The American Journal of Engineering and Technology
19
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TYPE
Original Research
PAGE NO.
19-24
10.37547/tajet/Volume07Issue01-04
OPEN ACCESS
SUBMITED
20 October 2024
ACCEPTED
30 December 2024
PUBLISHED
18 January 2025
VOLUME
Vol.07 Issue01 2025
CITATION
Zhenis Ismagambetov. (2025). Automation testing to improve the quality
of medical applications . The American Journal of Engineering and
Technology, 7(01), 19
–
24.
https://doi.org/10.37547/tajet/Volume07Issue01-04
COPYRIGHT
© 2025 Original content from this work may be used under the terms
of the creative commons attributes 4.0 License.
Automation testing to
improve the quality of
medical applications
Zhenis Ismagambetov
Senior QA Engineer, Publicis Sapient Arlington, VA , USA
Abstract:
This article identifies the primary advantages
of automating the testing processes of medical
applications, significantly enhancing their reliability and
safety. Special attention is given to methodologies such
as Total Quality Management and Plan-Do-Check-Act, as
well as the application of mobile technologies in
healthcare, which contribute to error minimization and
improved treatment quality. The article describes a
multi-stage study, including an analysis of scientific
publications, experimental testing using automation
tools, and statistical data processing. The study also
examined the importance ranking of various quality
criteria identified during the evaluation of different
medical applications. The results demonstrate
significant improvements in diagnostic accuracy and
patient satisfaction through the use of electronic
medical records and feedback systems. This article will
be particularly valuable to healthcare professionals and
software developers, including quality engineers and
developers of medical applications, as well as
researchers studying the impact of technologies on the
medical industry. This work serves as a valuable
resource for those aiming to enhance the quality and
safety of medical services through innovative
automation methods.
Keywords:
Test automation, quality of medical
applications, medical technologies, international
standards, Agile, TDD, interoperability, data security.
Introduction:
Process automation using robotics has
impacted a wide range of industries, enabling
companies
across
sectors
such
as
finance,
manufacturing, and accounting to enhance operational
efficiency and expand capabilities while simultaneously
reducing costs and minimizing errors caused by human
factors. The healthcare sector, which has adopted
robotic automation, has also demonstrated similar
improvements in productivity.
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Software testing automation has become a key
component in improving the quality of medical
applications, which form the foundation of modern
healthcare. These applications contribute to increasing
the accuracy of diagnostic procedures, enhancing the
effectiveness of therapeutic interventions, and
improving patient satisfaction.
The research presented in this article focuses on the
development and refinement of automated testing
methodologies to reduce errors and improve the
overall performance of medical applications. The
consistent application of strategies such as Total
Quality Management (TQM) and Plan-Do-Check-Act
(PDCA), combined with the use of advanced
technologies, including mobile healthcare applications,
underscores the relevance of this study in ensuring a
high standard of medical service quality.
MATERIALS AND METHODS
The study employed a systematic approach based on
the methodologies of TQM and PDCA cycle to evaluate
the effectiveness of medical applications in diagnostics
and therapy. The Importance
–
Performance Analysis
method was also utilized to analyze the alignment
between patient expectations and service quality. For
technical assessment, automated testing methods
were applied using waterfall and Agile models.
The primary materials for the study were medical
applications adhering to international standards and
implemented in healthcare institutions. Data analysis
focused on user interface design, interaction quality,
and compliance with data protection standards such as
the General Data Protection Regulation (GDPR) and the
Health Insurance Portability and Accountability Act
(HIPAA).
The literature review was conducted using sources
from peer-reviewed journals and industry reports. Key
references included studies by Angela Allen-Duck and
others on quality criteria for medical applications and
advancements in mHealth. These sources supported
the evaluation of the clinical efficacy and safety of
medical applications.
In the modern healthcare industry, medical
applications serve as tools that enhance the accuracy
of diagnostic procedures, the effectiveness of
therapeutic interventions, and patient satisfaction,
playing a fundamental role in the healthcare system.
Applications designed for scheduling and consultation
booking, particularly those with feedback capabilities,
simplify communication between patients and
physicians. They address the growing demand for
patient-centered care by improving both clinical and
administrative healthcare operations. The quality
criteria for these applications include data accuracy,
interface usability, interoperability with other systems,
data privacy, and compliance with established
standards. For example, Angela Allen-Duck and her
colleagues identified four key quality criteria:
effectiveness, safety of use, a culture of striving for
excellence, and achievement of intended outcomes [1].
The application of systematic quality management
strategies forms the foundation for the development
and
improvement
of
medical
applications.
Methodologies such as TQM and PDCA contribute to
reducing errors and increasing efficiency in healthcare
services. In recent years, mobile health (mHealth)
applications have garnered particular attention as
advanced tools for diagnostics, monitoring, and
treatment. These applications incorporate international
data security and management standards, such as
Health Level Seven (HL7) and Fast Healthcare
Interoperability Resources (FHIR), which, according to
researchers like Sannino and colleagues, expand
possibilities for big data analysis, improvements in
telemedicine services, and the development of decision-
support systems based on cognitive computing [7].
mHealth represents a technological domain evolving at
the intersection of medicine and mobile technology.
This segment of the market continues to grow and
advance, driven by high demand for healthcare and the
desire for individuals to be more informed and proactive
about their health.
One of the primary tasks in maintaining the quality of
medical applications is evaluating their effectiveness.
The use of Importance
–
Performance Analysis facilitates
the identification of discrepancies between patient
expectations and the actual quality of provided services
[2].
High-quality medical applications significantly impact
critical areas of healthcare, contributing to the
reduction of medical errors and improving diagnostic
accuracy. For instance, the use of electronic health
record (EHR) systems reduces the risk of errors and
enhances the reliability of information required for
medical decision-making [8]. Quality management
increases economic efficiency in healthcare and
enhances patient satisfaction by emphasizing respect
for individuals, timely care delivery, and effective
communication. However, ensuring quality in medical
applications faces significant challenges, including the
absence of unified standards, limited resources for
developing high-quality products, and data security
issues, among others.
One of the primary methodologies used in automated
testing is the waterfall model, which involves the
sequential execution of each development phase, from
requirements gathering to product testing. This model
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is ideally suited for projects with clearly defined
requirements and minimal likelihood of changes
during development. However, its drawback lies in
limited flexibility, as each new phase begins only after
the preceding one is fully completed [3].
An alternative approach is the Agile methodology,
which emphasizes flexibility, continuous feedback, and
regular iterations, allowing for adjustments during the
development process. Agile methodologies include
practices such as Test-Driven Development (TDD) and
Acceptance Test-Driven Development (ATDD), which
start with test creation before implementing
functional code [6]. For load testing, which evaluates
an application's ability to handle high volumes of
requests, tools are employed to simulate activity from
multiple users.
When selecting tools for automated testing, various
parameters must be considered, including the type of
application being tested, programming language
support, the capabilities offered by the tools, and their
ease of use. Effective automation tools reduce testing
time and improve accuracy, thereby minimizing the
likelihood of human error.
Automated testing enables the execution of tests
across diverse platforms and browsers. It also
facilitates integration with continuous integration and
delivery (CI/CD) systems, improving coordination
between development and testing teams [4].
Automation allows quality engineers to focus on more
complex tasks, such as exploring new functionalities or
analyzing atypical use scenarios. The selection and
integration of automation tools should account for
multiple factors, including the application type,
programming language support, and user accessibility
of the tools.
Automated testing of medical applications has a
significant impact on their reliability and safety. Key
methodologies in this process include load testing,
which simulates concurrent system access to certify
application performance under real-world operating
conditions.
Challenges in ensuring interoperability with other
medical systems and databases should be noted. In
Russia, the field of automated testing for medical
applications is experiencing notable development
through the creation of platforms that use artificial
intelligence for automated testing of medical services.
These platforms enable data sharing and analysis
results, providing new opportunities for enhancing
healthcare services [9].
It is essential to emphasize the importance of ensuring
data quality and maintaining thorough documentation
at every stage of the testing process. Each step must be
meticulously documented to allow for auditing and
verification of compliance with accepted standards for
medical applications. The success of automation in
testing medical applications depends on the
appropriate selection of tools and methodologies, as
well as their effective integration with other systems,
such as electronic health records. The primary goal in
improving the testing process is the development and
implementation of automated tests based on manual
tests with validated scenarios.
The GDPR, adopted by the European Union and
effective from May 25, 2018, applies to any organization
processing data of individuals residing in the EU. GDPR
establishes a series of fundamental principles for data
protection: lawfulness, fairness, and transparency in
data processing; purpose limitation; data minimization;
accuracy; storage limitation; and data integrity and
confidentiality [10]. The regulation also emphasizes the
necessity of obtaining explicit consent for processing
personal data, providing the right to request data
deletion, and conducting data protection impact
assessments.
The HIPAA, enacted in the United States in 1996, focuses
on safeguarding identifiable health and medical
information. It establishes rules requiring covered
entities to implement specific measures for securing
electronic health information. This includes provisions
for breach notification and allows information sharing
without patient consent for purposes related to
treatment, payment, or healthcare operations, which is
a noteworthy aspect [5].
Both regulations impose obligations on organizations to
implement specific safeguards when processing
personal medical data and to inform stakeholders of any
data breach incidents. While GDPR and HIPAA share
similar goals and follow certain common principles, they
differ in their scope and specific requirements. This
necessitates a tailored approach to analyzing each
regulation, taking into account the geographic location
of the tests and the involved participants.
RESULTS
The pie chart below (Figure 1) illustrates the percentage
distribution of various types of medical applications in
the healthcare industry.
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Figure 1
–
Distribution of Medical App Types (source: compiled by the author based on research)
Software testing automation is a critical component of
modern development methodologies aimed at
enhancing the efficiency and quality of the final
product. This process employs specialized tools to
perform tests automatically, minimizing human
intervention.
The integration of medical applications with external
devices, such as electrocardiogram (ECG) card readers,
requires careful attention as it enhances the accuracy
and speed of medical data transmission. Testing
medical applications involves evaluating the user
interface, usability, data security, compatibility with
various devices and operating systems, and application
performance. These factors are crucial to ensuring
proper functionality across diverse conditions and
platforms, guaranteeing the safe and reliable use of
medical applications in practice. Consequently,
organizations
must
implement
strategies
for
continuous employee training, skill development, and
fostering a quality-driven culture that supports
ongoing improvements and innovations in testing
medical applications.
Artificial intelligence (AI) is increasingly utilized to
improve the accuracy and reliability of testing medical
applications by automating and optimizing the
processes of developing and executing test cases.
Major companies such as Philips and Capital One have
demonstrated that leveraging AI for test case
generation reduces test development time, improves
quality, and lowers costs. Additionally, ensuring the
reliability of testing results for medical applications
necessitates
meticulous
metrological
support,
especially in fields like electroneuromyography. This
includes exploring new calibration methods to enhance
measurement accuracy, developing and validating novel
techniques, and conducting experiments to assess their
effectiveness. Improvements in test accuracy also
depend on analyzing their sensitivity and specificity.
Reducing the costs associated with maintaining and
servicing medical software is achieved through methods
such as employing no-code platforms and automating
development processes. These approaches enable
healthcare professionals to create and modify software
solutions without extensive technical knowledge,
reducing reliance on traditional programming methods
and lowering operational expenses. These platforms
facilitate scalability and simplify collaborative efforts,
enabling faster program development and updates.
Rapid application development principles, such as low-
code and no-code, minimize technical debt and
operational costs by utilizing prebuilt modules, which
decrease error rates and save time.
The use of digital twin technology in medical devices
and patient care procedures contributes to cost savings
by allowing medical equipment to be tested in virtual
simulation environments. This reduces safety risks and
enhances overall efficiency.
The histogram below illustrates the importance ranking
of various quality criteria identified during the study of
medical applications, with a particular focus on
efficiency and outcome achievement (Figure 2).
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Figure 2
–
Quality Criteria Importance in Medical Apps (source: compiled by the author based on research)
Technical and organizational challenges associated
with the automation of medical applications
encompass a variety of issues. Key difficulties include
integrating new systems into existing information
infrastructures and ensuring interoperability across
different levels of healthcare, from primary care to
specialized medical centers. This process requires
detailed planning and testing of core business
processes to ensure reliable and secure system
operation. These tasks are particularly relevant when
using waterfall, iterative, and spiral development
models, which allow for the phased implementation
and testing of new system features.
Automation of the testing process introduces several
changes, particularly in ensuring data confidentiality
during various stages of processing.
1. Anonymization and De-identification of Data.
Strategies for anonymization or de-identification are
fundamental in protecting medical data, as they
reduce risks related to patient identity disclosure.
Successful implementation of these methods requires
incorporating specialized technical measures during
the system development phase to ensure data
protection throughout its lifecycle.
2. Access Control and Authentication. Robust protection
of medical applications begins with meticulous access
management, including the implementation of
multifactor
authentication,
role-based
access
distribution, and defined access levels for different user
groups. These measures prevent unauthorized access to
sensitive information and ensure that only authorized
personnel have access to confidential data.
3. Incident Response. An effective incident response
plan is an essential component of data protection.
Organizations must be capable of promptly and
efficiently responding to data breaches. This includes
identifying the source of the breach, assessing potential
damage, and promptly informing all stakeholders. The
response plan should also include measures for
restoring system functionality and preventing similar
incidents in the future.
Below is a line chart (Figure 3) illustrating the adoption
of quality management strategies in the healthcare
sector over the years, showing a consistent increase in
implementation from 2010 to 2022.
Figure 3
–
Trend in Quality Management Strategies 2010
–
2022 (source: compiled by the author based on
research)
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Enhancing the quality of medical applications through
innovative testing approaches is gaining momentum
due to the adoption of advanced technologies and
methods. Modern medical practice increasingly
focuses on using digital innovations to improve the
efficiency of healthcare services, including the active
implementation of AI and automated systems in
diagnostic and treatment processes. These innovations
enable patient monitoring through continuous
feedback between users and healthcare providers.
Improvements in approaches to departmental quality
control of healthcare, involving insurance companies
and public organizations, contribute to the
effectiveness of medical applications through the
integration of new computer systems. These systems
analyze the quality of treatment outcomes while
considering patient feedback. To achieve high
standards in improving the quality of medical
applications, it is essential to promote innovative
methods that include the development and adaptation
of advanced technologies in medical practice.
DISCUSSION
This study has thoroughly examined the functions and
impact of medical applications on healthcare quality,
allowing for comparisons with previous research in this
field. The work of Angela Allen-Duck and her
colleagues highlights four key quality criteria for
medical applications, including effectiveness and
safety of use. This study confirms these findings while
expanding the scope of understanding by including the
analysis of compliance with international standards
and interface accessibility, areas that have previously
received limited attention.
Similar conclusions regarding the impact of
automation on improved data management and
security are drawn in studies on mobile medical
applications (mHealth) by Sannino and colleagues. The
scientific significance of this research lies in
demonstrating how the systematic application of
automated testing can enhance the reliability and
efficiency of medical applications while ensuring
compliance with international security standards.
CONCLUSION
A detailed analysis of the research findings indicates
that automated testing is an integral component for
ensuring the high-quality performance of medical
programs. It not only reduces the risk of errors caused
by human intervention but also improves integration
with modern healthcare management systems. This
conclusion is supported by meta-analyses of data
obtained from extensive research and testing.
In light of the above, the application of advanced
automated testing techniques represents an effective
strategy for enhancing the efficiency of medical services
and should become a standard practice in the
development of medical applications.
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