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Citation: Ansermino JM, Pillay Y, Tagoola A,
Zhang C, Dunsmuir D, Kamau S, et al. (2025)
Implementation of Smart Triage combined with
a quality improvement program for children
presenting to facilities in Kenya and Uganda:
An interrupted time series analysis. PLOS Digit
Health 4(3): e0000466. https://doi.org/10.1371/
journal.pdig.0000466
Editor: Ziad El-Khatib, Karolinska Institutet,
SWEDEN
Received: February 9, 2024
Accepted: January 8, 2025
Published: March 10, 2025
Copyright: © 2025 Ansermino et al. This is an
open access article distributed under the terms
of the Creative Commons Attribution License,
which permits unrestricted use, distribution,
and reproduction in any medium, provided the
original author and source are credited.
Data availability statement: Study materials
(protocol, data collection tools, data dictio-
nary, software, analysis code, and metadata)
are publicly available through the Pediatric
Sepsis Data CoLaboratory’s (Sepsis CoLab)
Dataverse on https://borealisdata.ca/dataverse/
ST_implementation. Due to the sensitive
nature of clinical data and the potential risk
RESEARCH ARTICLE
Implementation of Smart Triage combined
with a quality improvement program for
children presenting to facilities in Kenya and
Uganda: An interrupted time series analysis
J Mark Ansermino 1,2*, Yashodani Pillay1,2, Abner Tagoola3, Cherri Zhang 1,2,
Dustin Dunsmuir1,2, Stephen Kamau4, Joyce Kigo4, Collins Agaba5, Ivan Aine Aye5,
Bella Hwang2, Stefanie K Novakowski2, Charly Huxford2, Matthew O. Wiens1,2,5,
David Kimutai6, Mary Ouma6, Ismail Ahmed6, Paul Mwaniki4, Florence Oyella7,
Emmanuel Tenywa3, Harriet Nambuya3, Bernard Opar Toliva5, Nathan Kenya-Mugisha5,
Niranjan Kissoon8, Samuel Akech4, On behalf of the Pediatric Sepsis CoLab
1 Department of Anesthesiology, Pharmacology and Therapeutics, University of British Columbia,
Vancouver, British Columbia, Canada, 2 Institute for Global Health, BC Children’s Hospital and BC
Women’s Hospital + Health Centre, Vancouver, British Columbia, Canada, 3 Department of Pediatrics,
Jinja Regional Referral Hospital, Jinja, Uganda, 4 KEMRI-Wellcome Trust Research Programme,
Nairobi, Kenya, 5 World Alliance for Lung and Intensive Care Medicine in Uganda, Kampala, Uganda,
6 Department of Paediatrics, Mbagathi County Hospital, Nairobi County, Kenya, 7 Department of
Pediatrics, Gulu Regional Referral Hospital, Gulu, Uganda, 8 Department of Pediatrics, University of
British Columbia, Vancouver, British Columbia, Canada
* anserminos@yahoo.ca
Abstract
Sepsis occurs predominantly in low-middle-income countries. Sub-optimal triage contrib-
utes to poor early case recognition and outcomes from sepsis. Improved recognition and
quality of care can lead to improved outcomes. We evaluated the impact of Smart Triage
using improved time to intravenous antimicrobial administration in a multisite interventional
study. Smart Triage, a digital platform with a risk score and clinical dashboard, was imple-
mented (with control sites) in Kenya (February 2021-December 2022) and Uganda (April
2020-April 2022). Children presenting to the outpatient departments with an acute illness
were enrolled. A controlled interrupted time series was used to assess the effect on time
from arrival at the facility to intravenous antimicrobial administration. Secondary analyses
included antimicrobial use, admission rates and mortality (NCT04304235). During the
baseline period, the time to antimicrobials decreased signicantly in Kenya (132 and 58
minutes) at control and intervention sites. In Uganda, the time to antimicrobials marginally
decreased (3 minutes) at the intervention site. Then, during the implementation period in
Kenya, the time to antimicrobials at the intervention site decreased by 98 min (57%, 95%
CI 81-114) but increased by 49 min (21%, 95% CI: 23-76) at the control site. In Uganda,
the time to antimicrobials initially decreased but was not sustained and there was no sig-
nicant difference between intervention and control sites. At both intervention sites, there
was a signicant reduction in antimicrobial utilization of 47% (Kenya) and 33% (Uganda)
compared to baseline. There was a reduction in admission rates of 47% (Kenya) and 33%
(Uganda) compared to baseline. Mortality reduced by 25% (Kenya) and 75% (Uganda)
PLOS Digital Health | https://doi.org/10.1371/journal.pdig.0000466 March 10, 2025 2 / 17
PLOS DigitaL HeaLtH Implementation of Smart Triage for children in Kenya and Uganda
compared to the baseline period. We showed signicant improvements in time to intrave-
nous antibiotics in Kenya but not Uganda, likely due to COVID-19, a short study period
and resource constraints. The reduced antimicrobial use and admission and mortality
rates are remarkable and welcome benets. The admission and mortality rates should be
interpreted cautiously as these were secondary outcomes. This study underlines the diffi-
culty of implementing technologies and sustaining quality improvement in health systems.
Author summary
Implementing the Smart Triage platform and quality improvement program for children
in Kenya and Uganda resulted in inconsistent improvements in time to intravenous
antimicrobial administration. The time to IVA decreased significantly in Kenya during
baseline and reduced further during the intervention while increasing at the control site.
In Uganda the time to treatment initially decreased but was not sustained. The treatment
times were significantly influenced by the improvements during baseline data collection
and multiple external health system factors such as drug shortages, the COVID -19 pan-
demic, staff shortages and strikes. The dramatic reduction in treatment, admission, and
mortality rates should be further investigated.
Background
Among critically ill children, sepsis is the leading cause of preventable deaths globally. Coun-
tries in Sub-Saharan Africa report disproportionately high case fatality rates [1,2]. Many of
these deaths are from malaria, pneumonia and diarrheal diseases resulting in sepsis, which may
respond to time-sensitive treatment [3]. Early treatments, usually within the first hour of presen-
tation, improve survival rates [4,5]. However, delayed recognition and treatment within health
facilities remain significant barriers to reducing sepsis-related deaths and complications [6].
Critically ill children can be rapidly identified using triage, which prioritizes patients and
the provision of medical care according to illness severity [7]. The Emergency Triage Assess-
ment and Treatment (ETAT) system [8,9], the Pediatric South African Triage Scale (PSATS)
[10] and Integrated Management of Childhood Illnesses (IMCI) guidelines [11] were devel-
oped based on expert consensus to facilitate triage processes for low-resourced environments.
Despite adoption and scaling in Lower-and-Middle-Income Countries (LMICs), sustained
implementation into clinical practice at scale has been a persistent challenge, resulting in sub-
optimal impact [9,12,13]. Therefore, triage remains under-used in many LMIC settings and
patients are still frequently seen on a first-come-first-served basis [14].
Recent efforts to improve triage and treatment decisions include clinical decision support
tools implemented in digital platforms using guidelines based on expert opinion [15]. Digital
tools can also provide automated guidance based on real-world data using an individualized
precision public health approach [15] and data-driven feedback that can be used for quality
improvement (QI) by health workers [16] and to address local implementation challenges
[17]. Nevertheless, multifaceted system-wide interventions are still needed to effectively
improve care practices in complex, low-resourced settings [3].
We have developed, validated, implemented, and evaluated a digital triage platform called
Smart Triage [18–22]. The Smart Triage platform comprises data-driven risk assessment
(triage), patient and treatment tracking, a real-time dashboard, and automated reports to
for re-identification of research participants,
the de-identified dataset is available through
moderated access on the Sepsis CoLab
Dataverse on https://borealisdata.ca/dataverse/
ST_implementation and through the KWTRP
Research Data Repository Dataverse https://
kemri-wellcome.org/dataverse/. Access to
these data will be granted on a case-by-case
basis following approval from the authors and
the Data Governance Committees.
Funding: The funding for this project was pro-
vided by a Wellcome Trust (https://wellcome.
org/) Innovator Award (215695/Z/19/Z) to SA
and MA as co-principal investigators in January
2020. The funders played no role in the study
design, data collection, analysis, decision to
publish, or preparation of the manuscript.
Competing interests: The authors have
declared that no competing interests exist.
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PLOS DigitaL HeaLtH Implementation of Smart Triage for children in Kenya and Uganda
improve emergency case recognition and time-to-treatment. The Smart Triage algorithm
predicts admission, as a data-driven surrogate for illness severity, and is combined with
emergency and priority signs as guardrails to generate a triage tool suitable for all acutely ill
patients.
QI studies evaluating digital triage tools in low-income countries, under real-world con-
ditions, are scarce [13]. We report on the findings of a study investigating the Smart Triage
platform implementation at pediatric outpatient departments (OPD) of four tertiary hospitals
(two hospitals in Uganda and two hospitals in Kenya). We hypothesized that the Smart Triage
platform would reduce the time to treatment. The secondary aims were to evaluate the effect
of Smart Triage and QI on patient outcomes, including mortality, admission and readmission.
Methods
We use an interrupted time series analysis to assess changes in the time from arrival at the
facility to administration of an appropriate sepsis bundle of care following implementation of
the Smart Triage platform.
Study sites
This controlled interrupted time series study was conducted in the OPDs of four public ter-
tiary facilities in Kenya and Uganda. Facilities included one control and one intervention site
per country. In Kenya, the facilities were Mbagathi County Hospital (intervention site) and
Kiambu County Referral Hospital (control site). In Uganda the facilities were Jinja Regional
Referral Hospital (intervention site) and Gulu Regional Referral Hospital (control site).
Contextual and temporal factors
COVID-19-related lockdowns restricting gatherings and travel between districts were
enforced from 15 April-5 May 2020 and 7 June-19 July 2021 in Uganda. Outside of these
dates, a strict curfew and other infection prevention measures were observed from April 2020
to December 2021. The study was initiated in Kenya after in-country COVID-19-related
restrictions had ended. In addition, there were healthcare worker shortages due to strikes
(nurses in Kenya and junior doctors in Uganda). Planned staff rotations also significantly
impacted compliance with triage and QI. The control site in Uganda was added a year later
than the intervention site in Uganda due to delays in study initiation at the control site in
Kenya, which was initially planned as the sole control site.
Participant recruitment
All pediatric arrivals presenting with an acute illness to the OPD were eligible. The age limits
were in keeping with each hospital’s practice for pediatric admissions (Mbagathi 0-15 years,
Kiambu 0-15 years, Jinja 0-19 years and Gulu 0-12 years). Informed consent was obtained
from parents or legal guardians. Assent was obtained from children over 8 years (Uganda)
and 13 years (Kenya). We excluded trauma, burns, elective cases (surgery, change of dressing),
immunization visits or clinical review or follow-up appointments. Patients with trauma or
burns were classified as emergency during implementation.
Standard procedures
The implementation design and procedural information have previously been published [20].
In brief, a preintervention phase (baseline), interphase (model and technology development)
and intervention phase (implementation) were used. The implementation is described in this
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PLOS DigitaL HeaLtH Implementation of Smart Triage for children in Kenya and Uganda
report. We used a systematic method of sampling. Following consent, predefined candidate
predictor variables and outcomes were collected on patients waiting to be seen in the OPD
by trained study nurses using Android tablets on a custom-built mobile application [18,20].
Dedicated timekeepers, with at least a high school level education, logged the time of each IVA
treatment manually in the treatment room. The implementation and control sites continued
data collection for the entire study duration. Oxygen saturation (SpO2) and heart rate were
measured using the Masimo iSpO2 (Masimo Corporation, California, USA). Respiratory rate
was measured using the RRate Application directly on the tablet [23,24]. The Welch Allen
SureTemp 692 thermometer was used to measure temperature (Welch Allyn, New York, USA).
Smart triage platform
Using the baseline data at intervention sites, we developed and recalibrated a nine-variable risk
prediction model [19,25] and deployed this within a mobile application for triage. The triage
algorithm included independent triggers based on the ETAT triage guidelines [26]. The indepen-
dent triggers ensure that no danger or priority sign or single significantly abnormal vital sign is
missed. The Smart Triage platform comprises data-driven risk assessment (triage), patient and
treatment tracking using a Bluetooth Low Energy (BLE) tracking system called Smart Spot, a real-
time dashboard, and automated reports. The Smart Spot system uses a BLE tag (Fig 1) attached
to a color-coded lanyard around the caregiver’s neck. A BLE reader detects the signal strength
from each tag. Signal strengths that exceed the reader’s threshold are considered to be in that
room (thresholds differ based on room size). Readers were strategically located in the hospital to
provide the patient’s location as they move through the facility. Treatment tags in the treatment or
Fig 1. Bluetooth beacons with colour coded lanyards.
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PLOS DigitaL HeaLtH Implementation of Smart Triage for children in Kenya and Uganda
emergency room were used to track the time and type of treatment given. All the information col-
lected at triage and via the Smart Spot system was accessible through the clinical dashboard. The
dashboard was available to clinicians, laboratory, pharmacy, nutrition team and administrators
on laptops, tablets or computer screens. A large screen with a public-facing dashboard was dis-
played in the waiting areas. The public-facing dashboard displayed coded patient queues, health
information posters and videos targeting caregivers. The information was intended to improve
understanding of the triage process, Smart Spot system and general health information.
The feasibility and usability of the platform were assessed through questionnaires, direct
observation and usability scenarios using a think-aloud methodology [20]. Qualitative inter-
views were used to optimize the platform [21]. A cost-effectiveness study was completed to
determine utilization costs and implications for scale-up [22].
Implementation
Hospital triage staff routinely utilized the digital triage platform during the intervention
phase. Following triage with Smart Triage algorithm, families were given colour-coded
lanyards, which corresponded to the predicted level of risk, with an attached Smart Spot BLE
tag. Red lanyards corresponded to emergency cases, yellow to priority and green to non-
urgent cases.
The Smart Triage platform was implemented as part of the overall QI program that focused
on delivering a sepsis bundle of care. At implementation sites, healthcare workers were
trained on the importance of early sepsis recognition and treatment, in using the platform
and in performing QI using a train-the-trainer model. Weekly feedback reports were provided
using data collected by the Smart Triage platform. The reports included information on the
number of children triaged, triage categories, duration of triage, treatment times and metrics
of specific interest to the facility. The metrics and report layout were developed collaboratively
with facility leadership and implementors to improve data-driven QI processes at the facility.
Ongoing training, support supervision, job aides and manuals were also available to health
workers.
QI initiatives were aimed at optimal triage and time to antimicrobial administration and
were led and customized by each implementation facility. Triage QI initiatives included
reducing the time from arrival to triage, improving the triage completion rate, increasing
completeness of vital sign measurement, and creating a daily updated list of drug and supply
stock-outs to improve communication between departments and caregivers. The causes of
delayed time to treatment were identified by using a fishbone diagram. The workflow was
optimized at both sites with changes in patient queuing at admission, assessment, laboratory,
pharmacy and the emergency room. Specific benches were allocated for emergency cases. An
emergency pharmacy cabinet with after-hours access was installed in Uganda to reduce the
impact of delays in supplying drugs from the pharmacy. Stock-outs and staff shortages were
the most significant barriers identified at both sites (S1 Table).
Follow-up
In-hospital and post-discharge mortality and readmission data were collected during a phone
call at seven-days post discharge (or post-visit for those who were not admitted).
Outcome measures
The primary outcome was the time to IVA from the time of OPD arrival, among those treated
with IVA. The initially planned primary outcome was the time to receive an appropriate sepsis
bundle of care (antibiotics, antimalarials, oxygen or fluids if clinically indicated). However, it
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PLOS DigitaL HeaLtH Implementation of Smart Triage for children in Kenya and Uganda
became evident that IVA was most robustly collected by the timekeepers and most commonly
occurred after the other elements of the bundle.
Secondary outcomes included treatment rates, the admission rate, readmission rate (both
among those admitted and among those not initially admitted), length of stay among admis-
sions, and overall mortality at seven days. In addition, the time to IVA in each triage category
and the proportion of triaged patients was calculated.
Data management
All data was captured digitally, either on a tablet or through the study dashboard. At the end
of each day, data was uploaded directly to REDCap, on a Kenya Medical Research Insti-
tute (KEMRI) server in Kenya and on the central study server at the BC Children’s Hospital
Research Institute for Uganda.
Sample size
Based on the feasibility study [22], the increase in the proportion of children receiving a bun-
dle of care within one hour in this study was 21.4%, and the proportion of children receiving
IVA was 8.2%. Based on these observations, we estimated that using an alpha of .05, at a
power of 80%, we would require 10 600 triages (1290 treatments) in the pre-intervention and
post-intervention phases combined, to confirm this effect observed in the previous study.
Analysis
We used Mann-Whitney and Kruskall-Wallis (for continuous variables) and Fischer’s exact
test (for categorical variables) to compare patient characteristics for patients who received IVA
and outcomes between control and intervention sites at baseline and following implementa-
tion. A p-value of <0.001 was considered statistically significant.
We used a quasi-experimental interrupted time series to assess the immediate effects
and effects over time of the intervention but excluding the interphase period. The post-
implementation period was set to occur the day following the baseline period. Quantile
regression was used to estimate the change in median time to IVA (in minutes) per increase
in days since enrollment. The independent variable included each individual IVA adminis-
tration while a weekly median IVA was derived to visually summarize data. The regression
error terms were fitted using an Ordinary Least Square method. Separate regression analysis
was done for each of the control and intervention sites. We estimated secular trends for the
pre- and post- implementation period and compared the post-implementation period with
the counterfactual trend to determine immediate and longer-term effect of the implementa-
tion using slope and level changes, respectively. An interaction term with sites was fitted to
estimate the difference in median time to IVA between control and intervention sites.
A univariate logistic regression was fitted to obtain the odds ratio of admission, readmis-
sion, mortality, and receiving IVA between sites at baseline and following implementation as
well as within sites at the intervention sites for the full cohort and separately by triage catego-
ries. Confidence intervals were calculated using maximum likelihood.
Ethical considerations
Ethics approval was obtained from Makerere University School of Public Health (MUSPH)
Institutional Review Board (IRB00011353). The Ugandan National Council for Science and
Technology (UNCST) (HS528ES) in Uganda provided approval for study activities to be con-
ducted at Jinja Regional Referral Hospital and Gulu Regional Referral Hospital. The KEMRI
Scientific and Ethics Review Unit (SERU) (KEMRI/SERU/CGMR-C/183/3958) provided
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PLOS DigitaL HeaLtH Implementation of Smart Triage for children in Kenya and Uganda
approval for the study activities to be conducted at Mbagathi County Hospital and Kiambu
County Hospital in Kenya. The study was also approved by the University of British Colum-
bia Research Ethics Board in Canada (H19-02398-A006). The trial was registered on Clinical
Trials.gov. Identifier: NCT04304235, Registered 11 March 2020.
Results
There were 6784 children enrolled at baseline from three sites and 5410 at two intervention
sites and 6032 at two control sites during the implementation phase (Fig 2). There were a few
negative times as patients were identified as emergency cases and treated as soon as they arrived
before triage. In such cases, negative times were set to zero, and times greater than 8 hours were
censored at 8 hours. Triage was completed on 5331 (98.5%) children enrolled at the intervention
sites during the implementation period, which formed our analysis cohort. The median (IQR)
time for fluids and/or oxygen to be initiated was 70 (32-137) min compared to IVA at 210 (130-
304) min at all sites during both phases (S2 and S3 Tables). In addition, only 85 (13.6%) children
received fluids or oxygen after IVA. Time to IVA among children who went on to receive IVA
was thus used as the study endpoint for all analyses based on these observations.
In Kenya, the baseline time to IVA decreased by 133 min (44%, 95% CI: 93 to 171) at the
control site and by 57 min (37%, 95% CI: 38 to 77) at the intervention site (Fig 3). Paradoxi-
cally, there was a 74-min (76%; 95% CI: 54-94) increase in time to IVA following the 2-week
interphase period at the Kenya intervention site. During the implementation phase, the time
Fig 2. Flow diagram of the Smart Triage multisite interventional study in Uganda and Kenya indicating the progress through study phases at each site.
Control site was added later than intervention site in Kenya due to site initiation delays in intervention site and control site in Uganda resulting from the
COVID-19 pandemic.
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PLOS DigitaL HeaLtH Implementation of Smart Triage for children in Kenya and Uganda
to IVA decreased by 98 min (57%, 81-114) at the Kenya intervention site, while during the
same period it increased by 50 min (21%, 95% CI: 23-76) at the control site (Table 1).
In Uganda, the baseline time to IVA decreased by 3 min (2%, 95% CI: -12 to 5) at the
intervention site (Fig 4). During the intervention, the time to IVA initially decreased by 19 min
(8%, 95% CI: 19-21) compared to the end of baseline, but this was not sustained. The median
time to IVA during the baseline period was 16 min (6%, 95% CI: 6 to 40) more than the imple-
mentation phase. Time to IVA at the Uganda control site decreased by 13 min (6%, 95% CI:
6 to 20) during the implementation phase, but there was no statistically significant difference
between intervention and control sites (Table 2). We have no baseline data from the Uganda
control site, so we could not evaluate the trend over time for the entire study period.
In Kenya, there was a reduction in admission rates between the baseline and intervention
of 47% (OR: 0.51, 95% CI: 0.42-0.62, p<0.0001) (Table 3). Admission rates in the control site
were unchanged (4.7%). IVA use was also reduced at the intervention site compared to the
baseline by 47% (OR: 0.51 CI: 0.41-0.63) (Table 3). IVA use in the Kenya control site was also
unchanged (2.9%). Mortality within 7 days was also reduced by 25% (OR: 0.29, 95% CI: 0.14-
0.59) compared to the baseline period.
Fig 3. Interrupted time series analysis showing time to intravenous antimicrobials (IVA) in minutes at intervention and control sites in Kenya during baseline
and implementation periods. The vertical dotted black line indicates Smart Triage implementation. Secular trends are indicated by solid lines and counterfactual trends
are indicated by dotted colored lines. Weekly median time to IVA is summarized by “+”.
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PLOS DigitaL HeaLtH Implementation of Smart Triage for children in Kenya and Uganda
Table 1. Segmented regression analysis of time to intravenous antimicrobial administration (IVA) at interven-
tion and control sites during the implementation period.
Kenya Estimates (95% CI) Uganda Estimates (95% CI)
Intervention site
Start of period 171 (126 to 216) 234 (200 to 267)
Slope (mins/week) -2.0 (-0.3 to -3.7) 0.3 (-1.4 to 2.0)
End of period 73 (44 to 102) 246 (213 to 280)
Control site
Start of period 239 (138 to 341) 222 (183 to 261)
Slope (mins/week) 1.0 (-4.0 to 6.0) -0.2 (-1.3 to 0.9)
End of period 289 (214 to 364) 209 (163 to 255)
Comparison between intervention site and control site
Difference in slopes (mins/week) -3 (-8 to 2) -0.1 (-0.2 to 1)
Difference at the beginning of period (mins) -68 (-125 to -12) 12 (6 to 17)
Difference at the end of period (mins) -216 (-262 to -170) 33 (25 to 50)
Note: values are in minutes
https://doi.org/10.1371/journal.pdig.0000466.t001
Fig 4. Interrupted time series analysis showing time to intravenous antimicrobials (IVA) in minutes at intervention and control sites in Uganda during baseline
and implementation periods. The vertical dotted black line indicates Smart Triage implementation. Secular trends are indicated by solid lines and counterfactual trends
are indicated by dotted colored lines. Weekly median time to IVA is summarized by “+”. Due to delays related to COVID-19, the Ugandan control site was initiated
during the implementation phase at the intervention site.
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PLOS DigitaL HeaLtH Implementation of Smart Triage for children in Kenya and Uganda
In Uganda, the reduction in admission rate between baseline and intervention was 34%
(OR: 0.59, 95% CI 0.50-0.71) (Table 2). IVA use was reduced by 33% (OR: 0.62 CI: 0.52-0.75)
(Table 2). Mortality within 7 days was reduced by 75% (OR: 0.27, 95% CI: 0.08-0.84) at the
intervention sites compared to the baseline period.
In addition to the overall reduction in admission rates, there was a reduction in priority admis-
sion rates of 64% (OR: 0.35, 95% CI: 0.22-0.55) in Kenya (S4 Table) and 32% (OR: 0.62, 95% CI:
0.47-0.83) in Uganda (S5 Table). There was a 19% (OR: 0.79 95% CI: 0.54-1.17) reduction in the
admission rates of priority cases at the Kenya control site compared to the baseline. Emergency
cases who presented to the Kenya intervention site showed a 161 min (62%, 95% CI: 113 to 209)
reduction in time to IVA when compared to the Kenya control site (S4 Table). In Uganda, the
reduction was 42 min (17%; 95% CI: 13 to 71) compared to the baseline period (S5 Table).
Discussion
We report the results of a controlled interrupted time series analysis implementing the Smart
Triage platform to improve the time to treatment. We found an inconsistent reduction in
the time to IVA. The time to IVA at the start of baseline was much lower in Kenya than in
Uganda, with a much larger reduction in time to IVA during the baseline compared to the
intervention and control sites in Uganda. In Kenya, we observed an immediate increase in
time to IVA at both our intervention and control sites between the end of the baseline and
the start of the implementation phase. This effect could be explained by the timing of the
Smart Triage implementation, which started during the December holiday period when staff
shortages was common and there was staff changeover. In contrast, the sustained improve-
ment observed at the intervention site versus the control site supports implementation success
at this site. In Uganda at the start of the intervention, the control site had a lower time to IVA
than the intervention site and continued to improve over time while the intervention site
worsened. The comparison between sites is compromised by delayed initiation of the control
site due to the COVID-19 pandemic, the lack of simultaneous baseline data and the differ-
ences between two facilities in different countries with different confounders [27].
The consistent reduction of time to IVA administration at all sites during the baseline
period would indicate a system change associated with baseline data collection. A similar
Table 2. Uganda: enrolment, time to IVA, mortality, admissions and readmissions.
Uganda Control compared to intervention sites during implementation Baseline compared to implementation at the
intervention site
Intervention site Control site % diff Effect sizebBaseline % diff Effect sizec
Enrolled, N 1903 2855 1408
Time to IVA, median (IQR) 239 (171-303) 212 (129-295) 13 26.78 (0.88, 52.69) 255 (186-348) -6 -16.22 (-38.92, 6.48)
Admitted, n (%) 288 (15.2) 207 (7.3) 108 OR: 2.28 (1.88-2.75) 326 (23.2) -34 OR: 0.59 (0.50-0.71)
IVA, n (%) 268 (14.1) 196 (6.9) 104 OR: 2.22 (1.83, 2.70) 294 (20.9) -33 OR: 0.62 (0.52, 0.75)
IVA in admitted cases, n (%)a268 (100) 194 (99.0) 1 OR: 0.90 (0.44-1.85) 291 (99.0) 1 OR: 1.61 (0.91-2.86)
Time to IVA emergency, median (IQR) 202 (155-277) 195 (122-277) 4 3.73 (-33.53, 40.99) 244 (173-320) -17 -41.60 (-70.56, -12.64)
Time to IVA priority, median (IQR) 266 (205-328) 220 (120-299) 21 43.05 (-43.32, 129.43) 257 (194-377) 4 8.98 (-26.88, 44.85)
Readmitted, n (%) 27 (1.4) 17 (0.6) 57 OR: 2.44 (1.33-4.49) 37 (2.7) -48 OR: 0.53 (0.32-0.87)
Mortality within 7 days, n (%) 4 (0.2) 2 (0.07) 186 OR: 3.05 (0.56-16.67) 11 (0.8) -75 OR: 0.27 (0.08-0.84)
Note: athe denominator is total admitted; b reference is control site, OR is obtained by fitting univariate logistic regression with first column as the outcome and site as
the independent variable; creference is baseline, OR is obtained by fitting univariate logistic regression model with phase as the independent variable; IVA= intravenous
antimicrobial administration; diff=difference; OR=odds ratio.
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PLOS DigitaL HeaLtH Implementation of Smart Triage for children in Kenya and Uganda
Table 3. Kenya: Enrolment, time to IVA, mortality, admissions and readmissions at sites in Kenya.
Kenya Control compared to intervention sites at baseline Control compared to intervention sites during
implementation
Intervention site: Baseline
compared to implementa-
tion at the intervention site
Intervention
site
Control site % diff Effect sizebIntervention
site
Control % diff Effect sizeb% diff Effect sizec
Enrolled, N 2856 2520 3428 3177
Time (min) to IVA, median
(IQR)
115 (68-203) 239 (160-388) -52 -125 (-168, -81) 123 (68-213) 261 (193-392) -53 -139 (-179, -99) 7 8 (-21, 37)
Admitted, n (%) 255 (9.0) 119 (4.7) 90 OR: 1.99 (1.59-2.49) 164 (4.78) 148 (4.7) 3 OR: 1.03 (0.82-1.29) -47 OR: 0.51 (0.42-0.62)
IVA, n (%) 218 (7.6) 73 (2.9) 162 OR: 2.77 (2.11, 3.63) 138 (4.03) 92 (2.9) 39 OR: 1.41 (1.08, 1.84) -47 OR: 0.51 (0.41, 0.63)
IVA in admitted cases, n (%)a193 (88.5) 69 (94.5) -6 OR: 2.26 (1.42-3.58) 108 (78.3) 86 (93.5) -16 OR: 1.39 (0.88-2.21) -12 OR: 0.62 (0.40-0.95)
Time to IVA emergency cases,
median (IQR)
119 (65-185) 196 (149-366) -65 -79 (-136, -21) 101 (58-162) 262 (201-381) -61 -161 (-209, -113) -15 -18 (-46, 10)
Time (min) to IVA priority
cases, median (IQR)
108 (59-235) 303 (175-480) -64 -191 (-272, -109) 169 (68 -246) 258 (179-354) -34 -87.63 (-181, 5) 56 62 (-2, 127)
Readmitted, n (%) 20 (0.7) 13 (0.5) 37 OR: 2.39 (1.26-4.53) 21 (0.6) 27 (0.9) 28 OR: 0.72 (0.41-1.28) -13 OR: 0.88 (0.47-1.62)
Mortality within 7 days, n (%) 29 (0.4) 11 (1.0) -60 OR: 2.34 (1.17-4.67) 10 (0.3) 10 (0.3) -6 OR: 0.93 (0.39-2.24) -25 OR: 0.29 (0.14-0.59)
Note: adenominator is total admitted; breference is control site, OR is obtained by fitting univariate logistic regression with first column as the outcome and site as the independent variable; creference
is baseline, OR is obtained by fitting univariate logistic regression model with phase as the independent variable; IVA= intravenous antimicrobial administration; diff=difference; OR=odds ratio.
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PLOS DigitaL HeaLtH Implementation of Smart Triage for children in Kenya and Uganda
contamination has been observed in cluster randomized trials of digital health interventions
that mask the impact of the intervention [28,29]. The downward slope in time to IVA during
the baseline period would also suggest that this effect had not reached steady state.
What was surprising in our study was the decrease in treatment and admission rates in
both Uganda and Kenya. This finding may be due to adherence to triage criteria in almost all
patients and less reliance on personal preferences among clinicians. However, without more
robust long-term follow-up (>7 days) of all children after discharge we cannot be certain that
the lower admission rates reflect better triage practices. However, we did not see an increase
in antimicrobial utilization or readmission rates. A similar reduction in antimicrobial utiliza-
tion has been shown in a randomized trial using an electronic algorithm-guided management
platform [30]. These findings warrant additional investigation into the impact on longer-term
mortality following triage as post-discharge mortality is common and may also be more prev-
alent in patients who were not admitted [31].
The finding of a decrease in mortality by 25% (OR: 0.29, 95% CI:0.14-0.59) in Kenya and
by 75% (OR: 0.27, 95% CI: 0.08-0.84) in Uganda compared to the baseline period is similar
to an effect that has been reported in the US in a large 19 hospital American Academy of
Paediatrics Paediatric Severe Sepsis Collaborative that were focused on sepsis QI in emergency
department care [32]. In these collaboratives, there were shared criteria for sepsis, stan-
dardized screening tools and electronic health record (EHR) embedded tools (such as triage
screens and sepsis order sets). Improvements were noted in time to first clinical assessment,
fluid bolus and antibiotics, with an associated improvement in 30-day all-cause mortality from
2.3% to 1.4%. Interestingly, even sites newer to improvement work demonstrated process
metric improvement as well as mortality reductions, corroborating the benefits of an ‘all teach
- all learn’ QI collaborative methodology [32,33].
Our findings of inconsistent improvement are not surprising because the uptake and
effectiveness of clinical decision support tools have had mixed success due to complexities
in implementation contexts [15,34]. Beyond the setting, technology-supported innovations
face additional complexity in implementation that stems from each aspect of the innovation.
These complexities include addressing the clinical condition (especially when poorly charac-
terized like sepsis), interacting technology components, and obtaining organizational, user
and patient buy-in [35]. These factors are amplified in poorly resourced settings which are
plagued by overcrowding and understaffing in emergency departments, inadequate resources
for timely laboratory testing and imaging, inadequate stock of antibiotics, and inadequate
overall resources for paediatric sepsis management [16,36,37]. Thus, to be effective, digital
interventions addressing triage and QI must consider individual and systems-related factors
and processes that influence facility performance [38–40].
Sepsis care in children is complex. Consideration must be given to biology of both pathogens
(malaria, dengue, bacteria and viruses) and the patient including age and genetics; along with sea-
sonality of infections, resource availability (personnel, drugs, diagnostic tools) and access, timing
of presentation and progress of deterioration and co-morbidities such as malnutrition and HIV
[41]. Care occurs in facilities which are complex, dynamic environments continuously influenced
by process inputs, organizational structure and resources, culture and individual motivations [42].
In this milieu of constant shifts and changes it is not surprising that QI is difficult to initiate and
sustain. An assumption of the interrupted time series methodology is that the baseline trend is not
changing during the intervention [43]. By selecting a process indicator and prioritizing random-
ization and prospective enrollment on arrival at the facility; we hoped to account for confounding
related to variable case mix and severity, however this may not be the case.
The time to IVA was chosen because it is the most important factor in improving outcomes
from sepsis and easy to track and compare between hospitals [44]. However, IVA is vulnerable
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PLOS DigitaL HeaLtH Implementation of Smart Triage for children in Kenya and Uganda
to changes in resource availability [45,46]. For example, using the data-driven QI process, we
were able to identify that delivery of timely treatment was influenced not just by identification
of emergency cases, but also staff availability, drug and reagent shortages, and lab equipment for
diagnostics and caregivers’ ability to pay for the drug and other sundries. These issues were exac-
erbated by the COVID-19 pandemic and persisted throughout the study period. The best chance
for success, therefore, relies on a holistic approach which not only addresses the clinical elements
of the treatment guideline but also include supply chain management, maintenance policies and
coevolution of health systems infrastructure with pediatric triage and sepsis guidelines [27,47,48].
Strengths and limitations
The major limitation of this study is that the methodology chosen was unable to account for
the significant confounders during data collection such as the COVID-19 pandemic and sig-
nificant disruptions in supplies and staffing. We were also limited by the lack of synchronous
data during the baseline in Uganda. We underestimated the impact of data collection during
the baseline on time to IVA and would have preferred to have a longer period of observation
to allow for traction in QI initiatives and account for seasonality. Using timekeepers to track
treatment times manually at the point of care may have influenced clinical staff behaviour.
However, this was consistent between baseline, control and intervention sites.
The strengths of the study are completeness of data, and the adjustment of the intervention
using small iterative changes (responsive feedback), co-developed with the QI teams on site.
We actively addressed issues related to usability, training and staff rotations through contin-
ued training, resources, and job aides. Sustainability and continued staff-led problem solving
is supported through an ongoing QI program and partnerships with implementors.
Conclusion
Smart Triage was successfully implemented for routine use in two resource-constrained settings.
The improvement in time to IVA was inconsistent between sites and impacted by numerous
health system factors. However, the reduction in admission and treatment times and the reduc-
tion in mortality are benefits worthy of further investigations. We have also highlighted the sig-
nificant challenges in undertaking clinical evaluation of digital health tools in complex real-world
clinical settings. The results can be leveraged to understand delivery gaps, strengthen implemen-
tation strategy and methodology and inform future adaptions of the Smart Triage platform.
Supporting information
S1 Table. Delayed treatments and reasons for delays.
(PDF)
S2 Table. Characteristics of patients who received IV antimicrobials in the study.
(PDF)
S3 Table. Time to the components of sepsis bundle.
(PDF)
S4 Table. Kenya: Time to intravenous antimicrobial (IVA) based on of triage categories,
sites and periods.
(PDF)
S5 Table. Uganda: Time to intravenous antimicrobial (IVA) based on triage categories,
sites and periods.
(PDF)
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PLOS DigitaL HeaLtH Implementation of Smart Triage for children in Kenya and Uganda
Acknowledgements
We are grateful for the quality improvement efforts of facility staff and leadership at Jinja
Regional Referral Hospital and Mbagathi County Hospital. Further, we would like to thank
members of the Smart Triage research team based at Walimu, Uganda; Kenya Medical
Research Institute, Kenya; and the Institute for Global Health, Canada. This includes but is
not limited to, Savio Mwaka, Clare Komugisha, Bamwesigye Emmanuel, Annet Mary Nabwe-
teme, Busense Bosco, Kisaame Meshack Moshin, Kantono Miria, Nakasagga Barbra, Monero
Angel, Isaac Omara, Komakech Francis, Patricia Aloya, Joan Lamagi, Priscilla Antimango,
Reagan Obalim, Angela Wanjiru, Anastasia Gathigia, Felix Kimani, Mercy Mutuku, Emmah
Kinyanjui, Esther Muthoni, Faith Wairimu, Sidney Kipkorir, Verah Karasi, Victor Achiro,
Kevin Bosek, Brian Ochieng, John Mboya, Deborah Lester, Jessica Rigg, Katija Pallot, Parnian
Hosseini, Alishah Mawji and Edmond C K Li.
Author contributions
Conceptualization: J Mark Ansermino, Abner Tagoola, Stefanie K Novakowski, Matthew O.
Wiens, Nathan Kenya-Mugisha, Niranjan Kissoon, Samuel Akech.
Data curation: J Mark Ansermino, Yashodani Pillay, Cherri Zhang, Dustin Dunsmuir,
Stephen Kamau, Joyce Kigo.
Formal analysis: J Mark Ansermino, Yashodani Pillay, Cherri Zhang, Stephen Kamau, Joyce
Kigo, Paul Mwaniki.
Funding acquisition: J Mark Ansermino, Stefanie K Novakowski, Matthew O. Wiens, Samuel
Akech.
Investigation: J Mark Ansermino, Dustin Dunsmuir, Stephen Kamau, Joyce Kigo, Collins
Agaba, Charly Huxford, David Kimutai, Mary Ouma, Ismail Ahmed, Florence Oyella,
Emmanuel Tenywa, Harriet Nambuya, Samuel Akech.
Methodology: J Mark Ansermino, Stefanie K Novakowski, Matthew O. Wiens, Niranjan
Kissoon, Samuel Akech.
Project administration: J Mark Ansermino, Abner Tagoola, Dustin Dunsmuir, Stephen
Kamau, Ivan Aine Aye, Bella Hwang, Charly Huxford, Bernard Opar Toliva, Nathan
Kenya-Mugisha, Samuel Akech.
Resources: J Mark Ansermino, Ivan Aine Aye, Bella Hwang, David Kimutai, Nathan
Kenya-Mugisha.
Software: Dustin Dunsmuir.
Supervision: J Mark Ansermino, Yashodani Pillay, Abner Tagoola, Dustin Dunsmuir, Stephen
Kamau, Joyce Kigo, Collins Agaba, Charly Huxford, David Kimutai, Mary Ouma, Ismail
Ahmed, Florence Oyella, Emmanuel Tenywa, Niranjan Kissoon, Samuel Akech.
Validation: J Mark Ansermino, Samuel Akech.
Visualization: Dustin Dunsmuir.
Writing – original draft: J Mark Ansermino, Yashodani Pillay.
Writing – review & editing: J Mark Ansermino, Yashodani Pillay, Abner Tagoola, Cherri
Zhang, Dustin Dunsmuir, Stephen Kamau, Joyce Kigo, Collins Agaba, Ivan Aine Aye,
Bella Hwang, Stefanie K Novakowski, Charly Huxford, Matthew O. Wiens, David Kimutai,
Mary Ouma, Ismail Ahmed, Paul Mwaniki, Florence Oyella, Emmanuel Tenywa, Harriet
Nambuya, Bernard Opar Toliva, Nathan Kenya-Mugisha, Niranjan Kissoon, Samuel
Akech.
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PLOS DigitaL HeaLtH Implementation of Smart Triage for children in Kenya and Uganda
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