University of Wollongong: Big Data and Healthcare Delivery Value Chain

This report, originating from the University of Wollongong, examines the potential of big data analytics (BDA) in the healthcare sector, focusing on the healthcare delivery value chain (CDVC). The study reviews current peer-reviewed literature, highlighting BDA's impact on improving treatment outcomes and reducing costs within the CDVC framework. It defines big data using the 5 V dimensions (volume, variety, velocity, veracity, and value) and explores its applications, including better prediction of epidemics, improved treatment of diseases, and enhanced patient care. The report also discusses the CDVC model, its components, and the role of information technology in enabling transformations from siloed information environments to integrated systems. The research methodology involves a literature review, and the analysis identifies research gaps and future directions for big data research in healthcare, emphasizing the need for empirical studies to assess BDA's real potential in improving health outcomes and efficiency. The report is a part of the Faculty of Engineering and Information Sciences Papers and is available on Research Online.

University of Wollongong
Research Online
Faculty of Engineering and Information Sciences -
Papers: Part B Faculty of Engineering and Information Science
2018
Exploring the potential of big data on the
care delivery value chain (CDVC): a prelim
literature and research agenda
William J. Tibben
University of Wollongong, [e-mail]
Samuel Fosso Wamba
Toulouse Business School, [e-mail]
Research Online is the open access institutional repository for the University of Wollongong. For further information contact the UOW
[e-mail]
Publication Details
Tibben, W. J. & Fosso Wamba, S. (2018). Exploring the potential of big data on the health care delivery value chain (C
preliminary literature and research agenda. 51st Hawaii International Conference on System Sciences (HICSS 2018) (
Honolulu, HI 96822 USA: Hawaii International Conference on System Sciences (HICSS) HICSS Conference Office Depar
IT Management, Shidler College of Business University of Hawaii at Manoa. 2018

Exploring the potential of big data on the health care delive
(CDVC): a preliminary literature and research agenda
Abstract
Big data analytics (BDA) is emerging as a game changer in healthcare. While the practitioner lite
been speculating on the high potential of BDA in transforming the healthcare sector, few rigorou
studies have been conducted by scholars to assess the real potential of BDA. Drawing on the he
delivery value chain (CDVC) and an extensive literature review, this exploratory study aims to di
peer-reviewed articles dealing with BDA across the CDVC and discuss future research directions
Keywords
literature, chain, exploring, potential, big, data, health, (cdvc):, care, preliminary, delivery, value
research
Disciplines
Engineering | Science and Technology Studies
Publication Details
Tibben, W. J. & Fosso Wamba, S. (2018). Exploring the potential of big data on the health care de
chain (CDVC): a preliminary literature and research agenda. 51st Hawaii International Conferenc
Sciences (HICSS 2018) (pp. 2045-2054). Honolulu, HI 96822 USA: Hawaii International Conferenc
System Sciences (HICSS) HICSS Conference Office Department of IT Management, Shidler Colleg
Business University of Hawaii at Manoa. 2018
This conference paper is available at Research Online: http://ro.uow.edu.au/eispapers1/

Exploring the potential of big data on the health care delivery value chain
(CDVC): a preliminary literature and research agenda
William Tibben
University of Wollongong
[e-mail]
Samuel Fosso Wamba
Toulouse Business School
[e-mail]
Abstract
Big data analytics (BDA) is emerging as a game
changer in healthcare. While the practitioner literature
has been speculating on the high potential of BDA in
transforming the healthcare sector, few rigorous
empirical studies have been conducted by scholars to
assess the real potential of BDA. Drawing on the
health care delivery value chain (CDVC) and an
extensive literature review, this exploratory study aims
to discuss current peer-reviewed articles dealing with
BDA across the CDVC and discuss future research
directions.
1. Introduction
Increasing health care costs has become a critically
important public policy challenge around the world
[75]. While each country has its own unique history
and challenges there are good reasons to explore
emerging areas of scholarship that address
commonalities such as the need for greater efficiency
and efficacy of health care delivery. This paper seeks
to build on efforts to assess big data in health care by
reviewing research literature for its impact on health
care delivery. Understandably, many studies have
based their assessments using frameworks that have
their origins in extant health care delivery models [36,
44, 69, 73, 79]. In contrast, Porter and Teisburg’s care
delivery value chain (CDVC) is a framework that aims
to re-organize the delivery of health care to improve
treatment outcomes and reduce costs [53]. Hence, the
use of Porter and Teisburg’s CDVC model in this
paper to evaluate big data research aims to provide an
assessment of the transformative potential of big data
to facilitate changes in the way health care is delivered
Porter and Teisburg’s care delivery value chain
(CDVC) model seeks to radically change the
organization of health care delivery [53]. The primary
features of their CDVC is to promote better patient
focused treatment outcomes while improving
efficiencies in the delivery of health care services [26,
31, 32, 34, 52]. A central feature of Porter and
Teisburg’s CDVC relates to information and
information technology [53]. Accordingly, one
essential and strategic aspect, from their perspective, is
recognition of the role that information technology
plays in enabling transformations from the silo-ed
information environments of the past to integrated
systems across the whole health care value chain.
The reliance of health care and allied services on
patient data and related health care information
coupled to mobile technologies and the Internet of
Things (IoT) has contributed to enormous growth in
health care information. It is understandable that some
consider the potential of big data in health care
delivery [60]. Big data is defined in this paper using
the 5 V-related dimensions of volume, variety,
velocity, veracity and value [19]. What is yet to emerge
from this research activity is a meaningful
understanding of the relative impacts that big data
research is having in promoting improved health
outcomes for patients or improving efficiencies relative
to health costs. In order to create actionable insights
that address big data’s contributions to these two issues
of efficacy and efficiency in the delivery of health care
this paper undertakes a review of relevant literature
using Porter and Teisburg’s CDVC as an analytical
framework.
The rest of this paper is organized as follows:
Section 2 further explains the concept of care delivery
value chains as outlined by Porter and collaborators.
Section 3 defines big data and provides some
background to its attributes and its potential health care
impacts. Section 4 explains the methodology used for
the research. Section 5 presents the results of the
literatures review analysis. Section 6 moves on to
discuss these results and explore implications for future
research.
2. Health Care Delivery Value Chain
Porter’s and Teisburg’s care driven value chain
(CDVC) aims to re-orientate traditional health delivery
models to focus on providing value to the consumers of
health care. The CDVC represents a radical shift in the

delivery of health care away from supply side models
where health care is organized around the needs and
wants of doctors, hospitals and associated health care
units. In their 2006 monograph, Porter and Teisberg
reason that the fee-for-service model rewards health
care practitioners for their time and expertise without
sufficient regard for optimal treatment of the
underlying health condition from a patient and cost
perspective. This has led to inefficiencies in the
delivery of treatments. Their CDVC is organized on
the basis of “integrated practice units” that deliver
treatment for specific conditions over a full cycle of
care [53 p. 49]. As patients progress through the
treatment value chain it becomes possible to focus on
delivering each stage of care in more cost effective
ways. Thereby costs can be reduced without sacrificing
standards of care
The CDVC comprises of ten components (see
Figure 1). The integrated treatment cycle begins with
monitoring and preventing followed by diagnosing,
preparing, intervening, recovering rehabbing and
monitoring and managing. Each of these can be
divided into individual units of costs that can be
monitored. However, from the patient’s perspective,
this cycle of care should be integrated rather than
separated as indicated by the three top layers of
informing and engaging, measuring and accessing.
This is what delivers value to the patient.
The tenth component of the CDVC deals with
knowledge development. This covers a broad range of
activities such as physician and nurse training, results
management and tracking, process improvements and
technology development. For the purposes of this
paper, knowledge development has been limited here
to technology development. Porter and Teisburg
outline a specific role for information technology in
promoting the dissemination of results-based
information generated in the course of treatment [53].
They argue that such information enables competition
to flourish which also places downward pressure on
health care costs.
So, it is with these factors in mind that the paper
moves on to consider the impact of big data in relation
to health care delivery.
3. Big Data
The “Gartner’s Top 10 Strategic Technology
Trends for 2017” recognizes advanced analytics within
the Intelligent Apps as one of the ten top strategic
technology trends for 2017 that, when fully utilized by
firms, will help refine their offers and transform
customer experience [51]. Big data analytics (BDA) is
considered as a “holistic process to manage, process
and analyze 5 Vs (i.e., volume, variety, velocity,
veracity and value) in order to create actionable
insights for sustained competitive advantage” [19].
BDA recently has received much attention from both
practitioners and scholars because of its huge potential
in transforming firms across industry to achieve
sustained competitive advantage [13].
In healthcare, BDA offers many applications
including: better prediction of epidemics, treatment of
disease, improvement in the quality of life and
prevention of preventable deaths [45]. MacDonald [46]
identifies five big data trends in healthcare for 2017.
(i) Value-Based, Patient-Centric Care. This aims to
capitalize on technology to improve healthcare quality
and coordination by delivering “outcomes [that] are
consistent with current professional knowledge” (p. 1),
while reducing healthcare costs and avoidable overuse,
while providing support for reformed payment
structures
(ii) The Healthcare Internet of Things (IoT).
Alternatively known as the Industrial Internet, IoT is
characterized by a variety of devices that will be used
to monitor all types of patient behaviors including:
glucose monitors, fetal monitors, electrocardiograms,
blood pressure and medicines consumption. This
envisages a situation called “management of
exceptions” in which the need for direct physician
intervention is reduced because patients can followed
up by a nurse if an exception occurs.
(iii) Reducing Fraud, Waste, and Abuse. Here, the
author argues that BDA “can be a game changer for
healthcare fraud” because “predictive modeling” using
BDA tools can identify “inaccurate claims in a
systematic, repeatable way and generate a “2200%
return on their big data/advanced technology”
investment (p. 1).
(iv) Predictive Analytics to Improve Outcomes:
Using predictive modeling of health care records has
the potential to lead to early diagnosis and reduced
mortality rates. More generally, enhanced “accuracy of
diagnosing patient conditions, matching treatments
with outcomes, and predicting patients at risk for
disease or readmission” (p. 1) leads to better and more
efficient health outcomes
(v) Real-time Monitoring of Patients. The
generation of personalized health case data enables
“more proactive care to … patients by constantly
monitoring patient vital signs”.
The areas outlined by MacDonald resonate with
recent contributions to the academic literature. For
example Kohn et al. cite the potential that big data has
for better decision making in health care as well as a
greater autonomy for the patient in care management
[36]. Shah and Jyotishman similarly identify the
potential of big data for better integration of health care
data, knowledge-creation with consequent
improvements in practice [60].

Figure 1 Care Delivery Value Chain Framework (Based on [34, 53])
Using established frameworks to assess the extant
health care-related big data literature enables insights
to be developed about relevance of big data research,
research gaps and its potential to guide future research.
One recent paper uses a framework that draws on the
concept of a data life cycle to track data from its
capture, transformation to its consumption [73]. This
framework emphasizes the role of data governance to
support each phase. Another recent paper uses a health
care operations and supply chain management
(HOSCM) framework to assess the potential of big
data research to improve outcomes in health care
delivery [44]. The authors found that the HOSCM
framework was found wanting in revealing end-to-end
care delivery processes thereby not allowing patient
pathways to be adequately captured.
The extent to which these apparent failings are a
product of the traditionally silo-ed information
environments that Porter and his collaborators criticize
warrants attention. The application of the CDVC
provides an opportunity to assess the potential of big
data research to transform health care delivery by
focusing on patient value as well as unit health care
cost outcomes (see Figure 1). As a consequence the
objective of this review is summarized as: find how big
data and associated analytics contribute to the delivery
of healthcare with an emphasis on Porter and
Teisburg’s health care delivery value chain (CVDC).
The paper proceeds to outline the steps that were
taken to assess current literature in relation to CDVC.
4. Methodology
The methods that were adopted in this paper
enables an assessment to made of big data research
activity in health care delivery and to identify areas for
future research. The review of the literature was guided
by a protocol adapted from Fosso Wamba et al. [19,
20], Lim et al. [41] and Ngai et al. [49]. The protocol
consists of three phases: (i) creation of classification
framework; (ii) identification of relevant literature; and
(iii) application of classification framework to the
literature. The selected literature is limited to peer-
reviewed journal articles, articles-in-press and reviews
are reasoned to be the principal medium by which
academic and practitioners obtain and disseminate new
information [50].
4.1. Classification Framework
The framework used to classify papers is based on
Porter CDVC as detailed in Figure 1 [53, 34]. The
CDVC is made up of ten categories. Nine of the

categories (Informing and Engaging, Measuring;
Accessing; Monitoring and Preventing; Diagnosing;
Preparing; Diagnosing; Recovering Rehabbing and
Monitoring Managing) were used to classify the
selected literature.
Finally, each paper was considered for the area of
technology that was being advanced by the research.
Technology in this sense is defined as areas of
technical knowledge rather than referring more
generally to technical artifacts as evidenced in popular
usage of the term (see discussion in [42]).
4.2. Literature search strategies
In order to capture the most recent and relevant
research a keyword search from the past five years
(2012 to 2017) on the SCOPUS database was
undertaken. The SCOPUS database is considered an
appropriate choice to begin the literature search
because it is the largest abstract and citation database
of peer-reviewed literature holding more than 19,000
peer-reviewed journals. Only articles, reports and
articles-in-press were retained for further analysis
The keywords were derived from the research
statement to “find how big data and associated
analytics contribute to the delivery of healthcare with
an emphasis on Porter and Teisburg’s health care
delivery value chain (CDVC).” The following
keywords were derived: “big data” “business
analytics”, “analytics”, “healthcare” and “care delivery
value chain” (including variants: “healthcare delivery
value chain”, “health care delivery value chain” and
“CDVC”). The SCOPUS search is defined by the
following: KEY ("Big data" OR "business analytics"
OR "analytics" AND "healthcare") AND PUBYEAR
> 2012) OR (KEY ("Big data" OR "business analytics"
OR "analytics" AND "health care delivery value
chain") AND PUBYEAR > 2012 ) OR (KEY ("Big
data" OR "business analytics" OR "analytics" AND
"healthcare delivery value chain") AND PUBYEAR >
2012 ) OR (KEY ("Big data" OR "business analytics"
OR "analytics" AND "CDVC" ) AND PUBYEAR >
2012) OR (KEY ("Big data" OR "business analytics"
OR "analytics" AND "care delivery value chain")
AND PUBYEAR > 2012) AND (LIMIT-TO
(DOCTYPE , "ar") OR LIMIT-TO (DOCTYPE , "re")
OR LIMIT-TO (DOCTYPE , "ip")).
Our search started on April 27, 2017 and ended on
June 13, 2017. The initial search resulted in 134
articles. One paper was discovered to be redacted so
was immediately eliminated from the analysis. The
references for the 133 papers, including the abstracts of
all articles, were downloaded into Endnote, a reference
management software package. Groups were created to
categorize articles in the following fashion: 1. Nine
papers removed after abstract review by one author
because paper did not address either health care
delivery or big data as defined by [19]; and 2. 65
papers were removed after a review of full content by
both authors to ensure that articles addressed both big
data and health care delivery. At the end of this process
59 articles were deemed suitable for our research
objectives and were selected for classification.
Further scrutiny of the remaining 59 articles was
able to separate some articles on the basis of their
research approach. Seventeen articles were found to be
literature articles that did not report on research
findings beyond the outcome of their analysis of
articles. With the elimination of these the final number
of articles that were deemed suitable for classification
against the CDVC totaled 42 (see Table 1).
5. Results
In this section, the results of the review of past
literature that intersects both big data and the CDVC
delivery will be outlined.
5.1. Distribution of articles by publication date
The distribution of articles for the past five years is
shown in Figure 2. It can be seen that there is a clear
upward trend in the numbers of articles being
published. For 2012 only one article was retrieved with
only a small increase in 2013. At the time of writing
with approximately six months till years-end, the 2017
total had already exceeded the 2016 total of 25.
Figure 2 Distribution of articles by year of
publication
5.3. Distribution of articles by CDVC
framework
The allocation of research articles according to the
CDVC classification framework (see Figure 1) is
summarized in Table 2, 3 and 4. In Table 2 articles

Table 1. Articles selected for review and classification
Note: Some articles counted more than once because they cover more than one element of the CDVC
Table 3. Classification of articles using CDVC framework - treatment cycle
CDVC Article # %
Monitoring and preventing [1] [8] [9] [10] [16] [18] [21] [24] [29] [35] [38] [40] [43] [57] [71] [72] [59]
[58]
18 33%
Diagnosing [1] [6] [12] [15] [16] [18] [21] [25] [27] [37] [38] [54] [57] [58] [70] [78] 16 30%
Preparing [2] [4] [15] 3 6%
Intervening [2] [54] [59] [64] [70] 5 9%
Recovering rehabbing [3] [7] [54] [64] [70] 5 9%
Monitoring and managing [10] [29] [35] [40] [43] [68] [59] 7 13%
Total 54 100%
Note: Some articles counted more than once because they cover more than one element of the CDVC
Table 4. Big data technology development
Technology focus Article # %
Analytics [1]) [3] [4] [7] [17] [18] [25] [29] [33] [43] [57] [63] [68] [71] [74] 15 36%
Personalized medicine – IoT &
Web 2.0
[2] [6] [12] [24] [35] [38] [40] [59] [66] [76] [77] 11 26%
Computer science- processing
large data sets
[8] [10] [15] [16] [21] [27] [30] [58] [72] [78] 10 24%
Management & policy (resource
management and ethics)
[37] [54] [65] [70] 4 10%
User interface design (e.g. data
visualization)
[9] [64] 2 5%
Total 42 100%
Review articles [5], [11], [14], [22], [23], [28], [36], [39], [44] [55] [56] [61] [62] [67] [69] [73] [79]
Research articles [1], [2] [3] [4] [6] [7] [8] [9] [10] [12] [15] [16] [17] [18] [21] [24] [25] [27] [29] [30] [33],
[35] [37] [38] [40] [43] [54] [57] [58] [59] [63] [64] [65] [66] [68] [70] [71] [72] [74] [76],
[77] [78]
Table 2. Classification of articles using CDVC framework – patient value
CDVC Article # %
Informing [1] [2] [3] [8] [9] [16] [21] [24] [29] [40] [43] [59] [63] [65] [66] [76] [77] 17 24%
Measuring [1] [3] [4] [6] [7] [8] [9] [10] [12] [17] [18] [25] [27] [29] [30] [33] [35] [37] [38]
[40] [43] [54] [57] [58] [63] [64] [71] [72] [74] [76] [78]
31 46%
Accessing [2] [4] [8] [9] [10] [12] [16] [17] [24] [29] [37] [38] [40] [43] [54] [57] [64] [66]
[68] [72] [76] [78]
22 30%
Total 70 100%

were classified by patient value attributes of Informing
and Engaging, Measuring and Accessing. It can be
seen that the majority of big data articles (31) address
the element of Measuring. The allocation of articles to
Informing and Engaging and Accessing were 17 and
22 respectively. These three elements are common to
all phases of the CDVC and are identified by Porter
and his collaborators as providing patient value. All of
the reviewed articles can be placed in one of more of
these categories so ostensibly provides evidence that
the research described by the articles provides added
value to patients.
In relation to the CDVC treatment cycle (Table 3) it
can be seen that the first element of Monitoring and
Preventing has attracted the greatest number of articles
(18). The second highest treatment area is Diagnosing
(16). Interestingly, the areas of Preparing (3) and
Intervening (5) were allocated relatively few articles.
The final two areas of Recovering Rehabbing (5) and
Monitoring and Managing and Policy (7) similarly
received relatively small tallies. Hence, it can be seen
that the majority of research articles favors the early
phases of the CDVC treatment cycle (Monitoring and
Preventing and Diagnosing)
In order to develop a deeper understanding of the
nature of research that contributes to the CDVC it is
useful to review Table 4 which lists areas of
technology development represented in each of the
articles. From the table it is clear that the largest
number of articles describe big data-related
innovations in analytics (15). The next most popular
area of research concerns personalized medicine where
the Internet, Web 2.0, mobile technologies coupled
with bio sensors have made a significant impact (11).
A similar number of articles pertain to the concerns of
computer scientists and the need to process large data
sets in a timely manner (10). These papers are notable
for being technical in nature (notably Hadoop
Mapreduce framework and varying statistical
techniques) which use datasets obtained from
healthcare applications for their experiments. There are
only four papers that deal with relevant management
issues such as resource management and ethics. Also
connected to Computer Science is user interface design
(data visualization) where two studies into data
visualization are represented.
Finally, the results of Tables 2-4 are summarized in
a CDVC-technology matrix (Table 5). Its possible to
gain a sense of the areas in which big data research has
been making a greater or lesser impact. In relation to
Patient Value, it can be seen that there is a good
coverage of technology over the three levels. Notably,
Analytics can be seen to be making the greatest impact
in the areas of Measuring (14). None of the
management papers appear to have informed
Accessing sites of care.
Moving on to Health outcomes, Analytics has
made the greatest impacts on Monitoring and
preventing (6) and Diagnosing (4). Similarly,
Computer Science research has a similar concentration
in Monitoring and preventing (6) and Diagnosing (6).
For Personalized Medicine technologies the greatest
areas of impact are Diagnosing (3) and Monitoring and
Managing (3). The relatively few papers in
Management and Policy made the greatest tallies in
Intervening (3) and Recovering and rehabbing (3).
User Design papers were evident in Monitoring and
preventing (1) Intervening (1) and Recovering and
rehabbing (1).
6. Discussion
The results summarized in Tables 1-4 begin to
provide an insight into the potential impact of big data
research to alter the CDVC for healthcare. Each of the
areas of the CDVC were relevant to the articles
reviewed though its clear that some aspects of the
CVDC (Preparing, Intervening, Recovering and
Rehabbing) have attracted relatively few articles when
compared to other areas (Monitoring and Preventing
and Diagnosing).
It is in the treatment cycle element of Preparing
which has received the least attention from big data
researchers (3). Preparing is defined in the CDVC
framework as being made up of “Choosing the team”,
and “Pre-intervention preparations” (including Pre-
treatments). One can speculate that the lack of research
interest may reflect silo-ed arrangements of care where
treatment centers have largely determined the
composition of teams and treatments without much
consumer input.
There are three areas of technology research from
Table 4 represented here: Analytics (design of patient
care homes using outpatient data); Personalized
Medicine (twitter initiative organizing blood donations
in India) and Computer Science data processing
(determining optimal chemotherapy treatments from a
health records data set).
Moving on to Intervening in Table 3 a slightly
higher number of research articles (5) can be found
which are diverse in their foci. Intervening is
comprised of: “Ordering and administering drug
therapy”, “Performing procedures” and “Performing
counseling therapy”. Four of the research articles that
have been assigned are: once again, twitter initiative
for blood donations; limitations of EU health policy to
support personal medicine interventions; user interface
design (sepsis management using data visualization);
and management (US Medicare cost comparisons for

Table 5. CDVC - technology matrix
CDVC elements Analytics Personalized
medicine
Computer
science
Management &
Policy
User design
Patient value
Informing 5 7 3 1 1
Measuring 14 6 7 2 2
Accessing 3 4 5 0 1
Health outcomes
Monitoring and preventing 6 0 6 0 1
Diagnosing 4 3 6 1 0
Preparing 1 1 1 0 0
Intervening 0 1 0 3 1
Recovering rehabbing 2 0 0 3 1
Monitoring and managing 3 3 1 0 0
interventions across the US). The fifth article
discusses the potential of combining big data
analytics with virtual physiological human (PVH)
technology in treatments of disease research. The
technology mix from this category as revealed in
Table 5 indicates that one study is assigned to
personalized medicine (twitter), three papers to
Management and Policy (US Medicare cost
comparisons, EU health policy for personalized
medicine and PVH research policy) and one to User
Design.
Recovering Rehabbing in Table 3 indicates a
similarly low tally of big data articles with five
papers assigned. Recovering Rehabbing entails the
following: “Inpatient recovery”, “Inpatient and out-
patient rehab”, “Therapy fine- tuning” and
“Developing a discharge plan”. Looking to Table 5,
the five papers assigned are comprised of two from
analytics researchers (re-admissions), two from
Management and Policy (EU health policy for
personalized medicine and cost comparisons across
the US for readmissions) and one from data
visualization (dash boards for sepsis management).
The final treatment element considered in Table 3
is Monitoring and Managing. Analytics (e.g.
predicting readmissions monitoring after effects of
health shocks) and personalized medicine (e.g. bio
sensing, smart home monitoring) from Table 4 are
most strongly represented here. The CVDC-
technology matrix (Table 5) indicates that three
papers are drawn from analytics, three from
personalized medicine and one from Computer
Science. One can see a similarity in these research
articles with those performed in the Monitoring and
preventing stage. Opportunities to engage with
community members before or after their treatment
within health facilities seems to be better than that
once patients enter such facilities.
Broadly summarizing, it can be seen that there is
a strong representation of papers that engage with
patients before entry into and after treatment regimes
within health care facilities. Much less engagement
can be seen with health care delivery around
Intervening and Recovering Rehabbing suggesting
the need for more attention to these areas if big data
is to facilitate changes to all aspects of the CDVC.
The relatively small number of big data articles that
deal with Management and Policy can similarly be
seen as a future research need to address questions of
efficiency within the CDVC.
To that end, a need for greater engagement of big
data researchers with treatment (Preparing,
Intervening and Recovering Rehabbing) is one future
area of research the paper suggests. Another area is
the need for big data researchers to engage with
management and policy issues of CVDC. The case
for change to CVDCs is likely to be met with
resistance from entrenched interests. According to
Porter and his collaborators, the tendency to replicate
existing models of health care privileges the interests
of health and allied care practitioners over patient
value and lowering costs. The need for robust
evidence to agitate for such change outlines a
significant research challenge of big data researchers.
The findings from this paper have limitations in
that the selection of literature was from one database.
Further work in this area should initially seek to
broaden the literature base to confirm the initial
findings from this analysis.

7. Conclusion
This paper seeks to provide an informed
understanding of the impact of big data research on
health care delivery. Using the care delivery value
chain (CDVC) framework developed by Porter and
collaborators it has been possible to see what aspects
of health care delivery have benefitted most from big
data research and areas that have been given less
attention. Health care delivery involves many
different disciplines none more so than medical and
allied health professions. However, there is a
critically important role for IS scholarship too as
demonstrated through the examples outlined in
relation to CDVC. While there is an ongoing role for
big data research to address improvements in
treatments the paper finds that there is a greater need
for increased attention to management and policy
development that aims to promote more personalized
modes of care that create increased patient value
while simultaneously seeking to achieve greater
efficiencies in the delivery of health care.
8. References
[1] A. Abbas, M. Ali, M. U. Shahid Khan and S. U. Khan,
"Personalized healthcare cloud services for disease risk
assessment and wellness management using social media",
Pervasive and Mobile Computing, 28 (2016), pp. 81-99.
[2] R. A. Abbasi, O. Maqbool, M. Mushtaq, N. R. Aljohani,
A. Daud, J. S. Alowibdi and B. Shahzad, "Saving lives
using social media: Analysis of the role of twitter for
personal blood donation requests and dissemination",
Telematics and Informatics (2017).
[3] S. E. Abdelrahman, M. Zhang, B. E. Bray and K.
Kawamoto, "A three-step approach for the derivation and
validation of high-performing predictive models using an
operational dataset: Congestive heart failure readmission
case study", BMC Medical Informatics and Decision
Making, 14 (2014).
[4] S. Ajorlou, I. Shams and K. Yang, "An analytics
approach to designing patient centered medical homes",
Health Care Management Science, 18 (2015), pp. 3-18.
[5] A. Asante-Korang and J. P. Jacobs, "Big Data and
paediatric cardiovascular disease in the era of transparency
in healthcare", Cardiology in the Young, 26 (2016), pp.
1597-1602.
[6] G. Atluri, A. Macdonald, III, K. O. Lim and V. Kumar,
"The Brain-Network Paradigm: Using Functional Imaging
Data to Study How the Brain Works", Computer, 49
(2016), pp. 65-71.
[7] I. Bardhan, J. H. Oh, Z. Zheng and K. Kirksey,
"Predictive analytics for readmission of patients with
congestive heart failure", Information Systems Research,
26 (2015), pp. 19-39.
[8] F. A. Batarseh and E. A. Latif, "Assessing the Quality
of Service Using Big Data Analytics: With Application to
Healthcare", Big Data Research, 4 (2016), pp. 13-24.
[9] P. Calyam, A. Mishra, R. B. Antequera, D.
Chemodanov, A. Berryman, K. Zhu, C. Abbott and M.
Skubic, "Synchronous Big Data analytics for personalized
and remote physical therapy", Pervasive and Mobile
Computing, 28 (2016), pp. 3-20.
[10] H. Chen and Z. Fu, "Hadoop-Based Healthcare
Information System Design and Wireless Security
Communication Implementation", Mobile Information
Systems, 2015 (2015).
[11] A. Chluski and L. Ziora, "The application of mobile
technology management concept and big data solutions in
healthcare", Polish Journal of Management Studies, 12
(2015), pp. 37-47.
[12] G. Csepeli and R. Nagyfi, "Facebook diagnostics:
Detection of mental health problems based on online
traces", European Journal of Mental Health, 9 (2014), pp.
220-230.
[13] T. H. Davenport and J. G. Harris, Competing on
analytics: the new science of winning, Harvard Business
School Press, Boston, Massachusetts, 2007.
[14] R. R. Dewangan, D. Thombre and C. Patel, "Big data
technology in health and biomedical research: A literature
review", International Journal of Database Theory and
Application, 9 (2016), pp. 175-184.
[15] E. Elsebakhi, F. Lee, E. Schendel, A. Haque, N.
Kathireason, T. Pathare, N. Syed and R. Al-Ali, "Large-
scale machine learning based on functional networks for
biomedical big data with high performance computing
platforms", Journal of Computational Science, 11 (2015),
pp. 69-81.
[16] K. Feldman, D. Davis and N. V. Chawla, "Scaling and
contextualizing personalized healthcare: A case study of
disease prediction algorithm integration", Journal of
Biomedical Informatics, 57 (2015), pp. 377-385.
[17] S. Feuerriegel, "Decision support in healthcare:
determining provider influence on treatment outcomes with
robust risk adjustment", Journal of Decision Systems, 25
(2016), pp. 371-390.
[18] A. R. M. Forkan, I. Khalil, A. Ibaida and Z. T.
Member, "BDCaM: Big Data for Context-Aware
monitoring-a personalized knowledge discovery framework
for assisted healthcare", IEEE Transactions on Cloud
Computing, PP (2015).
[19] S. Fosso Wamba, S. Akter, A. Edwards, G. Chopin
and D. Gnanzou, "How ‘big data’ can make big impact:
Findings from a systematic review and a longitudinal case
study", International Journal of Production Economics, 165
(2015), pp. 234–246.
[20] S. Fosso Wamba, A. Anand and L. Carter, "A
literature review of RFID-enabled healthcare applications
and issues", International Journal of Information
Management, 33 (2103), pp. 875-891.
[21] J. Frizzo-Barker, P. A. Chow-White, A. Charters and
D. Ha, "Genomic Big Data and Privacy: Challenges and
Opportunities for Precision Medicine", Computer
Supported Cooperative Work: CSCW: An International
Journal, 25 (2016), pp. 115-136.
[22] D. Gu, J. Li, X. Li and C. Liang, "Visualizing the
knowledge structure and evolution of big data research in
healthcare informatics", International Journal of Medical
Informatics, 98 (2017), pp. 22-32.

[23] K. Y. He, D. Ge and M. M. He, "Big data analytics for
genomic medicine", International Journal of Molecular
Sciences, 18 (2017).
[24] C. Heavin and Y. O'Connor, "Starting with small
health data opportunities for Mhealth in Africa",
International Journal of Design and Nature and
Ecodynamics, 11 (2016), pp. 338-351.
[25] M. S. Hossain and G. Muhammad, "Healthcare Big
Data Voice Pathology Assessment Framework", IEEE
Access, 4 (2016), pp. 7806-7815.
[26] A. Ignatius, "Fixing a Broken System", Harvard
Business Review, 94 (2016), pp. 8.
[27] S. Istephan and M. R. Siadat, "Unstructured medical
image query using big data - An epilepsy case study",
Journal of Biomedical Informatics, 59 (2016), pp. 218-226.
[28] K. Jee and G. H. Kim, "Potentiality of big data in the
medical sector: Focus on how to reshape the healthcare
system", Healthcare Informatics Research, 19 (2013), pp.
79-85.
[29] L. Jiang and C. C. Yang, "User recommendation in
healthcare social media by assessing user similarity in
heterogeneous network", Artificial Intelligence in Medicine
(2017).
[30] P. Kalmane and J. S. Kallimani, "An approach for big
data analytics on log data of medical devices in healthcare",
International Journal of Applied Engineering Research, 10
(2015), pp. 23923-23932.
[31] R. S. Kaplan and M. E. Porter, "How to Pay for Health
Care", Harvard Business Review, 94 (2016), pp. 88-100.
[32] R. S. Kaplan and M. E. Porter, "How to Solve the Cost
Crisis in Health Care", Harvard Business Review, 89
(2011), pp. 46-64.
[33] K. Kaur and R. Rani, "A Smart Polyglot Solution for
Big Data in Healthcare", IT Professional, 17 (2015), pp. 48-
55.
[34] J. Y. Kim, P. Farmer and M. E. Porter, "Redefining
global health-care delivery", The Lancet, 382 (2013), pp.
1060–1069.
[35] T. W. Kim and J. H. Seu, "Big data framework for u-
Healthcare system", Life Science Journal, 11 (2014), pp.
112-116.
[36] M. S. Kohn, J. Sun, S. Knoop, A. Shabo, B. Carmeli,
D. Sow, T. Syed-Mahmood and W. Rapp, "IBM's Health
Analytics and Clinical Decision Support", Yearbook of
medical informatics, 9 (2014), pp. 154-162.
[37] E. Körpeoǧlu, Z. Kurtz, F. Kilinç-Karzan, S. Kekre
and P. A. Basu, "Business analytics assists transitioning
traditional medicine to telemedicine at virtual radiologic",
Interfaces, 44 (2014), pp. 393-409.
[38] H. J. La, "A conceptual framework for trajectory-
based medical analytics with IoT contexts", Journal of
Computer and System Sciences, 82 (2016), pp. 610-626.
[39] G. Z. Li and B. Y. Liu, "Big data is essential for
further development of integrative medicine", Chinese
Journal of Integrative Medicine, 21 (2015), pp. 323-331.
[40] Z. Liao, L. Kong, X. Wang, Y. Zhao, F. Zhou, Z. Liao
and X. Fan, "A visual analytics approach for detecting and
understanding anomalous resident behaviors in smart
healthcare", Applied Sciences (Switzerland), 7 (2017).
[41] M. K. Lim, W. Bahr and S. C. H. Leung, "RFID in the
warehouse: a literature analysis (1995–2010) of its
applications, benefits, challenges and future trends",
International Journal of Production Economics, 145 (2013),
pp. 409-430.
[42] S. Macdonald, Technology beyond machines, in S.
Macdonald, D. Lamberton and T. Mandeville, eds., The
trouble with technology, Francis Pinter, London, 1983, pp.
26-36.
[43] S. Mahmud, R. Iqbal and F. Doctor, "Cloud enabled
data analytics and visualization framework for health-
shocks prediction", Future Generation Computer Systems,
65 (2016), pp. 169-181.
[44] M. M. Malik, S. Abdallah and M. Ala’raj, "Data
mining and predictive analytics applications for the
delivery of healthcare services: a systematic literature
review", Annals of Operations Research (2016), pp. 1-26.
[45] B. Marr, How Big Data Is Changing Healthcare,
Forbes, 2015.
[46] C. McDonald, 5 Big Data Trends in Healthcare for
2017, MapR, 2017.
[47] E. W. T. Ngai and A. Gunasekaran, "A review for
mobile commerce research and applications", Decision
Support Systems, 43 (2007), pp. 3-15.
[48] E. W. T. Ngai, Y. Hu, Y. H. Wong, Y. Chen and X.
Sun, "The application of data mining techniques in
financial fraud detection: a classification framework and an
academic review of literature", Decision support systems
and electronic commerce 50 (2011), pp. 559–569.
[49] E. W. T. Ngai, J. K. L. Poon, F. F. C. Suk and C. C.
Ng, "Design of an RFID-based healthcare management
system using an information system design theory",
Information Systems Frontiers, 11 (2009), pp. 405-417.
[50] E. W. T. Ngai and F. K. T. Wat, "A literature review
and classification of electronic commerce research",
Information Management, 39 (2002), pp. 415-429.
[51] K. Panetta, Gartner’s Top 10 Strategic Technology
Trends for 2017, Gartner, 2016.
[52] M. E. Porter, "What is Value in Health Care?", The
New England Journal of Medicine, 363 (2010), pp. 2477-
2481.
[53] M. E. Porter and E. O. Teisberg, Redefining Health
cCare: Creating Value-based Competition on Results,
Harvard Business School Press, Boston, Massachusetts,
2006.
[54] V. Raghupathi and W. Raghupathi, "Exploring cost
and quality of medicare in the United States using
analytics", International Journal of Healthcare Information
Systems and Informatics, 11 (2016), pp. 1-18.
[55] D. Reena, S. Balvinder and K. S. Kumar,
"Opportunities and challenges of using Big Data Analytics
in Indian healthcare system", Indian Journal of Public
Health Research and Development, 7 (2016), pp. 238-243.
[56] J. F. Rodrigues, F. V. Paulovich, M. C. F. De Oliveira
and O. N. De Oliveira, "On the convergence of
nanotechnology and Big Data analysis for computer-aided
diagnosis", Nanomedicine, 11 (2016), pp. 959-982.
[57] P. K. Sahoo, S. K. Mohapatra and S. L. Wu,
"Analyzing Healthcare Big Data with Prediction for Future
Health Condition", IEEE Access, 4 (2016), pp. 9786-9799.
[58] P. Sampath, S. Tamilselvi, N. M. Saravana Kumar, S.
Lavanya and T. Eswari, "Diabetic data analysis in
healthcare using Hadoop architecture over big data",

International Journal of Biomedical Engineering and
Technology, 23 (2017), pp. 137-147.
[59] S. Schee Genannt Halfmann, N. Evangelatos, P.
Schröder-Bäck and A. Brand, "European healthcare
systems readiness to shift from 'one-size fits all' to
personalized medicine", Personalized Medicine, 14 (2017),
pp. 63-74.
[60] N. D. Shah and P. Jyotishman, Why Health Care May
Finally Be Ready for Big Data, Harvard Business Review
Digital, Harvard Business Review School Publishing Corp,
2014, pp. 2-5.
[61] V. Shetty and M. Yamaguchi, "Point-of-care testing in
the time of P5 medicine: A preface", Sensors and Materials,
28 (2016), pp. 1141-1148.
[62] R. Sreekanth, R. R. Gondkar and R. S. Ravindra Babu,
"Cloud computing architecture to process big data
healthcare information for health care industries",
International Journal of Applied Engineering Research, 9
(2014), pp. 4905-4911.
[63] U. Srinivasan and B. Arunasalam, "Leveraging big
data analytics to reduce healthcare costs", IT Professional,
15 (2013), pp. 21-28.
[64] J. G. Stadler, K. Donlon, J. D. Siewert, T. Franken and
N. E. Lewis, "Improving the Efficiency and Ease of
Healthcare Analysis Through Use of Data Visualization
Dashboards", Big Data, 4 (2016), pp. 129-135.
[65] A. Stylianou and M. A. Talias, "Big data in healthcare:
a discussion on the big challenges", Health and
Technology, 7 (2017), pp. 97-107.
[66] X. Tang and C. C. Yang, "Ranking user influence in
healthcare social media", ACM Transactions on Intelligent
Systems and Technology, 3 (2012).
[67] L. A. Tawalbeh, R. Mehmood, E. Benkhlifa and H.
Song, "Mobile Cloud Computing Model and Big Data
Analysis for Healthcare Applications", IEEE Access, 4
(2016), pp. 6171-6180.
[68] K. Topuz, H. Uner, A. Oztekin and M. B. Yildirim,
"Predicting pediatric clinic no-shows: a decision analytic
framework using elastic net and Bayesian belief network",
Annals of Operations Research (2017), pp. 1-21.
[69] C. W. Tsai, M. C. Chiang, A. Ksentini and M. Chen,
"Metaheuristic Algorithms for Healthcare: Open Issues and
Challenges", Computers and Electrical Engineering, 53
(2016), pp. 421-434.
[70] M. Viceconti, P. Hunter and R. Hose, "Big Data, Big
Knowledge: Big Data for Personalized Healthcare", IEEE
Journal of Biomedical and Health Informatics, 19 (2015),
pp. 1209-1215.
[71] L. L. Vie, K. N. Griffith, L. M. Scheier, P. B. Lester
and M. E. P. Seligman, "The Person-Event Data
Environment: Leveraging big data for studies of
psychological strengths in soldiers", Frontiers in
Psychology, 4 (2013).
[72] F. Wang, "Adaptive semi-supervised recursive tree
partitioning: The ART towards large scale patient indexing
in personalized healthcare", Journal of Biomedical
Informatics, 55 (2015), pp. 41-54.
[73] Y. Wang, L. A. Kung and T. A. Byrd, "Big data
analytics: Understanding its capabilities and potential
benefits for healthcare organizations", Technological
Forecasting and Social Change (2016).
[74] M. J. Ward, K. A. Marsolo and C. M. Froehle,
"Applications of business analytics in healthcare", Business
Horizons, 57 (2014), pp. 571-582.
[75] World Health Organisation, World Report on Health
Policy and Systems Research, World Health Organisation,
Switzerland, 2017.
[76] J. Wu, H. Li, S. Cheng and Z. Lin, "The promising
future of healthcare services: When big data analytics
meets wearable technology", Information and Management,
53 (2016), pp. 1020-1033.
[77] J. Wu, H. Li, L. Liu and H. Zheng, "Adoption of big
data and analytics in mobile healthcare market: An
economic perspective", Electronic Commerce Research and
Applications, 22 (2017), pp. 24-41.
[78] W. Xiang, G. Wang, M. Pickering and Y. Zhang, "Big
video data for light-field-based 3D telemedicine", IEEE
Network, 30 (2016), pp. 30-38.
[79] J. J. Yang, J. Li, J. Mulder, Y. Wang, S. Chen, H. Wu,
Q. Wang and H. Pan, "Emerging information technologies
for enhanced healthcare", Computers in Industry, 69
(2015), pp. 3-11.