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38

Artigo Original

tion applied at the nursing ward level, in order to raise

awareness, at real-time, regarding hand hygiene com-

pliance, individual behavior change and performance

optimization.The gamification solution aims at collect-

ing data from nurses procedures, provides real-time ac-

curate feedback to the nurses (the information is shown

in a screen at the nursing office). Involving nurses in

the design process of aligning the combined automated

monitoring systems with gamification with the nursing

processes, allowed a better understanding of their needs

and of the barriers facing nursing work at the ward [8].

ePharmaCare is a decision-supporting system to collab-

oratively manage medicine prescription and patient in-

formations in community pharmacies linked with online

patients.The systems supports an online pharmaceutical

service, implemented to bring the pharmacist closer to

the patient for medicine follow-up services (e.g. quick

identification of adverse events or the need for a new

prescription), valued by both [9]. Pharmacists’ lack of

time, improper time management and incurrect infor-

mation systems usage skills were the main barriers for

the full system adoption.Training, communication with

online patients and pharmacy services reorganization are

critical to ensure the correct implementation of the ser-

vice [10].

The use of decision-making algorithms is essential to

enable quick a proper use of the information for de-

cision-making. In these cases, the algorithms were de-

signed in close collaboration between researchers and

healthcare professionals and leveraging evidence-based

rules. The use of machine-learning is another level of

smart-decision-making, which will enable the delivery

of predictive models that can provide important infor-

mation to help urgent decision-making. Using machine-

learning generated epidemic patterns, the system will

be able to combine different data and issue an early epi-

demics alert. The system’s algorithms could integrate

national and international guidelines, norms and laws,

as another layer of possible analysis and guided action.

The system could provide valuable forecast capabilities

and use cognitive computing to detect patterns associ-

ated with malaria epidemics (e.g. by using IBM-Watson

Discovery Advisor functionalities) providing the right

context to smooth quick decision-making.This capabil-

ity needs to be comprehensively tested and its accuracy

properly measured (and corrected if necessary). The

systems requires as much as possible previous epidem-

ics situations in order to learn and be prepared to new

cases.

Information visualization models are also very impor-

tant.When new technology innovations are combined,

and assigning new features to a system, it allows a refin-

ing of the information, as well as a better performance

is expected.Timely reporting malaria cases is key, but it

is currently very fragile. It often depends on the profes-

sional’s availability and motivation. Hence, healthcare

people may benefit from improved working processes

and extra motivation to improve the case reporting.

The use of gamification (e.g., adapting tasks into the

form of a game to engage professional into improved

performance) could be particularly useful to improve

data entry quality since it depends on the full attention

the healthcare professional. Basic gamification func-

tionalities can be applied collaboratively with users to

encourage both ownership and behavior change, im-

proved resilience and awareness of the time-lapse from

information reception to action. It even can be linked

to a more transparent quality supported incentive

program in a ltter stage of the implementation. In the

same line, a "nudging technique" performance could

help guiding the professionals throughout the preven-

tion and control procedures, like in the OSYRISH pro-

ject, to promote nurses’ hand hygiene compliance self-

awareness and action.

The system’s approach to implementation should

strengthen the availability of information, its integra-

tion process as well as the decision-makers capacity to

act accordingly.The collaboration process is fundamen-

tal to establish a responsibility model so that they can

address regular quality processes improvement.The HP

should lead the construction of the system as they will

be using it, in real-time, for decision-making, assuring

it offers both safety and accurate procedures [11]. The

collaborative design process enables the alignment with

malaria elimination decision-making processes. It also

should allow to validade the confidence in making deci-

sions and if the system is an effective anchor to reduce

malaria at the end of the day.

Machine-learning systems are still not mature but im-

proving. The success of such a system depends on the

number of previous cases and on how elimination ac-

tions will be improved in real settings (i.e. if the systems

output are truly valuable to improve decision-making).

The organizational aspects are critical, since the system

only works if people are trainned and ready to use it.

Therefore, co-designed science research methodology

is a valuable to both engage health professionals and use

evidence-based knowledge in the design of innovative

systems that are designed to properly respond to what

specific public health professionals need. Additionally,

using a Lean approach, involving both management and

health practitioners, it will provide the understanding

wheather if the working processes are effectively de-

livering as promised, therefore benefiting the organiza-

tion by eliminating waste directly related to informa-

tion access, treatment and analysis, for example, reduc-

ing information errors or improving on quality of data

presentation [12].