Briti Deb

Satish N. Srirama

Frontiers in Genetics

PubMed

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OPINION ARTICLE
published: 03 September 2013
doi: 10.3389/fgene.2013.00171
Social networks for eHealth solutions on cloud
Briti Deb* and Satish N. Srirama
Mobile Cloud Laboratory, Institute of Computer Science, University of Tartu, Tartu, Estonia
*Correspondence: deb@ut.ee
Edited by:
John Hancock, University of Cambridge, UK
Reviewed by:
Jenny Ure, Edinburgh University, UK
Keywords: cloud computing, eHealth, crowdsourcing, social networks, semantic web, participatory medicine, utility computing/on-demand computing
INTRODUCTION
Traditional experimentation based health-
care solutions are constrained by limited
data that can confirm or refute the ini-
tial hypothesis. Big medical data in indi-
vidual Electronic Health Records, labs,
imaging systems, physician notes, medi-
cal correspondence and claims, provides
a resource for extracting complemen-
tary information that can enhance the
data available from traditional approaches
based on experimentation. Datamining
algorithms are being used to analyze data
to get a more insightful understanding
of human health, both preventive and
clinical. But despite their sophistication,
they are far from flawless. One way to
solve the problem is crowdsourcing citi-
zens connected in a social network, who
can provide data, get it analyzed, and con-
sume data for preventive health insights
(Swan, 2009). Several challenges come
along with it, for instance: performance,
scalability, speed, storage, and power,
which we believe could be addressed by
cloud-enabled social networks for eHealth
services. Such services could be com-
posed of many other services, for instance,
user authentication, email, payroll man-
agement, calendars, tele-consultation, e-
Prescribing, e-Referral, e-Reimbursement,
and alerting services, aiming to change the
way big medical data in social networking
web sites could be used making it action-
able to save lives.
This paper aims to explore the oppor-
tunities and challenges for realization of
cloud-enabled social networks for eHealth
solutions, by examining efforts already
underway, and recommending solutions to
improve it. We discuss a three-tier ecosys-
tem to advance this key field leveraging the
Cloud computing technologies. In Tier-
1 is “Build Sustainable eHealth System”
to create a foundation that facilitates
secure creation, storage, exchange, and
analysis of data between actors. In Tier-2 is
“Crowdsourced Social Networks for eHealth
Services” to utilize the power of crowd-
sourcing. In Tier-3 is “Increasing Access to
eHealth” to minimize risk and improve
patient outcome. Failure to address these
issues is believed to result in inefficient
use of big medical data toward preventive
healthcare.
THE THREE-TIER eHEALTH
ECOSYSTEM ON CLOUD
TIER 1: BUILD SUSTAINABLE eHEALTH
SYSTEM
Semantic interoperability
As healthcare institutes do not strictly
conform to a single commonly agreed
vocabulary/standard, integrating bio-
medical data using domain ontologies
is far from perfect (Della Valle et al., 2005;
Ulieru et al., 2006). We believe that such
diverse data in terms of volume, vari-
ety, and velocity, can be attempted to be
semantically integrated, shared, reused,
and made accessible, by using a top-level
ontology for integrating domain ontolo-
gies, semantic web standards such as RDF
for describing information, SPARQL as
an RDF query language, and OWL to
represent knowledge.
Compliance/accountability
Specific compliance/accountability
requirements can be enforced by laws and
regulations on organizations that collect,
generate or storemedical data, thereby dic-
tating a wide array of data related policies
such as, retention time, deletion process,
recovery plans, and sharing policy. Laws
such as the Health Insurance Portability
and Accountability Act (HIPAA) in the
US are already in force and complied with
by organizations like PatientsLikeMe.com.
The Federal Risk and Authorization
Management Program (FedRAMP) is
another law in the US enacted to assess
and authorize cloud products and ser-
vices. The dispersed geographic location
of cloud providers such as Amazon.com
opens the possibility of breach of compli-
ance, which could be addressed by Portable
Consent, and Institutional Review Board
could be enacted to monitor, approve, or
prevent the use of medical data on the
cloud.
Security and privacy
Hosting data in the cloud poses pri-
vacy concerns because the service provider
may access, accidentally or deliberately
alter, or even delete information. Methods
to obfuscate individual identity attributes
such as Zero-knowledge Technology or
Privacy Enhancing Technologies are cur-
rently not used in a pervasive manner
(Bertino et al., 2009) due to lack of gran-
ularity in the Access Control List, creating
privacy risks. Tomitigate some of the secu-
rity risks such as sensitive data access, data
segregation, bug exploitation, recovery,
accountability, and activity by malicious
insiders, solutions are being researched
such as cryptography, public key infras-
tructure (PKI), standardisation of APIs,
and virtual machine security.
Legislative influence
As the Cloud poses a challenge on “pos-
session,” “custody,” and “ownership” of
data, Terms of Service (TOS) agreements
become vital to clarify the different rights
to be assigned to different roles. The TOS
must also specify procedures to follow in
the event of an end of provider-customer
relationship, a merger of one provider with
another, bankruptcy, and insolvency. An
open challenge is how to ascertain legal
jurisdiction if disputes arise for geograph-
ically dispersed data. Patient Advocacy
Groups could play a role in influencing
advisory panels toward adopting better
laws to protect providers and consumers.
www.frontiersin.org September 2013 | Volume 4 | Article 171 | 1
Deb and Srirama Social networks for eHealth solutions on cloud
Revenue/financial model
Crowdsourced eHealth social networks are
mostly free of subscription fees, adver-
tising, banner ads or popups. Sale of
anonymized data, clinical trial aware-
ness programs, and market research sur-
veys constitute a major part of revenue.
In future, revenue model could increas-
ingly include health insurers, such as
the already implemented Health Savings
Account in US.
Reputation/credibility, quality control, and
transparency
The success of safety-critical sys-
tems depends largely on the reputa-
tion/credibility they enjoy in market.
Several non-technical challenges arises
from the change in the IT depart-
ment’s role from provider to consultant
(Khajeh-Hosseini et al., 2010), resulting
in an increased risk to customer satis-
faction, job quality, and job satisfaction,
tensions between the expectations of dif-
ferent groups, questioning the long term
organizational impact of Cloud migra-
tion on reliability, scalability, and cost
effectiveness.
TIER 2: CROWDSOURCED SOCIAL NETWORKS
FOR eHEALTH SERVICES
Personalized preventive health mainte-
nance comes against the backdrop of
several challenges such as difficulty in
understanding the causations of complex
diseases due to an incomplete understand-
ing of the complexities of biology, the
high cost of healthcare, an aging pop-
ulation, and a physician shortage. One
solution is to use social networks as a
platform to facilitate the participation of
millions of users in the crowd to realize
the 4P’s of medicine—preventive, per-
sonalized, predictive, and participatory.
Several eHealth social networks have
appeared, namely, patientslikeme.com,
hellohealth.com, medhelppc.org, curetoge-
ther.com, dailystrength.org, FacetoFace-
Health.com, 23andMe.com, Genomera.com,
QuantifiedSelf.com, DIYgenomics.org, pro-
viding a platform for people in the crowd
to compare their conditions with other
individuals, and identifying areas for fur-
ther scientific research on their own before
clinical symptoms appear. Studies have
shown typical challenges for a crowd-
sourced system (Doan et al., 2011) such
as (a) recruitment, retention, and eval-
uation of users, (b) merging/combining
contribution of users, (c) managing qual-
ity of contribution of users, (d) managing
query semantics, query execution, and
query optimization, and (e) improving
user interfaces.
In addition to identifying potential pre-
clinical symptoms, datamining algorithms
can be applied to the discussion forums
provided by the eHealth social networks to
identify epidemiological patterns such as
(i) patient behavior in response to a safety
event, (ii) efficacy and side-effects of drugs
that have not shown up in trials, thereby
helping to reduce time spent in clinical
trial, (iii) monitoring and participating
in real-world natural experiments, (iv)
anonymously sharing treatment, symp-
tom, progression and outcome data.
However, performance and adaptabil-
ity of eHealth social networks face chal-
lenge due to complexities in big data
handling, such as variety, velocity, vol-
ume, distribution, synchronization, fault
recovery, etc. To address the challenge of
distributing data and computation loads
over multiple processing units, largely
three main directions have being stud-
ied: (a) parallel computing frameworks
such as MapReduce, Iterative MapReduce,
and Bulk Synchronous Parallel (BSP),
(b) Graphics Processing Units, and (c)
Message Passing Interfaces.
In the MapReduce model, parallelism
is achieved by executing Map and Reduce
tasks concurrently. To achieve fault toler-
ance, data is replicated and failed tasks are
re-executed. The efficiency and scalability
of algorithms on the Cloud can be affected
by the characteristics of an algorithm,
necessitating a classification for algorithms
(Srirama et al., 2012). As the MapReduce
model is most suitable for embarrassingly
parallel tasks, i.e., parallel tasks having lit-
tle or no dependency between them, seri-
ous issues arise when working with graph
problems in social networks due to fac-
tors such as (a) long “start up” and “clean
up” times, (b) no way to keep impor-
tant data in memory between MapReduce
job executions, and (c) reading of all
data from file system (HDFS) after each
iteration and writing back there at the
end. Three main directions are currently
being pursued to address the challenges of
graph processing in parallel environment:
(i) restructuring algorithms for the non-
iterative MapReduce version, (ii) restruc-
turing non-iterative MapReduce algo-
rithms into iterative MapReduce ver-
sions using alternative MapReduce frame-
works (Twister, HaLoop, Spark), giving up
advantages of the MapReduce model such
as Fault tolerance and running multiple
concurrent reduce tasks, and (iii) alterna-
tive distributed computing models such as
BSP (Pregel, Hama, Giraph).
TIER 3: INCREASING ACCESS TO eHEALTH
Several challenges limit access to eHealth.
One such is the workflow challenge,
arising for several reasons such as the
inefficiency of current processes and the
dependency on paper to store data. It
is envisioned that in future, a physician
would enter patient data in an electronic
scheduling system on the Cloud, which
would be processed by some workflow to
automatically determine the most appro-
priate test, and the patient directly notified
of the possible options.
Semantically integrating diverse
patients medical records, census data, and
environmental samplings, and managing
scalability and load balancing, are some
of other major challenges while analyz-
ing big data. One approach to addressing
these is the use of virtualization tech-
nology, which allows applications to be
easily migrated from one physical server
to another, resulting in improved reliabil-
ity, scalability, business continuity, load
balancing, hardware maintenance, dis-
aster recovery, and better utilization of
processors and memory.
Yet another challenge to increasing
access to healthcare is providing ubiqui-
tous healthcare monitoring. Traditionally,
patients were “treated” only in hospi-
tal/clinic, which is expected to change
in future, as ubiquitous gadgets such as
mobile phones are now being increasingly
being used to track patients and keep
them compliant. Mobile cloud computing
is expected to arise as a prominent domain,
seeking to bring the massive advantages of
the Cloud to resource constrained smart-
phones, by following either the delegation
model or code offloading model (Flores and
Srirama, 2013). In the delegation model,
a mobile phone consumes services from
multiple clouds by following their Web
API, whereas, in the code offloading model,
Frontiers in Genetics | Bioinformatics and Computational Biology September 2013 | Volume 4 | Article 171 | 2
Deb and Srirama Social networks for eHealth solutions on cloud
a mobile application is partitioned and
analyzed so that the most computationally
expensive operations at code level can be
identified and offloaded to the Cloud for
remote processing.
DISCUSSION AND CONCLUSIONS
In this paper, we briefly analyzed the
opportunities and challenges for realiza-
tion of cloud-enabled social networks
for eHealth solutions, and proposed
a three-tier ecosystem to improve it.
Four main actors can be identified:
service providers (genomic counselors,
biomedical researchers), remedy providers
(eHealth social networks providing com-
puting and storage), health professionals,
and data provider/consumers. The chal-
lenges can be summarized into two
main groups. First, technical challenges
such as resource exhaustion attributed
to the ever increasing demand of the
Cloud resources, data transfer bottle-
necks attributed to the limited network
bandwidth, unpredictability of Cloud per-
formance attributed to the inability of
Cloud consumers to govern the virtual
architecture owned by Cloud providers,
data lock-in attributed to the disconti-
nuity of Cloud-based eHealth services,
compounded by the problem of semantic
interoperability when migrating the data
to another Cloud, and limitations of the
non-iterative MapReduce model, particu-
larly in scalable graph processing. Second,
non-technical challenges arising from the
change in the IT department’s role from
provider to consultant, affecting customer
satisfaction and overall service quality,
calling for stringent quality control and
transparency measures. To address these
issues, we proposed a three-tier eHealth
ecosystem. In future, we propose to: (i)
investigate the use of Parallel R packages to
leverage multi-processor systems to speed
computations with big data by explicit
parallelism, implicit parallelism, and
implementing map-reduce for Hadoop;
(ii) develop novel algorithms for parallel
classification and parallel search; and (iii)
develop a novel framework for semantic
integration of biological data in social net-
works leveraging the Cloud. We believe
that a combined strategy consisting of
semantic, algorithmic, and computational
approaches would be useful to solve many
problems in eHealth social networks on
the Cloud. Biological research would ben-
efit as researchers would be able to analyze
massive amounts of complex data much
more quickly, and generate hypotheses
faster. Finally, the authors believe that
research in that direction could enhance
the scale and scope of experiments that
are possible, resulting in an exponential
growth in knowledge, similar to the expo-
nential growth in data that we see today.
ACKNOWLEDGMENTS
This research is supported by the
European Regional Development Fund
through the EXCS, Estonian Science
Foundation grant ETF9287, Target
Funding theme SF0180008s12 and
European Social Fund for Doctoral Studies
and Internationalization Programme
DoRa.
REFERENCES
Bertino, E., Paci, F., Ferrini, R., and Shang, N. (2009).
Privacy-preserving Digital Identity Management
for Cloud Computing. IEEE Data Eng. Bull. 32,
21–27.
Della Valle, E., Cerizza, D., Bicer, V., Kabak, Y., Laleci,
G., Lausen, H. et al. (2005). “The need for seman-
tic web service in the eHealth,” in W3C workshop
on Frameworks for Semantics in Web Services,
(Innsbruck).
Doan, A., Ramakrishnan, R., and Halevy, A. Y. (2011).
Crowdsourcing systems on the world-wide web.
CACM 54, 86–96.
Flores, H., and Srirama, S. N. (2013). “Adaptive
Code Offloading for Mobile Cloud Applications:
Exploiting Fuzzy Sets and Evidence-based
Learning,” in The Fourth ACM Workshop on
Mobile Cloud Computing and Services (MCS 2013),
at The 11th International Conference on Mobile
Systems, Applications and Services (MobiSys 2013),
(Taipei, Taiwan: ACM), 9–16.
Khajeh-Hosseini, A., Greenwood, D., and
Sommerville, I. (2010). “Cloud migration: a
case study of migrating an enterprise it system to
iaas,” in Cloud Computing (CLOUD), 2010 IEEE
3rd International Conference on. IEEE (Miami,
Florida).
Srirama, S. N., Jakovits, P., and Vainikko, E. (2012).
Adapting scientific computing problems to clouds
using MapReduce. Future Gen. Comput. Syst. 28,
184–192. doi: 10.1016/j.future.2011.05.025
Swan, M. (2009). Emerging patient-driven health care
models: an examination of health social networks,
consumer personalized medicine and quantified
self-tracking. Int. J. Environ. Res. Public Health 6,
492–525. doi: 10.3390/ijerph6020492
Ulieru, M., Hadzic, M., and Chang, E. (2006). Soft
computing agents for e-Health applied to the
research and control of unknown diseases. Inf. Sci.
176, 1190–1214.
Received: 17 June 2013; accepted: 15 August 2013;
published online: 03 September 2013.
Citation: Deb B and Srirama SN (2013) Social networks
for eHealth solutions on cloud. Front. Genet. 4:171. doi:
10.3389/fgene.2013.00171
This article was submitted to Bioinformatics and
Computational Biology, a section of the journal
Frontiers in Genetics.
Copyright © 2013 Deb and Srirama. This is an open-
access article distributed under the terms of the Creative
Commons Attribution License (CC BY). The use, dis-
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in accordance with accepted academic practice. No use,
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Social networks for eHealth solutions on cloud

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Satish Narayana Srirama

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