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A n a i s d o I HM T
and risk-alerts (e.g. combining actual patterns of malar-
ia with both previous patterns and Centers for Disease
Control and Prevention (CDC) heuristic-based alerts),
and allowing to direct control interventions around de-
tected cases and outbreaks. Combing both GIS/DHIS2
and AI will enable real-time forecasts, provind the possi-
bility for helping improve malaria interventions response
time, hopefully anticipating and preventing the spread of
new cases. Combined together, these technologies have
the potential to strengthen the sustainability of data col-
lection processes, giving support to decision-makers and
fomenting the behavior change of public health decision-
makers in malaria risk management.
It is a fact that healthcare services need to be responsive
to epidemics, where a prompt and effective action will
make the difference.The smart malaria elimination tool
can facilitate malaria elimination needs identification and
support evidence-based decision-making, through real-
time access to data.This will also enable the creation of
bigger databases from which one can learn and improve
the response procedures.
The smart-surveillance information system (SSIS) tool is
designed, developed and implemented using the Design
Science Research Methodology (DSRM) framework [4].
This method ensures the adjustment to the public health
context. Through a collaborative and participatory pro-
cess teamwork is strongly promoted (e.g. between epi-
demiologists and information systems experts), aiming
at solving organizational problems by creating and evalu-
ating a shared and integrated information system. It de-
pends on the dedicated collaboration and contributions
of healthcare professionals (HP), including technicians
and administrative staff, as well as from top management
commitment.The DSRM establishes the base for the ar-
tefact construction process following six sequential steps
[5].Together with the HP, the researchers set-up priori-
ties, define objectives for the solution (e.g. the alerts re-
quired), pre-select important and necessary data, identi-
fying its sources' systems, etc.The designed system will
be finnaly implemented and demonstrated in the field
(Bengo region, in Angola), being submitted to constant
evaluation, with results communication, in form of con-
stant profiling and reporting.
Results and discussion
The DSRM first steps ask for the understanding of the
problem, identifying possible solutions. Only then, we
can advance to the next steps for implementation.The
benchmarking and learnt lessons with prior technology
innovations are also relevant.
A Public Health Information System has started (with
both problem identification and solution obejctives
definition) to be developed and will be tested in Bengo
– Angola. From the first step (problem identification)
there were some identified difficulties to the tool im-
plementation process. Currently malaria data still is re-
corded in basic text and calculation Excel sheets, and so
it is feebly consolidated, only enabling the production
of simple and basic graphics.There is the opportunity to
address these issues altogether, with the development
of a the smart-surveillance information system (SSIS),
which should integrate the following functionalities:
1) (Malaria) epidemiological surveillance: suspected
by confirmed cases; total severe cases by mortality;
2) Entomological features, for the vector control, as
entomological inoculation index;
3) Epidemics risk alerts;
4) Logistics: medicine stock and mosquito nets con-
trol and management, including received and distrib-
uted material.
Supporting the idea of creating the SSIS system that
actually address an intervention for epidemics surveil-
lance and management, in malaria elimination pro-
grams, the solution design could benefit from previous
experiences.
The Design Science Research Methodology, have been
used to implement several tools in Global Health and
Tropical Medicine, involving several healthcare inno-
vation systems, such as HAITooL, OSYRISH, and eP-
harmaCare, among others, always under collaborative
processes, following best-practices and helping to go
further through optimization for each of the cases.
HAITooL is an decision-support information system to
help preventing and controling hospital infections and
antibiotic prescription and resistance. Implemented
in two hospitals as a participatory process, each of the
two teams worked together with the researchers in the
design-science research process, in order to co-design
and implement an effective surveillance and decision-
support system, adapted to both hospitals clinical pro-
cessesa and socio-cultural context [6]. As a decision-
support system, HAITool uses smart algorithms to dis-
plays alerts, for example for an excessive antimicrobial
therapy duration or if antimicrobial therapy is not in
accordance with microbiology results, among others.
The system presents an integrated views of patient,
microbiology and pharmacy data, displayed in innova-
tive layouts and graphics, allows the visualization of pa-
tient clinical evolution, antibiotic consumption trends,
antibiotic resistant infections indicators and patterns.
Information turns to be easy and clear for the profes-
sional use [7].
OSYRISH is a decision-supporting information sys-
tem to help nurses improve hand hygiene in order to
reduce healthcare-acquired infections. It includes an
indoor location technology and a gamification applica-