Nursing
Process Supported by Expert System in the Application of the Glasgow and Braden
Scales in a Brazilian Public Hospital

Processo de Enfermagem Apoiado por Sistema Especialista
na Aplicação das Escalas de Glasgow e Braden em um Hospital Público Brasileiro

Edquel Bueno Prado Farias

https://orcid.org/0000-0002-7490-8148

Universidade
Anhembi Morumbi - [email-masked]

Dacyr Dante de Oliveira Gatto

https://orcid.org/0000-0003-2146-4819

Universidade Nove de Julho - Brasil.
[email-masked]

Marcio Romero

https://orcid.org/0000-0001-7595-9249

Universidade Nove de Julho - Brasil. [email-masked]

Maria Luiza Almeida de Oliveira Moura

https://orcid.org/0000-0003-3767-970X

Universidade Nove de Julho - Brasil. [email-masked]

Renato José Sassi

https://orcid.org/0000-0001-5276-4895

Universidade Nove de Julho - Brasil. [email-masked]

RESUMO

O processo de enfermagem padroniza a forma de
atender e diagnosticar o paciente e, durante sua execução, escalas de apoio são
utilizadas, como a de Glasgow e de Braden, que visam
agregar parâmetros na decisão clínica. Para apoiar a aplicação correta dessas escalas,
é possível utilizar técnicas da Inteligência Artificial (IA), como os Sistemas
Especialistas (SE). O hospital público, objeto de estudo deste trabalho, não
apresentava o processo de enfermagem informatizado e nem um SE. Desta forma,
informatizou-se o processo, e um SE foi usado para auxiliar na aplicação das
escalas. O objetivo deste trabalho, então, foi apresentar a implementação, em
hospital público, do processo de enfermagem apoiado por SE na aplicação das
escalas de Braden e Glasgow. A metodologia de
pesquisa adotada foi bibliográfica, aplicada e experimental. O processo de
coleta e validação dos dados desse estudo foi obtido a partir de entrevista dos
enfermeiros envolvidos no processo e na validação e aprovação da base de conhecimento
do SE pelos especialistas, tanto do hospital quanto dos contratados para
consultoria externa. Com a aplicação do processo de enfermagem informatizado,
padronizou-se o atendimento hospitalar, normalizando os processos relativos à
enfermagem e, assim garantindo assertividade do enfermeiro no processo de
tomada de decisão. Conclui-se que a implementação do processo
de enfermagem informatizado apoiado pelo SE apoiou a tomada de decisão no
hospital.

Palavras-chave:
Processo de Enfermagem Informatizado; Sistemas Especialistas; Escala de
Glasgow; Escala de Braden.

ABSTRACT

The nursing process standardizes the way to assist and diagnose the
patient and during its execution, support scales are used, such as the Glasgow
and Braden scales, which aim to aggregate parameters in the clinical decision.
To support the correct application of these scales, it is possible to use
Artificial Intelligence (AI) techniques, such as Expert Systems (ES). The
public hospital, the object of this study, did not have a computerized nursing
process or an ES. In this way, the process was computerized, and an ES was used
to assist in the application of the scales. The objective of this work, then,
was to present the implementation, in a public hospital, of the nursing process
supported by ES in the application of the Braden and Glasgow scales. The
research methodology adopted was bibliographic, applied
and experimental. The data collection and validation process of this study was
obtained from interviews with nurses involved in the process and in the
validation and approval of the SE knowledge base by specialists, both from the
hospital and from those hired for external consultancy. With the application of
the computerized nursing process, hospital care was standardized, normalizing
the processes related to nursing and thus guaranteeing the nurses'
assertiveness in the decision-making process. It is concluded that the
implementation of the computerized nursing process supported by the ES
supported decision making in the hospital.

Keywords: Computerized Nursing Process; Expert Systems;
Glasgow Scale; Braden Scale.

Recebido
em 31/08/2020.  Aprovado em 18/10/2020.
Avaliado pelo sistema double blind peer review. Publicado conforme normas da ABNT.

https://doi.org/10.22279/navus.2021.v11.p01-16.1397

1 INTRODUCTION

The Nursing Process (NP) is a methodological
instrument that favors quality and safety in patient care. For its
implementation, nurses need to consult a wide range of literature and
technical-scientific support material, which requires a lot of research time
for each diagnostic and prescription item (RAMOS; RODRIGUES; GONZAGA, 2018).

Palomares and Marques (2010) and Domingos (2017)
mention that, when computerizing the nursing process, the main contributions
are: time optimization, better organization, language standardization,
decision-making aid, ease of data retrieval and multi professional
communication, less redundancy, information integration as well as data
security and integrity.

Importantly, during the development of the
nursing process, scales are used, such as Glasgow and Braden's, to aggregate
parameters in clinical decision (diagnosis), evolution and patient history (RAMOS;
RODRIGUES; GONZAGA, 2018; WECHI et al.,
2017; OLIVEIRA; DIAS; SOUSA, 2018).

The Glasgow Coma Scale (GCS), also known as the
Glasgow Scale, is an essential tool for measuring a patient's level of
consciousness as it assesses their ability to open their eyes, communicate
verbally, obey commands and move their extremities.
For this reason, it has been used both in prehospital care and in Intensive
Care//Units (ICU) (SURABENJAWONG; SONMEETHONG; NAKORNCHAI, 2017).

The Braden scale is an essential instrument to
measure the risk of developing pressure ulcer, which is defined as an injury
caused by unrelieved pressure, resulting in decreased blood circulation and,
consequently, death and skin necrosis. (PEREIRA; LUDVICH; OMIZZOLO, 2016; WECHI et al., 2017).

To support decision making and application of
the Glasgow and Braden scales, the Artificial Intelligence (AI) technique
called the Expert System (ES) can be used. (PEEK et al., 2015).

An ES deals with real-world problems that would
most often require expert analysis and interpretation. Using a computer model
analogous to human reasoning, it is possible to reach conclusions like those of
a subject matter expert (MAGOC; MAGOC, 2011).

One of the differences between an ES and a
database program is the use of logical reasoning, using inference procedures
for problem solving. ES use “IF THEN” probability and production rules,
followed by related logical connectors within the scope of the issue at hand
(ESFANDIARI et al., 2014).

Thus, the aim of this paper was to present the
implementation, in a Brazilian public hospital, of the nursing process
supported by the ES in applying the Glasgow and Braden scales.

2 LITERATURE REVIEW

This section presents the literature review used in this paper where the
themes Nursing Process, Supporting Scales and Expert System were addressed.

2.1 Nursing Process

The Nursing Process (NP) emerged in the 1960,
based on the scientific method of observation, measurement, and data analysis,
being described by Virginia Henderson and elaborated
by Faye Abdellah (ALFARO-LEFEVRE, 2014).

In 1979,
in Brazil, the nurse Wanda de Aguiar Horta presented and defined the NP as the
dynamics of systematized and interrelated actions aimed at assisting human
beings, based on the theory of basic human needs (Horta, 2011). The NP,
however, only began to be implemented in health institutions in 2009, meeting
the requirements of the Federal Nursing Council (COFEN, 2009).

According to Resolution 358/2009 (COFEN, 2009),
the NP should be carried out throughout Brazil, deliberately and
systematically, by all public and private health institutions. A well-known and
used synonym for NP is the Nursing Care Systematization (NCS) (ALFARO-LEFEVRE,
2014).

The nursing process can be
seen as a set of systematic and interrelated actions, aimed at assisting
human beings. It is characterized by the interrelationship and dynamism of its
phases and steps.

Also, according to resolution 358/2009 (COFEN,
2009), the NP is organized in 5 steps: a) Collection of nursing data or nursing
history; b) Nursing diagnosis; c) Nursing planning; d) Nursing implementation;
e) Nursing assessment.

The
following steps of the NP are described:

a) Nursing Data Collection or Nursing History

It consists of interview and physical examination. The interview will
investigate the patient's or community's health situation, identifying problems
and needs for interventions. The physical examination consists of inspection,
palpation, percussion and auscultation, procedures that require theoretical
knowledge and appropriate technical skills to perform.

b) Nursing Diagnosis

At this stage, the nurse analyzes the collected data and the
individual's health status through the identification and evaluation of present
or potential health problems. The diagnoses are made according to the protocols
of the institution; the most used are North American Nursing Diagnosis
Association (NANDA) and CIPE.

c) Nursing Planning

Specific expected outcomes are determined, and necessary interventions
identified. Interventions should be targeted to achieve estimated outcomes as
well as to prevent, resolve or control changes encountered during nursing
history and diagnosis.

d) Nursing Implementation

It is the implementation of the care plan and the implementation of
actions or interventions determined in the nursing planning stage.

e) Nursing assessment

Systematic and continuous process of
verification of changes in the responses of the person, family or community at
a given time in the health-disease process to determine if nursing actions or
interventions have achieved the expected result. Here, there is also the need
for changes or adaptations in any of the NP steps. The following indicators are
used for assessment: Missing or Present; Better or Worse; Maintained or Resolved.
This stage is the evolution of nursing, which is the patient's assessment every
24 hours (RAMOS; RODRIGUES; GONZAGA, 2018).

The interrelationship between these phases is
fundamental. Therefore, improper data collection can lead to erroneous
conclusion of the problems presented and improper planning (DA SILVA REIS et al., 2016).

The nurse should know and use various
literature on nursing classification systems during the elaboration of NP.
Among them are the following classifications suggested by NANDA (KIM et al., 2018; LUCENA et al., 2018): the Nursing
Interventions Classification (NIC) (BUTCHER
et al., 2018; KIM et al., 2018;
LUCENA et al., 2018); the Nursing
Outcomes Classification (NOC) (MOORHEAD; JOHNSON; MAAS; SWANSON, 2015) ; and the International Classification for Nursing
Practice (ICNP) (GARCIA et al., 2020).

NANDA, in conjunction with NIC and NOC,
provides the nursing discipline with the elements of clinical data for
automated patient registration (MOORHEAD; JOHNSON; MAAS; SWANSON, 2015; KIM et al., 2018; LUCENA et al., 2018). NANDA is a must-have
literature composed of a compilation of nursing diagnoses that describes
patients' reactions to disease (BUTCHER et
al., 2018; KIM et al., 2018).

NIC is a comprehensive and standardized
classification of interventions performed by nurses and is responsible for
characterizing hospital interventions in a very specific way. The NOC measures
changes in patient status, i.e., documents the development of standardized
results for disease assessment (BUTCHER et
al., 2018; LUCENA et al., 2018).

2.2 Supporting Scales

During the NP stages,
the Glasgow and Braden scales are used to measure values that support diagnosis
and prescription. The Braden Scale is an essential instrument for measuring the
likelihood of patients developing pressure ulcers. It was published in 1987 in
the United States and was adapted and validated for Brazilian culture in 1999 (PEREIRA;
LUDVICH; OMIZZOLO, 2016; OLIVEIRA; DIAS; SOUSA, 2018; WECHI et al., 2017).

The Braden scale is
composed of six subscales (Gomes et al.,
2011; WECHI et al., 2017): Sensory
perception, activity, mobility, nutrition, friction
and shear. All contribute to the development of pressure ulcers and none of
them should preferably be evaluated over another (RITROVATO et al., 2015; OLIVEIRA; DIAS; SOUSA, 2018.

The six subscales
receive a score according to the commitment presented. To evaluate each of
them, one must resort to the definition of the parameters included in the
scale. Scores range from 1 to 4, except for the “friction” and “shear”
subscales, which range from 1 to 3. The sum of the six subscales ranges from 6
to 23. The lower the value, the greater the compromise presented and,
consequently, the greater the risk exposure (OLIVEIRA; DIAS; SOUSA, 2018).
Table 1 shows the Braden Scale.

Table 1 - Braden Scale

Description

1

2

3

4

Sensory
perception

Totally
limited

Very
limited

Slightly
limited

No
limitation

Moisture

Completely
wet

Very
wet

Occasionally
wet

Rarely
wet

Activity

Chained

Confined
to Chair Walks

Occasionally

Walks
Frequently

Mobility

Totally

Very
limited

Lightly
limited

No
limitations

Nutrition

Very
poor

Probably
inadequate

Adequate

Excellent

Friction
and Shearing

Problem

Potential

Problem

No
Problem

Source: COFEN (2009).

Patients
with a score equal to or greater than 16 points are
considered to be at low risk for developing pressure ulcers. Score
between 11 and 16 indicates moderate risk, and below 11 points indicates high
risk, as it shows that there was a significant organic functional decrease that
facilitates the onset of decubitus ulcer (GOMES et al., 2011; WECHI et al.,
2017).

Another
support scale used is the Glasgow Scale, an essential instrument for measuring
the patient's level of consciousness, as it assesses their ability to open
their eyes, communicate verbally, obey commands and
move their extremities. It is commonly used in both prehospital care and
intensive care units.

The
Glasgow Scale was developed and published in Lancet magazine in the 1970s by
Graham Teasdale and Bryan Jennett. Its initial
version was named Coma Index (CI), but after a statistical study of the scoring
system, it became the Glasgow Coma Scale (GCS) (TEASDALE; JENNETT, 1974;
TEASDALE; MURRAY, 2000; SURABENJAWONG; SONMEETHONG; NAKORNCHAI, 2017).

In Table 2, this
scale is illustrated.

Table 2 - Glasgow Coma Scale (GCS)

Glasgow
Coma Scale

Variables

Score

Eye
opening

Spontaneous

The voice

To pain

None

4

3

2

1

Verbal
Answer

Oriented

Confused

Inappropriate words

Incomprehensible words

None

5

4

3

2

1

Motor
Response

Obey commands

Finds pain

Withdrawal movement

Abnormal flexion

Abnormal extension

None

6

5

4

3

2

1

Maximum
Total

Minimum Total

Intubation

15

3

8

Source:
COFEN (2009).

Glasgow
Scale findings form the basis of clinical decision making, such as the need for
computed tomography, surgical intervention and / or drug modality (SURABENJAWONG;
SONMEETHONG; NAKORNCHAI, 2017).

2.3 Expert System

Expert System (ES) is an Artificial Intelligence (AI) technique
that emulates the behavior of human experts in their domain to solve complex
problems that require intensive use of specialized knowledge for their solution
(WAGNER, 2017).

For Weiss and Kulikowski
(1988), ES deals with complex real-world problems that would usually require
expert analysis and interpretation, and solves them
through a reasoned computational model of a human being in order to arrive at
the same conclusions as that of a subject matter expert.

An ES consists in structuring the knowledge and
experiences of human specialists through a computational representation that
allows the application of consistent computer processing methods (DYMOVA; SEVASTJANOV;
KACZMAREK, 2016).

To assist in the construction of ES, knowledge-oriented
tools called shells were created. A shell allows the system developer to worry
only about the knowledge representation of the expert, leaving it to the task
of interpreting the knowledge represented and executing it on a machine. In addition,
the shell provides explanations of how the system reaches a certain conclusion (NASER
et al., 2016). Thus, an ES has two
essential components in its architecture: the knowledge base (where knowledge
is stored) and the Inference engine (where the inference or deduction process
occurs).

The typical architecture of an ES, in addition to the
knowledge base and inference engine, contains the user interface and the
acquisition interface and the explanation module, as can be seen in Figure 1.
Importantly, not all ESs have the same structure, but overall, it is quite
similar (GUPTA; SINGHAL, 2013).

Figure 1 - Architecture of an ES

Source: Adapted from Gupta and Singhal
(2013).

The following is a description of the modules presented in Figure 1
(GUPTA; SINGHAL, 2013)

a)
Knowledge Base: is the module where the system
knowledge is stored; Its creation is the first step in the development of an
ES. It can be said that the great power of an ES lies in its knowledge base,
since it contains the knowledge concerning the domain of the problem to be
solved by the expert, which will be used in the decisions regarding the
application domain. The basis is the rules and facts of problem solving, that
is, it is like the intuition that a human expert can use to solve problems in a
domain. It can be stated that the rules and facts contained in the knowledge
base is the representation of all knowledge acquired from human specialists.
Such knowledge can be represented in various forms, namely: production rules, logic,
or semantic networks.

b)
Knowledge Acquisition Interface: Used to
add, modify, and update facts, attributes, and rules contained in the knowledge
base. This part of the ES interacts directly with the human expert and can be
brokered by a knowledge engineer who is responsible for its extraction and
representation. The process of changing the knowledge base can be something
simple, such as adding data, or more complex, such as debugging the base,
reordering priorities, among others.

c) Inference Engine: Is responsible to access the
knowledge base and decision making. To do this, it applies the knowledge
contained in the knowledge base, relying on user input and knowledge base rules
to reach any solution or conclusion. The Inference Engine is the central part
of an ES, responsible for the interaction between the user and the ES during a
query. It can be said to be the algorithm used to search the knowledge base.
The Inference Engine assesses the knowledge of the base and then directs the
resolution process. There are several formalities to implement the inference, research and conclusion process. The most used are forward
chaining and backward chaining. The inference engine drives the ES to the
expected solution, linking, in the case of the rule-based system, these to the
facts.

d) User
interface: Also known as data collection
module; is the module responsible for the interaction between the ES and the
user. This interaction can be console based (text) or graphical interfaces
(multiple choice windows). Preferably, one should interact with the user, using
natural language, in such a way that the user interacts with the ES just as he
would with a human expert. It is through this interface that the user proceeds
to input data, entering answers to system questions and receiving explanations
regarding queries made to the ES.

e)
Explanation Module: Has the function of justifying
the reasoning used by the ES and it is a mandatory requirement. The
Explanations Module usually provides a history, explaining step by step how the
ES reached a certain conclusion, why the ES reached that conclusion, and why
the ES did not reach another conclusion. To this end, the Explanation Module
determines in detail how and which rules were accepted
and which were rejected. Therefore, the Explanation Module is extremely
important for the user to understand what was the reasoning
used by the ES to arrive at a particular solution about a particular decision,
or about any fact or knowledge that he has stored in the base.

f) Expert System Core (Shell): The main
feature of an ES is the separation between the Knowledge Base and the Inference
Engine. This separation makes it possible to represent knowledge through
IF-THEN rules, which are closer to human thinking. It also allows the same
inference engine to be used to find the solution in ESs with different
knowledge bases; It also allows the knowledge base to be changed without
changing the algorithm used in the Inference Machine. The set formed by the
Inference Engine and User Interface is known as the ES Core or Shell.

The ES can be applied in several areas
and are based on assisting in problem solving and diagnostics, through
questions directed to users, which are based on decision rules elaborated
through the acquisition of knowledge from experts in the studied area. From the
users' answers, the system, through its inference engine, gives the user a
possible solution (MILLER et al., 2015, PANNU, 2015).

Some examples of the application of ES in
healthcare are: AIDS treatment aid (SOUZA, 2020), prostate cancer
diagnosis (PEREIRA; SCHAEFER; MARQUES, 2004; NIKITAEV, 2018), dengue symptom
analysis (HAIKAL, 2017), HPV Risk Prediction (DRABLE; MOL; LEGEY, 2014) and
Diagnosis of Blood Infections (SHORTLIFFE, 2019).

It is worth highlighting the work of Wagner
(2017) conducted robust research by collecting 311 case studies on the use of
higher education in various areas of knowledge between 1984 and 2016. The study
shows that the application of higher education went from medicine to several other areas of knowledge.

3 MATERIALS AND METHODS

The research methodology adopted
in this work was defined as bibliographic, exploratory
and experimental, with quantitative approach. The research can also be
considered as applied, since it aims to generate knowledge and use it in a
practical way, considering the results obtained (GIL, 2016).

The experimental research was
performed in 5 steps described below:

Step 1: The
Access application was used to create and normalize the database, which, in
this case, are evaluation protocols based on the Glasgow and Braden Scales. The
task of data acquisition was done from spreadsheets, books, interviews and
gathering prior knowledge of hospital specialists.

Step 2: Data were
selected and preprocessed with Excel.

Step 3: The data
were evaluated and validated by experts in the domain.

Step 4: The
computerized nursing process was implemented.

Step 5: ES was
implemented to support decision making, using the Expert Sinta,
developed by the Laboratório de Inteligência
Artificial (LIA, 1999) of the Universidade Federal do
Ceará. ES offers an inference engine. With the help
of the shell, the production rules were inserted into the rule base and
knowledge.

As already mentioned, the following steps of
the NP are: Nursing Data Collection or Nursing History; Nursing Diagnosis; Nursing
Planning; Nursing Implementation and Nursing assessment.

The computerization of the nursing process
aims to make the collection, treatment and use of these data as accurate as
possible. The ES stands out to support decision-making in raising patient signs
and symptoms with the Glasgow and Braden scales. Next, in Figure 2, we present
the components and steps of the computerized nursing process.

Figure 2 - Steps of the Computerized Nursing Process

1st step of NP:

Nursing History

Interview, physical
examination and survey of signs and symptoms of the patient.

2nd step of the NP

Nursing Diagnosis

Analysis of the client's health history
and definition

3rd step of the NP

Nursing Planning

Analysis of the diagnosis and preparation
of Nursing Intervention.

4th step of the NP

Nursing Implementation

Nursing Intervention prescription
application.

5th step of the NP.

Nursing Assessment

Nursing evolution occurs by closing a
24-hour cycle to. evaluate the client's health condition.

ES

1st and 5th step of the
Father:

Supports decision making at scales while
surveying client signs and symptoms.

Modulo (Glasgow) measures level of consciousness.

Module (Braden),
measures level of dependence.

Source: Authors (2019).

4 RESULTS DISCUSSION

As shown in Figure 3, the initial
interface of the computerized nursing process consists of the following
subsystems: patient registration, diagnosis, evolution and nursing history,
view prescription and end NP.

Figure 3 - Initial interface of the Nursing Process

Source: Authors (2019).

With the help of the shell, the
production rules were inserted in the rule base and knowledge, as shown in
Figure 4.

Figure 4 - Example of rule generated by ES.

Source: Authors (2019)

During the evolution of the NP, it
is possible to access the window of signs and symptoms. To fill it out, the
specialist consults the interface of an ES (Glasgow and Braden Scales) that
provides support for nurses' decision making.

The ES consists of two main
modules: Glasgow and Braden scale. Both were developed with the application of
ES. While using the Braden Scale module, the user visualizes the question
interface, which opens new windows until all conditions are fulfilled to answer
the question raised, as shown in Figure 5.

Figure 5 - ES Question Interface Braden Scale

Source: Authors (2019).

Inferring at the end of this
process an interface, in which the results with the answer to the question
raised by the nurse are visualized, as shown in Figure 6.

Figure 6 - ES Question Interface Braden Scale

Source: Authors (2019).

Inferring at the end of this
process an interface, which visualizes the results with the answer to the
question raised by the nurse, as shown in Figure 7.

Figure 7 - ES Result Interface Braden Scale

Source: Authors (2019).

In the Braden Scale module, the
user visualizes a question interface, which generates new windows until all the
questions raised are answered. After this step, the results appear in a new
interface, which is composed by the following tabs: query results, all values,
containing the data that were entered in the system, and the history through
which it is possible to evaluate how and why the system arrived at the proposed
result, as shown in Figure 8.

Figure 8 - ES Result Interface History (Braden Scale)

Source: Authors (2019).

The same process occurs during the
use of the Glasgow scale module, which opens new windows until all the
conditions are met to answer the question raised, as shown in Figure 9.

Figure 9 - ES
Scale Glasgow Interface questions

Source: Authors (2019).

Inferring at the end of this
process an interface, in which the results with the answer to the question
raised by the nurse are visualized, as shown in Figure 10.

Figure 10 - Glasgow Scale ES Result Interface

Source: Authors (2019).

The same process reported on the
Braden scale occurs on the Glasgow scale. In Figure 11, you can see the result
of the questions generated by the system.

Figure 11 - ES result interface history (Glasgow Scale)

Source: Authors (2019).

Once the Glasgow and Braden scale
metrics in the computerized nursing process are inferred, the next step is the
insertion of the diagnosis, from a series of options available in the system,
as well as suggestions for prescribing and treating nursing, as shown in Figure
12.

Figure 12 - Nursing Prescription Interface

Source: Authors (2019).

Accepting the suggestions and
making the necessary changes, as decided by the nurse, the system generates the
nursing prescription, as shown in Figure 13.

Figure 13 - Nursing prescription generated by the system

Source:
Authors (2019).

Once implemented the decision
support ES with the Glasgow and Braden Scales, and the computerized nursing
process, the following results were achieved: optimization of the nursing
process application time; standardization of patient assessment, prescription,
care and schedules; ease of patient continuity of treatment, even when
transferred to another ward; improvement in expert nurse assertiveness and
measurement of Glasgow and Braden scales; possibility of monitoring and
auditing of the whole process by nursing professionals; validation and approval
of the ES knowledge base by specialists from both the hospital and external
consultants.

5 CONCLUSION

With the application of the
computerized nursing process, the Brazilian Public Hospital care was
standardized, facilitating the continuity of the patient's treatment, even when
transferred to another clinic. The computer system has standardized all
nursing-related processes throughout the institution, including case
registration, assessment, care delivery, prescriptions, and schedules, thus
fully achieving the aims.

With this work, it was also
evidenced that the use of ES guaranteed greater assertiveness of nurses during
the decision-making process by collecting signs and symptoms and measuring them
through the Glasgow and Braden scales.

To validate and analyze the
results, the research was based on the nurses' feedback. Thus, despite the lack
of previous indicators, it was found that the nurse's gain in efficiency and
effectiveness was assisted by a computer system supported by an ES. One of the
biggest indications of this improvement was the reduction in the evaluation and
prescription time of NP, from 20 minutes to 7, on average.

It was evident
that the availability of the computerized nursing process with the ES modules
brought major improvements to the system, having greater impact on the history
and evolution of nursing.

A limitation is
the fact that the application was performed in only one hospital and the
absence of a mobile interface (cell phones and tablets) to access the system
during the physical examination, thus reducing rework.

It is worth
highlighting that the main challenge to be overcome was in the implementation
of the computerized nursing process was the initial resistance presented by
some nurses to use computer systems.

Another highlight
was the difficulty for nurses to use a computational device beside the bed
during the physical examination of the patient. This situation, which according
to some nurses generated rework because they needed to make notes of the
physical exam on paper and then transcribe them the system, which generated
rework.

It is suggested
as a continuity of this work, the implementation of the computerized nursing
process in other health units in order to attend a
larger number of patients and, consequently, a greater range of diagnoses. It
is also suggested to develop an interface with mobile devices to be used near
the patient's bedside.

Finally, with regard to SE, it is suggested the application of
another AI technique such as Case Based Reasoning (CBR) when considering a
scenario of cases on a case basis and no longer on knowledge. In this way, the
proposed technique would automatically update its database.

REFERENCES

ALFARO-LEFEVRE, Rosalinda.
Aplicação
do Processo de Enfermagem: Fundamentos
para o raciocínio clínico. 8. ed.
Porto Alegre: Artmed, 2014.

BUTCHER,
Howard K. et al. Nursing
Interventions classification (NIC)-E-Book. USA: Elsevier Health Sciences,
2018.

COFEN. Resolução COFEN-358 de 15 de outubro de
2009. Dispõe sobre a
sistematização da assistência de enfermagem e a implementação do processo de
enfermagem em ambientes, públicos ou privados, em que ocorre o cuidado
profissional de enfermagem. [2009]. Available
in: http://www.cofen.
gov.br/resoluo-cofen-3582009_4384.html. Access
at: 18
out. 2020.

DA SILVA REIS, Geísa
et al. Sistematização da assistência de enfermagem: vantagens e dificuldades na
implantação/Systematization of
nursing care: advantages and difficulties in implementation. Arquivos Médicos dos Hospitais e da Faculdade de Ciências Médicas da
Santa Casa de São Paulo, v. 61, n. 3, p. 128-132, 2016.

DOMINGOS, Camila Santana et al. A aplicação do processo de
enfermagem informatizado: revisão integrativa. Enfermería
Global, v. 16, n. 4, p. 603-652, 2017.

DRABLE, Rosana Gama; MOL, Antonio Carlos de Abreu;
LEGEY, Ana Paula. Avaliação
do uso da lógica nebulosa para previsão de risco de Papiloma Vírus Humano. RECIIS – Rev. Eletron.
de Comun. Inf. Inov. Saúde, v. 8, n. 3, p. 344-358, 2014.

DYMOVA, Ludmila;
SEVASTJANOV, Pavel; KACZMAREK, Krzysztof. A
Forex trading expert system based on a new approach to the rule-base evidential
reasoning. Expert Systems with
Applications, n. 51, p. 1–13, 2016.
DOI doi.org/10.1016/j.eswa.2015.12.028.

ESFANDIARI,
Nura; BABAVALIAN, Mohammad Reza; MOGHADAM, Amir-Masoud
Eftekhari; TABAR, Vahid Kashani.
Knowledge discovery in medicine: Current issue and future trend. Expert
Systems with Applications, v. 41, n. 9, p.  4434-4463, 2014.

GARCIA, Telma Ribeiro et al. Classificação
Internacional para a Prática de Enfermagem CIPE (R): Versão 2019/2020. Porto
Alegre: Artmed Editora, 2020.

GIL,
Antonio Carlos. Como elaborar projetos de
pesquisa. . ed. São Paulo: Atlas, 2002.

GOMES, Flávia Sampaio Latini; BASTOS, Marisa Antonini Ribeiro; MATOZINHOS, Fernanda
Penido; TEMPONI, Harieti Rotelli;
VELÁSQUEZ-MELÉNDEZ, Gustavo. Avaliação
de risco para úlcera por pressão em pacientes críticos. Revista da Escola de Enfermagem da USP,
v. 45, n. 2, p. 313-318, 2011.

GUPTA,
Swati; SINGHAL, Ritika. Fundamentals
and characteristics of an expert system. International Journal on Recent and Innovation Trends in Computing and
Communication, v. 1, n. 3, p. 110-113, 2013.

HAIKAL,
Ludmila. Prevenção da dengue utilizando o sistema especialista para big data
hadoop. Revista Academus,
v. 5, n. 1, 2017.

HORTA,
Wanda. Processo
de Enfermagem. Rio de Janeiro: Guanabara Koogan, 2011.

KIM,
Eun Jung et al. Analysis
of the Contents and Importance of Clinical Practicum Education in Adult Health
Nursing According to Nursing Intervention Classification (NIC) System. Health Communication, v. 13, n. 2, p. 205-216, 2018.

LIA, Laboratório de Inteligência
Artificial (1999). Expert SINTA: uma ferramenta para criação de sistemas
especialistas. Fortaleza: Universidade
Federal do Ceará, 1999.  Available in: www.lia.ufc.br.  Access at: 18 out. 2020.

LUCENA,
Amália de Fátima et
al. Validation of Nursing Outcomes and Interventions to Older Adults Care
with Risk or Frail Elderly Syndrome: Proposal of Linkages Among NOC, NIC, and
NANDA-I to clinical practice. International journal of nursing
knowledge, v. 30, n. 3, p. 147-153, 2018.

MAGOC,
Tanja; MAGOC, Dejan. Neural network to identify individuals at health risk. International
Journal of Artificial Intelligence & Applications, v. 2, n. 2, p. 104-114,
2011. DOI 10.5121/ijaia.2011.2208.

MILLER,
Anne; MOON, Brian; ANDERS, Shilo; WALDEN, Rachel;
BROWN, Steven; MONTELLA, Diane. Integrating computerized clinical decision
support systems into clinical work: A meta-synthesis of qualitative. International Journal of Medical Informatics,
v. 84, n. 12, p. 1009-1018, 2015.

MOORHEAD,
Sue; JOHNSON, Marion; MAAS, Meridean; SWANSON, Elizabeth.
NOC
Classificação dos Resultados de Enfermagem. 5. ed. Rio de Janeiro: Elsevier,
2015.

NASER,
Samy Abu; ALHABBASH, Mohammed Male Infertility Expert System Diagnoses and Treatment. American
Journal of Innovative Research and Applied Sciences, v. 2, n. 4, 2016.

NIKITAEV,
V. G. et al. Expert System for Histological Diagnosis of Prostate Cancer. KnE Energy, p. 328–332-328–332, 2018.

OLIVEIRA,
Raisa Leocadio; DIAS, Samya
Raquel Soares; DE SOUSA, Jairo Edielson Rodrigues B.
Use of scales in the assessment of pressure injury in critical patients/Utilização de escalas na avaliação de lesões por pressão em pacientes críticos/Utilización de escalas en la evaluación de lesiones por presión en pacientes. Revista de Enfermagem da UFPI, v. 7, n. 3, p. 54-60, 2018.

PANNU, Avneet.
Survey on expert system and
its research areas. International Journal of Engineering and
Innovative Technology (IJEIT), v. 4, n. 10, p. 104-108, 2015.

PALOMARES, Mariana Laura Esteves;
MARQUES, Isaac Rosa. Contribuições dos
sistemas computacionais na implantação da sistematização da assistência de
enfermagem. Journal of Health
Informatics, v. 2, n. 3, p. 78-82, 2010.

PEEK,
Niels; COMBI, Carlo; MARIN, Roque; BELLAZZI, Riccardo. Thirty years of artificial intelligence in medicine (AIME) conferences: A
review of research themes. Artificial Intelligence in Medicine, n.
65, p. 61-73, 2015.  DOI 10.1016/j.artmed.2015.07.003

PEREIRA,
M. A.; SCHAEFER, M. B.; MARQUES, J. L. B. Remote expert system of support the prostate cancer diagnosis. In: ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY,
26.; ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE, 2., 2004, San
Francisco, CA, USA. Proceedings […]. San Francisco: IEEE, 2004. p. 3412-3415.

PEREIRA, Mariane Onofre; LUDVICH,
Sabrina Cardoso; OMIZZOLO, Jaqueline Aparecida Erig. Segurança
do paciente: prevenção de úlcera por pressão em unidade de terapia intensiva. Inova
Saúde, v. 5, n. 2, p. 29-44, 2016.

RAMOS, Júnia Helena Furini; RODRIGUES, Rosangela da Costa; GONZAGA, Márcia Féldreman Nunes. Origem e importância da Sistematização da Assistência
de Enfermagem (SAE). Revista Saúde em Foco, n. 10, p.  923-936, 2018 .

RITROVATO,
Matteo; FAGGIANO, Francesco; TEDESCO, Giorgia;
DERRICO, Pietro. Decision-oriented health technology assessment: One step forward in
supporting the decision-making process in hospitals. Value in Health, v. 18, n. 4, p. 505-511,
2015.

SHORTLIFFE,
Edward Hance. Computer-based medical consultations: MYCIN. 2.
ed. New York:  Elsevier, 2012.

SHORTLIFFE,
Edward H. Artificial intelligence in medicine: weighing the accomplishments,
hype, and promise. Yearbook of
medical informatics, v. 28, n. 1, p. 257, 2019.

SOUZA, Ademar Rosa de. Estudo
comparativo avaliando três modalidades de diagnóstico médico: parecer
médico, buscas no Google e sistema especialista de apoio à decisão médica.
2020. 99 f. Tese (Doutorado em Fisiopatologia em Clínica Médica) - Universidade
Estadual Paulista Júlio de Mesquita Filho, São Paulo.

SURABENJAWONG,
Usapan; SONMEETHONG, Weeraphon;
NAKORNCHAI, Tanyaporn. Accuracy of Glasgow Coma Score and FOUR score: a prospective study in
stroke patients at Siriraj hospital. J. Med. Assoc. Thail, v. 100, n. 9, 2017.

TEASDALE,
Graham.; JENNETT, Bryan. Assessment of
coma and impaired consciousness: a practical scale. The Lancet, v. 304, n. 7872, p. 81-84, 1974.

TEASDALE,
G. M.; MURRAY, L. Revisiting the
Glasgow coma scale and coma score. Intensive Care Med, v. 26, p. 153-
154, 2000. DOI 10.1007/s001340050037.

WAGNER,
William. P. Trends in Expert System
Development: A Longitudinal Content Analysis of Over Thirty Years of Expert
System Case Studies. Expert Systems With
Applications, n. 76, p. 85-96, 2017. DOI 10.1016/j.eswa.2017.01.028.

WEISS, S. M.; KULIKOWSKI, C. A. Guia
prático para projetar sistemas especialistas. Rio de Janeiro: LTC – Livros
Técnicos e Científicos S.A, 1988.

WECHI, Jeane Silvestri et al. Escala de Braden:
instrumento norteador para a prevenção
de úlceras por pressão. Estima–Brazilian Journal of Enterostomal
Therapy, v. 15, n. 3, p. 145-151, 2017.