This data is the Saving young lives: Triage and treatment using the pediatric rapid sepsis trigger (PRST) tool study. Data collected for this study occurred from April 2020 to April 2022. Objective(s): This is a pre-post intervention study involving pediatric patients presenting to the study hospitals in seek of medical care for an acute illness. The purpose of this study was to develop a prediction model and to perform clinical validation of a digital triage tool to guide triage and treatment of children at health facilities in LMICs with severe infections/suspected sepsis. The study involved three phases: (I) Baseline Period, (II) Interphase Period, (III) Intervention Period. The study hospitals include 2 sites in Kenya (1 control site, 1 experimental site) and 2 in Uganda (1 control site, 1 experimental site). Data Description: Predictor variables were collected at the time of triage by trained study nurses using a custom-built mobile application. All data entered into the mobile application was stored an encrypted database. Data was uploaded directly from the mobile device to a Research Electronic Data Capture (REDCap) database hosted at the BC Children’s Hospital Research Institute (Vancouver, Canada). Outcomes were obtained from facility records or telephone follow-up at 7-10 days and the data was collected electronically. Time-specific outcomes were tracked using an RFID tagging system with study personnel as backup. Limitations: There is missing data and some variables were not collected at all sites. Ethics Declaration: This study was approved by the Makerere University Higher Degrees research and Ethics Committee (No. 743), the Uganda National Institute of Science and Technology (HS 528ES), and the University of British Columbia / Children & Women’s Health Centre of British Columbia Research Ethics Board (H19-02398 & H20-00484). NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at sepsiscolab@bcchr.ca or visit our website.

This data is from the Smart Triage + QI: A digital triaging platform to improve quality of care for critically ill children study. Data collected for this study occurred from December 2021 to July 2023. Objective(s): This is a pre-post intervention study involving pediatric patients presenting to the study hospitals in seek of medical care for an acute illness. The purpose of this project was to implement Smart Triage + QI to improve the quality of care at four health care facilities in Uganda. The primary objective of the program is to enable healthcare workers to recognize the most urgent children more rapidly and allocate existing resources more efficiently. The second objective is to use the proactive processes of QI to identify and examine opportunities for ongoing improvement to strengthen the health system. The study involved two phases: (I) Baseline Period, and (II) Intervention Period. Phase II also involved a community sub-study at 1 site to identify key messaging for an appropriate methods for disseminating educational materials for VHTs and caregivers on Smart Triage. Data Description: Data was collected at the time of triage by trained study nurses using a custom-built mobile application. All data entered into the mobile application was stored an encrypted database. Data was uploaded directly from the mobile device to a Research Electronic Data Capture (REDCap) database hosted at the BC Children’s Hospital Research Institute (Vancouver, Canada). Outcomes were obtained from facility records or telephone follow-up at 7-10 days and the data was collected electronically. Starting in June 2022, outcomes were also collected via automated follow-up (SMS/WhatsApp) messages at one site. Time-specific outcomes were tracked using an RFID tagging system with study personnel as backup. Limitations: There is missing data and some variables were not collected at all sites. Ethics Declaration: This study was approved by the Makerere University Higher Degrees research and Ethics Committee (SPH-2021-41), the Uganda National Institute of Science and Technology (HS 1745ES). NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at sepsiscolab@bcchr.ca or visit our website.

A simulation of complex clinical processes is a challenging task and suitable methods need to be found which can capture the influence of relevant factors and their relationships. The Manchester triage system (MTS) is widely used in German emergency departments (ED), however the impact on patient waiting times remain difficult to predict. The purpose of this work is the assessment of MTS particularly with regard to the waiting times of different degrees of severity. The methodology of agent based simulation was found suitable for the ED domain and the agent based simulation tool SeSAm was chosen due to its intuitive user interface and easy adaption of the simulation models. Altogether four agent classes could be implemented based on the information derived from a process model. The model permits a dynamic simulation of the ED processes and a reliable assessment of patient waiting times. In addition, the implementation of a triage nurse allowed the simulation of the triage process and a direct comparison to the current state without a standardized triage procedure. Essential influencing factors (e.g. number of patients, manning level) were implemented and their effects on the ED processes and patient waiting times assessed. The simulation runs delivered correct results based on the underlying process model and the collected statistical data. The process flow and the waiting times of an ED could be mapped exactly. In all simulation runs the waiting times of high triage levels (MTS-levels 1 and 2) could be reduced. Especially patients of MTS-level 2 in the waiting area of the ED benefit significantly from the implementation of a standardized triage procedure and the associated permanent monitoring.

IntroductionNatural language processing (NLP) uses various computational methods to analyse and understand human language, and has been applied to data acquired at Emergency Department (ED) triage to predict various outcomes. The objective of this scoping review is to evaluate how NLP has been applied to data acquired at ED triage, assess if NLP based models outperform humans or current risk stratification techniques when predicting outcomes, and assess if incorporating free-text improve predictive performance of models when compared to predictive models that use only structured data.MethodsAll English language peer-reviewed research that applied an NLP technique to free-text obtained at ED triage was eligible for inclusion. We excluded studies focusing solely on disease surveillance, and studies that used information obtained after triage. We searched the electronic databases MEDLINE, Embase, Cochrane Database of Systematic Reviews, Web of Science, and Scopus for medical subject headings and text keywords related to NLP and triage. Databases were last searched on 01/01/2022. Risk of bias in studies was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST). Due to the high level of heterogeneity between studies and high risk of bias, a metanalysis was not conducted. Instead, a narrative synthesis is provided.ResultsIn total, 3730 studies were screened, and 20 studies were included. The population size varied greatly between studies ranging from 1.8 million patients to 598 triage notes. The most common outcomes assessed were prediction of triage score, prediction of admission, and prediction of critical illness. NLP models achieved high accuracy in predicting need for admission, triage score, critical illness, and mapping free-text chief complaints to structured fields. Incorporating both structured data and free-text data improved results when compared to models that used only structured data. However, the majority of studies (80%) were assessed to have a high risk of bias, and only one study reported the deployment of an NLP model into clinical practice.ConclusionUnstructured free-text triage notes have been used by NLP models to predict clinically relevant outcomes. However, the majority of studies have a high risk of bias, most research is retrospective, and there are few examples of implementation into clinical practice. Future work is needed to prospectively assess if applying NLP to data acquired at ED triage improves ED outcomes when compared to usual clinical practice.

Emergency department (ED) overcrowding leads to delayed care, increased patient risk, and inefficient resource use. The MIMIC-IV-Ext Triage Instruction Corpus (MIETIC) addresses this by providing 9,629 structured triage cases from MIMIC-IV, aligned with the Emergency Severity Index (ESI). MIETIC supports large language model (LLM) training for AI-assisted triage, improving accuracy, consistency, and risk assessment. The dataset includes chief complaints, vital signs, demographics, and medical history, ensuring realistic triage decision-making. Developed through automated quality control and expert validation, MIETIC enhances model performance in high-risk and moderate-risk classification. Available in CSV formats, MIETIC enables research in clinical NLP, AI-driven triage, and decision-support tools. The dataset module includes:

Structured triage cases with ESI labels. Triage case generation prompts for instruction tuning. Expert-validated samples for quality control. SQL scripts for data extraction and validation, hosted on GitHub.

MIETIC provides a standardized, reproducible dataset to advance AI-driven emergency triage, optimizing accuracy, efficiency, and resource allocation.

The Hospital Services Volumes dataset includes various service-related metrics and indicators related to the service volumes in the hospitals of Nova Scotia Health and the IWK. The data is collected from multiple sources within the health system, including Hospital Inpatient, Emergency, and Surgical Databases. Measures included in this dataset include emergency visits, emergency visit triage score (CTAS), patient admissions, patient discharges, and surgeries completed. The data is aggregated and anonymized to ensure privacy and does not contain any personally identifiable health information. This data set is used to build the Action for Health Public Reporting and the goal of this project is to provide accessible healthcare information to the general public, researchers, and analysts in order to improve understanding and foster improvements in the healthcare system in Nova Scotia.

Objective(s): To ensure countries can effectively benefit from digital health investments, digital adaptation kits (DAKs) are designed to facilitate the accurate reflection of clinical, public health and data use guidelines within the digital systems countries are adopting. DAKs are operational, software-neutral, standardized documentation that distill clinical, public health and data use guidance into a format that can be transparently incorporated into digital systems. Objective(s): This DAK provides operational requirements for implementing Smart Triage recommendations in digital systems. With a focus on triage care, this DAK aims to provide a common language across various audiences – triage and other programme managers, software developers, and implementers of digital systems – to ensure a common understanding of the appropriate health information content within a defined health programme area, as a mechanism to catalyse the effective use of these digital systems. The key objectives of this DAK are: to ensure adherence to clinical, public health and data use guidelines, and facilitate consistency of the health content that is used to inform the development of a patient-centred digital tracking and decision-support (DTDS) system; to enable both health programme leads and digital health teams (including software developers) to have a joint understanding of the health content within the digital system, with a transparent mechanism to review the validity and accuracy of the health content; and to provide a starting point of the core data elements and decision-support logic that should be included within DTDS systems for Smart Triage. Acknowledgements: The Institute for Global Health (IGH) is grateful for the contributions of its collaborators. This digital adaptation kit was coordinated by Dustin Dunsmuir, Charly Huxford, Yashodani Pillay, Justine Behan, and Mark Ansermino of IGH; and Fredson Tusingwire and Aine Ivan Aye Ashebukara of the World Alliance for Lung and Intensive Care Medicine in Uganda (WALIMU). NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at sepsiscolab@bcchr.ca or visit our website.

This test dataset consists of one table of variables collected in PRIEST dataset. The PRIEST (Pandemic Respiratory Infection Emergency System Triage) Study for Low and Middle-Income Countries (DP - PRIEST)

Once literature triage system is ready it is time to actually try to apply if to records that do not have any label in order to find the subset that does describe TF-TG interactions (are relevant). This is the corpus that has to be labeled by the systems created (hopefully) during the hackathon. To make the results more useful we have pre-selected records that do mention TFs by exploiting either automatic human TF mention recognition or external references from databases that have manually curated information on transcription factors (from GeneRif or UniProt). This means that these abstracts should be enriched with TF relevant records. This record has the same format as the training data except that the last column with the class label is missing.It contains PMIDs and Abstracts.Name: greekc_triage_unlabelled_v01.tsvExample:Format: tsv-separated columns (PMID, PubAnnotation JSON formated results of Pubtator for this record together with the automatically detected gene mentions using GnormPlus providing the Entrez Gene Identifiers together with the mention offsets, i.e. start and end character positionsPubAnnotation format description: http://www.pubannotation.org/docs/annotation-format/PubTator record retrieval description:https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/curl.htmlWarning: This file is quite big!

Background: Prioritization of acutely ill patients in the Emergency Department remains a challenge. We aimed to evaluate whether routine blood tests can predict mortality in unselected patients in an emergency department and to compare risk prediction with a formalized triage algorithm.

Methods: A prospective observational cohort study of 12,661 consecutive admissions to the Emergency Department of Nordsjælland University Hospital during two separate periods in 2010 (primary cohort, n = 6279) and 2013 (validation cohort, n = 6383). Patients were triaged in five categories by a formalized triage algorithm. All patients with a full routine biochemical screening (albumin, creatinine, c-reactive protein, haemoglobin, lactate dehydrogenase, leukocyte count, potassium, and sodium) taken at triage were included. Information about vital status was collected from the Danish Central Office of Civil registration.

Multiple logistic regressions were used to predict 30-day mortality. Validation was...

Comparative analogical and digital triage times between the groups of the two countries.

Abstract

Introduction: Telephone triage services (TTS) play an increasing role in the delivery of healthcare. The objective of this study was to characterize the users of a TTS for non-critical emergencies, describe the types of advice given and their subsequent observation, and assess the influence of TTS on the use of the healthcare system in a sanitary region of Switzerland. Methods: Data from a TTS based in the French part of Switzerland were analyzed. This service consists of a medical contact center for non-critical emergencies, with trained nurses available 24/7. A random selection of 2,034 adult calls was performed between July and December 2018. Research students contacted users 2 to 4 weeks after the initial call and assessed sociodemographic and clinical data, as well as the impact of the advice received on the use of the healthcare system. Results: A sample of 412 (22.2%) users was included in the analyses. The average age was 49.0 (SD 20.4) years; 68.5% were women and 72.8% of Swiss origin. The two main recommendations provided by nurses were to consult the emergency department (ED) (44.6%, n=184) and to contact a physician on duty (33.2%, n=137). The majority of users followed the advice given by the nurses (substantial agreement [k=0.79] with consulting the ED and perfect agreement [k=0.87] with contacting a physician on duty). We calculated that calling the TTS could decrease the intention to visit the ED by 28.1%. Conclusion: TTS for non-critical emergencies have the potential to decrease the use of ED services.

Analysis unit

412 individuals

Universe

Humans

Sampling procedure

In this cross-sectional study, a research collaborator randomly selected calls each week from all TTS calls made during a 4-month period (July 24 to September 27, 2018, and October 23 to December 17, 2018) by using STATA software (Stata Corp 2015, College Station, Texas, USA).

Mode of data collection

Users were contacted by phone by trained research university students (not necessary medical students) who collected the data. During the phone encounter, participants provided oral consent, after which the students recorded their answers on a secured software system (REDCap). The consent was given orally and the answer was transcribed into the Redcap form. If the answer was negative, the interrogation was interrupted. For each included participant, a research assistant also retrieved data from the TTS database that had been recorded during the initial call by the nurse. The data from the records were used for the exact date of the call and the time of the call. These data were retrieved from the registration form and added to the secure folders (REDCap) by the research assistant.

Objective: Our aim was to develop a risk stratification model to predict the presence of a potentially more sinister injury in patients exposed to a whiplash trauma. Methods: The study base comprised of 3,115 residents who first sought healthcare contact within one week after being exposed to a whiplash trauma between 1999-2008, from within a defined geographical area, Skaraborg County in south-western Sweden. Information about gender, age, time elapsed prior to seeking care, type of health care contact, and hospitalization was retrieved. Eighteen potential risk factors were identified and evaluated using multivariate logistic regression. Results: Of 3,115 patients, 215 (6.9%) required hospital admission so theoretically 93% could have been initially assessed by primary health care. However, only 46% had their first contact in primary health care. All patients had symptoms resulting in a diagnosis of whiplash injury. Four risk factors were found to be associated with hospital admission: commotio cerebri (OR 31, 19-51), fracture / luxation (OR 11, 5.1-22), serious injury (OR 41, 8.0-210), and the patient sought care during the same day as the trauma (OR 5.9, 3.7- 9.5). These four risk factors explained 27 % of the variation for hospital admission and the area under curve (AUC) was 0.77 (0.74-0.80). Ninety-six percent of patients (2,985) had only a whiplash injury with none of the other four risk factors. These could be split into those attending health care the same day as the trauma, 1,737 (56%) with a 7.1% risk for hospital admission, and those attending health care later, 1,248 (40%) with a 1.3% risk for hospital admission. Conclusion: Patients with no signs of commotio cerebri, no fracture/luxation injury, no serious injury, comprising 96% of all patients exposed to a whiplash trauma can initially be referred to primary health care for initial assessment. However, those contacting the health care the same day as the trauma should be referred to a hospital for evaluation if they can't get an appointment with a general practitioner the same day.

Background: Many hospitalized children in developing countries die from infectious diseases. Early recognition of those who are critically ill coupled with timely treatment can prevent many deaths. A data-driven, electronic triage system to assist frontline health workers in categorizing illness severity is lacking. This study aimed to develop a data-driven parsimonious triage algorithm for children under five years of age. Methods: This was a prospective observational study of children under-five years of age presenting to the outpatient department of Mbagathi Hospital in Nairobi, Kenya between January and June 2018. A study nurse examined participants and recorded history and clinical signs and symptoms using a mobile device with an attached low-cost pulse oximeter sensor. The need for hospital admission was determined independently by the facility clinician and used as the primary outcome in a logistic predictive model. We focused on the selection of variables that could be quickly and easily assessed by low skilled health workers. Results: The admission rate (for more than 24 hours) was 12% (N=138/1,132). We identified an eight-predictor logistic regression model including continuous variables of weight, mid-upper arm circumference, temperature, pulse rate, and transformed oxygen saturation, combined with dichotomous signs of difficulty breathing, lethargy, and inability to drink or breastfeed. This model predicts overnight hospital admission with an area under the receiver operating characteristic curve of 0.88 (95% CI 0.82 to 0.94). Low- and high-risk thresholds of 5% and 25%, respectively were selected to categorize participants into three triage groups for implementation. Conclusion: A logistic regression model comprised of eight easily understood variables may be useful for triage of children under the age of five based on the probability of need for admission. This model could be used by frontline workers with limited skills in assessing children. External validation is needed before adoption in clinical practice. NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at sepsiscolab@bcchr.ca or visit our website.

Background: Many hospitalized children in developing countries die from infectious diseases. Early recognition of those who are critically ill coupled with timely treatment can prevent many deaths. A data-driven, electronic triage system to assist frontline health workers in categorizing illness severity is lacking. This study aimed to develop a data-driven parsimonious triage algorithm for children under five years of age.

Methods: This was a prospective observational study of children under-five years of age presenting to the outpatient department of Mbagathi Hospital in Nairobi, Kenya between January and June 2018. A study nurse examined participants and recorded history and clinical signs and symptoms using a mobile device with an attached low-cost pulse oximeter sensor. The need for hospital admission was determined independently by the facility clinician and used as the primary outcome in a logistic predictive model. We focused on the selection of variables that could be quickly and easily assessed by low skilled health workers.

Results: The admission rate (for more than 24 hours) was 12% (N=138/1,132). We identified an eight-predictor logistic regression model including continuous variables of weight, mid-upper arm circumference, temperature, pulse rate, and transformed oxygen saturation, combined with dichotomous signs of difficulty breathing, lethargy, and inability to drink or breastfeed. This model predicts overnight hospital admission with an area under the receiver operating characteristic curve of 0.88 (95% CI 0.82 to 0.94). Low- and high-risk thresholds of 5% and 25%, respectively were selected to categorize participants into three triage groups for implementation.

Conclusion: A logistic regression model comprised of eight easily understood variables may be useful for triage of children under the age of five based on the probability of need for admission. This model could be used by frontline workers with limited skills in assessing children. External validation is needed before adoption in clinical practice.

The Virtual Healthcare Triage System market is rapidly evolving, driven by a pressing need for efficient patient management and the increasing demand for accessible healthcare solutions. These systems utilize advanced telemedicine technology to assess patients' conditions remotely, facilitating prompt medical guidan

The London Trauma Office has released a mid-year report to show the activity associated with the Major Trauma Centres (MTC) in London.

The information reported at either London or MTC level:

• Number of patients who triggered the Triage Tool and were taken to a MTC Emergency Department
• Outcome of Triage Tool Positive patients taken to a MTC by London Ambulance Service (LAS) or Helicopter Emergency Medical Service (HEMS)
• Triage Tool Positive patients by mechanism of injury
• Triage Tool Positive admissions to ED by time of day
• Seniority of Doctor in the ED at each MTC
• The median time (hours) from arrival at MTC to CT Scan
• All patients held in TARN (Trauma Audit & Research network) by age
• Breakdown of Trigger Steps- Trauma Triage Tool for May.

Data source: London Trauma Office (http://www.londontraumaoffice.nhs.uk)

1. The spatial and temporal dynamics of landscape structure yield ecological constraints that may limit or promote the recovery of functioning habitat within human-modified ecosystems. In planning restoration and conservation measures to optimize outcomes for biodiversity, such constraints should be evaluated at multiple scales. 2. This paper presents a multi-scale methodology based on the concept of triage that incorporates landscape and regional spatial context and temporal dynamics to prioritize restoration and conservation. 3. In applying the framework to a large Brazilian Atlantic forest study site that underwent substantial forest cover changes between 1990 and 2002, our results demonstrate the utility of this framework for navigating between different trade-offs inherent to biodiversity conservation. 4. Landscapes are ranked in accordance with indicators that evaluate the relative urgency of intervention, feasibility of recovery, and importance for broader-scale biotic flows. 5. Synthesis and applications. Efforts to enhance biodiversity through restoration and/or conservation may be hampered when decisions are based exclusively on contemporary landscape structure, and not made through a historical perspective. In making such trade-offs explicit, this framework can aid practitioners in defining the most appropriate set of restoration or conservation strategies given the ecological constraints, biodiversity goals, and available budget.

This table describes Communications Center compliance with Medical Priority Dispatch System (MPDS) performance standards. Performance targets are based on Accredited Center of Excellence (ACE) standards set forth by the International Academies of Emergency Dispatch.

Objective(s): The Smart Triage Quality Improvement Training Program covers the basic concepts of the Quality Improvement process and provides a framework and tools that can be used to train staff on QI. Core learning components include: 1) understanding what QI is; 2) the QI model for improvement; and 3) QI methods and tools. Data Description: This dataset includes the following materials for use in the Smart Triage Quality Improvement Training Program: 1) Quality Improvement Guide; 2) QI Activities Workbook. Materials were originally developed through a partnership with Walimu and the University of British Columbia. All materials are provided in the English language. Data Limitations: These materials were designed for the Ugandan context and may not be generalizable to other settings. Data Ethics Declaration: NA Funding Source(s): BC Children's Hospital Foundation; Grand Challenges Canada; Mining4Life; Wellcome NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at sepsiscolab@bcchr.ca or visit our website.