A framework for secure healthcare systems based on big data analytics in mobile cloud computing environments

International Journal of Ambient Systems and Applications (IJASA) Vol.2, No.2, June 2014
DOI : 10.5121/ijasa.2014.2201 1
A FRAMEWORK FOR SECURE HEALTHCARE SYSTEMS
BASED ON BIG DATA ANALYTICS IN MOBILE CLOUD
COMPUTING ENVIRONMENTS
Ahmed E. Youssef
College of Computer & Information Sciences, King Saud University, Riyadh, KSA
Faculty of Engineering, Helwan University, Cairo, Egypt
ABSTRACT
In this paper we introduce a framework for Healthcare Information Systems (HISs) based on big data
analytics in mobile cloud computing environments. This framework provides a high level of integration,
interoperability, availability and sharing of healthcare data among healthcare providers, patients, and
practitioners. Electronic Medical Records (EMRs) of patients dispersed among different Care Delivery
Organizations (CDOs) are integrated and stored in the Cloud storage area, this creates an Electronic
Health Records (EHRs) for each patient. Mobile Cloud allows fast Internet access and provision of EHRs
from anywhere and at any time via different platforms. Due to the massive size of healthcare data, the
exponential increase in the speed in which this data is generated and the complexity of healthcare data
type, the proposed framework employs big data analytics to find useful insights that help practitioners take
critical decisions in the right time. In addition, our proposed framework applies a set of security
constraints and access control that guarantee integrity, confidentiality, and privacy of medical information.
We believe that the proposed framework paves the way for a new generation of lower cost, more efficient
healthcare systems.
KEYWORDS
Electronic Health Records; Big Data; Mobile Cloud Computing; Information Security; Healthcare
Systems, HL7.
1. INTRODUCTION
The 21st century Healthcare Information Technology (HIT) has created the ability to
electronically store, maintain, and move data across the world in a matter of seconds and has the
potential to provide healthcare with tremendous increasing productivity and quality of services. It
permits each provider to have his own database of patients' Electronic Medical Records (EMRs).
Previous studies of the value of connected EMR systems estimated a potential efficiency savings
of $77 billion per year at the 90% level of adoption; added value for safety and health could
double these savings [10]. One problem in today's EMR systems is that they are highly
centralized, each Healthcare Provider (HP) has its own local EMR system. This makes health
information for any patient dispersed among different HPs and, therefore, its retrieval will be a
challenge. The ability to universally access all patient healthcare information in a timely fashion
is of utmost important [1, 10, 58]. Health information needs to be accessible and available to
everyone involved in the delivery of patient healthcare from the researchers attempting to find
causes, treatments, and cures for diseases to the patients themselves. Therefore, a high level of

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data integration, interoperability, and sharing among different healthcare practitioners and
institutions is required in order to deliver high-quality healthcare to the patients they serve [54].
The revolution in healthcare data size is another problem in today's Healthcare Information
Systems (HISs). This revolution is not just about the massive size of healthcare data, but we also
witness an exponential increase in the speed in which this data is generated and a complex
varieties of data type (i.e., structured, semi structured, unstructured). The Development of new
technologies such as capturing devices, sensors, and mobile applications is a major source of
healthcare data. Additional sources are added every day; patient social network communications
in digital forms are increasing, collection of genomic information became cheaper and more
medical knowledge/discoveries are being accumulated. Such big healthcare data is difficult to
process or analyze using common database management tools. Obviously, capturing, storing,
searching, and analyzing healthcare big data to find useful insights will improve the outcomes of
the healthcare systems through smarter decisions and will lower healthcare cost as well, however,
it requires efficient analytical algorithms and powerful computing environments. Finally, the
increased reliance on networked healthcare data brings new challenges to securing medical
records in EHR systems. Authenticating individuals and authorizing global secure access to
patients’ records are vital security requirements. Physical face-to-face methods of identifying and
authenticating patients and providers no longer apply; methods of electronic identification and
authentication are required. Moreover, electronic records are susceptible to inappropriate access,
compromised data integrity, or widespread unauthorized distribution. New security measures are
needed to secure patients’ records on HISs.
In this paper we introduce a framework for secure HISs based on big data analytics in mobile
cloud computing environments. This framework provides a high level of integration,
interoperability, and sharing of EHRs among HPs, patients, and practitioners. It integrates distinct
EMRs of a patient from different HPs distracted among different cities, states, and regions and
store them in the Cloud data storage areas. Mobile Cloud computing technology [57-61] provides
a fast Internet access, and provision of EHRs from anywhere and at any time with high
availability. Due to the massive size of healthcare data, the exponential increase in the speed in
which this data is generated and the complexity of healthcare data type, the proposed framework
employs big data analytics to find useful insights that help practitioners take critical decisions in
the right time. Our proposed framework applies a set of security constraints and access control
that guarantee integrity, confidentiality, and privacy of medical data. Authenticated healthcare
providers, practitioners, and patients are authorized by the Cloud Service Providers (CSPs) at
different levels of privilege and permissions to securely access EHRs and retrieve patients’
information. We believe that the proposed framework paves the way for a new generation of
lower cost, more efficient healthcare systems.
The rest of this paper is organized as follows: section 2 discusses the problem of integrating
patients' EMRs dispersed among different CDOs. Section 3 discusses how mobile cloud
computing solution improves integration, interoperability, and availability of EHRs. Section 4
explores the issue of "Big" healthcare data analysis. Security issues associated with EHR are
discussed in section 5. Our proposed framework for HISs is given in section 6. Finally, in section
7, we give our conclusion remarks and future work.
2. INTEGRATING ELECTRONIC MEDICAL RECORDS
Today, there is a widespread use of EMRs or EHRs systems. These terms are used
interchangeably by many in both healthcare industry and health science literature; however, they
describe completely different concepts according to Health Information and Management System
Society (HIMSS) Analytics [6, 7]. An EMR is a formatted record of patient health information

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owned by a hospital or any healthcare provider. The data in the EMR system is the legal record of
what happened to the patient during his encounter at the CDO and is owned and managed by one
CDO [6]. A significant disadvantage to EMRs is that they cannot be easily and accurately
electronically shared and distributed.
On the other hand, HIMSS defined the EHR as “a longitudinal electronic record of patient health
information generated by one or more encounters in any care delivery setting. Included in this
information are patient demographics, progress notes, problems, medications, vital signs, past
medical history, immunizations, laboratory data, and radiology reports. The EHR automates and
streamlines the clinician's workflow” [7]. The definition of EHR system is “the set of components
that form the mechanism by which EHRs are created, used, stored, and retrieved” [8, 9]. EHRs
are typically composed of a subset of EMRs maintained by each CDO and is assigned to a
patient. In [10] Zhang and Liu compared between EMR and EHR from different perspectives.
This comparison is shown in Table 1.
Table 1: A comparison between EMRs and EHRs [10]
EMR EHR
Definition The legal record of clinical
services for patient within a
CDO.
A subset of EMRs from one or
more CDOs where patient
received clinical services.
Owner Owned by one CDO Owned by several CDOs
Customer and
Usage Scope
EMR systems are supplied by
enterprise vendor and
installed by hospitals, health
systems, clinics, etc.
EHR systems are run by
community, state, or regional
emergence, or national wide
emergence organization.
Right of
patients
Patients can get access to
some EMR information once
authorized by the EMR owner
Patients are provided with
interactive access as well as the
ability to append information.
Interoperability
with other
CDOs
Each EMR contains the
patient’s encounter in a single
CDO. It does not contain
other CDO counter data.
Sharing information among
multiple CDOs.
An EHR provide a mean of communication among clinicians contributing to the patient’s care by
sharing health information between different EMR systems in different CDOs. The challenge here
is how to integrate distinct EMRs scattered in different CDOs in different cities, states, and
regions to create unified EHRs. In our framework, the Cloud provides a solution for this problem
by networking the CDOs and collecting patients’ EMRs from the interconnected CDOs. It is also
desirable to have a unified standard format for EHRs to support interoperability and data sharing.
3. HEALTHCARE MOBILE CLOUD COMPUTING
In recent years, mobile devices have started becoming abundant in many healthcare applications.
The reason for the increasing usage of mobile computing is its ability to provide a tool to the user
when and where it is needed regardless of user movement, hence, supporting location
independence. However, it suffers some inherent problems such as limited scalability of users and
devices, limited availability of software applications, resources scarceness in embedded gadgets,
frequent disconnection and finite energy of mobile devices. In healthcare sector, the effect of
these limitations is magnified due to the massive size, excessive complexity, and rapid generation
of healthcare data. As a result, a wide range of healthcare applications are difficult to run in

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mobile devices such as radiology processing and recognition, patients' social networking data
management, genomic information and sensor data applications. In addition, progress in
interoperation and sharing data among different EMR systems has been extremely slow due to the
high cost and poor usability. What is needed is an environment that is capable of capturing,
storing, searching, sharing and analyzing healthcare big data efficiently to provide right
intervention to the right patient at the right time.
Cloud computing [15-29] provides an attractive IT platform to cut down the cost of EHR systems
in terms of both ownership and IT maintenance burdens for many medical practices. Cloud
environment can host EHRs and allows sharing, interoperability, high availability, and fast
accessibility of healthcare data. Cloud Computing (CC) platforms possess the ability to overcome
the discrepancies of mobile computing with their scalable, highly available and resource pooling
computing resources. The main idea behind CC is to offload data and computation to a remote
resource provider (i.e., the Internet) which offers broad network access. The concept of offloading
data and computations in the Cloud, is used to address the inherent problems in mobile computing
by using resource providers (i.e., cloud resources) other than the embedded devices themselves to
host the execution of user applications and store users’ data. The problems are addressed as
follows: 1) by exploiting the computing and storage capabilities (resource pooling) of the cloud,
mobile intensive applications can be executed on low resource and limited energy mobile devices,
2) the broad network access of the cloud overcomes the limited availability and frequent
disconnection problems since cloud resources are available in anywhere and at any time, 3) the
infrastructure of cloud computing is very scalable, cloud providers can add new nodes and servers
to cloud with minor modifications to cloud infrastructure, therefore; more services can be added
to the cloud, this allows more mobile users to be served and more portable devices to be
connected [60].
A study by Juniper Research states that the consumer and enterprise market for cloud-based
mobile applications is expected to rise to $9.5 billion by 2014 [61]. In healthcare sector we
believe that this environment is very promising and is expected to change how healthcare services
are provisioned. Mobile cloud computing technology will contribute to healthcare sectors in the
following ways:
• Integrating healthcare data dispersed among different healthcare organizations and social
media.
• Providing a shared pool of computing resources that is capable of storing and analyzing
healthcare big data efficiently to take smarter decisions at the right time.
• Providing dynamic provision of reconfigurable computing resources which can be scaled
up and down upon user demand. This will help reduce the cost of cloud-based healthcare
systems.
• Improving user and device scalability and data availability and accessibility in healthcare
systems.
Healthcare cloud can provide two deployment Models. These models describe the level of data
sharing among different CDOs, patients, and practitioners when using the cloud. These models
are:
• Private healthcare cloud: The cloud infrastructure is owned solely by a CDO. It may be
managed by the CDO or a CSP and may exist on or off premise. The CSP provides the
same capability in terms of security and privacy protection as those in the EMR system
running by a CDO.
• Community healthcare cloud: The cloud infrastructure is shared by several CDOs and
supports a specific community that has shared concerns (e.g., mission, security

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requirements, and policy). It is managed by a CSP or by the CDOs and may exist on or
off premise.
Healthcare services provided by healthcare clouds are classified as follows:
• Software as a Service (SaaS): Healthcare applications, such as EHRs, are hosted as a
service and provided to practitioners, healthcare providers, and patients across the
Internet, with no need to install and run on their own computer. Hosted applications can
be accessed through web browsers from various client devices such as laptops, PDAs and
cell phones. Multiple users can share the applications and avoid the trouble associated
with software maintenance, upgrading and the need for additional licenses.
• Platform as a Service (PaaS): PaaS is a development platform that allows healthcare
providers to not only deploy but also design, model, develop and test healthcare
applications directly on the Cloud. It supports work in groups on collaborative healthcare
projects where project team members are geographically distributed. This requires PaaS
to provide development infrastructure including tools and programming languages.
• Infrastructure as a Service (IaaS): healthcare providers can directly use independent
virtual machines that isolate the underlying physical hardware of the cloud from them.
They can dynamically provision/release virtual computing resources based on their
increasing/decreasing resource demand.
4. HEALTHCARE BIG DATA
Improving healthcare services and reducing medical cost are the ultimate goals of nations
worldwide. However, the revolution of healthcare data size remains an obstacle that hinder
achieve this goal. In 2012, worldwide digital healthcare data was estimated to be equal to 500
petabytes and is expected to reach 25,000 petabytes in 2020 [62]. Obviously, capturing, storing,
searching, sharing and analyzing such big data to find useful insights will improve the outcomes
of the healthcare systems through smarter decisions and will lower healthcare cost as well,
however, traditional database management tools are no longer suitable to process these data. New
efficient algorithms are required to accomplish this task. For example, in the United States, more
than 71 million individuals are admitted to hospitals each year, according to the latest survey
from the American Hospital Association. Studies have concluded that in 2006 well over $30
billion was spent on unnecessary hospital admissions. The Heritage Provider Network (HPN)
arises the question: "Can we identify earlier those most at risk and ensure they get the treatment
they need?" and it believes that the answer is "yes". To achieve its goal of developing a
breakthrough algorithm that uses available patient data to predict and prevent unnecessary
hospitalizations, HPN sponsored the Heritage Health $3 Million Prize Competition. Winning
solutions will use a combination of several predictive models and the winning team will create an
algorithm that predicts how many days a patient will spend in a hospital in the next year. Once
known, HPs can develop new care plans and strategies to reach patients before emergencies
occur, thereby reducing the number of unnecessary hospitalizations. This will result in increasing
the health of patients while decreasing the cost of care [62].
Big data analytics is motivated in healthcare through the following aspects [62]:
• Healthcare data is now growing very rapidly in terms of size, complexity, and speed of
generation and traditional database and data mining techniques are no longer efficient in
storing, processing and analyzing these data. New innovative tools are needed in order to
handle these data within a tolerable elapsed time.

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• The patient’s behavioral data is captured through several sensors; patients' various social
interactions and communications.
• The standard medical practice is now moving from relatively ad-hoc and subjective
decision making to evidence-based healthcare.
• Inferring knowledge from complex heterogeneous patient sources and leveraging the
patient/data correlations in longitudinal records.
• Understanding unstructured clinical notes in the right context.
• Efficiently handling large volumes of medical imaging data and extracting potentially
useful information and biomarkers.
• Analyzing genomic data is a computationally intensive task and combining with standard
clinical data adds additional layers of complexity.
5. SECURITY ISSUES
In cloud-based HIS, security should be the top priority from day one. Patients’ data should be
protected with comprehensive physical security, data encryption, user authentication, and
application security as well as the latest standard-setting security practices and certifications, and
secure point-to-point data replication for data backup. These security issues have been extensively
investigated for cloud computing in general [13, 14, 30-53]. A major challenge to healthcare
cloud is the security threats including tampering or leakage of sensitive patient’s data on the
cloud, loss of privacy of patient’s information, and the unauthorized use of this information.
Hence, a number of security requirements should be satisfied by healthcare cloud computing
systems. The main security and privacy requirements for healthcare clouds are discussed below
[10, 13, 14]:
• Authentication: in a healthcare cloud, both healthcare information offered by CSPs and
identities of users (HPs, practitioners, and patients) should be verified at the entry of
every access using user names and passwords assigned to users by CSPs.
• Authorization: is an essential security requirement that is used to control access priorities,
permissions and resource ownerships of the users on the cloud. Each cloud user is granted
privileges based on his account. Patient can allow or deny sharing their information with
other healthcare practitioners or CDOs. To implement patient consent in a healthcare
system, patient may grant rights to users on the basis of a role or attributes held by the
respective user.
• Non-repudiation: implies that one party of a transaction cannot deny having received a
transaction nor can the other party deny having sent a transaction. In a healthcare system,
technologies such as digital signatures, timestamps, confirmation receipt, and encryption
can be used to establish authenticity and non-repudiation for patients, CDOs, and
practitioners.
• Integrity and Confidentiality: integrity means preserving the accuracy and consistency of
data. In the healthcare system, it refers to the fact that EHRs have not been tampered by
unauthorized use. Confidentiality is defined by the International Organization for
Standardization (ISO) in ISO-17799 as "ensuring that information is accessible only to
those authorized to have access". Confidentiality and integrity can be achieved by access
control and encryption techniques in EHR systems.
• Availability: For any EHR system to serve its purpose, the information must be available
when it is needed. High availability systems aim to remain available at all times,
preventing service disruptions due to power outages, hardware failures, and system
upgrades. Ensuring availability also involves preventing denial-of-service (DoS) attacks.

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6. THE PROPOSED FRAMEWORK
One of the most important challenges in developing HISs is to provide mechanisms to analyze
and obtain patients’ information from multiple EMR/EHR repositories accurately, securely and
fast. In the previous sections, we discussed the requirements for a new generation of secure HIS
in a cloud computing environment based on big data analytics. In this section, we propose a
framework for developing such systems. Figure 1 shows the proposed framework which consists
of the following main components:
The Cloud: the first component is the cloud environment itself that hosts patient information and
provides different types of service to authorized users. The cloud can be used as private or
community environment according to the required level of data sharing. It makes these records
ubiquitously accessible for patients, HPs, and practitioners. This component was discussed in
detail in section 3.
The EHR: the second component is the EHR where distinct patient records are integrated from
different units (pharmacy, registration, Lab,...) in many CDOs distracted in a city, state, or region
and stored in the cloud. One critical issue here is the ability to link patients to their records
without errors. This is achieved by the usage of a hybrid linking approach that combine statistical
matching and Unique Patient Identifier (UPI) approach [1, 12, 55, 58]. The statistical algorithm is
used first to retrieve patients’ records from EMRs in different interconnected CDOs. The records
are integrated and stored to form EHRs. UPI for each patient can then be used as a primary key to
filter patient’s information in EHRs and eliminate errors occur during statistical matching.
The Security Model: the third component is the one that guarantees protecting security and
privacy in HIS. Encryption algorithms such as AES [2] and RC4 [3] and authentication
techniques such as One Time Password (OTP) [4] and Two Factor Authentication (2FA) [5] can
be applied to protect EHR from tampering and unauthorized access. The healthcare authority can
now setup groups of clinician authorized to access patients’ EHRs. It is also responsible for
defining privileges for each group according to their specialization, disease type and complexity,
and the role they play in treatment. The group may contain specialists, doctors, nurses, lab
technicians, pharmacists, or other practitioners. Group members can be from different hospitals,
cities, or regions; hence, the cloud facilitates sharing of medical records among group members to
give perfect consultation. The healthcare authority assigns to each group digital signatures to sign
the medical certificates provided by them. Signed medical certificates are available to the patient
and are stored in his/her EHRs. The patient can authenticate diagnosis and consultations through
these digitally signed certificates. For example, allowing specific group to access the medical
record for patient-A, his family medical records, give the appropriate treatment decision,
medications, and report the status of his health. Each member in one group may have different
privileges according to his job and profession. On other hand, the patient is allowed to see only
his medical records, access the results of medical tests, validate the integrity of his records, and
verify the authenticity of medical certificate given by a group with a digital signature. The
researchers take the statistical information without knowing the full information about the
patients. The health insurance companies may verify the integrity of treatment decisions to their
customers.
The Big Data Analytics: this component can deploy different tools that analyze multi-terabyte
EHR databases in the cloud and give real-time insights about the data. CSPs offer many big data
analytics tools such as:

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• Google Big Query: uses Google’s cloud infrastructure to store and query massive
datasets in few seconds by enabling super-fast SQL like queries.
• MapReduce: is a software tool that enables cloud users to write application to process in
parallel a vast amount of data on large clusters. It is divided into two parts: Map,
a function that splits work to different nodes in the distributed cluster, and Reduce,
another function that collects the work and resolves the results into a single value.
The CDOs: this component includes different healthcare organizations distributed across the
country. Each organization consists of several clinical and administrative departments such as
Radiology, Lab, Pharmacy, Billing, etc. These departments are integrated via the EHR unit in the
cloud which enable data sharing and interoperability. Communications between different CDOs
departments and the EHR is made possible using HL7 protocol which is a structured standard that
is used as a means of exchanging information between healthcare applications. It defines a format
for the transmission of health-related information [63].
The proposed framework for a new generation HISs aims to achieve the following goals:
• Provide healthcare services of high quality and low cost to the patients using a
combination of big data, cloud computing and mobile computing technologies.
• Take advantage of the massive amounts of healthcare data and provide right intervention
to the right patient at the right time.
• Provide personalized healthcare to the patient.
• Protect the security and the privacy of healthcare data.
• Bridge big data, mobile-cloud computing, information security and medical informatics
communities to foster interdisciplinary works between them.
FIG. 1: A FRAMEWORK FOR EHR BASED ON MOBILE CLOUD COMPUTING AND BIG DATA ANALYTICS

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7. CONCLUSIONS AND FUTURE WORK
This paper proposes a framework for secure Health Information Systems (HISs) based on big data
analytics in mobile cloud computing environment. The framework provides a high level of
integration, interoperability, and sharing of EHRs among healthcare providers, patients and
practitioners. The cloud permits a fast Internet access, sharing, and provision of EHRs by
authenticated users. Big data analytics helps analyze patient data to provide right intervention to
the right patient at the right time. The proposed framework applies a set of security constraints
and access control that guarantee integrity, confidentiality, and privacy of medical data. The
ultimate goal of the proposed framework is to introduce a new generation of HISs that are able to
provide healthcare services of high quality and low cost to the patients using this combination of
big data analytics, cloud computing and mobile computing technologies. In the future we plan to
design and implement HIS based on the proposed framework.
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