ED M&M meeting minutes. A total of 1443 patients were treated by NPs, with ages ranging from 0 to 98 years (median 40 years). Australasian Triage Score (ATS) 3 cases made up 30% of the workload. Other than ATS 3 cases, time to being seen was better than the general department. There were very few unexpected representations, complaints or adverse events. This study supports the view that NPs working in a regional emergency department can safely manage a variety of patients outside a fast-track model, with a wide age range and a variety of triage categories and diagnoses. We believe this has important implications for the provision of emergency care, especially in regional and rural Australia.

Silver Fast Track – Prioritising People Over 75 in the Emergency Department (ED)

Abstract Background The National Emergency Medicine Programme prioritised the care of older adults in a three-year Unscheduled Care Improvement Programme, coinciding with the “Integrated NCAGL Acute Operations and Older Persons body of work on Older Adults with Frailty in Acute hospitals”. In May 2023 a three-month pilot in the ED commenced to reduce the time for people over the age of 75 awaiting assessment by a doctor. Using the Manchester Triage system, a separate filing box on triage (Category 3+) was created to identify all people aged over 75 years who would then be prioritised to be seen within 4 hours of arrival. In addition, if a person aged over 75 years was waiting over 4 hours to be seen they would be re-triaged and seen within 15 minutes (Category 2). Methods A Prospective analysis of waiting times for people aged over 75 years, assessing: (1) time to be assessed by the Older Persons Assessment and Liaison (OPAL) Team; (2) time to see the ED doctor and (3) time to discharge. Results Four months prior to commencement, the median time from arrival to be seen by OPAL was 10 hours 58 mins. Following commencement, for the remainder of 2023, the median time from arrival to seen by OPAL was 6 hours 36 mins. The time to see the doctor reduced by 49 mins and time to discharge reduced by 1 hour 19 minutes. Conclusion The category 3+ for patients aged over 75 years is now an established feature in the ED. It has enabled the OPAL team to undertake earlier assessment of the patients journey alongside the medical team ensuring a more collaborative discharge pathway. In the future, we hope to further reduce the time to be assessed by OPAL through the introduction of clinical frailty scoring at ED triage &amp; expanding the team.

Assessment of the effectiveness of hospital external disaster functional drills on health care receivers’ performance, using standardized patients and mass cards simulation: a pilot study from Saudi Arabia

BackgroundGiven the increasing frequency of disasters globally, it is critical that healthcare systems are prepared for these mass casualty events. The Saudi health system’s preparedness for mass casualty incidents needs to be more robust, potentially due to limited disaster drills and inadequate standardized patient (SP) simulation training. This study aims to (i) assess the performance of front-line hospital staff in Saudi Arabia through a functional drill and (ii) evaluate the drill’s effectiveness using SP and MAC-SIM cards, providing detailed insights into its design and execution.MethodsA functional drill was conducted at a government hospital in Riyadh, Saudi Arabia, on December 19, 2022, using a cross-sectional approach with two phases. 141 healthcare receivers served as subjects, while 23 volunteers acted as SPs. The scenario simulated a building collapse to assess the emergency department (ED) response, interdepartmental communication, and surge capacity. Data were collected through direct observation of healthcare practitioners’ interactions with the SPs, analysis of SP data, and participant feedback. Quantitative data were analyzed descriptively, while qualitative data were examined for patterns and themes related to simulation performance and effectiveness.ResultsThe hospital receivers’ performances demonstrated accurate triage categories. The ED team assessed most patients (67%) in less than 5 min. For patients requiring definitive care, such as intensive care unit, 95% spent less than 2.5 h in the ED. Most patients (65%) required ‘other treatments’. Communication was efficient in the triage zone and the yellow treatment area. Participants’ feedback on using MAC-SIM cards during the simulation was overwhelmingly positive with 82.61% reporting that MAC-SIM use helped them respond better. Experienced SPs (paramedics) with prior disaster knowledge and experience outperformed inexperienced SPs (nurses) in the functional exercise.ConclusionThis groundbreaking study is the first in the Arabic Gulf region to use SPs with MAC-SIM cards in functional drills. The findings highlight the potential of simulation exercises to improve hospital team knowledge and performance when responding to disasters. Multiple evaluation techniques can effectively identify participant strengths and weaknesses, informing future disaster improvement plans. This information is a valuable resource for Arabic and middle-income countries where disaster medicine is still developing.

Length of stay and associated factors among pediatric patients in the pediatric emergency unit of the Tikur Anbessa specialized hospital, Addis Ababa, Ethiopia

BackgroundLength of stay in the emergency department is used as a quality indicator to gauge the overall efficiency of emergency care. The performance measure was used to evaluate the quality of care provided in the emergency department.ObjectiveTo assess the length of stay and associated factors among pediatric patients admitted to the pediatric emergency unit of Tikur Anbessa Specialized Hospital in Addis Ababa, Ethiopia.MethodsAn institution-based prospective cross-sectional study design was employed. A systematic random sampling technique was used to select the study participants. Data were collected via semi-structured, interviewer-administered questionnaires and chart reviews. Analysis was performed via the Statistical Package for Social Science software version 27. Binary logistic regression analysis was conducted to identify variables associated with the length of stay. The study was conducted in the Pediatric Emergency Unit of Tikur Anbessa Specialized Hospital in Addis Ababa, Ethiopia, from March 15 to April 15, 2023.ResultsA total of 268 patients participated in the study, with a response rate of 97.81%. The majority of the participants were male (157, 58.6%), with a median age of 3 years. The study revealed that 180 participants (67.2%) experienced a prolonged length of stay. The variables significantly associated with prolonged length of stay included residency (AOR = 2.04, CI: 1.03, 4.025), triage category (AOR = 3.25, CI: 1.08, 5.974), number of investigations (AOR = 2.381, CI: 1.038, 5.462), and waiting for imaging (AOR = 4.230, CI: 1.638, 10.93).ConclusionMany pediatric patients stayed in the emergency room for more than 24 h because of factors such as residency, triage category, number of investigations, and the need for imaging. To address this, we recommend streamlining triage processes, increasing imaging resources, providing additional staff training, developing integrated care pathways, and advocating for policy changes to increase emergency room efficiency and improve patient outcomes.

Learner evaluation of an immersive virtual reality mass casualty incident simulator for triage training

BackgroundTo minimize loss of life, modern mass casualty response requires swift identification, efficient triage categorization, and rapid hemorrhage control. Current training methods remain suboptimal. Our objective was to train first responders to triage a mass casualty incident using Virtual Reality (VR) simulation and obtain their impressions of the training’s quality and effectiveness.We trained subjects in a triage protocol called Sort, Assess, Lifesaving interventions, and Treatment and/or Transport (SALT) Triage then had them respond to a terrorist bombing of a subway station using a fully immersive virtual reality simulation. We gathered learner reactions to their virtual reality experience and post-encounter debriefing with a custom electronic survey. The survey was designed to gather information about participants’ demographics and prior experience, including roles, triage training, and virtual reality experience. We then asked them to evaluate the training and encounter and the system’s potential for training others.ResultsWe received 375 completed evaluation surveys from subjects who experienced the virtual reality encounter. Subjects were primarily paramedics, but also included medical learners as well as other emergency medical service (EMS) professionals. Most participants (95%) recommended the experience for other first responders and rated the simulation (95%) and virtual patients (91%) as realistic. Ninety-four percent (94%) of participants rated the virtual reality simulator as “excellent” or “good.” We observed some differences between emergency medical service and medical professionals regarding their prior experience with disaster response training and their opinions on how much the experience contributed to their learning. We observed no differences between subjects with extensive virtual reality experience and those without.ConclusionsOur virtual reality simulator is an automated, customizable, fully immersive virtual reality system for training and assessing personnel in the proper response to a mass casualty incident. Participants perceived the simulator as an adequate alternative to traditional triage and treatment training and believed that the simulator was realistic and effective for training. Prior experience with virtual reality was not a prerequisite for the use of this system.

Redirection of low-acuity emergency department patients to nearby medical clinics using an electronic medical support system: effects on emergency department performance indicators

BackgroundOvercrowded emergency departments (EDs) are associated with higher morbidity and mortality and suboptimal quality-of-care. Most ED flow management strategies focus on early identification and redirection of low-acuity patients to primary care settings. To assess the impact of redirecting low-acuity ED patients to medical clinics using an electronic clinical decision support system on four ED performance indicators.MethodsWe performed a retrospective observational study in the ED of a Canadian tertiary trauma center where a redirection process for low-acuity patients was implemented. The process was based on a clinical decision support system relying on an algorithm based on chief complaint, performed by nurses at triage and not involving physician assessment. All patients visiting the ED from 2013 to 2017 were included. We compared ED performance indicators before and after implementation of the redirection process (June 2015): length-of-triage, time-to-initial-physician-assessment, length-of-stay and rate of patients leaving without being seen. We performed an interrupted time series analysis adjusted for age, gender, time of visit, triage category and overcrowding.ResultsOf 242,972 ED attendees over the study period, 9546 (8% of 121,116 post-intervention patients) were redirected to a nearby primary medical clinic. After the redirection process was implemented, length-of-triage increased by 1 min [1;2], time-to-initial assessment decreased by 13 min [-16;-11], length-of-stay for non-redirected patients increased by 29 min [13;44] (p < 0.001), minus 20 min [-42;1] (p = 0.066) for patients assigned to triage 5 category. The rate of patients leaving without being seen decreased by 2% [-3;-2] (p < 0.001).ConclusionImplementing a redirection process for low-acuity ED patients based on a clinical support system was associated with improvements in two of four ED performance indicators.

The effects of applying artificial intelligence to triage in the emergency department: A systematic review of prospective studies.

Accurate and rapid triage can reduce undertriage and overtriage, which may improve emergency department flow. This study aimed to identify the effects of a prospective study applying artificial intelligence-based triage in the clinical field. Systematic review of prospective studies. CINAHL, Cochrane, Embase, PubMed, ProQuest, KISS, and RISS were searched from March 9 to April 18, 2023. All the data were screened independently by three researchers. The review included prospective studies that measured outcomes related to AI-based triage. Three researchers extracted data and independently assessed the study's quality using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) protocol. Of 1633 studies, seven met the inclusion criteria for this review. Most studies applied machine learning to triage, and only one was based on fuzzy logic. All studies, except one, utilized a five-level triage classification system. Regarding model performance, the feed-forward neural network achieved a precision of 33% in the level 1 classification, whereas the fuzzy clip model achieved a specificity and sensitivity of 99%. The accuracy of the model's triage prediction ranged from 80.5% to 99.1%. Other outcomes included time reduction, overtriage and undertriage checks, mistriage factors, and patient care and prognosis outcomes. Triage nurses in the emergency department can use artificial intelligence as a supportive means for triage. Ultimately, we hope to be a resource that can reduce undertriage and positively affect patient health. We have registered our review in PROSPERO (registration number: CRD 42023415232).

Machine Learning Model Reveals Determinators for Admission to Acute Mental Health Wards From Emergency Department Presentations.

This research addresses the critical issue of identifying factors contributing to admissions to acute mental health (MH) wards for individuals presenting to the emergency department (ED) with MH concerns as their primary issue, notably suicidality. This study aims to leverage machine learning (ML) models to assess the likelihood of admission to acute MH wards for this vulnerable population. Data collection for this study used existing ED data from 1 January 2016 to 31 December 2021. Data selection was based on specific criteria related to the presenting problem. Analysis was conducted using Python and the Interpretable Machine Learning (InterpretML) machine learning library. InterpretML calculates overall importance based on the mean absolute score, which was used to measure the impact of each feature on admission. A person's 'Age' and 'Triage category' are ranked significantly higher than 'Facility identifier', 'Presenting problem' and 'Active Client'. The contribution of other presentation features on admission shows a minimal effect. Aligning the models closely with service delivery will help services understand their service users and provide insight into financial and clinical variations. Suicidal ideation negatively correlates to admission yet represents the largest number of presentations. The nurse's role at triage is a critical factor in assessing the needs of the presenting individual. The gap that emerges in this context is significant; MH triage requires a complex understanding of MH and presents a significant challenge in the ED. Further research is required to explore the role that ML can provide in assisting clinicians in assessment.

Diagnostic Performance of a Deep Learning-Powered Application for Aortic Dissection Triage Prioritization and Classification.

This multicenter retrospective study evaluated the diagnostic performance of a deep learning (DL)-based application for detecting, classifying, and highlighting suspected aortic dissections (ADs) on chest and thoraco-abdominal CT angiography (CTA) scans. CTA scans from over 200 U.S. and European cities acquired on 52 scanner models from six manufacturers were retrospectively collected and processed by CINA-CHEST (AD) (Avicenna.AI, La Ciotat, France) device. The diagnostic performance of the device was compared with the ground truth established by the majority agreement of three U.S. board-certified radiologists. Furthermore, the DL algorithm's time to notification was evaluated to demonstrate clinical effectiveness. The study included 1303 CTAs (mean age 58.8 ± 16.4 years old, 46.7% male, 10.5% positive). The device demonstrated a sensitivity of 94.2% [95% CI: 88.8-97.5%] and a specificity of 97.3% [95% CI: 96.2-98.1%]. The application classified positive cases by the AD type with an accuracy of 99.5% [95% CI: 98.9-99.8%] for type A and 97.5 [95% CI: 96.4-98.3%] for type B. The application did not miss any type A cases. The device flagged 32 cases incorrectly, primarily due to acquisition artefacts and aortic pathologies mimicking AD. The mean time to process and notify of potential AD cases was 27.9 ± 8.7 s. This deep learning-based application demonstrated a strong performance in detecting and classifying aortic dissection cases, potentially enabling faster triage of these urgent cases in clinical settings.

A comparative study between point‐of‐care hemoglobin (capillary samples) and laboratory hemoglobin result (venous samples)

AbstractObjectivePoint‐of‐care tests (POCTs) are commonly utilized in emergency departments. The purpose of this study was to evaluate accuracy and clinical utility of capillary POCT Hemocue (POCT Hcue) in emergency department setting.MethodsThis was a retrospective study. Accident and emergency department attendances ≥18 years old with a capillary POCT Hcue result and a paired venous hemoglobin (Hb) result were eligible. Statistical analyses including Pearson coefficient and Bland–Altman plot were performed to assess the relationship and agreement between capillary POCT Hcue and venous Hb.ResultsOne hundred and seven patients were included in the analysis. 59% were female. 67% of the patients were from triage category 3. Scatter plot revealed a linear relationship between capillary POCT Hcue and venous Hb, with a correlation coefficient calculated to be 0.95. The mean difference was −0.30 on the Bland–Altman plot with the limits of agreement from −1.83 to +1.22.ConclusionCapillary POCT Hcue had acceptable agreement with venous Hb in emergency department setting.