CLOUD COMPUTING71Dissertation Factors affecting the adopt

CLOUD COMPUTING
71
Dissertation: Factors affecting the adoption of cloud computing
in healthcare
Shiva Kumar Pagadala
University of the Cumberlands
Advanced Research Methods
DSRT 839
Dr. Bryian Ramsey
03/04/2022
Abstract
In medical care, cloud technology allows hospital treatment.
This research intends to evaluate variables impacting cloud-
based diagnostic medical alternatives by clinical staff.
Regression analysis tests were employed to assess the
conceptual framework and outcome findings. Based on
multivariate regression tests, the results demonstrated that all
control variations perceived beneficial, the relative advantage
of usages, perceived risk, productivity, and availability have a
numerically substantial effect apart from organizational
commitment and interoperability with the reliant involved in the
decision making. Findings reflect the influence and relevance of
the response variable and illustrate the crucial role such
parameters play in consumers' inclination to employ central data
centers in the healthcare industry. These results also corroborate
results from earlier relevant investigations. Findings from this
study in clinical technology would give greater emphasis to
these aspects.

Table of Contents
Chapter One: Introduction
Overview………………………………………………………………
……...
Background and Problem
Statement………………………………………….
Chapter Two: Literature Review
Introduction
Cloud computing has intensely grown to be one of the most
deployed services because of its relative benefits and
advantages to firms, organizations, and enterprises. There are
four main service deployment models of cloud computing,
whereby the models differ according to physical and
foundational infrastructure layers (Amron et al., 2017). The
models include hybrid cloud, community cloud, private and
public cloud. The central service model is a platform as a
service, software as a service, and infrastructure as a service.
The complexity of healthcare information systems has been the
leading cause of the shift from traditional to modern mobile-
based technology systems, as cloud computing helps incorporate
solutions to the technologies while adopting new information
technology outsourcing (Amron et al., 2017). Apart from
improving service quality and meeting various healthcare needs,
cloud computing also aids in storing and sharing information
such as electric health records and opening new horizons for
patients (Amron et al., 2017).
On a theoretical framework, all cloud computing models
applicable shall be examined in review on technological aspect,
organization-environmental framework aspect, and
technological innovation. All the currently and internally
adopted technologies used in an organization and accessibility

shall be reviewed (Gao &Sunyaev, 2019). The organizational
context will also include organizational constructions like
scope, managerial structure, and size. In contrast, the
environmental context will comprise all the factors related to
the healthcare facility's environment and operates as an industry
and the competitors.
The various contexts will respectively present the opportunities
and the constraints for technological innovation. The
technological-organizational-environmental framework being
the organizational-level theory, will provide a different
perspective of framework that will include internal and external
factors (Amron et al., 2017). The framework will classify and
determine the factors. It will also provide free space for
categorizing the various attributes in different contexts in a
broader realm as the cofactors in every context are obtained
from previous studies.
Once the literature has been reviewed, the TOE framework is
combined with the various theories to identify specific factors.
The framework will also be integrated with the other models
that offer a more comprehensive range of constructs to have a
sizeable theoretical ground for understanding different adoption
attributes (Gao &Sunyaev, 2019). The two-theory study that
needs to be integrated here is the diffusion of innovation and
technological-organization-environmental theory. The two
models complement one another in terms of knowledge of
innovation attributes, explaining different inter-organizational
levels instead of just individual stories (Amron et al., 2017).
The advantage of this framework is that it only allows and
encourages researchers to identify many factors concerning
different contexts (Ali et al., 2018). However, the most frequent
factors chosen by the most empirical types of research due to its
importance in the adoption of cloud computing remain the most
reviewed one then it shall be followed by its adoption on the

organization's top management support, barriers perceived
under the healthcare facilities, and organizational readiness. In
addition to competitive pressure, rules and regulations are under
the environmental aspect (Amron et al., 2017).
Literature review
Cloud technology in health facilities is a kind of cloud
computing that is used to improve patient treatment. It can solve
some of the shortcomings of healthcare systems by using cloud
computing (Ali et al., 2018). The capacity to access medical
information from any location and at any time can significantly
enhance quality healthcare. Cloud computing may be utilized to
get accessibility to health information and, as a result, improve
the efficiency of medicine delivery. Because cloud computing is
often used in a sharing and accessible setting, it is vulnerable to
criminal data theft, assaults, and data redundanc y (Amron et al.,
2017).
Widespread security threats hinder the adoption of cloud
technology solutions in the medical sector. Healthcare
professionals are wary about cloud computing because
cybercriminals may obtain sensitive medical information (Ali et
al., 2018). As a result, legitimate security issues exist. Cloud
services service vendors should address security issues to foster
more confidence between healthcare professionals and clients.
According to a study, an increasing amount of healthcare
institutions want to adopt cloud technology solutions to take
advantage of cloud technology (Ali et al., 2018). The
deployment of digitalization has, as initially stated, provided
numerous implementations for the healthcare sector,
particularly in terms of planning and safety. Regardless of the
critical issues associated with delivering clinical attention, the
hospital sector is very complicated. Healthcare involves a
diverse range of participants with diverse objectives and
business characteristics (Ayoobkhan & Asirvatham, 2017). As a
result, cloud computing in medicine is complex, and its

implementation via healthcare organizations can only be
guaranteed under most conditions.
Many significant issues connected with these circumstances
should be considered while establishing cloud computing
systems (Amron et al., 2017). A cloud computing deployment
choice made without careful consideration of the deciding
variables may not only impede the effective use of cloud
technology in the medical system. Still, it can also create issues
for the organization.
Electronic health record cloud computing solutions are changing
medicine by offering users new procedures that were not
previously feasible a generation ago. According to experts,
advanced online healthcare cloud computing technologies will
eventually become the creative foundation of modern healthcare
delivery (Ayoobkhan & Asirvatham, 2017). Highlighted the
advantages of adopting e-health cloud technology platforms to
improve diagnostics in various scenarios, including Cl oud
computing gadgets offer doctors with clinical information
guidelines that help them enhance their patient's recovery time
effectiveness (Amron et al., 2017).
Cloud computing opportunities in healthcare
Regarding several types of research, medical errors in the
healthcare industry are primarily because of inadequate limited
access and communication to patient records. CC was perceived
as a possible way of improving healthcare performance and
reducing medical errors, just as increasing service delivery,
investment returns, and even medical research. Moreover, data
can be transferred throughout several models with CC, which is
recently absent in several healthcare facilities. CC could enable
doctors' references, electronic health records, prescriptions, and
diagnoses to get shared throughout multiple models. This is
currently taking place in the radiological sector, with several
facilities utilizing cloud computing to share images and save
money on storage (Gao et al., 2018). CC has allowed hospitals,

medical practitioners, and even pharmaceutical businesses to
collaborate to share patient data, leading to more efficiency and
excellent quality services.
Moreover, due to carbon emissions, the old ICT models have
not been environmentally friendly. Due to cloud computing
enables lower energy use based on usage, the data storage
facilities are inexpensive to operate. It also needs few materials
for cooling, thus being releasing fewer harmful emissions into
the surrounding. This is one of major motivators for CC use in
healthcare as the surrounding impact is minimized. Lastly, due
to human life being valuable and the resource in medicine being
limited, eHealth services match a cost-effective context where
the patients along with the company benefit from the new
technology through enhancing the patient service quality via a
shared, powerfully integrated platform along with coordinating
care, the medical process just as minimizing IT infrastructure
cost of investing thus leading to a better healthcare surrounding,
(Atianashie Miracle &Adaobi,, 2014.)
Benefits of CC in Healthcare
Multiple benefits accompany CC adoption in the sector of
healthcare. The first benefit is that CC brings economic
relevance to organizations of healthcare. It is unexpected for
CC to offer economic benefits. In general, the utilization of CC
is due to short-term interests in the economy. Several pieces of
research such that CC has the principal merit of low cost I
healthcare. CC plays the role of reducing overall IT
maintenance tasks and thus aids them in preventing likely IT
reinvestments. The second advantage is that CC mainly targets
the clinical tasks through leveraging great scalability. Some
research shows that the old health IT focused on administrative,
financial, and even strategic functions instead of clinical
activities. Such findings promote the urgent need to utilize CC
to remedy the old health deficiencies in the context of the
healthcare company's clinical activities. CC adoption has the

advantage of great scalability with a focus on the clinical
sectors. The third benefit is that CC supports patient-
centeredness. A conservative yet well-acknowledged perception
of health IT is medical staffs are critical users of IT
applications, and several existing IT applications focus heavily
on physicians. Yet, it has been claimed that the great potential
of CC is that it realizes patient-centeredness, which is a
promising upcoming trend of health IT. Moreover, several
surveys suggest that CC innovatively includes patient family
members for realizing patient-centeredness. It has also been
noted that CC promotes service flexibility and mobility (Gao et
al., 2018).
Factors influencing CC adoption in healthcare
Identification of factors affecting adoption in cloud computing
innovations in deploying HIS has obtained insufficient attention
in academic literature; thus, there is a lack of rigor and
systematic analysis studies. The following are factors affecting
the perception towards using eHealth:
System integrity domain factors
In the system integrity domain, system features have been
known to affect the way persons manage tasks. This is usually
featured through the personal perception of a model to finish
specific task demands. Regarding Hsiu-Fen, establishing the
successful utilization of innovations needs examining model
features as outside variables for adopting with regards to
driving a belief of its usefulness and effectiveness. The first
system characteristic can be compatibility which refers to how
the modern model fits the personal existing values and current
needs. The research postulates that determining the fit of HIS
and personal needs is essential for structuring the utilization
behavior of the model (Meri et al., 2017). Compatibility is
another essential aspect to be considered when examining the
current innovation adoption. With regards to Jen-Her,
compatibility of HIS vitally affects approval by professionals of

healthcare.
The next factor, compatibility, refers to the degree to which
innovation is viewed as hard to understand and employ. It has
been known that using CC in the healthcare domain can lead to
some issues for people lacking technological expertise, just as
IT specialists. This is due to the ease of integrating cloud HIS
into healthcare sectors, leading to higher utilization chances.
Nevertheless, the complexity of new roles and services in cloud
HIS can be challenging for professionals lacking expertise in
technology. The model's complexity is linked with how persons
view innovations to be relevant to their experiences. Moreover,
it gets correlated with the mental efforts of users needed to
utilize the system.
In addition, there is data security and privacy as another
component of the system integrity domain. The continuous
increase in using trending innovations in healthcare practices
led to the modulation of conventional approaches to handling
patient data. In current healthcare practices, healthcare experts
with various privileges require accessing patient data through
any device at any moment in the cloud. Thus, various
documents have shown the relevance of security focusing on
users using CC. Security vulnerabilities get defined as a threat
with concerns of destruction, data modification, and denial of
service just as disclosure, fraud, waste, and abuse. Users have
been warned not to trust the current security measures. With CC
security focus is more feasible, thus having the security of
cloud HIS positively affects physicians' approval of CC (Meri et
al, 2019). Besides the system integrity domain, there are other
factors needed for considerations, they include;
Hardware and software modularity
In the context of hardware modularity, there are multiple
limitations of present hardware in the healthcare organizations
that recourses generalization of using the HIS. Ensuring that

there is enough hardware modularity can enhance healthcare
application's scalability by embedding critical devices for
developing and debugging various health-associated issues from
a department to several ones (Meri et al., 2017). Thus, giving
healthcare employers enough hardware equipment that vitally
elevates their confirmation of the effectiveness to handle along
with communicating healthcare data over departments. In
software modularity, the process linked with performing
multiple tasks within a system can somehow be associated with
modularity for tailoring process development just as
comprehensibility.
Several research asserted that the effectiveness of regarding the
factor when it
turns to CC adoption where there is assurance that the previous
platforms can manage cloud application could elevate the
acceptance of healthcare employees to use cloud services.
Moreover, the organizations need to ensure that their software
is up to date to aid in maintaining the correct innovation with
regards to flexibility of the IT infrastructure that conforms to
means that applications get reconfigured with fewer efforts.
Ensuring modularity of software and hardware can impact the
worker's view in a positive way; thus, employing CC in
healthcare is much efficient (Meri et al., 2017).
Internet network
It has been noted that the lack of connectivity in internet
networks like one of the determinants limiting using the
adoption of innovation. Network refers to the infrastructure of
telecommunication needed for connecti ng multiple healthcare
areas and the users in a state. However, the workers still face
more difficulties in sharing and communicating the health-
linked data throughout departments because of the limited
accessibility to enough basic infrastructure. Several researchers
suggest a need for excellent network quality to influence the
adoption of CC positively. According to Steinbart and Nath,
computer network refers to a critical element driving company

management (Osplabs, 2018). Thus, network factors can
positively influence users' usage experience because they shape
IT use decisions.
Training availability
Training refers to access resources that companies give workers
to achieve the expertise required for operating and using
innovation. The workers are aided by training availability
through boosting their confidence in utilizing the cloud
technology in the model, like understanding each capability of
the provided innovation can make them approve it more. With
regards to Eley and his colleagues claimed the relevance of
delivering such resources for taking advantage in telemedicine
innovations within care and management of the patient. It is
also known that the lack of training resources can lead the users
to have opposing views on the innovation expectations, (Meri et
al., 2017)
The impacts to the adoption of cloud computing in health care
Like any other new technology, cloud computing needs to be
thoroughly tested since it is widely used (Ali et al., 2018). In
the perspective of both potential and difficulties, just several
doctoral dissertations have comprehensively investigated the
influence of cloud technology on care services information
technology. Considering the views of administration,
infrastructure, and safety, this research seeks to find the
challenges that affect the incorporation of cloud computing into
the health sector and the recommendations on the steps to be
carried out to help overcome these challenges, the influence that
clouds computing has to the health sector and the methods of
facilitating the acceptance of cloud computing into the health
sector so that considerable improvements can be seen (Amron et
al., 2017).
Administration challenges of cloud computing
The key issues are the absence of consumer confidence in safety
and confidentiality issues in the data sector of the

organizational lethargy, lack of accountability, and adherence
by doubtful vendors (Dauwed et al., 2018). Challenges occur
when their crucial information and expedition programs shift to
cloud services, whereby vendors might not assure their data
protection with safeguards' efficiency (Govinda,
&Ramasubbareddy, 2018). Societal opposition to information
exchange and conventional operational environments is a
typical cloud service administrative difficulty. A sales contract
may not be binding to enable the customer to audit their data in
certain situations (Gao &Sunyaev, 2019). Records management
breach might have a severe effect on the tactics of a data center
and hence on the ability to fulfill its purpose and objectives.
Technology challenges of cloud computing
Numerous cloud-related technological issues involve assets
fatigue, operational volatility, information shut, memory
management, constraints, and defects in vast and complex cloud
environments. Due to heavy rivalry, significant suppliers crowd
the marketplace, and many cloud vendors overextend computing
power to lure consumers (Shepherd, 2019). To sustain income,
the moral framework cut shortcuts so that they may, for
instance, block connectivity to cloud assets, employ outdated
virtualization, or install older Processor architecture.
This disparity among consumer expectations and what the
supplier can realistically supply is a significant technological
issue for the cloud client to give maximum support to their
users (Osplabs. 2018). Mobile check is also a significant
problem in certain circumstances; cloud customers may need to
relocate information or capabilities to another supplier or return
to the technology system. If the supplier stops commerce or
function, software clients may notice a transmission power
congestion due to limited actual network infrastructure. Another
particular technological concern is flawed in massive data
centers (Sharma et al., 2020). Relative to IT platforms, the
mistakes in these enormous dispersed architectures are harder to

troubleshoot.
Security challenges
IT usage has various data security threats, such as hacker
assaults, network outages, natural catastrophes, partition
breakdown, urban planning, functionality, improper
cryptography access control, and misuse of privileges
(Shepherd, 2019). Isolation breakdown, governance
functionality, bad cryptographic critical administration, and
authority misuse are particular threats to cloud technology. If
somehow the supplier does not isolate the services, it might
pose significant safety problems.
In the illustration, a client wishes to remove data stored in a
computing server; as with other web browsers, this would not
instantly lead to genuine data erasure. The information is still
stored on the drive but still missing (Sharma et al., 2020). Other
clients utilize physical hardware in numerous tenancies. In this
situation, a foreign entity might obtain removed data from
another client. These pose a more significant risk to internet
clients than underlying servers. Improper access control may
lead to loss of cryptographic algorithms, exposure of private
keys or credentials to hostile persons, or illegal verification
usage.
Despite the benefits of cloud computing in e-health services,
information security is questionable. Security problems have
turned more complex in cloud models and need additional
investments to deploy data management policies. For each
predictable event in health care cloud computing, primary data
need to be registered and accepted by the important people to
propose and take the required measures. The data kept in cloud
virtualized surroundings can be accessed via several people;
thus, healthcare cloud computing has multiple issues and
concerns involving data transmission and access control.
Meanwhile, when users keep and transfer the information on the

cloud, the integrity of the data set and issues associated with
moving data is quite a difficult task. However, when health
information is stored in the cloud, patients lose physical control
of their private information (Mehraeen et al., 2017).
Achieving security along with privacy within e-health is very
crucial in obtaining the aims of utilizing modern technology.
This is necessary as digitizing health-associated data along with
sharing them can lead to various kinds of attacks. Various
government health institutions have developed a framework for
ensuring an excellent level of safety and privacy. For example,
the HIPAA was put across by US Congress in 1996 as federal
law applies for the US healthcare industry. Following HIPAA
guidelines, a combination of valuable security and privacy
requirements must be put to effectively utilize e-Health, (Azeez
& Van der Vyver., 2019).
As noticed, the safe exchange of e-health information within the
company needs standards for security measures and privacy
protection. There have been vital privacy and security issues
with e-health systems. One of which is access control and
authentication. This is needed to make sure there is proper
confidentiality and authorization for patient records. Reliable,
robust, and standards forms of authentication are specifically
relevant requirements for safeguarding patient privacy. The
second issue is data integrity. Ensuring integrity is one of the
crucial keys in e-health systems like HER since it guarantees
the precise of data, thus reducing error and enhancing patient
safety. Incorrect input of staff data between paper and
electronic medical records leads to such errors (Bajrić, 2020).
The next issue is system availability which is needed for
attaining the continuity of EHR to ensure the best services. It
claims that the system does not need to be considered to a
particular time of day; otherwise, the physician's job will get
difficult since decisions can never be made in real-time as

needed. Another issue is data loss which is essential to
safeguard against. In the case of data loss, there is a need to
achieve data recovery, which can get challenging due to
hardware and software errors, security attacks, or network
errors. The last security issue is network security. Data
protection is crucial when the security of critical assets depends
on network security. Disruption to network functionality and
denial service attacks can impact healthcare delivery, Bajrić,
(2020).
Unintended consequences
Apart from safety concerns, Meeks and his colleagues show that
the IT solutions like her can contribute to other unintended
consequences due to issues of usability, disruptions of clinical
processes, and unsafe workarounds. The unintended
consequences of HER solutions cast doubts on the system's
reliability for improving the quality of care and reducing
medical errors. Commonly, unintended consequences are hard to
classify into a category since they are side effects of hidden
social-technical system factors, Vanderhook & Abraham.,
(2017).
Possible solutions to challenges facing cloud computing
The solutions to administration challenges can be solved by
enabling a transparent platform that assures the consumers that
their data is well handled. In cases of any threats occurring in
the cloud services of the healthcare patients, the administration
should be able to respond while alerting the clients on taking
specific measures that might mitigate the risk, such as changing
the log-in credentials (Osplabs, 2018). The healthcare
administration also needs to guarantee that their consumer's
data are backed up in case of any emergency the private details
are secure from being tampered with. Another measure that the
administration needs to put in place to entice more consumers
and hold on to their previous clients is providing a recovering
method and setting up a customer care service where queries

concerning their data can be audited (Meri et al., 2018). On the
side of the healthcare service vendors' technology solutions on
cloud computing technology, creating an online platform should
be a basic necessity (Osplabs, 2018). Computing mobile
healthcare applications with broad interaction capabilities,
patient registration, patients' hospital information,
communication, healthcare, and more need to be catered. That's
where the professionals can construct customized bridge
eHealth cloud apps with desktop and Mobile compliance and
sophisticated capability to build new features and integrate with
external networks.
On the security challenges, specific measures and conditions
need to be applied in the cloud technology software to eliminate
hackers who aim to tamper with the patients' data (Osplabs,
2018). This involves creating many coded layers w ith
information technology experts capable of mitigating any risk
and alarming it whenever there are signs of an invasion.
Implementing a rigorous software solution safety procedure and
evolving hospital culture ensures data protection is reasonable
progress (Sabbah et al., 2019). There is also a need to educating
and equipping Information systems professionals to manage
safety concerns with a suitable response.
Cryptographic solutions
The ongoing advancements of IT along with data
communication strengthen the exchange of greatly sensitive
medical data. Electronic health systems get widely utilized, and
several facilities depend on transmission and the receipts of
medical information. Over past years, several security models
were introduced to monitor the privacy of patients and ensure
the safety of medical data exchanged. Cryptography was a
technique that frequently gives security for HIS. Cryptographic
approaches allow one to several computations to occur directly
from data encrypted, regardless of defining them. As such, the
schemes f encryption with homo-morphic structures gets helpful

in creating confidentiality agreements, where the confidential
information stays safeguarded during the exchange, processing,
and even storage.
Cryptography refers to compiling, validating information, and
sharing it through conferences to monitor the data set up for
investigation. They have been countless approaches suggested
for protecting patient health care information. Nevertheless,
cryptography methods can either be symmetric-key or
asymmetric-key, where the previous utilization of similar
encryption along with coding key while the other utilizes
various keys (Sivan &Zukarnain, 2021). Moreover, there is the
use of Access Control Manager, where the managers of access
control utilize tokens for accessing their classified records kept
in the cloud where servers perform users’ identification and
determine the access rights through AAM servers.
Authorization as solution
Authorization typically gives the extent to which approved
users can enter the system after a correct authentication. It
usually comes for a system after correct authentication and
giving user privileges to access the system. In computer models,
RBAC refers to a model that gives limited entry based on
privileges for the user after correct authentication. The models
of RBAC involve RBAC0, which is the basic model with basic
needs of security with regards to access control (Keshta &
Odeh, 2021). The roles, Permissions, and users get defined
regarding the model based on tasks based on Function to
Permission and then User to Permission. It is based on rules
where a user can serve as a member of various roles, and every
role can contain several users.
The second model is RBAC1 which was introduced for
increasing the efficiency of models at the administration level.
The role hierarchy is of much help when assigning roles to the
juniors and seniors in a company. It aids the senior role to have
entry to each type of data related to the junior role. However,

the junior cannot access senior data. The following model is
RBAC which is used in models that cardinality is relevant and
utilizes rules of RBAC0 and extra factors for promoting the
model's security. It is where the permissions get added for
functions of establishing separation among roles. Lastly, there
is RBAC3 which is often recalled as the Unified model because
it works on each rule of RBAC0 and the other models. RBAC3
aids more constraints and Role Hierarchies. The model is
favorable for systems where classified interaction needs to
occur (Rashid et al., 2020)
Data Security and Privacy Needs
To enhance the trust in the innovation, CC application in
healthcare has several securities needs to be accomplished. The
following are the needs of security and privacy for healthcare
application of CC;
Authenticity
This requirement refers to the truthfulness of attributions,
origins, intentions, and commitment. It is responsible for
ensuring that the entity demanding access is authentic. In the
healthcare model, the information given by healthcare givers
and the identities of entities using such information needs to be
verified through the Act of Authentication. The authentication
of information is likely to pose unique issues, like man-in-the-
middle attacks, and is frequently minimized by combining
usernames and passwords. Multiple cryptographic protocols
involve most forms of endpoint authentication, particularly for
preventing such attacks. Within models of healthcare, both
information given by providers along with the identities of
consumers need to get verified at each access, Abouelmehdi et
al., (2017).
Confidentiality
Confidentiality refers to the act of making sure the patient
health information is stored wholly unhanded over to

unapproved entities. Delegating the data controls to the cloud
increases the risk of data compromises like the data turned
accessible to augmented counts of parties. There is an elevation
in data compromise threats because of the elevated counted
parties, devices, and even e applications included. To make
doctor links work effectively, patients must trust the model to
safeguard data confidentiality. If the patient feels that
information is compromised, he/she can choose the information
to disclose to the doctor in the future. The data threat can harm
the relationship between doctor and patient and even hamper the
correct medical diagnosis or treatment (Keshta &Odeh, 2021).
Availability
For CC in healthcare to serve its role, information needs to
present each time. An essential and often overseen aspect in
HIS is the availability of information in crucial situations
involving carrying out operations even when the authorities
misbehave along with the capability of ongoing running even in
chances of the security breach. Because of power outages,
system upgrades, hardware failure, and denial-of-service
attacks, great availability models have to avoid service
disruptions. It has the main motive of countering services that
can compromise availability (Desai et al, 2011.). It needs to be
capable of preserving the usability of the records after the
enforcement of the HIPAA privacy and security rules.
Integrity
The following requirement ensures that health data gets
captured through the model to an entity are precise along with
consistent with intended information and are never modified in
any way during storage (Vithanwattana et al., 2017). Utilizing
the cloud for an actual application such as eHealth needs perfect
reliability for given services. Each eHealth cloud data and
services need to have zero errors. Unfair treatment based on
erroneous data can carry essential consequences on the health of
patients. The Security Rule of HIPAA claims that covered
entities need to implement procedures and policies for

safeguarding electronic individual healthcare information from
incorrect alteration. In healthcare, services storing and
manipulating the patient data need to implement verification
functionality and integrity functionality, such as nonmedical
applications, through means of checksum, before utilizing the
data. When integrity fails to check, the application in healthcare
must report errors and needs terminate regardless of processing
the data.
Access Control
Access control refers to a mechanism for managing access to
patient's health information that grants entry to illegitimate
entities. The policy of access control relies on privileges and
the rights of every authorized practitioner through patients or
even a trusted third party. Multiple solutions have been
suggested for addressing access control and security concerns
(Desai et al., 2011.). The RBAC along with ABAC are the top
popular models for applying CC in healthcare.
Audit
This refers to a security measure that makes sure the healthcare
model is safe. Audit suggests recording the user's activities in
the healthcare models in chronological order, like maintaining a
log of each access along with modification of data. Both
HITECH and HIPAA need users within healthcare providers'
organizations to be put accountable for addressing the patient's
safeguarded health information. There are various approaches
for maintaining audit controls on the information (Al-Shura et
al., 2018).
Data freshness and remanence
Data remanence is the residual representation of data that got
erased nominally. This can cause an unintentional attack on data
confidentiality. In the healthcare model, data integrity and
confidentiality are never enough if the freshness of data is never
regarded. Data freshness demands that the health records of
patients need to be fresh along with up-to-date. Delays in

sending and storing outdated notifications have consequences of
data inconsistency, particularly in crucial situations.
Secure transmission
This requirement deals with HIPAA Security Rule, which states
that the covered entities need to implement technical security
measures for guarding against unauthorized access for the
electronic safeguarded health information shared over networks
of electronic communications. The 2009 HITECH Act widens
the rule to business associates. All the rules of HIPAA cover
communication between the covered entities of HIPAA, where
the focus is on an adversary who wishes to attain confidential
medical information from viewing the network communication
among two communicating nodes (Al-Issa et al., 2019)
Nonrepudiation
Repudiation threats are focused on users that deny their
signature authenticity after the access of health data. For
example, both the doctors and patients can deny signature
authenticity after health data is misused in a healthcare
scenario. Similar to electronic commerce, CC in healthcare can
use digital signatures along with encryptions for establishing
nonrepudiation and authenticity (Ianculescu et al., 2020)
Complexity and Compatibility
Other issues arising from the use of cloud computing can be
complexity and compatibility. Complexity refers to the level of
the perceived difficulty of understanding along with using
technology. Meanwhile, compatibility refers to the extent to
which an innovation is viewed as consistent with the current
values, needs of potential adopters, along with their previous
experiences. Complexity and compatibility are vital
determinants of adopting cloud computing, (Al-Shura et al.,
2018).
Compatibility issue in CC
Introducing and studying new health IT solutions is crucial for

considering the sake of enhancing their capability. However, it
can attribute to critical interoperability complications among
systems. As stated, with the relevant expansion of
implementation of new HER solutions, it has been noted that the
more development of various and heterogeneous models, the
problems of interoperability and compatibility are encountered.
While different EMRs, EHRs projects, and software get
implemented in health bodies, various analytic services are
abundant. Such latter is in the form of machine learning models
(Khennou et al., 2018).
Compatibility can get regarded as a critical factor in the
decision of deploying an innovation. When companies think of
deploying the cloud, compatibility with previous applications is
a noticeable concern. This concern rises from the little control
that cloud clients have over the rendered computing platforms
by the provider; this makes providers assure flexibility.
Nevertheless, providers can alter the rendered platforms
whenever they want, regardless of the customer's approval.
From the developer side, there is a rise in attention toward
compatibility centered on attaining an excellent level of
integration for innovation (Al-Hujran et al., 2018).
Complexity in CC
Cloud computing along HPC is driven by end-user demand for
further greater scale and performance. To attain such needs,
heterogeneous resources, typically in the form of novel
processor designs, are getting integrated into cloud platforms
along with HPC models. The side effect of this is greater
complexity, specifically in the case of hyper scale cloud
services where the scale of infrastructure, applications, and
several end-users is multiple orders of magnitude greater than
overall-aim computing and HPC. Such complexity in large-scale
models leads to vital management, reliability, security, and
security issues (Lynn, 2018).
Emergence and the associated concept of self-organization, self-

management, and separation of concerns are design principles
that get proposed like potential solutions for handling
complexity in huge-scale distributed information models. The
complexity of hyperspace cloud models makes it very infeasible
for cloud services providers to foresee and manage manually.
Each possible configuration, end-user operations, and
component interactions on a detailed level due to significant
levels of dynamism in the model. Self-organization has roots in
natural sciences and studying realistic models. It has long been
recognized that greater-level outputs in dynamic models can
serve as an emergent effect of minimum-level inputs (Lynn,
2018).
There are various keys to note for having a secure cloud
architecture. One of which has a single sign-on. Within the
cloud computing ecosystem, workers log in to multiple
applications along with services. This makes it hard to use
strong authentication at the user level. To counter such an issue,
streamline security management along with implementing
single-on users for the cloud. This allows users to access
multiple applications and services in cloud computing
surrounding via a single point, thus enabling strong
authentication at the user level (Tripathi & Mishra., 2011).
The second point to note is increasing availability. Access to
cloud services needs to be available each time, even in times of
maintenance. This makes crucial business data kept in the cloud
be always present to cloud users, minimizing network
downtime, thus increasing business profit. Therefore, can be
performed through implementing significant availability
innovations like active clustering, dynamic server load
balancing, and ISP load balancing within the network
infrastructure. Moreover, there is a need for a defense in depth
method. There is a need to have proper layered protection
consisting of perimeter safety and intrusion detection and
prevention components within the network, (Tripathi & Mishra,

2011).
The use of Cloud Technology and Health Visuals
Mammography is understood as the method of seeing the
organism, more especial of humanity, using various techniques
to detect, evaluate, or cure various diagnostic problems
(Hartmann et al., 2019). Several detection methods are available
depending on the equipment utilized and the volume of material
included in the picture regarding the body region being
inspected. Screening tests comprise X-rays, scanning
techniques, magnetic resonance imaging, radiography,
sonography, and Computed Tomography.
Throughout generations, microfilm served as the primary
medium for previewing and sharing diagnostic pictures among
clinicians. Unfortunately, the video was prohibitively
expensive, cumbersome, and inefficient due to the possibility of
being lost. Shortly on, Digital Drives developed and partly
supplanted film as a storage medium. Discs are cheaper costly,
more portable, and offer larger memory capacity than other
media. Despite this, CDs are susceptible to injury and may
remain illegible, result in information failure as a result.
In response to the change in emphasis from providing patient-
centered care to promoting patient-centered care, the
Photographic Acquisition and Communicating Network were
developed to save, handle, acquire and display healthcare
pictures (Silva et al., 2014). Image Acquisition and
Communicating Network is composed of four components:
imaging equipment, storing facilities, terminals for picture
viewing, and a connection that connects all of the components
of the system. The transmission and dissemination of medical
image analysis are accomplished via the Diagnostic Scanning
and Connectivity in Healthcare protocol. DICOM allows for
exchanging medically diagnostic data across healthcare
equipment within the same organization, but not amongst

pharmaceutical products in other organizations.
The conversion of medicinal pictures to electronic platforms has
had a significant positive impact on healthcare facilities
regarding expense reduction, increased efficiency, and the
promotion of cooperation amongst health providers (Silva et al.,
2014). Cloud technology enables surgeons to use rebuilding
equipment hosted in the cloud by signing in to their physical
work area using their existing credentials. As clinicians analyze
pictures, only instructions and sorting occur at the workplace
area, whereas the analytical interpret takes place on cloud
storage. Identification procedures are shown on users' gadgets
while doctors are working.
Additionally, cloud computing enables users to obtain content
from any location and at any time as a timely update is made,
eliminating this need for system integration installation and
management, reducing overall IT cost. According to some
estimates, IT costs are expected to be reduced by twenty percent
each year as a result of cloud computing.
How cloud technology is popular in the Field of medical
The hospital sector makes use of a diverse variety of
information technology applications, ranging from basic
productivity applications such as desktop publishing to more
sophisticated biological equipment such as positron emission
tomography machines and everything in between (Nomoto et al.,
2014). Nonetheless, as opposed to other sectors, the medical
industry is approximately ten to fifteen years behind the curve
when it comes to adopting information technology.
Hospitals' financial accounts were just the first piece of health
information technologies to be used; it was not until the nineties
that health information systems were introduced. An EHR
system is a phrase that refers to a platform that consolidates all
of a participant's clinical records, and the name information

management system is also taken to characterize this
technology, per the investigator. A journal article healthcare
file was the primary method of storing customer records,
including diagnostic pictures, doctor remarks, pharmaceutical
prescriptions, and other relevant health records. Electronic
health records were moved to offer more patient-centered
hospital services by reducing medical mistakes, ensuring timely
access to correct information, and improving safety procedures.
Clients would reap the advantages of new innovation if they had
sufficient knowledge and received adequate coaching in its
usage. For example, adopting EMRs requires extensive user
training as well as significant upfront financial investment
before any advantages can be realized.
When compared to other sectors, the acceptance and application
of information technology (IT) services in hospitals is sluggish.
This, according to the leading analyst, is due to the inability of
IT technologies to demonstrate significant reductions in
operating expenses while simultaneously improving patient
outcomes (Bhatt & Peddoju, 2016). In addition, the
interconnected economies across the medical industry were
identified as a contributing factor to the delayed implementation
of information technology; these interconnected sectors
comprise health treatment, hospital coverage, the authorities,
and the employment force. It was claimed that delaying IT
deployment in the health industry would allow participants to
benefit from earlier literature on IT technologies' acceptability
and deployment in hospitals, which would help implement these
workarounds in the prospective.
In addition, user acceptability is a critical aspect to consider;
past expertise with failed solutions may make it more
challenging to embrace new information technology
innovations. Anxieties that introducing innovative IT
alternatives will disrupt the traditional workload of medical
practitioners are one of the factors contributing to the sluggish

adaptation of innovative IT alternatives (Bhatt &Peddoju,
2016). IT workers role in the care services take responsibility in
the sector serve as both authorized personnel and service
operators; thus, knowing the requirements and aspirations of
their companies may be beneficial in the phase of various
digitalization, since employees are a part of the consideration
process in this sector. The need for medicinal treatments has
risen significantly in recent years, leading to a greater in the
weight of medical information as well as an amount of monetary
expenses.
Virtualization, as a new information emerging technology, has
the opportunity to boost the medical Field by creating an
opportunity to regulate and improve service delivery, improving
specific, and facilitate information exchange from anywhere at
any time, enabling better the performance of public sector to
sick people and improving the overall patient experience. It is
projected that approximately above thirty percent of healthcare
organizations are already using a virtualized technology, with
the residual of more than seventy five percent of public health
organizations intending to use cloud - based services in the next
few years.
Cloud computing offers memory space that is expandable to
meet the requirements of the business, resulting in a reduction
in healthcare information technology costs. Cloud-based nursing
informatics storage provides medical professionals with the
advantages of efficient management and updating of their
records and improved control over admissions, thus reducing
the likelihood of illegal access to patient records (Changming,
2017). Furthermore, cloud-based medical storage capacity
allows for participants in the medical sector to maintain
conformity to regulatory requirements since digital formats of
material can be handled, analyzed, and safeguarded much more
easily than paper-based formats.

According to the experts, investigators have brought awareness
that the cloud hosting are supplied by third parties, posing
dangers to end users since they have no influence from over
security and compliance of their content kept on the Internet. To
be sure, interacting with a private entity is fairly unusual in the
traditional IT context, therefore these dangers are not unique to
the cloud system (Changming, 2017). Although this is the case,
present and prospective cloud services users are considered to
be coldly logical in their concerns since the confidentiality
threats are exacerbated in the cloud infrastructure when
contrasted to the conventional IT ecosystem.
It is important to note that, in the cloud architecture, existing
customers are not only exposed to outside data security threats,
which are common among traditional IT systems, but they are
also exposed to internal dangers. While other clients who are
backed by a certain cloud service get illegal entry to cloud
storage, this is referred to as a hazard. The activities of cloud
services must address these concerns and provide customers
with the assurance that personal information is correctly kept,
discreetly guarded, and safeguarded. Infringement of legal
rules, as well as the fines and sanctions that arise from them,
must be avoided.
The use of big data technology, ubiquitous electronics, and the
Internet of Things (IoT) in hospitals
In the course of their daily operations, healthcare professionals
produce and gather a significant quantity of knowledge from a
diverse range of institutional systems, including computerized
medical data, radiological pictures, drugstore purchases,
medication records, laboratory results, and workers
compensation information (Devi, 2020). With each passing year,
the amount of this digital information continues to increase at
an enormous speed, attributable mostly to developments in the
provider landscape, including as rewards for effective use of
electronic health records and a move for valuation monetization

strategies. In order to effectively handle a huge number of data,
a considerable amount of processing capacity and network
connectivity are required. Healthcare organizations are being
forced to spend more in IT facilities when it comes to on disk
space as the volumes of information being stored grows
(Navale&Bourne, 2018). This has resulted in the archiving of
significant amounts of healthcare data not being regarded as a
feasible alternative for storing such data. Cloud technology, on
either way, makes it possible to do any big data activity by
providing a huge amount of space and computational power.
Virtualized statistical methods also assist clinicians in
improving patient management by converting medical
information into practical information.
Individual tools in CC of healthcare industry
There are various examples of how the cloud, along with its
services have been employed in biomedical task. In genomic,
the usage of CC ranges from an application to finished virtual
machines with several applications. One of the individual tools
used is the
BLAST which is a commonly used tool within bioinformatics
research. A server image of BLAST is able to be hosted on
Azure, AWS, along GCP public clouds for allowing users to
operate stand-alone searches with BLAST. Users also submit
searches utilizing BLAT via the NCBI API to operate on Google
Compute Engine and AWS. Moreover, the Microsoft Azure
platform gets leveraged for executing huge BLAST sequence
databases from NCBI, operate different BLAST programs on
specified input from the sequence databases, and produce
visualizations from results for analysis that is easy (Devi,
2020). Azure also gives ways of creating a web-based user
interface for tracking and scheduling the BLAST match tasks,
managing users, visualizing results along with performing basic
tasks.

Another tool is the Cloud Aligner which is known for its speed
and full-characterized MapReduce-based equipment tool for
mapping sequences, tailored for being able to handle the long
sequences, while Cloudburst gives great sensitive short read
mapping with MapReduce. Great-throughput sequencing
analysis is able to be conducted by the Eoulsan package
integrated into cloud IaaS surrounding (Navale&Bourne, 2018).
For entire genome resequencing analysis, Crossbow remains the
scalable software pipeline. Crossbow integrates Bowtie, a
memory-efficient, and ultrafast short read aligner, along with
SoapSNP, which is the genotype, in an automatic parallel
pipeline running in the cloud.
Microsoft Azure platform in healthcare
One of the most common cloud platforms is Microsoft Azure
which is a provider involving the IaaS, PaaS along with SaaS
developed by Microsoft utilizing its network of data centers that
keeps on expanding all over the globe, as it gives a variety of
CC, storage along with application services for every kind of
users from business sector along with institutions. It enables the
development of applications and services for each kind of user
from the business sector and institutions. It enables the
development of services and applications, examination,
dissemination, and management over the vast worldwide
network using special tools and frameworks. Privacy and
security are also involved on the Azure platform. The platform
provides the ability to store data safely with ready entry while
increasing the storage space at any moment, depending on the
business activity of the user. In the case of CC services, the cost
is based on space consumed, and the total expense is computed
regarding hours of access along with data usage. It also gives
the chance of developing applications with ease along with
publishing compatible apps worldwide on each web platform
and mobile phone spread, along with being able to create and
respond rapidly with the capability of managing the web
applications along with examining and publishing them with

ease, Hassen et al., (2020)
Workflows and platforms
Integration of phenotype, genotype, and clinical data is crucial
for biomedical research. Biomedical platforms give an
ecosystem for creating an end-to-end infrastructure used for
storage and analysis of data acquitted. One of the platforms is
the Galaxy which is an open-source used for data-intensive
research. For huge-scale data analysis, Galaxy gets hosted in
cloud IaaS. It can achieve a highly scalable and reliable cloud-
based workflow model for the coming-generation sequencing
analyses when integrated with Globus Provision. As a scalable
and robust pipeline for NGS analysis is required for the
diagnostic task in clinical laboratories, Cloud Man is present on
AWS cloud infrastructure. It can be utilized as a platform for
sharing tools, data, and analysis consequences. Enhancements in
utilizing Cloud Man for analysis in genetic variant has been
done through minimizing storage expenses for clinical analysis
task, Navale&Bourne, (2018).
Electronic Health data
Electronic health is the use of electronic communication within
the sector of healthcare. The technical progress within mobile
communication along with networking innovation has resulted
in the development of individual systems in e-health. Tele-
health is a remote clinical and non-clinical service.
Telemedicine is a remote clinical service for patients in remote
areas. With the elevating prevalence of tablets and mobiles, it is
extensively utilized for supporting the practice of healthcare;
developing e-health models, mobile health, telemedicine, e-
visits, and e-consultation have turned into an increasing need.
Such models are utilized for ongoing monitoring, predictions,
diagnosis, and treatment (Mahmoud et al., 2020). As a result,
they result in minimized healthcare costs. Patient monitoring
models are viewed as important services within mobile health.
It aids patients in performing daily activities while the
important signs are kept on the radar.

IoT, together with cloud services, add more capabilities that can
get divided into three service layers known as IaaS that gives
great volume storage like Amazon S3 storage service, PaaS that
offers both services and storage like Google App, and SaaS. CC,
together with WSN, gives promising monitoring models that
facilitate and enhances QoS. It enables actual-time entry to
patient data at a time and from any place, allowing
preprocessing, analyzing, along sharing data, providing great-
volume storage in minimized cost along with reducing the cost
in hospitals along with clinics. With regards to S. Pandey, he
presented a model that combines mobile computing and CC for
analyzing ECG data, where a cloud surrounding was developed
for gathering people’s health data like ECG data along with
disseminating them to cloud-based information repository that
facilitated analysis of data through software services hosted
within the cloud. Another person known as Risso provided a
Cloud Mobile system used for monitoring respiratory diseases;
the system monitor patient Spo2 through wearable sensors then
utilizes mobile devices for transmitting to the cloud system,
Mahmoud et al., (2020)
ECG Monitoring System
IoT model has enabled designing small devices capable of
sensing, processing along with communicating, enabling
sensors, embedding devices along with other things developed
for assisting the clarifications of the surrounding. This includes
the ECG monitoring model, which has been developed along
with greatly utilized in the healthcare sector for the past few
decades and significantly keeps on evolving over time because
of the rise of smart enabling innovations. The architecture of
the ECG monitoring system is known to be beneficial in
healthcare. In the architecture of ECG, the right silos give
processing along with storage services to each process in four
horizontal layers. For instance, processed actual-time ECG
signals can use edge/fog computing. Also, the cloud
infrastructure allows storage along with processing services at
several stages of the ECG monitoring lifecycle. Moreover, at

the core of its design, security and privacy of data are crucial
characteristics that need to be supported in each process where
data gets gathered, transferred, analyzed, processed, accessed,
and along visualized by several stakeholders. It is also
suggested that blockchain technology can be combined to give a
trusted, immutable, and decentralized ledger for several
transactions which outdo existing approaches, techniques, and
mechanisms. It gives a great level of transparency for ensuring
privacy and security (Serhani et al., 2020)
The ECG monitoring model is a diagnostic cardiac issue thus
plays a critical role. This turns the implementation of the
Portable POC model at an inexpensive cost critical with the aim
of monitoring the cardiovascular health of patients regardless of
competing with daily routine. Moreover, a portable ECG
monitoring model with limited shape elements is activated with
BLE (Xu, 2020). Indeed, it is true that the healthcare
monitoring model in healthcare has experienced vital growth,
along with portable healthcare monitoring models with rising
technologies that are turning great focus to several countries
globally nowadays.
Smart health monitoring approaches, smart home, smart
packing, smart city, industrial sites, smart climate, along
agricultural fields are the key applications of IoT. The
tremendous key use of IoT is providing health and surrounding
condition tracking facilities. Heart rate along with body
temperature are two key vital indicators for the health of
humans (Islam et al., 2020).
IoT applications in healthcare
There are other key main applications of IoT in healthcare. One
of which is control of medical equipment and medication
control. IoT is used in monitoring the entire process of delivery,
production, anti-counterfeit, along with tracing medical
equipment and the medications for safeguarding public medical
safety. The aspects of the application include robust actual -time
monitoring, which makes sure that medicine is delivered and

gives a storage environment. The next aspect is the anti -
counterfeiting of medicine and therapeutic equipment for
recognizing RFID tags placed on a product is hard and unique
for recreating. For example, if medicine data is kept in the open
database, the patients, along with emergency clients, are able to
check the label as per the record set in the open database along
with distinguishing the fake meds efficiently and effectively.
IoT is capable of managing medical information, which involves
various applications. One of the applications is patient location
and tracking, which is a critical asset since it allows prompt
reactions in urgent assistance is required. This is specifically
for pathologies involving cognitive along with perceptual
disorders, epilepsy, Down's syndrome, and even
neurodegenerative diseases. The second application is the
medical equipment along with medication tracking, the correct
records of therapeutic equipment along with drug, uses the
necessary data of item use, specific data of item linked with the
unfavorable occasion, the origin of the item with quality issues,
patients who have used item with quality problems along with
districts the item with quality problems hasn’t been used (Syed,
2017). IoT relying on smart bundling approaches for medication
boxes can be used for drug management. The approaches have
controlled fixing depending on delaminating materials along
with constrained through remote communication.
According to Syed the third application, neonatal anti-theft
model, which integrates the identification management, neonatal
anti-theft along with pathway entry for avoiding the random
coming along with going of strangers for giving workable along
with robust protection for the coming babies. The following
application is in fall prevention along with fall detection due to
falls being serious health issues with elders. Detection of falls
can be grouped into ambient sensor-based, wearable devices
along with vision-based. The next application is that the
Medical Emergency Management aids dependable along with
productive data stockpiling along with review strategies for

RFID technology. Blood data management is another key
application as it can maintain the strategic distance from the
little limit downside of the bar tags along with acknowledging
non-contact recognizable proof for lessening blood pollution,
realizing several-target ID along with increment the efficiency
of information assortment. Lastly, the alarm system is an
application instance where the alert framework aids patients
with sending crisis trouble signals in the continuous tracking
along with monitoring of patients and clinic therapeutic
apparatus, Mohammed, 2020)
Internet of things has the capability of connecting medical
devices, sensors and healthcare professionals with an aim of
offering quality medical care especially in remote areas.
Internet of things play an important role towards improving
safety of patient, cost of healthcare is minimized and health
care facilities become easily accessible. Efficiency is a key
factor in health care and internet of things creates operational
efficiency (Martino et al., 2018). IOT enhances technologies,
healthcare services and application in coming up with solution
to various issues in healthcare. By use of internet of things
clinical analyses such as blood sugar test can be conducted at
home hence no need of patient to stay at hospital during the
treatment period. A device like smartwatch can enhance
diagnosing of diseases in health care and doing some necessary
monitoring. Internet of things technology has transformed
hospital-centric healthcare system to a patient centric system.
Through use of telecommunication services clinical data from
healthcare can be communicated from remote areas.
Telecommunication devices are combined with some advanced
technologies which includes machine learning and big data
analysis in order to make an improvement towards accessibility
of healthcare services and facilities. Internet of thi ngs has
enhanced; independence and it has distributed the ability of
human to interact with outside environment internet of things
play a major role of enhancing communication in health

industry. In healthcare internet of things is associated with
increased accuracy, less cost and it can make predictions about
their future in a well-organized manner. Sensors created which
are applied to human body through wearing enhances collection
of physiological information concerning human body and this
information includes things such as temperature, pressure rate,
electrocardiograph among many others. Internet of things
devices can also help in collecting information in the
environment which includes temperature, humidity rate and
time. The collection of these informa tion assists in making
progressive decisions on patient health and their existing
conditions.
Architecture of healthcare internet of things
Framework of internet of things used in healthcare aims at
maximizing advantages of internet of things technology and
cloud computing with the medicine field. Internet of things
offers a framework necessary from transforming patient data
from various sensors within the medical devices to a certain
healthcare network (Bhatt & Bhatt, 2017). The basic component
of internet of things in healthcare are publisher, broker and
subscriber.
According to Bhatt & Bhatt Publisher contains network of
sensors that are connected and many other medical devices that
can work individually or combination with others with an aim of
recording important patient information. Information such as
blood pressure, oxygen saturation and others once recorded in
the publisher they are sent continuously through network and to
the broker. The use of broker in internet of things of healthcare
is to process and store acquired data into the cloud. Subscriber
function is to monitor patient information continuously. the
monitored information can be accessed and visualized through
technological devices such as smartphone, computer and table
(Yuehong et al.,2016) Once the publisher has processed data it
gives observation concerning any physiological anomaly or
change in the patient health condition. The HIOT makes

assimilation in discrete component to hybrid grid. In hybrid grid
a specific purpose is dedicated to each component in the
internet of things network and the clouds in network of
healthcare.
HIOT technologies
In coming up with healthcare internet of things technology used
is so crucial since the technology used plays an important role
in enhancing ability of IOT system. The technology used are
identification technology, communication and location
technology. By use of identification technology accessibility of
HIOT system becomes possible and the access is from
authorized sensor that can be existing in a remote location
(AlBar & Hoque 2019). The identification follows a process of
assigning a unique identifier to each entity which is authorized
hence it can be easily identified hence unambiguous data
exchange is achieved. Communication technology used allows
connection among different entities in healthcare internet of
things network. The technology of communication is divided
into short range and medium range communication technology
(Meri et al., 2019). Short range communication technology is
used to establish communication among objects that are within
limited range. Medium range communication support
communication over long distances in healthcare sector.
Confidentiality
It's a common worry when it comes to cloud computing
alternatives that the database housed by suppliers is not as safe
as that kept on board. Client documentation is deemed delicate,
and a strong level of security must be preserved to ensure that
such data is only available to those who have been granted
permission to do so. Despite the fact that the cloud provides a
variety of advantages and protective features, cloud storage is
still vulnerable to hacking (Misra et al., 2019). With the
growing amount of client records and the growing number of
efforts to change healthcare via digitalization, database
integrity and access are becoming more prevalent. Furthermore,
individuals are also worried about the safety of their

information, which underscores the need to sustain high-level
security requirements for information protection. Cloud servi ces
are susceptible to the same security risks as conventional
network infrastructure and are therefore not recommended.
Despite the fact that they provide more access control methods
and techniques, dedicated servers are nevertheless viewed with
suspicion by the medical sector as far as database issues are
concerned.
Teleclouds are beginning to appear in cloud computing.
It is becoming clear that the confluence of network connection
and the cloud will be a compelling method for providing
medical services to distant places shortly. In many nations, most
doctors and specialists are concentrated in metropolitan areas
and urban areas (Indrakumari et al., 2020). In order to provide
modern care centers, only urban regions are equipped with
them, abandoning clients in remote regions without assistance.
In order to solve this issue, the usage of a telecloud is
recommended, which allows doctors and hospital experts to
identify and administer clients from a range in actual manner
and at a reasonable cost. As medical professionals become more
aware of the benefits of telemedicine services, the use of the
communication cloud is anticipated to rise in a number of
nations, providing significant development possibilities for
participants in the medical high - tech sector.
There are problems with uniformity and compatibility.
Among the most essential needs for any medical institution firm
is getting insight into patient’s records quickly and easily. In
the case of a facility's data migration to the Internet, the
information is kept on devices and technologies managed by the
provider, which may present some technical challenges (Martino
et al., 2016). Platforms and gateways for cloud computing are
the most difficult obstacles for companies seeking cloud

technology compatibility. Because of a shortage of standards
amongst cloud vendors, data exchange across cloud-based tools
is limited, resulting in problems with mobility.
Increasing demand for medical and non-healthcare information
organization is anticipated, which would help drive the
industry's growth forward
Depending on the type of outcomes achieved, the medical cloud
technology business may be split into two major classifications:
healthcare professional services and pharmaceutical insurer
systems (Nomoto et al., 2014). According to the projection,
healthcare professional services, which comprise medical record
platforms and nonclinical data management, are expected to
grow at the highest rate over this time period. Per the survey,
the expansion of the market for virtualized of the named
alternatives can be ascribed to the positive effects that
virtualized of the mentioned solutions provide, like on disk
space, effortless interoperability of application domains,
application of appropriate techniques, conformance, data
management, and assurance.
The cloud infrastructure installation choice is projected to get
the largest circulation
The remote patient monitoring industry is classified into the
below named categories of network architectures: community
cloud, confidential cloud, and blended cloud (Michels&
Walden, 2021). Community cloud is the most often used
deployment style in medical cloud technology. In the Field of
medical cloud technology, the community cloud is by far the
greatest often utilized implementation type. According to
forecasts, the confidential cloud implementation option is
expected to make up the bulk of the economic share. In contrast
to giving administrators complete power over their networks,
the assumption on private cloud provides a very reliable secure

environment. Confidential cloud hosting is an excellent choice
for larger organizations that have in-house IT staff who are also
knowledgeable about security issues. Furthermore, on premise
private cloud allows companies to deploy their own applications
while still complying with any regulatory constraints, which
will result in increased use in the coming years.
Machine Learning in CC serves a crucial role in diagnosing
diseases
Worldwide, breast cancer is a vital cause of death among
women. Early detection followed by rapid treatment can
minimize the risk of death because of breast cancer. It has been
recently noted that machine learning in CC serves a crucial role
in diagnosing diseases, but predominantly in individuals staying
in remote areas where the facilities of medicines are less.
Diagnosis models based on machine learning serve like
secondary readers along with assist radiologists’ incorrect
diagnosis of diseases. In contrast, cloud-based models can aid
support the telehealth services along with remote diagnostics.
Methods based on ANN have caught the attention of several
researchers for exploring their capabilities for diagnosing
diseases. Extreme Learning Machine refers to a variant of ANN
having huge potentials as a solution for several classification
issues.
Cloud computing services got more critical for creating living
surroundings. In addition, CC services can be utilized to
monitor patients, older adults, and disabilities within remote or
inaccessible towns and villages in several underdeveloped
nations, where medical facilities and expertise are never readily
present. In such areas, women affected with breast cancer get
rendered undiagnosed, and ultimately, it gets late when reaching
doctors present to larger cities. Doctors can use CC for
diagnosing patients that cannot be reached because they lack
financial resources. They can utilize CC for guidance via

telehealth along with telemedicine which involves the sharing
of several medical data like great-resolution biomedical images
and patient video conferences from remote sectors to the other
locations, where the physicians along with massive healthcare
centers are situated, (Lahoura etal. 2021)
For disease diagnosis and medical image analysis, machine
learning approaches are widely utilized in the medical sector. It
has various functions like pattern recognition, fraud detection,
disease prediction, along image segmentation. Yet, the
conventional machine learning methods are never enough for
substantial medical databases. Thus, great performance
computing gets used in machine learning data to achieve
efficient and accurate diagnosis of massive medical data. A
subgroup of machine learning is deep learning which relies on
learning data illustrations to classify features, along with using
supervised and unsupervised machine learning techniques. The
technique is advanced, possessing the ability to discriminate
data features without man intervention. It has the capability of
producing robust and representative characteristic layer by layer
in neural networks. Data utilized for deep learning are achieved
from various sources with correct data types and significance
for developing suitable models for managing data analysis.
Typical data analysis models involve clustering, neural network,
classification, and other efficient models.
The neural network is a crucial element in deep learning that
replicates biological models for communication node
distribution and information processing. The deep learning
process involves RNN, RBM, AE, MLP, and even CNN. With
the functions of storing, handling along with processing are
complex for huge-sized medical images, there is a need for
great performance processors are needed for processing medical
images. Hence, CNN gets used in the medical field for
processing massive medical data. It is a feed-forward neural
network that aids in modeling sequential data. It also aids in

predicting and classifying diseases and decision-making in
times of disease diagnosis by using several approaches. Thus, it
can aid the clinicians in detecting and characterizing the critical
features in huge image series (Deepika et al., 2021)
Home care services in healthcare 4.0
With the elevating numbers of the aging population, there is a
rise in demand for home care services, thus transformi ng aged
care at home from open-loop human-dominated models to
closed-loop HRS. However, there is the expectation that
significant issues are being faced in the emerging
interdisciplinary sector, which gets addressed through the
enabling innovations originated from I4.0. Healthcare 4.0 is
utilized for depicting the gradual rise of the fact that is
elevating several innovations, particularly CPS, incubated in the
manufacturing sector, are being employed in healthcare. The
evolution context elevates quantities of CPS to shape digital
healthcare models, including products, services, and
technologies involving intelligent sensing, actuation, BDA, AI
IoT, Fog, and CC.
There are various features enabled by Closed Loop feedback,
such as affective human-robot-interaction. HRI has turned into
an important research topic in cross-disciplinary fields of
behavioral science, psychology, and cognitive science in the
last few decades. It involves both developments of robots with
man involvement and the study of how man and robots interact.
However, healthcare robots need to communicate with users for
performance optimizing. Thus, robots need to effectively
recognize, respond and interpret emotions expressed by man,
provided such emotions convey crucial aspects of interactions
like thoughts and feelings.
The second feature enabled is seamless infrastructure-robot-
interaction. Many points’ smart devices and open-loop models
have been created to expand IoT and intelligent infrastructures.
Enabled by the closed-loop feedback, CPS-HRS integrates each
intelligent device in home surroundings to promote and advance

seamless infrastructure-robot interaction. Thus, more detailed
and abundant data on the condition of infrastructure and
surrounding ecosystems' impact on health care can be gathered.
Thirdly, there is a feature of Human-Robot-Symbiosis where
several advanced robots entered the human workplace with
quick advances in robotic innovation. The robots can run
alongside a man with particular work without the need for
natural barriers. Lastly, there is the feature of collaborative
robot’s teleoperation, a promising solution for the absence of
home caregivers to the aging population, (Yang et al., 2020)
CPS in healthcare
In quick summary, CPS gets designed for replacing the
embedded models. They assimilate current computing
innovation with information and communication systems. The
CPS combines the microcontroller processing model's
characteristics and communication networks with the physics of
the whole system functioning in the actual world. Regarding
Humaid, the concept of CPS has three critical components in
healthcare: computation, communication, and monitoring. The
devices have a natural component like sensing along with a
networking component for communication. The model is
utilized like a collection of sensor data that communicates with
controller; CPS is poised to play a crucial role within the
healthcare industry. The multiple types of research on
healthcare's CPS focus on an actual-time innovative sensor
model that monitors and warns of patients' conditions in
healthcare. This involves telemedicine models that qualify
remote healthcare service facilities and self-controlled remotely
functioning service robotics, which aid patients with actual
activities. CPSs within healthcare are responsive to users,
network model medical devices, and life-critical who
cooperatively treat patients in complex medical situations
(Malapane, 2020)
How did Fog computing rise in healthcare 4.0
However, the cloud is not favorable for critical applications.
The application based on the cloud has various issues associated

with significant bandwidth needs, safety and security, and
intermittent delay issues. Healthcare applications need real -time
monitoring. Cloud can never fulfill actual-time needs. The data
gets moved to the cloud and back to the application leads to
delay. Such issues are vital to healthcare, where a timely and
correct response is required for saving a life. Models of cloud -
based enable data from various sites and devices to get
gathered, and output is then set to the desired device, leading to
response delay and needing incredible bandwidth for massive
data. Data safety and user privacy are key significant issues.
Such reasons make persons much hesitant to utilize the cloud
(Pareek et al., 2021)
Despite multiple drawbacks of the CC paradigm, primarily
associated with communication between end devices and
datacenter hosting cloud services, specifically in latency, cost,
bandwidth, and connection availability, all contribute to
limiting uses of CC. The proliferation of pervasive mobile
devices worsened the situation, considerably challenging the
cloud paradigm. Indeed, in multiple contexts, the cloud never
meets each requirement of the application. Various concepts,
terms, along with expressions have been coined for solutions
and lead to the rise of fog computing for transferring some CC
services to edge network, close to devices of the user along with
possibly partially depending on users' device resources along
with distributing the load among end devices and old CC
datacenters along with bringing local-term safety, low-latency
rates along with faster responsiveness while aiding to promote
performance scalability for the entire system.
Fog computing promotes on-time service delivery and
significantly mitigates several problems associated with the
cloud, like the cost overhead, delays, and jitter while
information gets transferred to the cloud. It also aids user
mobility, interface, resource heterogeneity, and shared data
analytics to address the needs of shared applications needing

low latency. In addition, it eases the management along with the
programming of storage, computing, and networking services
between end devices and data centers. Fog computing refers to a
powerful tool for supporting the decentralized along with
intelligent processing of the data volumes produced by IoT
sensors used for integrating cyber and physical surroundings,
thus aiding the IoT to attain its vast potential (Aceto et al.,
2020)
Mobile Computing in Healthcare
When there is an issue in developing nations in the sector of
infrastructure innovation, there is a rising need to use mobile
cloud computing, which remains a modern concept, along with
the concept of e-health and the applications and needs for the
application. Mobile health is one of the critical issues to be
addressed, having variants of entry health services among them
poor health infrastructure in most nations and existence of
deficiency in man resources working in the health field and high
cost of health services that are limited to financial resources
availability. By comparing mobile health with the computer and
network e-health services wired, mobile health services allow
users to have access to health services with much more ease and
comfort regardless of the place and time. Mobile health refers to
use concerning cell devices like many smartphones, mobile
applications, network and individual sensors that collect
information from the man, to give services health. One of the
key benefits of mobile cloud computing, giving it the
popularity, is minimizing the power consumption and
augmenting manipulator practice through expanding serious
resource runs from mobiles to clouds. The most common
complaint about the paradigm is the short battery life and the
limited supply and capacity of storage. Mobile CC has
familiarized offloading, where data is kept, and additions get
finished in remote cloud-like substitute of the mobile device.
The merit of the method involves green cloud surrounding and
energy-efficient mobile network, Ali et al., 2018)
Regardless of the merits, MCC poses, one of the top difficulties

in using MCC is the incorporation of security along with the
privacy of classified information. Currently, MCC is
extensively included in cloud based-health monitoring, but
because of the absence of accurate security, it is getting less
attention as it needs. Such challenges need immediate
addressing to make MCC more favorable. Security of HI is one
of the iterative procedures and changes to healthcare environs .
By employing new schemes to promote the quality and
effectiveness of HI, it is imperative to reconsider HI's security
practices and policies. Recognizing threats and securing HI is
one of the demanding challenges in healthcare.
However, there is the MES scheme which is a modular
symmetric cryptographic algorithm. Utilizing the scheme, the
first module with entropy-based Key production is actualized on
MCC client-side along with the second extender module
intended for extension and compression of Health records
before transferring data to the cloud (Yuehong et al., 2016).
The rendered modules are done on crypto-cloud, and the multi-
cloud-based storage gets carried out. As a result, the modular
scheme at various layers drives us to multi-layered modular
security of records at the cloud in healthcare. Through this
approach, even cloud service givers are incapable of
approaching HI because each giver approaches the enciphered
health record version. This way protects the HI from insider
entry just as outside access. The various features of intended
schemes in the MCC surrounding area ensure HI's
confidentiality against a hacker or the third parties, complete
control of patients to their health data, and unwanted trials to
access HI can get restricted. The next feature is that it gives a
requirement-centric method against ensuring HI confidentiality.
There is also the feature of only patients having entry to HI. In
addition, based on needs can be distributed to others which
involve the experts and specialists. The last key feature is that
layered modeling with modularity can support against the
attacks of intruders to HI, Shabbir et al., (2021)

Due to the industry's dependence on solution vendors to fuel
category development, the Medtech segment
An industry for medical cloud technology is segmented into two
groups depending on the elements that are utilized in the
system: equipment and operations. According to projections, the
hospitality industry will grow at the highest rate of any
comparable field over the projected timeframe (Alagumalai et
al., 2019). This phenomenon may be connected with the
recurring structure of activities, including orientation and
learning, setup, system upgrades, consultancy and servicing,
and the high development level of this industry. The emergence
of software packages and the required additional to ensure
application interoperation are anticipated to boost the demand
for a broad variety of programs with significantly extra others.
This costing system is composed for the largest share of the
economy according to data from IDC.
In estimating the expansion of the overall medical cloud
industry, a component to consider is the billing system that may
be separated into two major subgroups: remuneration plans and
flash fee structures (Tanjung et al., 2019). Whenever it pertains
to prepaid cards, the payroll option accounted for the largest
share of the industry in the previous years. The notion that the
remuneration strategy, often referred to as the convenience
capitalist structure, allows medical professionals to take
advantage of the most up-to-date technology technologies while
retaining low operating costs could contribute.
Standard frameworks for CC in healthcare
Compliance with HIPAA is essential in each case where the
patients' individual information gets shared, like clinical
research, along with continuity of care is needed. All the efforts
entry patient information needs to be within the scope. Through
the CoronaVirus Aid, Relief, along with Economic Security Act,
some HIPAA regulations applied to telehealth get relaxed, along

with telehealth coverage for Medicare got expanded.
Consequently, patients' entry to medical services elevated,
along with medical institutions, can enhance infection control,
reduce general risk to public health, and provide needed
medical services. HIPAA regulation safeguards the privacy of
personally identifiable health data and ensures lawful use and
dissemination of health data to promote great-quality health
care.
The HITECH Act, created by OCR of HHS, places Bas under
HIPAA Security Rule compliance and HITECH Act's security
and privacy provisions. From consecutive announcements and
statements between April and February in 2003, HHS
emphasized HIPAA's correct functions instead of its
dysfunctions, listing several social benefits obtained through it.
HHS believed that through harnessing information innovations,
HIPAA could ensure correct privacy security and maintain the
common-sense balance between protecting patients' privacy and
giving the best quality care (Yuehong et al., 2016). In addition,
HHS affirmed that the function does not interact with
physicians’ capability of treating their patients along with
enabling vita public health projects like surveillance of
outbreaks and reporting adverse drug event (Kim &Josh, 2021)
With the rise and widespread deployment of cloud computing
solutions, HIPAA-covered entities, along with business
associates questioning how they can take merit of CC while
adhering to regulations security and privacy of electronic PHI.
This guidance aids such entities involving cloud services givers
in clarifying HIPAA obligations. The following guidance is
concerned with cloud resources given by CSP, which is an
entity legally distinguished from a covered entity considering
the use of its services. The HIPAA Security, Privacy, and
Breach Notification Rules create vital safety for PHI
information that involves limiting the usage and disclosure of
such data, safeguards from inappropriate use and disclosures,

and personal rights with concern to health information. The
covered entity, along with business associates, needs to comply
with applicable provisions of rule in HIPAA. A covered entity
is a health plan, a health caregiver, or a clearinghouse that
conducts specific billing and payment associated transactions
electronically. The business associate refers to an entity, other
than members of the workforce of the covered entity,
performing activities or functions on behalf of covered entity
involving creating, receiving, maintaining, or transmitting PHI
on behalf of other associates in business ("Cloud Computing",
2021)
Conclusion
According to predictions, the Software as a Service model will
offer the most significant solutions in the E-Health sector. The
industry might well be classified into three categories based on
the subscription service that is being used: SaaS, Iaas, and Paas.
Several benefits over on basis systems, like safety, reduced cost
of operation, quicker proposed action, and fewer initial
operating expenses contributed to the SaaS model accounting
for the biggest portion of the market. The use of cloud
technology is becoming more popular among medical providers
and clients equally, in particular to fulfill their requirements
(Serrat, 2017). That’s why cloud technology is an inter-service
that can handle an unlimited number of clients while also
enabling data at any point in the world, which makes it
particularly appealing. Because a supplier or a wireless carrier
maintains it, this solution may assist in minimizing upkeep
expenses while also increasing the effectiveness with which
facilities are used.
The following study aims in fulfilling the gas via the proposed
study model. It suggests a framework that studies the key
drivers along with challenges in e-health. It has been suggested
that cloud computing is the result of two main trends in IT. The
first trend is that cloud computing gives efficiency. Healthcare

companies can benefit from such features by minimizing the
capital expense linked with the on-premises deployment of the
data center. Datacenters for hospitals are very expensive due to
the lifesaving nature of the companies and the necessity for the
data center to maintain the mission. To finish this, assurance of
excellent availability along with zero downtime of the ser vices
is required. Zero downtime and high availability are complex
technical issues needing several resources, thus an expensive
option. In the non-cloud scenario, companies tend to have
several ranks of redundancy along with excellent availability
accompanied by disaster recovery. This can include several data
centers operated by others to obtain service availability and
compliance with government regulations.
The second trend is that CC, as claimed, gives IS along with
business agility which is cloud services hallmark. CC gives
companies with functions and features for improving their
business operations. Excellent availability along with disaster
recovery characteristics of CC is crucial for healthcare and
enables quick recovery. The service providers can offer
production and prebuilt ready tools and platforms, increasing
the development speed of the application in the organization.
These can be combined with previous systems via correct-
documented API and provider support, Al-Marsy et al., (2021).
Healthcare managers and professionals are searching for
strategies to elevate health information management flexibility,
efficiency, and cost-effectiveness. CC was a new computing
model with promises of more flexibility, more efficient, and
inexpensive IT services for users. It has been claimed that the
model is an excellent opportunity for healthcare settings to
enhance information management. Furthermore, when
organizations regard moving to cloud services, it requires
examining and addressing new model challenges along with
assessing the capabilities for achieving the goal along with
identifying strategies for the implementation ("Cloud

Computing for Health Information Management", 2021)
Chapter 3
Health care main aim is to improve our health by diagnosing,
treating and preventing diseases. In the process of ensuring that
the above activities are achieved big data is generated each day.
In health care big data analytics shifts data management and its
technique from structured to semi structured or even
unstructured data and from a static terminal environment to a
ubiquitous cloud-based environment. Big data analytics in
health sector uses rapid increase in data volumes to come up
with patterns and trends in the inherent complexity of the data
and to come up with solution or make smarter decisions. In real
sense big data analytics can assist in improving the quality and
reduces tremendous cost of healthcare delivery. Cloud
computing is the ultimate choice for big data and big data
analysis in health sector. As we know cloud computing brings
about advantages such as flexibility, security, parallel
processing and virtualization of resources.
Data collected in healthcare for analysis
In health care some of the big data collected for analysis via
cloud computing includes data from patient from electronics
health records, computerized physician order entry system,
clinical decision support system, medical services and sensors.
Other type of data collected which is of less patient relation
includes emergency care, new feeds and research articles. This
type of data is usually tremendous become it volume is huge,
the data is diverse and it requires higher analysis speed (Aceto
et al., 2020). Usually, this data collected from healthcare is
huge and it synthesized by 7Vs which are:
Volume
The data volume is that huge making traditional and new data
types to become a storage problem, this huge data volume also
affects complexity of data analysis. The volume of data in

healthcare is increasing each day due to conversion of exis ting
paper data form to new electronic emerging form. Examples of
these new forms include 3d images and genomics. These new
forms are cloud based and they offer effective collection,
storage and management of huge data volumes.
Variety
Semi structured and unstructured data is being generated each
day from new channels and emerging technologies such as
social media and internet of things. In health sector the semi
structured and unstructured data is being generated in
continuous manner through information movement from paper
medical records to EHRs and electronic media records. It also
includes data such as reading from medical devices, some
clinical data accounting which are electronic and bills and
statistics used to find out the possibility of risk occurr ence.
According to Ali et al., (2018) some of ways in which
unstructured data can be found in health care includes in paper
medical records, prescriptions, notes which are written via
hands, x rays and many other images that may be taken. Other
ways in which data used in cloud computing is generated in
healthcare includes from fitness devices, genomics, research
and social media.
Velocity
This is all about speed of data generating, retrieving, processing
and analyzing. The velocity of data generation in health care is
increasing each day due to real time generation algorithms.
Request combining data flow with business activities and
process of decision making. According to Lo’ai et al., (2016),
some of the data generated in health care can be static and it
includes paper medical records, x-rays the other can be medium
velocity or even real time for example operating room monitors.
Creating of up-to-date results is so crucial for example in
clinical decision support it assist in making right time decisions
in a rapid manner.

Veracity
Veracity is about accuracy and compliance of the data. This is
difficult especially for data in health care which comes in form
of big data from numerous sources. Veracity in health care
affects patient safety due to risk of low-quality data.
Variability
This is about how the data can be understood. Let for example
look at sophisticated algorithm which is so essential in getting
the real and correct meaning of social media comments which
can be a data type in health care.
Visualization
It all about readability and accessibility of the data presentation
and it requires numerous spatial and temporal parameters and
at least a relationship between them.
Value
In health care efficient tools and skills are so necessary in
analyzing huge amount of data to come up with exact results
and make an improvement in outcomes at health sector.
Clinical data in health care
According to Lo’ai this data type is collected during the course
of ongoing patient care or as a part of formal trial clinical
program. This data is of much use in cloud technology of health
care and it exist in forms such as electronic health records,
administrative data, claims data, patient data, health surveys
and clinical trial data. Electronic media record is the purest
form of electronic clinical data since it obtained at the point of
care of the health facility. In clinical data the information
collected includes administrative and demographic information,
diagnosis, treatment, information on drugs prescription, test
carried out in a lab, hospitalization data and insurance on
patients. Administrative data is all about hospital discharge and

this data type is reported to government agencies. Claim data in
health care give details on billable interactions between the
insured patient and health care delivery system. This data type
can be either of outpatient, in-patient, pharmacy and enrollment
and it of much use in cloud base analysis. In cloud computing
data on health survey offers accurate evaluation of population
health, national surveys and the prevailing national surveys.
Clinical research dataset in cloud computing is available
through national or discipline specific organizations.
Methods used to collect the data for analysis
Data from health care which is needed in cloud computing needs
to be collected. This data though does not flow along the health
sector in a cohesive standardized way. Methods such as
conducting health survey, administering enrollment and bill
records, medical records can enhance data acquisition for cloud
computing. Some of these methods are traditional and uses a lot
of paperwork however they can become so reliable in cloud
computing when converted to electronic form. To enhance this
process a good computing infrastructure should exist and be set
in the right manner in the health sector. The infrastructure
should consist of the necessary hardware and software settings
(Aziz & Guled, 2016). Let look at a scenario of collection of
data involving ethnicity race and language in hospital. At such
case cloud computing together when combined with health
information technology can assist in improving on data
collection by incorporating individual personal health record
and later utilizing them electronically in electronic health
record and other data systems. The data contained in electronic
health record includes on medical imaging, socio behavioral and
data on environment.
Electronic health records benefits to a health care
Electronic health records offer several advantages to health
sector and the first one is that it has enhanced healthcare
professionals to have improved access to medical information

and history of a patient. It has information on medical
diagnosis, prescriptions, data concerning identifiable allergies
and information regarding lab tests. It reduces the time needed
to recognize and treat several medical conditions. It assists in
reducing logistical errors hence reducing number of drug
allergies by eliminating errors in medication dose. By use of
cloud computing and related technology healthcare
professionals can now access over web based and electronic
platforms to make an improvement in medical practices
significantly by use of automatic reminders and prompts
concerning vaccination, abnormal screening in the lab, cancer
screening and other checkup which are done periodically (Dang
et al.,2019). Data acquired through cloud computing
electronically enhances communication among multiple health
care providers and patients. This comes about due to less
paperwork involved. EHR enhances faster retrieval of data
regarding the quality of the healthcare. This is of much benefit
to employee health insurance programs and can increase the
cost of health insurance benefits.
Some other methods of data collection for analysis in health
care and which engages cloud computing includes use of
internet of things, use of electronic health records, electronic
media records and means such as phones.
Insights from analysis health care data via cloud computing
According to Alexandru et al., (2016) use of cloud computing in
health sector is more than just storing data and cloud
architecture since through it now healthcare providers are
gaining more efficiency, optimized workflow and lower
associated cost in healthcare delivery. Analyzing of big
healthcare data by cloud computing is advantageous to both the
patients and the healthcare providers. It assists in cutting down
the operational expenses and at the same time it allows health
care provider to offer high personalized health care. The
analysis also results to improved patient outcomes by the clouds

using amp up patient engagement with their own health plans by
allow their own access to healthcare data.
The following factors can be contributed to cloud computing
data analytics:
Access to high power analytics.
By application of big data analytics and artificial intelligence
algorithm on data stored on cloud patient can power up medical
research. A cloud enhances processing of large data set to use in
health sector to be easier and more feasible. It also assists in
coming up with a more personalized care plan for patients
especially on individual level. Cloud computing data analytics
ensures that all data concerning the patient are stored in right
manner and none is missing. It allows easy retrieval of relevant
information regarding the patient.
Patient owns their data.
`
Cloud computing gives patient control over their health. This is
possible by boosting their participation in decisions concerning
their health. This leads to informed decision making by acting
as a tool for patient education and engagement. It allows easier
retrieval of patient records and medical images. Back up in
cloud computing system makes retrieval of data to become
simpler.
Allows telemedicine capabilities.
Combination of cloud computing with healthcare improves
healthcare related functions such as telemedicine, post
hospitalization care plans among others. Through cloud
computing access of health care through telehealth becomes so
possible (Aceto et al., 2020). Apps on telemedicine enhance
convenience of health care delivery and at the same time
improve the experience of the patient.
Cloud computing in health sector of developing countries

Cloud computing gives about better potentials to make changes
in the way healthcare sector functions in Iraq. Expert nowadays
is considering cloud computing as the new wave of web in the
health sector. Government is playing a key role in development
of cloud computing across the economy especially in health
sector. Cloud computing offers several advantages when
compared to other type of technology used in healthcare
especially the tradition ones. Some of the advantages associated
with cloud computing includes reduction in cost, storage
increase, automation level increase, standardization, high
flexibility levels and increase in employee’s mobility. Most
healthcare sectors are still dealing with traditional computing
(Shepherd, 2019). Most of the factors that can explain the
current state of computing in health sectors includes lack of
information technology expertise in government health sector
which affects the availability and quality of information
technology services being delivered, the other factor is
underutilization of information technology resources is spread
through.
According to Shepherd (2019) government healthcare secures
hardware and software and end up using just half of their
capacity and this leads to capital wastage, each health care has
its own technology and platform hence making it so hard to
achieve interoperability with other organiza tion in medical
sector hence coming up with information technology solution
becomes a complicated thing. Other challenges associated with
adoption of cloud computing in health care includes lack of
users to near ubiquitous internet access, cloud systems must be
equivalent to or be better than current standalone system and
barrier between government institutions which should be opened
and security and privacy of user’s data must be defined clearly
and protected. For developing countries adoption of cloud
computing in health sector is related to a lot of problems. One
includes lack of political leadership to push adoption of such
technology. most developing countries perceives cloud

computing technology as a disruptive technology that is not yet
mature with the lack of industry specific conformity to
standards.
In introducing of cloud computing in health sector it may be
faced with high level of related risks and cost. In adopting
cloud computing in health sector two technology are taken into
account which are diffusion of innovation and technology
organization and environment framework. Expert in cloud
computing and healthcare emphasize in the need to combine the
two theories when studying and adopting this technology in
order to eliminate any possibility of being ignorant on some
aspects which could create a big effect on strategy used for
adoption.
Innovation characteristics
According to (Kadhum & Hasan 2017) the theory of diffusion of
innovation explains why, how and what rate new technology and
ideas spread throughout the culture. This theory in adopting
cloud computing in health sector is based on characteristics of
technology and perception of user in the system. This theory
views innovation to be communicated throughout certain set of
channels over certain period of time and within a social system.
In simple this theory is all about characteristics that affect
adoption of innovation and they are relative advantage,
observability, compellability, and compatibility and trial
ability.
Relative advantage
This is a key factor making health sector to switch into cloud.
This is all about how the new technology being considered to be
better than the old one. It all about the measure on how the new
technology can bring some benefits to the health sector. In this
perceptive cloud computing is considered to be better
technology than the traditional ones in the health sector
(Kadhum & Hasan 2017). Cloud computing technology gives

health sector opportunity to respond to new needs in healthcare
so fast. Relative advantage is also viewed in time saving
perspective.
Complexity in health sector due to cloud computing
This is the measure on how innovation is perceived to be
relatively difficult to understand and use. For example, in
health sector people are already used to a certain routine and
system. Hence upon adoption of cloud in medical sector training
and extra efforts are needed to get the new technology into the
health sector. Lack of expertise and new technology expert
creates a lot of problems (Keshta & Odeh 2021). Complexity
negatively influences adoption of cloud in the health sector.
Compatibility
This is all about how innovation fits with existing values,
previous practices and current needs. Compatibility of cloud
with existing infrastructure is an essential factor to consider
when one is deciding to adopt cloud in healthcare sector. The
cloud used in healthcare should be compatible with the existing
infrastructure in terms of solution it’s supposed to offer. If the
cloud technology being adopted in health sector is not
compatible changes can be done to enhance compatibility. High
compatibility will positively influence cloud adoption in the
health sector.
Materials and Methods
Security concern in cloud computing.
When a health care sector moves to the clouds new layers of
complexity for data securing are added hence affecting adoption
decision. The healthcare security team should ensure once cloud
computing is adopted in its medical services patient data are
protected. Necessary infrastructure should be there to enhance
security. Hence security concerns will negatively affect
advantage gains of cloud computing in the health sector
(Vithanwattana et al., 2017). In adopting cloud technology in
the health sector, it’s so important to look at technology

readiness and it includes infrastructures and information
technology human resources. This measures the readiness of
health sector to adopt the new cloud computing technology.
Technology readiness positively influences adoption of cloud in
the health sector.
Cloud computing as a modern technology has brought various
benefits to the industry of the health of Iran. In Iran the study
was conducted through qualitative approach and through in-
depth interviews with an aim to identify factors affecting use of
cloud in health system of Iran. In Iran cloud computing is a
novel phenomenon that why qualitative method is the ultimate
approach method so that to understand the underlying issue in
more details. Series of interviews were carried out and the
people involved had work experience in field of information
technology and health cloud computing (Aceto et al., 2020).
Selection of people to engage in the study was done through
snowball sampling method and sustained to reach diffusion. The
interview was enhanced by interview guide who was designed
by researchers and five open questions were used to collect the
required necessary information. The mode of interviewing was
face to face and by telephone interviews.
According to Aceto et al., (2020) due to the fact that interviews
were accomplished through snowball sampling methodology at
interview end, interviewees had to introduce other individuals
with at least some experience in health and information
technology. Implementation of texts was via word 2013
software and research team studied them carefully after every
interview. To enhance scientific accuracy of data member
checking was done regularly. This also increased the quality
control of the data. More study was conducted quantitatively to
lead to a better understanding of the subject.
Results
Results obtained are in form of technical, organizational,
economic and security advantage.

Technical advantages
One of the technical advantages is reducing the time of software
installation and selection using the cloud facilitating data
mining and analysis of health data using cloud forecasting and
planning for the future in health system. The other advantage is
finding demographic patterns with high resource storage and
analysis in cloud computing (Alexandru et al., 2016). Other
technical benefit includes high accessibility, basic capabilities
and feature of cloud elasticity and flexibility upgrade of
information technology.
Organizational advantage of using cloud in health of Iran
According to Dang et al., (2019) increase in the efficiency of
information technology related processes in hospitals time
saving and quick handling of system. Accelerating hospital
services since there would be no need for full time specialist in
hospital. Other benefits from the study include increase in high
level capabilities.
Economic advantages
Hospital expenses will reduce, support of high rank
organization of the cloud due to economic and technical
benefits. According to Alexandru et al., (2016) for small
hospital cost effectiveness of clouds will increase, no need for
employment of internal staff for maintenance and support.
There will be reduction in direct financial and hardware
management expenses in the cloud.
Security advantage
According to Rajabion et al., (2019) an increase in data security
in clouds due to data concentration and definition of security
protocols in each layer. There would be an upgrade in
identification and security.
Cloud computing overall benefits to the health sector
According to Darwish et al., (2019) use of cloud computing in

healthcare offers a lot of benefits when compared to the use of
in-house client-server system. Cloud computing offers
economic, operational and functional advantage. The economic
benefit includes cost flexibility and reduction in cost. In cloud
computing heavy capital expenditure are avoided due to the fact
that information technology resources are acquired in demand
and their mean of payment is through operating expense. The
cost of cloud computing also includes cost of staff resources
needed to deploy and maintain information technology
resources (Darwish et al., 2019). The need for extra healthcare
provider skilled information technology resources and the cost
related are reduced when using cloud services. This occurs
mostly when using IAAS and PAAS platforms but even during
the use of SAAS solution cloud service provider is under the
whole responsibility and control.
Operational perspective and advantage of using cloud
computing in healthcare occurs in that there is ability to offer
scalability and ability to adjust to demands in a rapid manner.
Cloud services offers better security and privacy for healthcare
data. The better security comes about in that cloud providers
data center are highly secured and protected not only from
outsiders but also from insider threats. This is possible through
administrative, physical and technical methods that are under
control of an expert in security. Use of cloud services in health
sector enhances offering of sophisticated security controls.
These security controls include encryption of data, fine-grained
access controls and access logging. Use of cloud services also
minimizes the need for scarce information technology security
skills within the medical sector (Aceto et al., 2020). The
providers of cloud services functio ns in a manner that they have
all needed information technology skills and the cost of those
skills is spread across many customers.
In healthcare functionalities are well enabled by use of cloud
services. The information technology system used offers
potential for broad interoperability and integration. The

standards used by cloud services in healthcare follows some
certain protocols and the cloud services are based on the
internet. Clod services enhances sharing of information in an
easy and secure Rajabion et al., (2019). By use of cloud in
healthcare rapid development and innovation is enhanced and
this occurs mostly for mobile and internet of things devices.
Use of clouds enables remote access to data and applications
through the internet this is made possible by use of wireless
systems and wired ones. This remote access can be from
anywhere and at any time. If there is a need for access to a
much larger ecosystem of health providers, payer, life sciences
and information technology partners then cloud computing is
the ultimate choice in the health sector.
The ultimate benefit of using clouds in health sector is wide
new ranges of capabilities that clouds offer. These better
offered services increase the necessary opportunity to extend
the available capabilities in health organization staff, with an
aim of implementing improved ways of working and with an
aim of offering new services to patients. Cloud services have
the ability to support healthcare provider staff cognitive
capabilities hence mitigating medical mistakes and reduce
patient adverse events. These cloud services include intelligent
business process management suites and case management
framework. Capabilities associated with cloud computing in
health sector assists in facilitating personal health maintenance,
improvement in diagnose of diseases, acquiring better cases
outcomes and assist in making transformation from volume to
volume-based care.
Guidance for leveraging cloud computing in healthcare
In order to ensure successful deployment of cloud-based
healthcare solutions some steps should be taken into and to start
with is coming up with business case for cloud computing
(Vithanwattana et al., 2017). examining the most appropriate

cloud deployment and service models, make ensure all security
and privacy requirements are addressed, integrate with existing
enterprise systems, make negotiation on cloud service
agreement and monitor key performance indicators and lastly
manage the cloud environment.
High value cloud computing services for healthcare
There are various cloud services offered in the healthcare and
they cover wide range of capabilities. One of the services
offered is population health management. According to
Vithanwattana et al., (2017) in population health management
cloud and big data services assist in tracking diseases hence
informing the population on the areas where risk exist.
HealthCare’s can come up with the infrastructure necessary to
support these services by minimum cost by utilizing cloud
computing. The other offered service is care management
support and healthcare are utilizing cloud-based practices
management, medical records and medical image archiving
solution and the solutions offers cost effective implementations
and this offload tasks from hospital information technology
department hence making this department to focus on other
tasks within the health sector. By use of cloud diagnostic
support are offered and this occurs by organizations developing
new SAAS products and services that assist in coming with
expert at health sector at a lower cost. For faster screening of
diabetic patients combined automated image analysis tools with
user friendly telemedicine are used (Vithanwattana et al., 2017).
Through use of cloud services in health sector image handling
services are offered. This type of services enables healthcare
organizations to make a scale on storage services at an
affordable price. Cloud computing enhances medical
practitioner assistance in the medical sector. Through the use of
cloud services medical practitioners are able to search huge
amount of data hence coming up with more reasonable treatment
plans. Some of the existing tools includes flatiron oncology

cloud suite of tools for oncology and by use of this tool patient
utilizes offered services hence exploring their medical issues
and collaborate with those treating them (Amron et al., 2017)..
The other service is patient connectivity to the health service
providers. Another service is data distribution service that
enhances exchange of data especially key health related data
between organizations.
Discussion
Based on findings in the research very soon cloud computing
will be the main things in health center of Iran. Currently most
public and private hospitals in Iran are equipped with hospital
information system and electronic health program is followed
and established by health ministry. Cloud computing has offered
better opportunity for analyzing the produced data in health
system by offering storage and computing resources (Dang et
al., 2019). This has created more possibilities for data mining
and analysis of patterns from the health data.
Contexts influencing adoption of cloud technology innovation
process in health care
There are three context influencing technology innovation
process and to start with is technological context. According to
Ahmadi et al., (2019) this context includes the technology
currently and internally being used within the organization and
in addition to external and obtainable ones which are accessed
by the organization. The other context is organizational context
and it all about organizational structures which includes size,
scope and managerial structure. When it comes to
environmental context it’s all about factors relating to the
environment in which organization exists and operates. The
environment can be industry and competitors (Sivan,
&Zukarnain, 2021). The three contexts can work as either
constraints or opportunities for clod computing technology

innovation in the healthcare.
Conclusion
In deduction cloud computing has impacted health sector in a
positive manner. The cloud computing process in health care is
possible by establishing the necessary infrastructure that
consists of the necessary software and hardware tools. Some of
the data collected in health care via cloud computing includes
data on patient from electronics health records, automated
physician order admission scheme, clinical decision holds up
system, checkup services and sensors. Other category of data
collected includes emergency care, new feeds and research
articles. The methods used to collect this data are in electronic
form and they include EHR and EMR. Some methods of data
collection are in non-electronic form whoever once data is
acquired it converted in form understandable by cloud
computing technology. The data analyzed through cloud
computing is of much use in giving maximum health cover to
patients and it assists in helping patient make informed decision
concerning their health and in things such as telemedicine
capabilities.
Adoption of cloud computing in health sector will continue to
evolve. The evolution includes expanded usage of healthcare
internet of things devices, storage and analysis of vast amount
of healthcare information among others. Cloud computing
facilities application of various technologies that includes big
data analytics, cognitive computing, mobile collaboration and
exchange of information with an aim of accelerating delivery of
advanced healthcare solutions. Hybrid cloud deployment offers
healthcare providers the flexibility necessary in deploying
workloads and data based on business related risks.
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YOUR ABBREVIATED TITLE 1
YOUR ABBREVIATED TITLE 17
For the header, Type: your abbreviated title in all capital letters.
(No more than 50 characters, including spaces). The page
number is also in the header, flush right starting with 1.

The entire document should be double spaced with Times 12
Font.
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Your Approved Dissertation Title Here in Upper and Lowercase
Letters
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degrees.
First and Last Name
University of the Cumberlands
Type University of the Cumberlands on line 7.
Month and Year of Graduation
Month and Year only should be typed on line 8.

Approval for Recommendation
Include a copy of the signed form.
This dissertation is approved for recommendation to the faculty
and administration of the University of the Cumberlands.
Dissertation Chair:
Dissertation Evaluators:
Acknowledgements is where you thank those who have helped
you achieve this goal.
Acknowledgments
There are many to whom a debt of gratitude is owed for their
assistance in conducting this research…. (It is appropriate to
thank key faculty, friends, and family members, as well as

ministers and God. It is advisable to limit the comments to one
page)
The word “Abstract” should be centered and typed in 12-point
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Abstract
This study examined the differences………………
Do not indent the first paragraph in the abstract.
Table Of Contents
Chapter One: Introduction
Overview………………………………………………………………
……………...1
Background and Problem
Statement………………………………………………….2
List of Tables
Table 1: Name of the
Table…………………………………………………………………1

List of Figures
Figure 1: Name of the Figures
…………………………………………………………………1
Chapter One
Introduction
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here.
Overview
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here.
Background and Problem Statement
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problem statement here.
Purpose of the Study
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Research Questions
Theoretical Framework
Limitations of the Study

Assumptions
Definitions
Summary
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here.
Chapter Two
The literature review should be a minimum of 20 pages of
synthesized literature.
Review Of Literature
Introduction
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Main Heading (level 2 heading)
Subheading should be flush left, Bold italic, Title Case Heading
(level 3 heading)
Subheading should be indented, boldfaced, Title Case Heading,
ending with a period. (level 4 heading)
Summary
Chapter Three

Procedures And Methodology
Introduction
Research Paradigm (qualitative or quantitative)
Research Design
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Data Collection Sources
In Data Collection Resources section, reference Informed
Consent and IRB approval placed in Appendices.
Statistical Tests
Summary
Chapter Four
Research Findings
Introduction

Participants and Research Setting
Analyses of Research Questions
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Supplementary Findings (if any)
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Chapter Five
Summary, Discussion, And Implications
Introduction
Practical Assessment of Research Question(s)
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Limitations of the Study

Implications for Future Study
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References
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