Smart Triage

Smart Triage is a digital clinical support platform that rapidly identifies severely ill children with infections on arrival at a hospital. The platform comprises data-driven risk assessment (triage), patient and treatment tracking, a real-time dashboard and automated reports to improve emergency case recognition and reduce treatment times. Smart Triage is designed for low-resource settings where the number of children arriving at hospital is high, children present with higher disease severity, and hospitals are staffed by healthcare workers with limited training. To encourage staff led, contextually appropriate solutions, we used an iterative, user-centered approach and implemented Smart Triage together with a QI program.

Study locations: Uganda, Kenya
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1 to 10 of 11 Results
Geographical validation of the Smart Triage Model by age group
Jun 13, 2024 - Smart Triage - Manuscripts
Zhang, Cherri; Wiens, Matthew O; Dunsmuir, Dustin; Pillay, Yashodani; Huxford, Charly; Kimutai, David; Tenywa, Emmanuel; Ouma, Mary; Kigo, Joyce; Kamau, Stephen; Chege, Mary; Kenya-Mugisha, Nathan; Mwaka, Savio; Dumont, Guy A; Kisson, Niranjan; Akech, Samuel; Ansermino, J Mark, 2024, "Geographical validation of the Smart Triage Model by age group", https://doi.org/10.5683/SP3/4OI0BI, Borealis, V2
Background: Age is an important risk factor among critically ill children with neonates being the most vulnerable. Clinical prediction models need to account for age differences and must be externally validated and updated, if necessary, to enhance reliability, reproducibility, a...
Digital adaptation kit for Smart Triage: operational requirements for implementing Smart Triage recommendations in digital systems
May 10, 2024 - Smart Triage - Implementation Materials
Institute for Global Health, 2023, "Digital adaptation kit for Smart Triage: operational requirements for implementing Smart Triage recommendations in digital systems", https://doi.org/10.5683/SP3/8CMJEF, Borealis, V2
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...
Repeatability of RRate measurements in children during triage in two Ugandan hospitals
Mar 14, 2024 - Smart Triage - Manuscripts
Asdo, Ahmad; Mawji, Alishah; Omara, Isaac; Aye Ishebukara, Ivan Aine; Komugisha, Clare; Novakowski, Stefanie; Pillay, Yashodani; Wiens, Matthew O; Akech, Samuel; Oyella, Florence; Tagoola, Abner; Kissoon, Niranjan; Ansermino, J Mark; Dunsmuir, Dustin, 2024, "Repeatability of RRate measurements in children during triage in two Ugandan hospitals", https://doi.org/10.5683/SP3/XO7BVV, Borealis, V1, UNF:6:I2BQlBPFVKmzukX6X6Amsg== [fileUNF]
Background: Pneumonia is the leading cause of death in children globally. In low- and middle-income countries the diagnosis of pneumonia relies heavily on an accurate assessment of respiratory rate, which can be unreliable in nurses and clinicians with less advanced training. In...
Smart Triage - Manuscripts(University of British Columbia)
Mar 14, 2024
This sub-Dataverse hosts manuscript project data and supplementary materials related to the Smart Triage program of research.
Quality Improvement Training Materials 2.0 - Smart Triage
Quality Improvement Training Materials 2.0 - Smart Triage
Nov 15, 2023 - Smart Triage - Implementation Materials
Huxford, Charly; Dunsmuir, Dustin; Pillay, Yashodani; Ashebukara, Ivan Aye; Tusingwire, Fredson; Novakowski, Stefanie; Behan, Justine; Hwang, Bella; Ansermino, Mark; Lester, Deborah; Kissoon, Niranjan; Tagoola, Abner, 2023, "Quality Improvement Training Materials 2.0 - Smart Triage", https://doi.org/10.5683/SP3/AJEOYG, Borealis, V1
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 mode...
Health Worker Training Materials ~ Smart Triage
Nov 8, 2023 - Smart Triage - Implementation Materials
Huxford, Charly; Dunsmuir, Dustin; Pillay, Yashodani; Ashebukara, Ivan Aye; Tusingwire, Fredson; Novakowski, Stefanie; Behan, Justine; Pallot, Katija; Hwang, Bella; Ansermino, Mark, 2023, "Health Worker Training Materials ~ Smart Triage", https://doi.org/10.5683/SP3/XFCOHR, Borealis, V1
Objective(s): The Smart Triage Health Worker Training Program uses a train-the-trainer model to improve the quality of triage care. Core learning components include: 1) understanding what triage is; 2) effective triaging using the Smart Triage platform; and 3) best practices for...
Smart Triage - Implementation Materials(University of British Columbia)
Jul 28, 2023
This sub-dataverse contains materials to be used in the implementation of the Smart Triage Program, including a Digital Adaptation Kit (DAK), caregiver counselling materials, and training materials.
Smart Triage Jinja Data De-identification
Mar 18, 2022
Mawji, Alishah, 2022, "Smart Triage Jinja Data De-identification", https://doi.org/10.5683/SP3/MSTH98, Borealis, V1
This dataset contains de-identified data with an accompanying data dictionary and the R script for de-identification procedures.
Smart Triage QI
Jun 9, 2021
Lester, Deborah; Ansermino, J Mark; Kissoon, Niranjan (Tex); Tagoola, Abner, 2021, "Smart Triage QI", https://doi.org/10.5683/SP2/HEISQJ, Borealis, V1
Smart Triage QI is a quality improvement training package tailored for Uganda and supported by the Smart Triage platform. The package, including four slides decks with speaker notes and accompanying training manual, cover the basic concepts of the Quality Improvement process and...
Smart Triage Jinja Model
May 19, 2021
Mawji , Alishah, 2021, "Smart Triage Jinja Model", https://doi.org/10.5683/SP2/10BME4, Borealis, V1
This dataset contains 1) A spreadsheet of the de-identified data used to develop the model, along with the accompanying data dictionary, and 2) The R code written to derive, validate and assess the model.
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