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Information and Communication Technology
to Enhance the Implementation of the
Integrated Management of Childhood Illness:
A Systematic Review and Meta-Analysis
Andrea Bernasconi, MD, MSc; Marco Landi, MSc; Clarence S. Yah, PhD;
and Marianne A.B. van der Sande, PhD
Abstract
Objective: To evaluate the impact of Information and Communication Technology (ICT) on the implementation
of Integrated Management of Childhood Illness (IMCI) and integrated Community Case Management (iCCM)
through a systematic review and meta-analysis (PROSPERO registration number: CRD42024517375).
Methods: We searched MEDLINE, EMBASE, Cochrane Library, and gray literature from January 2010 to
February 2024, focusing on IMCI/iCCM-related terms (Integrated Management of Childhood Illness, IMCI,
integrated Community Case Management, iCCM) and excluding non-ICT interventions. A meta-analysis
synthesized the effect of ICT on clinical assessment, disease classification, therapy, and antibiotic pre-
scription through odds ratio (OR; 95% CI) employing a random effects model for significant heterogeneity
(I
2
>50%) and conducting subgroup analyses.
Results: Of 1005 initial studies, 44 were included, covering 8 interventions for IMCI, 7 for iCCM, and 2
for training. All digital interventions except 1 outperformed traditional paper-based methods. Pooling
effect sizes from 16 studies found 5.7 OR for more complete clinical assessments (95% CI, 1.7-19.1; I
2
,
95%); 2.0 for improved disease classification accuracy (95% CI, 0.9-4.4; I
2
, 93%); 1.4 for more appro-
priate therapy (95% CI, 0.8-2.2; I
2
, 93%); and 0.2 for reduced antibiotic use (95% CI, 0.06-0.55; I
2
99%).
Conclusion: This review is the first to comprehensively quantify the effect of ICT on the implementation
of IMCI/iCCM programs, confirming both the benefits and limitations of these technologies. The cus-
tomization of digital tools for IMCI/iCCM can serve as a model for other health programs. As ICT
increasingly supports the achievement of sustainable development goals, the effective digital interventions
identified in this review can pave the way for future innovations.
ª2024 THE AUTHORS.Published by ElsevierInc on behalf of Mayo Foundationfor Medical Educationand Research. This is an open accessarticle under
the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)nMayo Clin Proc Digital Health 2024;2(3):438-452
From 1990 to 2021, the global under-five
mortality rate decreased by 59%.
1
How-
ever, child survival continues to be a
critical issue, with w13,800 under-five deaths
daily in 2021, predominantly from prevent-
able diseases in Low-income and Middle-
Income Countries (LMIC). To address this,
World Health Organization and United Na-
tions International Children’s Emergency
Fund introduced the Integrated Management
of Childhood Illness (IMCI) strategy in
1996.
2
The IMCI represented an important
innovation, adopting a holistic strategy for
treating children who were sick at first-level
health care facilities. It encompasses a compre-
hensive assessment of a child’s overall health,
including their nutritional and immunization
status.
3
From a clinical perspective, this strat-
egy involves a set of evidence-based guidelines
that propose effective interventions for health
workers (HW), even those with minimal med-
ical backgrounds and working with limited or
non-existent diagnostic support. The IMCI al-
lows HWs to diagnose and manage the top 5
main diseasesdmalaria, measles, malnutri-
tion, diarrhea, and pneumoniadthat account
for 70% of the mortality among children un-
der 5 years old.
3
From the Institute of Tropical
Medicine Antwerp, Nationa-
lestraat 155, Antwerpen,
Belgium (A.B., M.A.B.v.d.S.);
Julius Center for Health Sci-
ences and Primary Care,
Global Health, University
Medical Centre Utrecht,
Utrecht University, Utrecht,
the Netherlands (A.B.,
M.A.B.v.d.S.); Human Singu-
larity, Milan, Italy (M.L.); and
Health Sciences Research
Office (HSRO), University of
the Witwatersrand, Johan-
nesburg, South Africa (C.S.Y.).
REVIEW
438 Mayo Clin Proc Digital Health nXXX 2024;2(3):438-452 nhttps://doi.org/10.1016/j.mcpdig.2024.06.005
www.mcpdigitalhealth.org nª2024 THE AUTHORS. Published by Elsevier Inc on behalf of Mayo Foundation for Medical Education and Research. This is an open
access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
The IMCI has regularly updated its proto-
cols to align with specific countries’health
profiles
4
and to include new diseases.
5
Its
adoption varies from full national implementa-
tion in some countries to regional or facility-
based adoption in others and many incorpo-
rating only its training without broader health
system reforms.
6,7
The IMCI comprehensive
implementation has been linked to a 15%-
50% reduction in child mortality
8,9
and con-
tributes to the sustainable development goal
of fewer than 25 deaths per 1000 live births
by 2030 by improving the quality of care
alongside relevant cost savings.
10
In addition,
the IMCI promotes responsible antimicrobial
use,
11
which is crucial amid the increasing
antibiotic resistance threat.
12
On the basis of
these promising results, the IMCI was subse-
quently streamlined into integrated Commu-
nity Case Management (iCCM), empowering
community health workers (CHW) to treat
pneumonia, diarrhea, and malaria, provide
nutritional assessments, and offer health
advice in underserved areas.
13
Despite their potential, the IMCI and
iCCM have seen suboptimal utilization
because of common constraints in LMICs,
such as health care system fragmentation,
inadequate training, poor supervision, short-
ages of essential drugs, and high staff turn-
over.
7,14
In Western Kenya, for instance, the
implementation reached just 14%, well below
the optimal 68% threshold.
15
HWs often view
the IMCI consultations as protracted and
burdensome, with an increase in workload
and patient waiting time.
7,16,17
Also, the
IMCI training, comprising 8 days of theory
and 3 days of clinical practice, has been criti-
cized for being lengthy, costly, and inade-
quately resourced.
16,18
A South African study
revealed a significant decline in adherence to
IMCI guidelines over time, with less than 2%
of HWs applying the protocol 32 months after
training.
19
The effectiveness and efficiency of
health care service delivery depend on the
commitment and motivation of the HWs to
adhere to guidelines.
20
Inadequate staff
commitment can considerably contribute to
poor quality services.
21
Surveys performed in
several countries have indicated that the lack
of motivation is the second most critical issue
within the health care workforce, preceded
only by staff shortages.
20
In the past decade, Information and Commu-
nication Technology (ICT) has enhanced health
care by providing personalized, efficient care
and quick, and accurate diagnostics.
22,23
In
LMIC, ICT extends its reach to remote and under-
served areas.
24
Digitized protocols can increase
guideline adherence through guided support in
diagnosis, treatment, and follow-up, and inte-
grates data analysis.
25
We hypothesize that ICT
can improve adherence to medical protocols
and enhance the impact of IMCI/iCCM programs.
This systematic review evaluates the integration of
ICT into these programs, focusing on how ICT
improves care delivery, training, and child health
outcomes. This includes aspects such as medical
examination, diagnosis, therapy, and the rational
use of antibiotics.
METHODS
This systematic review followed Preferred
Reporting Items for Systematic Reviews and
Meta-Analyses (PRISMA) guidelines
26
and is
registered in PROSPERO (CRD42024517375).
Inclusion Criteria
We considered studies focusing on ICT interven-
tions integrated into IMCI/iCCM programs and
used by HWs or caregivers. These interventions
included Electronic Health Records (EHRs), Clin-
ical Decision Support Systems (CDSS), mHealth,
Short Message Service (SMS), software applica-
tions, health information exchange and commu-
nication platforms, telehealth, and any other
technology finalized to improve the delivery of
IMCI/iCCM programs. We evaluated both quan-
titative and qualitative outcomes. Our research
encompassed various study designs, including
randomized controlled trials (RCT), quasi-
experimental, observational, mixed-methods,
and qualitative research. Studies comparing the
different digital interventions were excluded.
Search Strategy and Selection
Databases searched included MEDLINE,
EMBASE, and Cochrane library. We focused
solely on terms related to IMCI or iCCM (Inte-
grated Management of Childhood Illness, IMCI, inte-
grated Community Case Management, iCCM)and
excluded all the studies not involving ICT. Grey
literature, including unpublished research and
conference abstracts, were sourced from Google
Scholar, OpenAlex, GreyNet, and IEEE Xplore us-
ing the additional keywords “Digital Health”and
IMCI THROUGH ICT: REVIEW AND META-ANALYSIS
Mayo Clin Proc Digital Health nXXX 2024;2(3):438-452 nhttps://doi.org/10.1016/j.mcpdig.2024.06.005
www.mcpdigitalhealth.org 439
“eHealth/mHealth”. We included studies in En-
glish, French, and Spanish from January 2010,
to February 2024, marking the publication of
principles for digital development in 2010.
27
De-
tails of our search strategy and Population, Inter-
vention, Comparator, Outcome, and Study
Design framework are in the supplementary files
(Supplementary File 1, available online at
https://www.mcpdigitalhealth.org/).
Data Extraction
Two authors (A.B. and C.S.Y.) independently
screened titles and abstracts, resolving dis-
agreements with a third reviewer (M.L.). A
weighted kcoefficient of Cohen was calculated
to assess the degree of agreement between the
2 reviewers. On retrieving the full texts, one
author (A.B.) extracted the data, which was
then double-checked by another one
(C.S.Y.). The extracted data included study
design, implementation country, sample size,
digital intervention type, and results and con-
clusions of the study. References of these arti-
cles were also reviewed for additional relevant
ones.
Bias Assessment
The quality of the
studies was evaluated
by 2 authors (A.B. and
C.S.Y.) using the Joanna
Briggs Institute critical
appraisal tools and the
mixed methods
appraisal tool specif-
ically for mixed-method
or convergent design
studies. The risk of bias
was categorized as high,
medium,orlow.Given
the inherent characteris-
tics of involving digital
devices and their com-
parison to those without
ICT interventions,
blinding of interventions
was deemed inappli-
cable. Also, in this case,
we assessed the degree
of agreement between
the raters through a
weighted kcoefficient
of Cohen.
Meta-Analysis
The meta-analysis evaluated the effect of ICT on
assessment completeness for children who were
sick, disease classification accuracy, therapy
appropriateness, and antibiotic reduction. These
outcomes were measured in comparison with
the standards set by the IMCI protocols. We calcu-
lated pooled effects using odds ratios (OR) and
95% CI, focusing on intention-to-treat analysis.
We reported the degree of inter-study heterogene-
ity using the Cochrane’s Q test. Where a significant
heterogeneity was present (P10 or I
2
>50%) we
applied a random effects model, with further sub-
group analysis for specificeffects.Thedecisionto
include or exclude studies in the meta-analysis
was made jointly by 2 authors (A.B. and C.S.Y.),
with a third author consulted in case of disagree-
ment (M.L.). The R, version 4.3.1,
28
and the
“meta”package
29
were used for this analysis.
Included studies are presented based on their
integration with either the IMCI or the iCCM pro-
grams. We define e-IMCI (electronic IMCI) and e-
iCCM (electronic iCCM) interventions as those
where the program has been enhanced by an
1005 Records identified from:
PubMed (n=575)
EMBASE (n=409)
Gray literature (n=14)
Cohrane Library (n=5)
BMJ Health & Care Informatics
(n=2)
Records screened (n=985)
IdentificationScreeningIncluded
Records excluded based on study
exclusion criteria (n=921)
Reports sought for retrieval
(n=64) Reports not retrieved (n=0)
Reports assessed for eligibility
(n=64)
Studies included in the review
(n=44)
Studies included in the meta-
analysis (n=16)
Reports excluded:
Comparison between two digital tools (n=6)
No digital health intervention involved (n=4)
No IMCI/iCCM involved (n=3)
Only study protocol presented (n=3)
Instances of duplicate publication (n=3)
Publication before 2010 (n=1)
Records removed before screening:
Duplicate records removed
(n=20)
FIGURE 1. PRISMA flow diagram of the studies selection process.
MAYO CLINIC PROCEEDINGS: DIGITAL HEALTH
440 Mayo Clin Proc Digital Health nXXX 2024;2(3):438-452 nhttps://doi.org/10.1016/j.mcpdig.2024.06.005
www.mcpdigitalhealth.org
ICT solution. In comparison, we define p-IMCI
(paper IMCI) and p-iCCM (paper iCCM) as the
standard implementations based on paper.
RESULTS
Our search yielded 1005 results, from which
we identified 20 duplicates. After excluding
922 studies for lacking relevance to ICT based
on their titles and abstracts, we retrieved 64
full texts for a detailed review.
By applying our inclusion or exclusion
criteria, out of the 64 retrieved articles, 20 were
excluded for reasons presented in the PRISMA
flow diagram (Figure 1) and in supplementary
files (Supplementary File 2, available online at
https://www.mcpdigitalhealth.org/), resulting in
44 studies included in our analysis and 16 in
the meta-analysis.
The interrater reliability for selecting the
included studies, measured using Cohen’sk,
was 0.72, suggesting good agreement between
the reviewers.
30
Characteristics of Included Studies
Among the 44 included studies (in details in sup-
plementary files, Supplementary File 3, available
online at https://www.mcpdigitalhealth.org/), 27
focused on IMCI,
25,31e56
14 on iCCM,
57e70
and
3 on distan ce learning.
71e73
These studiescollec-
tively represent 8 ICT interventions for IMCI, 7
for iCCM, and 2 for IMCI training.
Most studies were RCTs (n¼11: including 8
cluster RCTs,
31,46,56,60,62,68,69,73
2 stepped-wedge
design studies
40,67
and 1 RCT),
53
mixed-
methods research (n¼9)
38,41,44,47,49,59,65,70,71
and
qualitative assessments (n¼8).
32,37,50,52,55,63,64,72
Theotherstudieswerequasi-experimental
(n¼4),
36,39,48,51
diagnostic accuracy studies
(n¼2),
34,57
observational studies (n¼2),
43,58
1
cost-analysis study,
61
and 1 stakeholder analysis.
66
We also retrieved 6 reviews that were important for
contextualizing the development and use of these
digital tools: 2 focused on ALgorithms for the
MANagement of Acute CHildhood illnesses
(ALMANACH)
33,54
development, one on
ePOCT,
45
2 on ICT integration in IMCI
25,35
and
1 on integrated e-diagnostic approach (IeDA)
adoption in Burkina Faso.
42
The research spanned 16 LMICs, with a
major focus on Africa (n¼36 studies) and
Asia (n¼3). One study was multicentre
(Bangladesh, Burkina Faso, and Ecuador).
57
A total of 77,291 children were enrolled in the
quantitative analysis, reporting a wide range of out-
comes, with several investigations examining more
than one at the same time. The most explored out-
comes were therapy appropriateness
(n¼11),
34,36,39,40,46,51,59,60,62,65,68
user satisfaction
(n¼11),
37,38,44,47,49,50,52,59,64,65,72
completeness
of assessment (n¼10),
31,34,36,39,40,46,48,49,51,59
antibiotic prescription (n¼7),
31,36,39,43,47,48,53
and disease classification (n¼6).
31,34,40,46,57,65
All the interventions were CDSS based on
predictive decision tree algorithms. CDSS align
HWs’inputs with a computerized database to
offer precise point-of-care recommendations
to guide in clinical assessment, physical exami-
nation, treatment, and follow-up planning or
referral. However, the CDSS approach varied.
In Ghana, an interactive voice response-
system aids caregivers in symptom severity
assessment.
34
mPneumonia merges software-
based breath counting and pulse oximetry for
enhanced diagnosis for pneumonia.
55
In India
a digital IMCI also includes algorithms for
neonatal care.
49
The MEDSINC, a web-based
platform, diverges from standard decision tree
solutions by using bayesian pattern recognition
to prevent misdiagnoses from singular data
point overemphasis.
57
Furthermore, MEDSINC
analyses clinical history, symptoms, and vital
signs employing cluster-pattern data for a
comprehensive clinical risk assessment.
70
Among all the ICT inventions retrieved, only
the electronic-Integrated Management of Child-
hood Illness (eIMCI)
44
in South Africa and the
interactive voice response system
34
were not mo-
bile device-based.
Table
2,7,8,31e34,36e41,43e47,49e63,65e70,72,73
summarizes the findings for the 17 ICT inter-
ventions. Although the majority (n¼13,
76.5%) focused on their effectiveness, only
a few published their algorithms (n¼6,
33.3%) or provided a cost-analysis (n¼3,
17.6%).
Risk of Bias Assessment
Excluding cost and stakeholders’studies, out of 36
studies, 21 (58.4%) presented a low risk of bias, 12
(33.3%) a medium risk, and 3 (8.3%) a high risk.
Major concerns in RCTs included nonblinding of
outcome assessors (91.9%). For 50% of the mixed
methods with a quantitative, nonrandomized
component, it was not clear if participants were
representative of the target population. For a
IMCI THROUGH ICT: REVIEW AND META-ANALYSIS
Mayo Clin Proc Digital Health nXXX 2024;2(3):438-452 nhttps://doi.org/10.1016/j.mcpdig.2024.06.005
www.mcpdigitalhealth.org 441
quasi-experimental study, concerns existed about
the independence of the control group (100%)
and for the qualitative ones about the cultural
orientation of the researcher and the representa-
tiveness of the participants’voices (87.5%). The
weighted kcoefficient of Cohen among the raters
was very good (0.83).
30
Details of the bias assess-
ment are in the supplementary files
(Supplementary File 4, available online at https://
www.mcpdigitalhealth.org/).
Digital Integrated Community Case Man-
agement tools
We found information on digital iCCM across
Bangladesh, Burkina Faso, Ecuador, Liberia,
Malawi, Mozambique, Niger, Nigeria, Uganda,
and Zambia.
Supporting LIFE electronic CCM Application
Supporting LIFE electronic CCM application
(SL e-CCM) gathers patient details, symptoms,
and vital signs like breathing rate through a
tap screen.
64
SL e-CCM found a higher accu-
racy in diagnosing (81% vs 58%, P<.01)
without therapeutic improvement (P¼.27).
65
It increased urgent referrals and reduced
repeated consultations (P<.01), but hospital
admissions remained unchanged (P¼.3).
67
Qualitative assessment highlighted that HWs
praised its ease of use and efficiency, despite
TABLE. Evidence Gathered for Each Intervention Included in This Review
Interventions No. System Countries
Algorithm
published Efficacy Effectiveness
Qualitative
assessment
Cost-
analysis
Distance learning
DIMCI 2 Tanzania N/P No Yes
63
Yes
72
Yes
72
Low-cost tablets 1 Uganda N/P No Yes
73
No No
Integrated community case management
CommCare mHealth
application
1 CommCare Niger No No Yes
69
No No
DHIS 2 & J2ME 2 DHIS 2 &
J2ME
Zambia No No Yes
62
No Yes
61
inSCALE 2 CommCare Mozanbique and Uganda No No Yes
60,68
No No
MEDSINC 2 Web-based
platform
Bangladesh, Burkina Faso,
Ecuador, and Nigeria
Yes
57
No Yes
57,70
Yes
57,70
No
Mobile phone use 1 N/A Uganda No No Yes
59
Yes
59
No
ODK-Liberia 1 ODK Liberia No No No Yes
58
No
SL e-CCM
application
5 Honeycomb Malawi No No Yes
65,67
Yes
65,66
No
Integrated management of childhood illness
ALMANACH 9 CommCare Tanzania, Afghanistan, Nigeria,
Somalia, and Lybia
Yes
54
Yes
33,53
Yes
31
Yes
36,39,43,52,56
No
eCare 1 Mangologic DRC, CAR, Mali, and Niger No Yes
47
No No No
eIMCI SA 4 South Africa No No Yes
38,41,46
Yes
38,44
No
eIMCI TZ 2 Tanzania No No Yes
51
Yes
50
No
ePOCT 1 Tanzania Yes
45
Yes
a
Yes
a
Yes
a
No
IeDA 4 CommCare Burkina Faso, Niger, Mali, and
India
No No Yes
40
Yes
37
Yes
40,49
IVR-system 1 IVR Ghana Yes
34
Yes
34
No No No
mPneumonia 2 ODK Ghana Yes
55
No No Yes
32
No
a
Excluded from this review as it was not compared against standard care.
Abbreviations: ALMANACH, ALgorithms for the MANagement of Acute CHildhood illnesses; DHIS, district health information software; DIMCI, distance learning IMCI;
eIMCI SA, electronic-IMCI South Africa; eIMCI TZ, electronic-IMCI Tanzania; IeDA, integrated e-diagnostic approach; IMCI, integrated management of childhood illness;
inSCALE, Innovations at Scale for Community Access and Lasting Effects; IVR, interactive voice response; J2ME, Java 2 Micro Edition; N/P, not pertinent; ODK, Open Data Kit;
SA, South Africa; SL e-CCM, supporting LIFE electronic CCM; TZ, Tanzania.
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technical and network challenges resulting in
poor integration into their workflow. Howev-
er, scalability appears jeopardized by inability
to access past records and discrepancies be-
tween digitalized and paper guidelines.
64
Innovations at Scale for Community Access
and Lasting Effects
Innovations at Scale for Community Access
and Lasting Effects (InSCALE), developed by
the Malaria Consortium with input from the
Ministry of Health and CHWs of Uganda
and Mozambique, aim to improve the iCCM
performance, morale, and satisfaction. It sup-
ports diagnosing (featuring a respiratory rate
counter), treating, and referring children, and
identifying danger signs in pregnancy. A sup-
plementary application for supervisors in-
cludes data management for analysis,
facilitating timely reporting.
60
Piloted in
Uganda and Mozambique, the application
considerably increased treatment appropriate-
ness by 26% (Mozambique)
68
and 11%
(Uganda)
60
and by 15% considering the
pooled effect (P<.01). However, no significant
improvements were noted in community
CHWs utilization (P¼.06), motivation
(P¼.4), and knowledge (P¼.5).
60
MEDSINC
MEDSINC helps CHWs in clinical severity
assessment, triage, treatment, and follow-up.
It found a specificity correlation of 84%-99%
with health care professionals’suggestions.
57
Users praised MEDSINC for its user-
friendliness and effectiveness in job perfor-
mance
57,70
and an unpublished Nigerian
study indicated a 41% rise in iCCM adherence
with MEDSINC.
70
A recent update featuring a
machine learning-based malaria algorithm
further improved its malaria detection sensi-
tivity (from 43% to 60%) and specificity
(from 64% to 79%).
74
Other e-iCCM ICT Solutions
In Zambia, a mobile platform using District
Health Information Software (DHIS 2) and
Java 2 Micro Edition (J2ME, a Java emulator
for the Android system) slightly improved
overall treatment rates (66% vs 63%),
increased correct pneumonia treatment by
21%, and
enhanced
supportive supervision by 18%. However,
these improvements were not statistically sig-
nificant (P>.05). Only CHWs logistic supply
significantly improved (P<.05).
62
In Uganda, CHWs used mobile phones for
data entry and immediate server uploads,
ensuring record accuracy and prompt medi-
cine resupplies through SMS. Correct child
management was at 93%, comparable to
94% in usual care (P>.05). Additionally, this
approach improved treatment planning, sup-
ply management, and logistics, eliminating
stockouts (P>.05).
59
In Niger, a CommCare
mHealth application led to considerable
improvement in quality of care regarding child
assessments and referral decisions, without
notably enhancing treatment.
69
In Liberia, a
tool based on open data kit has been evaluated
after 4 years of piloting for its connectivity
before scaling up, but further information
about its effectiveness are unavailable.
58
Digital Integrated Management of Childhood
Illness tools
Evidence supporting the enhancement of care
quality through IMCI digitalization initially
emerged from a pilot in Tanzania in 2008.
75
Although initially based on a modest sample of
23 consultations, subsequent confirmation in a
larger study underscored significant improve-
ments in clinical assessments (71% vs to 21%),
diagnoses (91% vs to 83%),
50,51
and communi-
cation between HWs and caretaker
56
(P<.05).
At the time of this review, digital IMCI was
in use in Tanzania,
51
Malawi, Adamawa State
(Nigeria),
39
India,
49
, Niger,
42
Guinea, Burkina
Faso,
40,42
Mali,
54
South Africa,
41
Kenya,
76
So-
malia,
25,48
Zambia
76
and in use to Médecins
sans Frontières’missions.
47
It was not possible
to obtain evidence for all these digital tools.
Some digital adaptations of the protocols,
such as ALMANACH,
54
ePOCT,
45
mPneumo-
nia,
55
and eCARE,
47
deviate from traditional
IMCI by introducing new point-of-care tests
and diseases.
Integrated e-Diagnostic Approach
Integrated e-Diagnostic Approach (IeDA)
encompassing e-coaching, EHR, e-learning,
and DHIS 2 integration, launched in Burkina
Faso in 2010 by the Ministry of Health and
Te-
rre
IMCI THROUGH ICT: REVIEW AND META-ANALYSIS
Mayo Clin Proc Digital Health nXXX 2024;2(3):438-452 nhttps://doi.org/10.1016/j.mcpdig.2024.06.005
www.mcpdigitalhealth.org 443
des Hommes, was in 2023 used in 85% of pri-
mary health care centers, covering 92% of
consultations with 90% guideline adherence.
It serves over 350,000 children monthly,
registering over 8 million.
42,77
IeDA improved
diagnosis accuracy (79% vs 54% in control)
and therapy, with a 15% reduction in anti-
biotic
40
(P<.05) and 1.6 USD million annual
savings.
42
It is also used for epidemic surveil-
lance.
78
Accepted by 91% of HWs for its user-
friendliness, it boosts caregiver satisfaction,
though tablet slowness and limited common
illness guidance are drawbacks.
37
IeDA has been piloted and adapted also to
Mali (2017),
42
Niger (2019),
42
India (2020,
state of Jharkhand),
49
and Guinea (2022),
42
and soon in Bangladesh.
79
IeDA’s latest
version includes also algorithms for antenatal
care
80
and newborn
49
.
ALgorithms for the MANagement of Acute
CHildhood illnesses
The ALgorithms for the MANagement of Acute
CHildhood illnesses (ALMANACH) was devel-
oped in Tanzania in 2011 by the Swiss TPH
and then implemented by the International
Committee of the Red Ccross in conflict zones
such as Afghanistan (2015),
33,36
Nigeria
(2016),
36,43
Somalia (2020),
48
and Libya
(2022).
25
Because it operates in unsafe or
war zones, for security reasons it does not
store any individual data and uploads aggre-
gated data to DHIS 2. ALMANACH signifi-
cantly improved clinical assessment in
Tanzania (71% vs 21%),
31
Afghanistan (84%
vs 24%),
36
and in Nigeria (58% vs 46%),
obtaining a better diagnosis (91% vs 83%,
Tanzania),
31
and treatment (85% vs 35%,
Afghanistan;
36
48% vs 30%, Nigeria)
39
(P<.05). These results have been achieved by
reducing antibiotic prescriptions by
80%
31,36,48,52
and, at follow-up, child recov-
ery was higher in comparison with usual
care
43,53
(P<.05). ALMANACH was also, in
general, well accepted by the HWs.
52
In
Nigeria, after scaling up, the tool was handed
over to the local health authority of Adamawa
by the end of 2021. At the state level, the anti-
biotic prescription rate has been reported to
decrease from 78% in 2018 to 19%-21% in
2023.
81
On the basis of the experience gained
with ALMANACH, the Swiss TPH developed
ePOCT, which combines advanced algorithms
with an oximeter and includes a C-reactive
protein point-of-care test that helped safely
reduce antibiotic prescriptions and improve
confidence in management.
45
eCare
eCare aims to support Médecins sans Frontièr-
es’s HWs. It has shown promise in reducing
antibiotic prescriptions to 25% and covering
90% of clinical situations.
47
However, its effec-
tiveness is not documented in peer-reviewed
journals, with current findings primarily pre-
sented at conferences.
electronic-Integrated Management of Child-
hood Illness
In South Africa, the electronic-Integrated Man-
agement of Childhood Illnes (eIMCI) was
launched in KwaZulu-Natal in 2018.
19
Its
adoption found promise and was well-
received,
38
but the COVID-19 pandemic hin-
dered its full implementation, with usage vary-
ing from 0 to 66.5%.
41
Excluding screenings
for anemia, tuberculosis, HIV, and malnutri-
tion, conventional care outperformed e-IMCI
in symptom classification, treatment effective-
ness (91% vs 82%), and reduced unnecessary
antibiotic use (5% vs 10.5%)
46
(P<.01).
mPneumonia
In Ghana, PATH piloted mPneumonia, a tool
combining IMCI algorithms with a breath
counter and oximeter, to improve respiratory
infection diagnostics.
55
Health administrators
found it implementable, whereas HWs deemed
it user-friendly and helpful for accurate patient
care, enhancing confidence in diagnosis and
treatment. The main challenges were device
charging and the time required for usage.
32
Meta-Analysis
The meta-analysis evaluated the effectiveness
of ICT in 4 areas, as follows: (1) completeness
of clinical assessment (5 studies; 1,828 inter-
vention children vs 2,988 control children);
(2) disease classification accuracy (5 studies;
1,933 vs 2,433); (3) therapy appropriateness
(10 studies; 10,766 vs 13,685); and (4) anti-
biotic reduction (7 studies; 3,236 vs 3,100).
Significant results favored the intervention
in 100% of the studies (5/5) for clinical assess-
ment completeness, 80% (4/5) for disease clas-
sification accuracy, 40% (4/10) for therapy
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444 Mayo Clin Proc Digital Health nXXX 2024;2(3):438-452 nhttps://doi.org/10.1016/j.mcpdig.2024.06.005
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appropriateness, and 83.3% (6/7) for anti-
biotic reduction. Conversely, 2 studies related
to therapy appropriateness significantly
(P<.05) favored the control arm.
Using a random effects model, we calcu-
lated the pooled effect sizes as follows: an OR
of 5.7 (95% CI, 1.7-19.1; I
2
, 95%) for the
completeness of clinical assessment; an OR of
2.0 (95% CI, 0.9-4.4; I
2
, 93%) for disease clas-
sification accuracy; an OR of 1.4 (95% CI, 0.8-
2.2; I
2
, 93%) for the appropriateness of therapy;
and an OR of 0.2 (95% CI, 0.06-0.55; I
2
, 99%)
for the reduction in antibiotic use (Figure 2).
In subgroup analysis of clinical assessments,
variations in effect size were linked to the inter-
vention (IeDA and eIMCI in Tanzania showing
more improvements), with heterogeneity drop-
ping to 51% in samples with fewer than 600
children. For diagnostic accuracy, better results
were associated with the iCCM program and so-
lutions like ALMANACH, IeDA and SL e-CCM
application. Therapy was significantly more
often inappropriate for observational studies,
whereas antibiotic prescription reduction
correlated with study quality (low), design
(RCT and cRCT), and intervention (ALMA-
NACH and eCare). Details of the subgroup anal-
ysis are present in Supplementary File 5,
available online at https://www.
mcpdigitalhealth.org/.
ICT solutions for IMCI training
ICT has been used to enhance IMCI training,
with a Tanzanian study showing distance
learning is as effective as standard courses but
70% cheaper. This course lasts 10-12 weeks,
mixing face-to-face sessions, self-study, and clin-
ical practice, supported by SMS communication
with facilitators.
71,72
In Uganda, a study
compared traditional training with tablet-based
videos for pneumonia diagnosis, showing im-
provements in the intervention arm, though
not statistically significant (P>.05).
73
DISCUSSION
This review reported that ICT considerably
facilitated IMCI and iCCM implementation,
mainly by improving protocol adherence.
The digitalization of IMCI/iCCM algorithms
through a CDSS technical approach ensured
crucial steps, such as checking for stiff necks
in febrile children, and emphasized often over-
looked tasks like measuring temperature and
counting breaths. According to the meta-
analysis, children evaluated using digital tools
were 6 times more likely to receive a through
clinical examination and twice as likely to be
accurately diagnosed. Moreover, these chil-
dren had a 40% higher chance of receiving
suitable treatment with an 80% reduction in
unnecessary antibiotic prescriptions. The inte-
gration of ICT into IMCI/iCCM programs
brings additional advantages, such as
improved cure rates,
43,53
more streamlined
patient referrals,
67,69
and better communica-
tion between HWs and caregivers.
43,56
The
HWs generally recognized the important con-
tributions of ICT: digital tools were lauded for
their user-friendliness, enhancing accuracy in
care, and bolstering diagnostic confi-
dence.
32,37,52,64
Regarding the IMCI training,
it benefited from ICT by shortening course du-
rations and reducing costs.
71
Furthermore,
many digital tools incorporate educational ele-
ments like videos, images,
34
and even gamifi-
cation elements,
57
offering effective hands-on
training that enhances learning through prac-
tical application. In addition, digital systems
facilitate rapid updates and adaptations to
evolving guidelines or specific local require-
ments without the need for further extensive
retraining.
36
However, although there were many posi-
tive outcomes, our analysis also identified
some negative aspects. The meta-analysis
found that disease classification accuracy was
somewhat subdued, and the appropriateness
of therapy exhibited considerably varied re-
sults. For both of these outcomes, the CI
crosses one, which implies that there is no sta-
tistical evidence that the interventions have a
beneficial effect. This may suggest that digital
tools can enhance adherence to guidelines,
compelling HCWs to follow protocols
completely and providing diagnostic sugges-
tions. However, the final decision remains
with the human agent using the tool. Even
when protocols are correctly followed, it is
the HCW who determines the disease classifi-
cation and prescribes therapy. These decisions
can be influenced by various factors outside
the guidelines, such as personal experience,
disease prevalence in the area, pressure from
caregivers, and drug availability. Furthermore,
it is important to note that adherence to guide-
lines is often measured by the HCW’s response
IMCI THROUGH ICT: REVIEW AND META-ANALYSIS
Mayo Clin Proc Digital Health nXXX 2024;2(3):438-452 nhttps://doi.org/10.1016/j.mcpdig.2024.06.005
www.mcpdigitalhealth.org 445
Bernasconi et al (2019)41 137 235 184 404 1.67 [1.21; 2.31] 21.6%
Sarrassat et al (2021)42 551 695 729 1343 3.22 [2.61; 3.99] 22.0%
Bernasconi et al (2018)38 152 181 158 404 8.16 [5.23; 12.73] 21.0%
Mitchell et al (2013)28 390 550 139 671 9.33 [7.18; 12.13] 21.8%
Rambaud-Althaus et al (2017)25 160 167 128 166 24.49 [5.80; 103.43] 13.7%
Random effects model
Heterogeneity: I2=95%, W2=0.8611, P<.01
1828 2988 5.66 [1.68; 19.07] 100.0%
Study
Completeness of clinical assessment
Events Total
Experimental Control
Odds ratio OR 95%-CI WeightEvents Total
0.01 0.1 1 10 100
Horwood et al (2024)47 59 109 61 105 0.85 [0.50; 1.46] 18.4%
Sarrassat et al (2021)42 450 572 767 1049 1.36 [1.06; 1.73] 21.1%
Mitchell et al (2013)28 500 550 555 671 2.09 [1.47; 2.97] 20.3%
Rambaud-Althaus et al (2017)25 89 167 56 166 2.24 [1.44; 3.49] 19.4%
Boyce et al (2019)55 432 535 206 452 5.01 [3.77; 6.65] 20.8%
Random effects model
Heterogeneity: I2=93%, W2=0.3921, P<.01
1933 2443 1.97 [0.87; 4.44] 100.0%
Study
Disease classification accuracy
Events Total
Experimental Control
Odds ratio OR 95%-CI WeightEvents Total
0.2 0.5 1 2 5
Horwood et al (2024)47 124 152 126 139 0.45 [0.23; 0.92] 8.2%
Kabakyenga et al (2016)61 4124 4552 5818 6276 0.76 [0.66; 0.87] 10.6%
Sarrassat et al (2021)42 437 567 836 1074 0.96 [0.75; 1.22] 10.3%
Biemba et al (2020)64 1252 1899 1134 1791 1.12 [0.98; 1.28] 10.6%
Soremekun et al (2023)58 782 1543 928 1941 1.12 [0.98; 1.28] 10.6%
Boyce et al (2019)55 156 223 120 186 1.28 [0.85; 1.94] 9.7%
Kallander et al (2023)62 808 1176 804 1304 1.37 [1.16; 1.61] 10.5%
Bernasconi et al (2018)38 96 181 139 404 2.15 [1.51; 3.08] 9.9%
Bernasconi et al (2019)41 151 306 119 404 2.33 [1.71; 3.18] 10.1%
Rambasud-Althaus (2017)25 105 167 37 166 5.90 [3.65; 9.56] 9.4%
Random effects model
Heterogeneity: I2=93%, W2=0.4224, P<.01
1,0766 1,3685 1.37 [0.84; 2.21] 100.0%
Study
Therapy appropriateness
Events Total
Experimental Control
Odds ratio OR 95%-CI WeightEvents Total
0.2 0.5 1 2 5
Shao et al (2015)30 130 842 525 623 0.03 [0.03; 0.05] 14.5%
Bernasconi et al (2018)38 57 181 348 404 0.07 [0.05; 0.11] 14.3%
Rambaud-Althaus et al (2017)25 25 167 116 166 0.08 [0.04; 0.13] 14.0%
Somalia et al (2024)49 102 639 355 611 0.14 [0.11; 0.18] 14.6%
Rambaud-Althaus et al (2015)48 63 252 17 37 0.39 [0.19; 0.79] 13.5%
Bernasconi et al (2019)41 131 189 314 404 0.65 [0.44; 0.95] 14.4%
Schmitz et al (2021)44 290 966 291 855 0.83 [0.68; 1.01] 14.7%
Random effects model
Heterogeneity: I2=99%, W2=1.4592, P<.01
3236 3100 0.18 [0.06; 0.55] 100.0%
Study
Antibiotic reduction
Events Total
Experimental Control
Odds ratio OR 95%-CI WeightEvents Total
0.1 0.5 1 2 10
FIGURE 2. Forest plot for completeness of clinical assessment, disease classification accuracy, therapy appropriateness, and antibiotic
reduction of the ICT interventions reviewed. ICT, information and communication technology; OR, odds ratio.
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to all prompts and the entry of all requested
data, as many applications do not allow skip-
ping essential checks. However, this does not
guarantee that all entered data are accurate.
Some data may be inputted without asking
the caregiver, to save time, or may be incorrect
because of limited clinical skills. Other data
may be entered approximately to expedite
the consultation, such as breath count or tem-
perature. We cannot even exclude that, in the
case of digital tools based on decision tree al-
gorithms, the HCWs may eventually start
manipulating the answers by inputting data
to bypass additional prompts or to match a
diagnosis they already have in mind. It is chal-
lenging to disentangle what may truly enhance
these outcomes. Broadly, it would be possible
to identify a range of interventions focusing on
people and processes, such as sample verifica-
tion of diagnoses through ex-post verification
visits and follow-ups, more accurate and
continuous supervision, on-the-job coaching,
staff performance appraisals, and improved
governance. However, these interventions are
often expensive and may not be feasible in
resource-constrained environments.
Other specific challenges were explicitly
highlighted by the authors in the reviewed
studies. The SL e-CCM application struggled
with integration, visibility, and funding
66
and
required continuous support from the Minis-
try of Health and humanitarian organizations
for long-term success.
63
For IeDA, staff turn-
over in remote areas of Burkina Faso and suf-
ficient supervision by district teams were
problematic, constrained by budget limita-
tions, vehicle accessibility, and time availabil-
ity.
37
Maintaining user motivation without
appropriate incentives emerged as a wide-
spread long-term challenge across various in-
terventions. A commonly reported complaint
was increased consultation time with digital
tools,
36,46,52
from 11 minutes in early inter-
ventions
50
to 28 minutes,
46
likely due to the
use of more advanced and comprehensive
tools. None of these obstacles, however, have
prevented a smooth implementation of digital
tools. Only in South Africa the attempt to digi-
talize the IMCI protocol encountered a setback
due to limited computer literacy, equipment
and staff shortages, and lack of adequate su-
pervision and evaluations. The eIMCI’s
recording demands also conflicted with
existing clinic programs, heightening adminis-
trative workload
38
and further problems like
chart mismatches, medication omissions, and
printout errors led the HWs to favor p-
IMCI.
44,82
The South African experience
prompts us to reflect more broadly on how
the local political and administrative context
can influence the implementation of digital
health. By 2016, there were nearly 150
mHealth projects supporting HWs in LMICs,
yet few achieved substantial scale.
83
Key issues
remain and include fragmented donor and
implementer platforms, unstable political sup-
port, failed integration, unsustainable business
models, and inadequate regulatory guide-
lines.
84
Thus, establishing country-level digital
health leadership is essential for upholding
standards, fostering investment in neglected
areas, and crafting adaptable solutions for
enduring e-health systems.
85
At a technical
level, sustainability could benefit from integra-
tion with health information systems and
EHRs, ensuring interoperability. The EHRs
enable patient tracking, summary assessments,
and data consolidation for routine reports,
while health information systems monitors
IMCI/iCCM programs through indicators like
illness incidence and treatment outcomes.
86,87
Although many e-IMCI/iCCM in this review
upload data to DHIS 2, only IeDa and e-
IMCI in South Africa were designed for auto-
matic synchronization with the central
server.
38,78
Furthermore, this review found
scant evidence of the impact of these interven-
tions on health managers and policymakers.
Ideally, stakeholders should easily access a
large volume of clinical data, but most
research focus on care delivery by HWs rather
than data utilization. Moreover, the reviewed
literature rarely assessed if the software was a
digital public goods, open and free for public
benefit. This omission is partly because of
the studies’age and the recent emergence of
the digital public goods concept.
88
In addi-
tion, most interventions lack a cost-
effectiveness analysis, which is crucial for in-
vestment prioritization and policy guidance
in resource-constrained systems. Likely, these
gaps could be filled soon as the World Health
Organization is pushing to facilitate digital
transition in LMICs and Standards-based, Ma-
chine-readable, Adaptive, Requirements-
based, and Testable guidelines have been
IMCI THROUGH ICT: REVIEW AND META-ANALYSIS
Mayo Clin Proc Digital Health nXXX 2024;2(3):438-452 nhttps://doi.org/10.1016/j.mcpdig.2024.06.005
www.mcpdigitalhealth.org 447
recently published to facilitate digitalization of
evidence-based guidelines.
89
The antenatal
care version of IeDA is already a product of
this innovative approach.
80
We finally identified 2 more challenges for
implementing digitalized tools from the expe-
riences we examined, as follows: (1) balancing
the completeness and accuracy of the tool with
practical constraints, such as maintaining
reasonable consultation times, a manageable
number of diseases to treat, limited availability
of diagnostic tools and tests, and varying levels
of clinical skills; (2) finding a compromise be-
tween adapting to local epidemiological pro-
files (which enhances diagnostic accuracy but
complicates standardization of procedures
and data collection) and maintaining standard-
ization (which simplifies processes, ensures
consistent data collection, and facilitates quick
deployment, but may reduce relevance and
diagnostic accuracy).
Our systematic review has also several lim-
itations. First, it is limited by strict adherence
to exclusion criteria, which led to the omission
of a few newer studies comparing different
digital interventions with each other. Second,
while using the Population, Intervention,
Comparator, Outcome, and Study Design
framework, there is a potential for subjective
bias in selection, data extraction, and interpre-
tation, which we tried to mitigate through a
third-party consultation. Third, the meta-
analysis presented high heterogeneity; sub-
group analyses were conducted to explore
this, but meta-analysis might not be the
optimal method to pool the effect size in this
context. Also, the funnel plots (see supple-
mentary files, Supplementary File 6, available
online at https://www.mcpdigitalhealth.org/)
exhibit clear asymmetry. Fourth, our evidence
search might have missed some gray literature,
particularly in local languages. However, to
optimize our search results and account for
potential shifts in ICT terminology over recent
decades, we concentrated exclusively on terms
associated with IMCI or iCCM, and we selec-
tively excluded sources that did not incorpo-
rate ICT approaches. Fifth, for many studies,
limited follow-up and small sample sizes pre-
clude reliable predictions regarding their sus-
tainability. Follow-up studies should be
conducted to confirm these results over the
long-term. Consequently, the results cannot
be fully generalized and should be considered
from a critical perspective. Sixth, we cannot
exclude the high risk of bias associated with
non-blinded studies, as the intervention,
such as a tablet in the hands of the HCW, is
clearly visible. The use of new technology
may influence the judgment of caregivers
and healthcare workers, leading them to
perceive the tablet or smartphone as a superior
advancement in itself.
CONCLUSION
This systematic review and meta-analysis is the
first comprehensive evaluation of ICT’s impact
on IMCI/iCCM programs and reports im-
provements through enhanced guideline
adherence and health system strengthening.
The decade-long evolution of digitized IMCI/
iCCM programs anticipated the Standards-
based, Machine-readable, Adaptive, Require-
ments-based, and Testable guidelines’
approach and offers a blueprint for digital
public health interventions in LMICs, paving
the way for future innovations.
This review underscores the potential of
ICT in enhancing health program implementa-
tion in LMICs. ICT can considerably improve
delivery, efficiency, and effectiveness. Through
dedicated investment in infrastructure,
training, and development, alongside robust
policy frameworks, LMICs can leverage tech-
nology to advance their health agendas, ulti-
mately contributing to global health equity
and the attainment of sustainable development
goals.
POTENTIAL COMPETING INTERESTS
The authors report no competing interests.
DECLARATION OF GENERATIVE AI AND AI-
ASSISTED TECHNOLOGIES IN THE WRITING
PROCESS
During the preparation of this work the author
Dr Andrea Bernasconi used ChatGPT3.5 to
improve readability. After using this tool/ser-
vice, the author reviewed and edited the con-
tent as needed and take full responsibility for
the content of the publication.
SUPPLEMENTAL ONLINE MATERIAL
Supplemental material can be found online at
https://www.mcpdigitalhealth.org/. Supple-
mental material attached to journal articles
MAYO CLINIC PROCEEDINGS: DIGITAL HEALTH
448 Mayo Clin Proc Digital Health nXXX 2024;2(3):438-452 nhttps://doi.org/10.1016/j.mcpdig.2024.06.005
www.mcpdigitalhealth.org
has not been edited, and the authors take re-
sponsibility for the accuracy of all data.
Abbreviations and Acronyms: ALMANACH, ALgorithms
for the MANagement of Acute CHildhood illnesses; CHW,
Community Health Worker; CDSS, Clinical Decision Sup-
port System; DHIS, District Health Information Software; e-
iCCM, electronic iCCM; e-IMCI, electronic IMCI; EHR,
Electronic Health Record; HW, Health Worker; ICT, Infor-
mation Communication Technology; iCCM, integrated
Community Case Management; IeDA, Integrated e-Diag-
nostic Approach; IMCI, Integrated Management of Child-
hood Illness; inSCALE, Innovations at Scale for Community
Access and Lasting Effects; J2ME, Java 2 Micro Edition;
LMIC, Low-income and Middle-Income Countries; OR,
Odds Rate; p-iCCM, paper iCCM; p-IMCI, paper IMCI;
PRISMA, Preferred Reporting Items for Systematic Reviews
and Meta-Analyses; RCT, Randomized Controlled Trials; SL
e-CCM, Supporting LIFE electronic CCM; SMS, Short
Message Service
Correspondence: Address to Andrea Bernasconi, MD,
MSc, Alt-Moabit 74, 10555, Berlin, Germany (doigb@
libero.it).
ORCID
Andrea Bernasconi: https://orcid.org/0000-0002-1299-
7114; Marianne A.B. van der Sande: https://orcid.org/
0000-0002-4778-6739
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