Exploring the Potential of Big Data on the Health Care Delivery Value Chain

University of Wollongong
Research Online
Faculty of Engineering and Information Sciences -
Papers: Part B Faculty of Engineering and Information Sciences
2018
Exploring the potential of big data on the health
care delivery value chain (CDVC): a preliminary
literature and research agenda
William J. Tibben
University of Wollongong, [e-mail]
Samuel Fosso Wamba
Toulouse Business School, [e-mail]
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Publication Details
Tibben, W. J. & Fosso Wamba, S. (2018). Exploring the potential of big data on the health care delivery value chain (CDVC): a
preliminary literature and research agenda. 51st Hawaii International Conference on System Sciences (HICSS 2018) (pp. 2045-2054).
Honolulu, HI 96822 USA: Hawaii International Conference on System Sciences (HICSS) HICSS Conference Office Department of
IT Management, Shidler College of Business University of Hawaii at Manoa. 2018

Exploring the potential of big data on the health care delivery value chain
(CDVC): a preliminary literature and research agenda
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.
Keywords
literature, chain, exploring, potential, big, data, health, (cdvc):, care, preliminary, delivery, value, agenda,
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 delivery value
chain (CDVC): a preliminary literature and research agenda. 51st Hawaii International Conference on System
Sciences (HICSS 2018) (pp. 2045-2054). Honolulu, HI 96822 USA: Hawaii International Conference on
System Sciences (HICSS) HICSS Conference Office Department of IT Management, Shidler College of
Business University of Hawaii at Manoa. 2018
This conference paper is available at Research Online: http://ro.uow.edu.au/eispapers1/1277

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].