Open Innovation Whitepaper

Business white paper
Transform the Healthcare
and Life Sciences industry
through open innovation:
Why open innovation matters to Healthcare and Life Sciences and how
technology can enable IT for innovation

About the Author
Bhuvaneashwar Subramanian is a program manager
and subject-matter expert with the HP Life Sciences
Market and Sales Intelligence Practice of Global
Analytics—Corporate Strategy and Alliances Division.
He collaborates extensively on strategy formulation,
thought leadership, and sales enablement initiatives
for the life sciences industry.
Subramanian contributes thought leadership on cloud
computing in life sciences and industry studies on
translational research. He also provides his expertise
to national and international life sciences organizations
and is a member of the Open Innovation Task Force
for Nanobiotechnology at the University of Waterloo in
Canada.
Bhuvaneashwar Subramanian holds a master’s degree
in molecular genetics from Banaras Hindu University
in India. He has an MBA in international business from
Edith Cowan University in Australia.
Business white paper | Healthcare and Life Sciences

Table of contents
4 Introduction
5 The case for open innovation: An outcome of the post genomic era
8 Enabling open innovation in healthcare and life sciences organizations:
A 3 step technology driven agenda
9 1. Siloed R&D environments to open R&D environments
10 2. Unidirectional marketing environments to co-creative marketing
environments
11 3. Clinical diagnosis as a predictive approach
16 Enabling the patient centric innovation ecosystem: A vision for the future

4
The healthcare and life sciences industry has been traditionally designed around the templates
and structures followed by the manufacturing industry. The linear model of operations with
specific departments such as RD, manufacturing, distribution, and marketing, operating in a
siloed environment, has served well for over a century of the industry’s focus on blockbuster
therapies. The reason for its success thus far has been largely due to the product push
model wherein the lack of sufficient specialisms outside the pharmaceutical and healthcare
organization, helped command a premium for the therapies and the services therein. However,
since the turn of the century, the industry has been witness to a transformation that is driven
by a wave of changes at the technological, political, and economic level including a better
understanding of the human genome, increasing healthcare costs, failing pipelines, and most
importantly the demand from multiple stakeholders such as payers, providers, and patients to
see positive outcomes from care delivery and therapeutics.
Being a technology intensive environment, the healthcare and life sciences industry is heavily
reliant upon the development and availability of new technologies such as medical devices
and therapeutics. From a therapy and medical technology development perspective, studies
advocate a strong need for convergence across the medical device and pharmaceutical
segments. Interestingly, the challenges for these segments are not very different. Both
segments continue to battle with optimizing the cost of developing a device and therapy
respectively and furthermore they are equally concerned with designing devices and therapies
that maximize the value of care delivery.
On the contrary, healthcare delivery has witnessed spiraling costs that skew healthcare
spending across developed and developing countries. For instance, recent reports by WHO
and Ernst and Young1
,2
claim that rich countries, such as the United States, spend 16 times
more than developing and underdeveloped nations of the world, even though the rate of drug
approval has dropped by 50%.
The widening schism between innovating technologies for improved care delivery and the cost
of care delivery calls for a new form of coordination across the three fundamental segments
of the healthcare and life sciences industry namely, healthcare providers, payers, and
pharmaceutical and life sciences organizations in order to design and deliver innovations that
are tailored to the individual patient or patient populations. This paper offers a perspective on
how the concept of open innovation is influencing the healthcare and life sciences industry and
offers a roadmap for organizations to transform into patient centric innovation engines driven
by mobility, security, and big data.
1
The World Health Organization, World Health
Report: Health Systems Financing—the path to
universal coverage, 2010
2
“Beyond Borders—Global Biotechnology Report.”
Ernst and Young, 2012
Introduction

Emergence of
personalized
medicine
Precompetitive
collaboration to
improve pipelines
Focus on pay for
performance
Convergence of
information technology
with biomedical
technologies
Rise of patient
social networks
Figure 1: Megatrends driving the need for open innovation in Healthcare and Life Sciences
5
The case for open innovation: An outcome of the post
genomic era
In the traditional sense of the term, open innovation has been defined by Henry Chesbrough,
the HBS Professor and proponent of the open innovation theory, as the use of purposive inflows
and outflows of knowledge to accelerate internal innovation, and to expand the market for
the external use of innovation, respectively3
. The typical implication of open innovation by
definition has been to facilitate technology acquisition through a spectrum of activities that
range from structured programs such as technology licensing and precompetitive partnerships
to more complex and disarrayed models of ideation such as crowdsourcing. Among the many
industries that have embraced this concept, life sciences in particular have demonstrated a fair
degree of engagement in the open innovation process. Typical examples of open innovation
programs in the healthcare and life sciences include the Innovative Medicines Initiative, the PD2
and TB program by Eli Lilly and corporate venture incubation programs such as the CEEED and
Open Lab for Neglected Diseases by GlaxoSmithKline. Recent entrants to the open innovation
space, such as Pfizer with the Centers for Therapeutic Innovation and Daiichi Sankyo labs
with its TaNeDs program, have engaged in research and therapy development partnerships
with universities and talented scientists, respectively to facilitate the development of product
pipelines and accelerate the development of therapeutics4
. From a healthcare standpoint, the
Harvard Medical School leveraged open innovation principles towards generating ideas for
translational research, even as medical device companies are collaborating with institutions
such as the NHS in the UK to assess the cost benefits of medical technologies and their
alternatives5
. In addition to these initiatives by pharmaceutical and healthcare organizations,
more than 10 global initiatives are underway to drive collaboration across an array of topics
ranging from personalized medicine, proteomics, and disease based programs6
. Reinstating the
growing importance of open innovation in the health and life sciences industry, a recent survey
by the Economist magazine revealed that 63% of the surveyed participants from innovation
rich life sciences organizations saw tangible benefits from the adoption of open innovation,
particularly around enriching their intellectual property7
.
3
“Open Innovation : State of the Art and Future
Perspectives.” Technovation. Huizingh.E.2010
4
“Crowdsourcing Pharma Drug Development.”Life
Sciences Leader. 2012
5
“Experiments in Open Innovation at Harvard
Medical School.” MIT Sloan Management Review.
Guinan.E, Boudreau K.J.,Lakhani.K.2013
6
“Knowledge Networks and Markets in Life
Sciences.” OECD, 2012
7
“Sharing the Idea: The Emergence of Open
Innovation Networks.” Economist, 2007

6
Interestingly, the investment in open innovation initiatives by the industry has been raised post
facto the sequencing of the human genome. The post genomic model fundamentally brought
about five fundamental changes/megatrends in the industry, some driven by politico-economic
forces and others by technology and regulatory implications, thereby calling for a new model of
therapeutic design, development, and delivery.
Figure 2: Open Innovation Trends and IT Implications in Healthcare and Life Sciences
• Creation of disruptive business segments based on
converging diagnostic, therapeutic, and computational
technologies
• New forms of equity through co-creation investment
pools
• IT specific implications: Opportunities in driving
transformative knowledge management and
stakeholder connectivity solutions
• Emergence of multiple revenue streams
• Increased bargaining power for large pharmaceutical
companies while targeting emerging and under-
developed markets
• Leverage crowdsourcing to build product portfolios
• IT specific implications: Building converged
infrastructure, cloud and tracking solutions for
information exchange and analytics to make better
informed decisions
• Emergence of profit sharing models across commonly
addressed disease segments
• Potential for creation of technology commercialization
units with equity by iP creators
• IT specific implication: Increased opportunity for
outsourcing key IT processes and development of IT
enabled diagnostics and drug delivery systems
• Opportunities for Cloud, Big Data Management, and
Mobility Solutions to improve stakeholder connectivity
and shift bargaining power to healthcare providers
from Pharmaceutical Companies
Emergence of the personalized medicine industry:
Significant understanding of the human genome led pharmaceutical and new age
biotechnology companies to consider designing therapies and companion diagnostics, based
on biological proteins, called biomarkers. Their ability to light up in the event of a disease has
spawned a range of therapies that enable the treatment of the right disease, at the right time,
at the right level. Industry estimates suggest that the market for personalized therapeutics or
therapies tailored to the biochemical and genetic makeup of a patient would grow into a $452
billion market, largely driven by innovations in the fields of molecular diagnostics, customized
nutrition, the wellness program, targeted therapeutics, and digital patient centric services that
include the influx of electronic health records and remote patient monitoring technologies.
While the industry is still in its infancy, with 30 products in the pipeline and 2 molecules
approved by the FDA, the concept of personalized wellness and personalized medicine has
attracted worldwide programs spearheaded by respective regions such as the Innovative
Medicines Initiative and the Personalized Medicine Council, CSDD, HUPO, and HUGO which are
focused on driving research towards developing targeted therapeutics across the spectrum
of diabetes, infectious diseases, and cancer. In addition, leading universities and companies
focused on biomedical research are engaging crowdsourcing and gamification platforms such
as FoldIT and EteRNA to solve complex problems in biology6,8
.
6
“Knowledge Networks and Markets in Life
Sciences.” OECD, 2012
8
“The New Science of Personalized Medicine”
PriceWaterHouse Coopers. 2012
Trend 1
Increase in externally
secured IP for
Cancer, Diabetes and
Neurosciences
Trend 2
Increase in activity
channeled through
public or corporate
disease-themed
consortiums
Trend 4
Combination of
techniology
acquisition and
business models
rampant in the
current life
sciences
ecosystem
Trend 3
Corporate
Venture Capital
is emerging as a
preferred channel for
technology incubation
and acquisition
• General Implication: Potential to impact realization of
personalized and translational medicine in high growth
areas
• IT specific implication: Potential to increase demand for
knowledge management and collaboration solutions

7
Engaging in collaboration to improve pipelines:
The realization of tailoring medicines to the patient’s genetic makeup has proved costly to
pharmaceutical and biotechnology organizations. Over the last two decades, the cost of drug
discovery has increased by 250%, even as drug approvals have dropped by 50%, with less than
20 new molecular entities being approved each year2
.
In the wake of these challenges, pharmaceutical and biotechnology companies have embarked
on alternative models to improve pipelines such as technology in licensing, precompetitive and
public-private partnerships, and knowledge brokering platforms. Innovation platforms such as
Innocentive, innovation incubators such as The Pfizer Incubator, and precompetitive alliances
such as the Pistoia Alliance, signal the transformation of the research and development
model in life sciences organizations, into a collaborative environment that is created on the
understanding that knowledge and technology for the creation of new therapies cannot all
reside within a single organization9
.
Focus on pay-for-performance:
An unlikely outcome of the genomics revolution and the emergence of genome sequencing
services such as 23 and Me, is the focus of healthcare and life sciences organizations to deliver
patient care and therapies that are effective. While studies suggest that an estimated $500
billion will be spent on pharmaceuticals by 2020, increasingly healthcare payers are driving
coverage for healthcare services and increasing coverage for a broader spectrum of ailments.
Consequently, some healthcare plans are becoming inclusive of companion diagnostics and
are engaging in partnerships that facilitate the value based pricing of therapies developed by
pharmaceutical majors.
An outcome has been the collaboration between payers, providers, and pharmaceutical
companies to design research agendas, define patient populations and therapeutic
performance, and to identify the most profitable means to bring therapeutics on to the market
and improve patient adoption and coverage10
.
Emergence of patient social networks:
Pressures such as the advocacy for pay-for-performance and the emergence of the internet
as a disruptive source of information, has lead healthcare and life sciences organizations to
identify multiple ways to interact with patients and across the healthcare expert community.
Consequently, disease discussion platforms such as PatientsLikeMe and specialized expert
discussion platforms such as Doc2Doc have facilitated increased patient participation in
making informed choices about maintaining personal health and determining the right course
of therapy respectively. At the other end, healthcare organizations are using social media to
communicate disease risk in a specific region, talk about new services and specialisms, and
facilitate online appointments with clinical specialists. Similarly though in limited measure,
life sciences organizations are engaging in social media to communicate insights on research
in their laboratories, and engaging key influencers to discuss the efficacy of therapeutics.
The varied levels of engagement fundamentally drives process innovation by gathering ideas
and opinions from patients and practitioners alike thereby steering medical practice and life
sciences research towards improved efficiency11,12
.
Convergence of biomedical technology with information technology:
An upcoming trend in the healthcare and life sciences industry, is the increasing integration of
information technology into clinical practice and biomedical research. IT enabled healthcare
has mostly become a mandate in developed countries with the influx of mobile health, remote
patient monitoring, robotic surgery, telemedicine, wearable patient monitoring solutions, and
digitally enabled healthcare environments, which in turn have brought about significant cost
savings to care delivery in terms of time invested in delivering quality care. The life sciences end
of the spectrum has engaged IT as an enabler of high throughput biology and automated drug
discovery experiments, with innovations such as electronic lab notebooks and sophisticated
genetic mapping algorithms, forming the backbone of large scale experiments conducted
by HUPO and HUGO. Furthermore, downstream processes in manufacturing and sales and
marketing, have leveraged ERP solutions and mobile real time social communication solutions
to drive sales force13
.
2
“Beyond Borders—Global Biotechnology Report.”
Ernst and Young, 2012
9
“Open Innovation in the Pharma Industry.”
Genetic Engineering and Biotechnology News.
2012
10
“Value Based Pricing for Pharmaceuticals:
Implications of the shift from volume to value.”
Deloitte. 2012
11
“The Health of Innovation: Why Open Business
Models Can Benefit The Healthcare Sector.” Irish
Journal of Management. Davey S.M. et al. 2010
12
“Social Media Likes Healthcare: From Marketing
to Social Business.” Price WaterHouse Coopers.
2012
13
“Creative Destruction of Medicine.” Eric Topol

• Siloed RD environments to
open RD environments
• Unidirectional marketing to
co-creative marketing
• Clinical diagnosis as a
predictive approach
• Suppliers as partners in
innovation
• Buyers as partners in the
creative process
• Substitute technologies and
new entrants as value
enabling assets
• Regulators as participant
gatekeepers in the
development process
• Precompetitive partnerships
• Open source information
• Knowledge brokering
Focus on redesigning
relationships with
competitors and
incumbents
Selective
implementation of
open innovation
initiatives
Facilitate knowledge
network models to
drive innovation
Figure 3: Enabling open innovation environment in Healthcare and Life Sciences
8
Enabling open innovation in healthcare and life sciences
organizations: A 3 step technology driven agenda
Interestingly, the scope of open innovation by its very nature, engages information technology
as a crucial backbone to facilitate knowledge exchange, collaboration, and the transfer of
intellectual property. A host of business models in the healthcare and life sciences sector, such
as CaBIG, Innocentive, and Collaborative Drug Discovery, plus several examples discussed
earlier in this article, are built on robust technology enabled environments incorporating cloud
and mobility solutions, coupled with strong analytical foundations that facilitate the evaluation
of biomedical and demographic data. Furthermore, the initiatives designed by healthcare
and life sciences organizations to open up the knowledge sharing process are but few and
far between and are yet to have a widespread impact as transformative drivers of change
towards making open innovation design a dominant theme across the healthcare and life
sciences sector. However, the context around the case for open innovation and the supporting
examples are suggestive of a clear technology enabled agenda for healthcare and life sciences
organizations to transform themselves into open innovation hubs:
• Selective implementation of open innovation initiatives: In the first instance, healthcare
and life sciences organizations, need to critically examine their objectives for engaging in open
innovation and the key activities across the value chain that would benefit significantly from
a collaborative and democratic knowledge exchange process. The typical examples of open
innovation in the healthcare and life sciences industry point to research and development
activities, as the most common area of engagement in open innovation. This is primarily
because research and development is governed by three fundamental factors:
–– High cost of infrastructure design and skilled manpower
–– Complex knowledge flows coupled with targeted specificity
–– Significant time frames
However, this approach for open innovation is based on a product development approach.
While the reduction of costs and time frames are significant towards driving the research and
development agenda, the scope of open innovation as the concept evolves, will be applicable
towards driving process improvements which are far more critical to the healthcare and life
sciences industry.
That said, it is important for healthcare and life sciences organizations, to identify critical
processes across the sequence of activities that demonstrate scope for collaborative
engagements. The healthcare and life sciences value chain, as it stands today is linear, but
draws upon multiple sources of information and capabilities that make it possible to engage
an open model to generate significant output. While healthcare institutions and life sciences
companies have been viewed as distinct entities and standalone industries, the primary
requirement is to view the entities as an integrated continuum that facilitates the passage of
medicines from the RD labs of the pharmaceutical organization to the bedside of the patient in
the hospital and beyond.

9
Taking a continuum view of the process flows brings forth 3 transformation points—activities
that carry the opportunity to embrace an open innovation model for the healthcare business:
1. Siloed RD environments to open RD environments:
The transformation of siloed RD environments into open RD environments, implies
creating an infrastructure that facilitates the exchange of ideas and research across partners.
However, life sciences and healthcare organizations engaged in active research must consider
carefully the kind of projects in their research portfolio that would derive maximum value
in the open innovation model. For instance, shifting complex discovery projects based on
intensive computational biology tools, high throughput experiments, and diseases with high
global burden towards the open innovation model, could enable healthcare and life sciences
organizations to generate significant cost savings and enrich the pipeline. A good example is the
widespread focus on cancer research and infectious diseases which have spawned several open
innovation initiatives. In both cases research activities and investment by private, public, and
commercial entities has been tremendous owing to the increasing complexity of biomolecules
involved in disease etiology. Furthermore, studies on these diseases employ sophisticated
modelling software and generate data that runs into several terabytes. The transition of
research projects mirroring this nature into the open innovation model, however, can be made
possible purely by creating an environment that facilitates:
• Sharing clinical and research data
• Real time collaboration between pharmaceutical companies, payer organizations, and
clinicians to design research studies and identify critical medical needs for patient pools within
specific regions and healthcare institutions
• Engagement of patient pools interested in research to contribute ideas and participate in the
drug design and development process
• Idea evaluation and technology licensing from universities and smaller biomedical companies
An intuitive technology based approach to facilitate the transition towards open RD
environments would be to take an incremental approach towards primarily designing a
cloud enabled collaborative environment that would facilitate the interlinking of biomedical
databases and resident project management infrastructure to facilitate data exchange and
communication between hospitals, research institutions, and payer organizations, particularly
if the projects warrant usage of epidemiological data. At the same time it would be worthwhile
for the participating stakeholders to be engaged through custom mobile solutions such as
applets or a common network that facilitates data sharing and analysis over mobile platforms
such as tablets and smartphones. The mobility component would become particularly useful
in a hospital setting, where adoption is far more prevalent and enables doctors to make real
time decisions on the go and discuss patient records from a translational research perspective
towards identifying the right therapies for the patient. However, a critical piece that would tie
in both the collaborative infrastructure and the mobile communication platform would be the
presence of a robust analytics platform that can be deployed, either as a standalone module
for respective participating stakeholders, or as an underlying foundation layered over the cloud
based collaborative platform.
Increasingly, structured data and unstructured data analytics techniques are becoming
foundational in helping drive in silico drug modelling, patient cohort modelling and facilitating
the design of personalized therapeutics, and patient management programs.

10
That said, healthcare and life sciences organizations, would be most benefited with a
comprehensive analytics solution that would facilitate real time evaluation of data and generate
insights that can be communicated across collaborating stakeholders.
In effect, transformation of research and development laboratories into responsive and
interactive environments that are attuned to the participative healthcare stakeholder would
facilitate improvements in therapeutic development, care delivery, and help gain a better
perspective of the patient pool, whose needs the organization intends to serve.
2. Unidirectional marketing environments to co-creative marketing environments:
Marketing activities in the pharmaceutical and life sciences sector account for the highest costs
in the value chain. A fair measure of these costs emerges from extensive sales force trainings
and market research into disease etiologies and competitive dynamics and analyzing feedback
on product launches. However, marketing professionals in pharmaceutical and life sciences
organizations can transform existing activities around market research and product launch by
leveraging technology enabled solutions to obtain real time data. Pharmaceutical companies
can make their marketing functions agile by engaging a judicious mix of analytics and cloud
based collaboration tools, to tap into the minds of doctors and patients. For instance, if your
company is seeking to understand the latest trends and technologies employed to treat ovarian
cancer, the first step is to integrate an analytics and collaborative solution that would help
identify the key resources to seek information and empower them with the means to provide
inputs that can be collected systematically, from a range of environments spanning the clinic,
the hospital, and even the patient’s home. These resources could range from a whole array of
people including your sales force, physicians, patients, and even payer organizations. Applying
big data techniques, typically unstructured data and heuristics on historical revenue data from
prescription sales, social media conversations on your company website, and online healthcare
communities your organization intends to target, would create a wide range of implications.
Firstly, physicians would indirectly contribute towards helping your organization get critical
insights on disease patterns and prescription behavior. Secondly, engagement of sales force
through collaborative tools communication media on tablets and smartphones, with physicians
would provide important feedback and inputs on the performance of the drug and probability of
its prescription.
A second facet of transforming marketing environments in life sciences companies is to create
avenues for physicians to participate in product development. While not quite rampant in
the biotechnology and pharmaceutical sector, medical device companies such as Medtronic
have invested in open innovation platforms that facilitate collaboration between physicians
and product development teams. Marketing departments of life sciences organizations can
facilitate cloud enabled collaborative environments that can be accessed by physicians across a
multitude of platforms ranging from their desktop PC, tablets, and mobile devices or channels
such as social media for physicians to contribute to product design and product development
ideas both from the perspective of testing, requirements gathering, and prototyping in terms of
the ideal product designs that may be suitable for doctors to employ.
Engaging with the end customers in unconventional ways such as these, with the leverage of
analytics and cloud based collaborative platforms, would transform marketing departments
from unidirectional “push” focused entities into agile and co-creative environments that involve
physicians and patients in designing and delivering therapies that are efficacious.

11
3. Clinical diagnosis as a predictive approach:
Redefining the way clinical diagnoses are conducted is a potent transformative force that
can propel healthcare towards becoming participative and accurate in delivering the right
cure to the right patient at the right time. While the statement echoes the conventional
definition of personalized medicine, the ability to get there is driven by enabling a change in
the process of diagnosis and cure. The emergence of biomolecular options such as the ability
to sequence the genome at less than $1000 and edit defective genome sequences—a direct
implication for gene therapy, coupled with techniques to map the individual’s risk for disease
against a comparable disease population worldwide, calls for healthcare providers to forge
collaborations with scientists engaged in high throughput biology, genome sequencing, risk
profiling companies, wellness companies, and patients to facilitate diagnoses that are all
encompassing and inclusive of drug profiles, patient genetic risk, and personalized wellness.
A case in point is the approach taken by physicians at the Baylor College of Medicine to treat
genetic diseases14
. In a bid to identify treatable genetic diseases against the non-treatable ones,
doctors at the institute called for an analysis of active gene sequences in the total genome
of patients suffering from neurological diseases or exome sequencing, after diagnosing the
disease in the traditional manner. The exome sequences were analyzed using proprietary
analytics platforms and provided insights to the physicians on delivering medical intervention
only for those genetic diseases where treatment options were available. In effect the example
signals the possibility of engaging predictive services such as exome sequencing as de facto
practices of the future to provide personalized treatment approaches to patients, governed by
genetic anomalies or otherwise.
In other words, healthcare providers need to increasingly adopt technologies such as
sophisticated analytics tools, mobility solutions, and collaborative environments to evaluate
patient medical history in addition to genetic information and pharmacogenomic risk and
collaborate with organizations that provide the input to design the best treatment course for
patients. Typical areas where analytics and collaborative platforms could be leveraged to drive
predictive diagnosis or provide the patient a personalized plan aligned to disease risk include:
• Core disease diagnosis—Mapping symptoms for a specific disease to biomolecular anomalies
based on gene sequence data set analysis of the patient and leveraging insights from
sequence analysis to diagnose the disease.
• Defining treatment options—Unlike conventional approaches of prescribing a pill for a
disease, a shift may be enabled from treating the disease to facilitating the wellbeing of the
patient. That said, analytics and collaborative solutions can be employed to gather inputs from
genome sequencing data to identify disease risk and design a pharmacogenomics profile,
leverage information on patient dietary requirements and habits to develop nutrition plans
based on a patients genotype coupled with exercise programs to provide a holistic treatment
option that facilitates cure of the disease and wellness of the patient at the same time.
14
Clinical Whole Exome Sequencing for the
Diagnosis of Mendelian Disorders.”The New
England Journal of Medicine. Yang.Y. et al. 2013

Stakeholder
competitive
positioning
Current trend Open innovation model Emerging
examples
Suppliers Low bargaining power
focused on raw materials
Complex supplier chains where
shift is from cost to strategic project
based partnerships
CROs and core Research only or
manufacturing setups for big
pharma
Buyers
(Patients)
Low bargaining power
driven by expert opinion
Participatory approach enables
patients make an informed decision
Transparency Life Sciences
and FoldIT engage consumers
to design proteins and clinical
trials
New entrants Relatively High entry bar-
riers for startups
New Entrants are seen as resources
for value chain partnerships
Bioclusters such as the Massa-
chussets Bio, BayBio, are good
examples for collaborative
involvement of new entrants
Substitution
dynamics
Substitutes viewed as
relatively low threats
Substitution technologies are being
viewed as integral to product and
technology evolution
Point of Care Sustainable
diagnostics by the FIND con-
sortium
Regulatory
bodies
Regulation is a stringent
and rate limiting factor for
therapeutic development
Regulation is expected to be more
stringent due to complexity of IP
and engage as a partner in the
progress of innovation
IMI Platform launched by the
European Union has integrated
regulatory bodies to accelerate
diffusion
Competitors Competition focused on in
house innovation
Low, higher engagement in precom-
petitive activities
Pistoia Alliance
Academics Engaged largely for tech-
nology licensing
Engaged in Technology Develop-
ment through corporate funded
activity
Pfizer, Centers for Therapeutic
Innovation, EU-AIMS led by
Roche
Figure 4: Shifting Dynamics of the Competitive Forces in the Healthcare and Life Sciences Value Chain
12
Focus on redesigning relationships with competition and incumbents:
Based on the identification of key elements that can be driven through the open innovation
model, it is crucial to design relationships with the organization’s incumbents to make the
innovation environment functional.
• Leveraging suppliers as partners in innovation: In the open innovation model, healthcare and
life sciences organizations will need to interact with supplier communities from the lens of a
partner in innovation. Typical interactions towards extracting immense value from the supplier
community would include the provision of feedback on product development, engaging with
suppliers in designing products, as is observed in the interactions of major medical device
companies and healthcare providers.
• Leveraging buyers (patients and doctors) as participants in the creative process: Examples
such as the Medtronic Open Innovation Initiative that enrolls doctors in the product
development process and the business model of Transparency Life Sciences to involve
patients in designing clinical trials reinforce an important need to engage end customers in
the healthcare and life sciences industry to benefit from an open innovation model. Typically,
organizations will need to invest in cloud enabled technology that facilitates the capture of
ideas and collaboration platforms that facilitate customers to communicate and share assets
involved in solution development in a tiered manner. At this juncture, it is essential for the
organization to invest in the right levels of security tools that may be configured to enable
access and contribution of data.
• Leveraging substitute technologies and new entrants as value enabling assets: Life sciences
organizations in particular need to view substitute technologies and new entrants to the
industry as value enabling assets towards improving the business outcomes of the industry.
While the premise of substitute technologies and new entrants is a fundamental reason for
open innovation, the enablement of the paradigm shift will require organizations to leverage
information technology towards integrating them at various levels. A typical technology
enabler towards facilitating collaboration with new entrants and substitute firms in the
pharmaceutical industry would be leveraging analytics to evaluate therapy adoption outcomes
using supportive technology from the new entrant and thereafter engage in co-development
partnerships via a secure cloud based collaborative environment to facilitate resource
optimization in bringing new therapies to market.

Pr
ecompetitive knowledg
e
bro
keringmodel
manageme
ntmodel
management model
Knowledge
Opensourcek
nowledge
Figure 5: Knowledge networks supporting open innovation
• Typically focused on single complex diseases with
high revenue potential for which treatments are
needed and skills are spread across major
healthcare and life sciences organizations
• Works with a hybrid cloud model that facilitates
delineation of tacit and codified knowledge forms
driven by appropriate security measures
• Ideal for co-creation
environments
• Leverage hybrid cloud and
mobility platforms to facilitate
data and solution contributions
on a secure platform
• Driven by Government
mandates and public/private
partnerships
• Engage public cloud and robust
analytics platforms to handle
huge datasets and facilitate
independent in silico research
by participating stakeholders
while releasing the results on
public domain
13
• Viewing regulatory bodies as participant gatekeepers in the development process: A key
incumbent in the open innovation process is a regulatory body like the FDA. In a typical
environment, the FDA would serve as the final decision maker in the releasing of a new therapy
or drug onto the market. However, precompetitive alliances such as the Innovative Medicines
Initiative are leveraging the resident expertise at the FDA to guide the development of
therapies. It must be noted that engaging regulatory bodies as a key stakeholder in the device
and drug development process, particularly in complex drug discovery and development
projects, may help generate a high quantum of unstructured data and enable identification
of key performance indicators that may help define the critical path. The emergence of
unstructured and structured data from public-private partnerships with the FDA and other
bodies would necessitate investment in robust analytics platforms to identify target disease
populations and predict the potential success of a drug during its early development stages,
even as development times are reduced significantly.
Facilitating a knowledge network model to drive innovation:
The enablement and success of an open innovation environment in the healthcare and life
sciences industry will be fulfilled only through the incorporation of knowledge across the
right outlets and channels. From the perspective of the healthcare and life sciences industry,
knowledge is typically recorded into biomedical databases, electronic medical records, and
electronic lab notebooks. However, a central challenge for the industry is to engage a common
platform to facilitate information transfer from one data set to another, due to the variable
formats of data storage. A second challenge is to define the intellectual property that can
be shared against that which must be retained within the organization. While the answers to
most of these challenges are still to emerge due to lack of suitable technology maturity, it is
evidently possible to design a knowledge network of participants and tailor information flows
across innovation partners. Primarily, healthcare and life sciences organizations need to define
the levels of participation across stakeholders in order to facilitate this transfer of knowledge.
Typically the participation modules would include precompetitive partnerships, open source
information, and knowledge brokering.
The precompetitive knowledge management model is based on facilitating collaboration
across projects around a specific disease focused drug discovery effort for which a wide range
of informative inputs and shareable intellectual property will need to be marshalled. Capturing
knowledge in this environment would require investment in a hybrid cloud ecosystem between
the participating organizations, so as to selectively share internal databases relating to the
molecule, patient records pertaining to treatments administered for the specific disease and

14
leverage collaborative tools to share data and communicate research specific requirements.
Typically, engagement in the precompetitive knowledge management model will involve
delineating codified knowledge with tacit knowledge that can be shared with the collaborator
and enabling security measures that selectively filter the stakeholders and data that can be
accessed across the participating organizations.
The Open Source Knowledge Management Model is typical of public-private partnerships with
a strong government backing that facilitates hosting of patient data, clinical trial data, and
biomolecular data sets on a public cloud environment. Organizations must consider driving
an open source knowledge portal if the projects are driven by a strong government mandate
and the work is particularly in an area where enormous data sets may need to be received
and evaluated. Consequentially, the open source management model also calls for a robust
analytics platform, given that it may facilitate independent scientists and clinicians in the
healthcare domain to contribute to public data sets in formats that are not standardized.
A second important outcome of designing the open source knowledge environment is the
identification of potential relationships between disease data and patient data so as to generate
inputs towards building a predictive medicine model. Clearly, the freely accessible nature of
data suggests that implementation of an underlying security protocol that may reduce the
instances of irrelevant entries and record duplications and deletions.
The Knowledge Brokering Model of Engagement in Life Sciences would best work in a co-
creation environment wherein a network of specialists and end customers of healthcare
services and life sciences products could provide solutions or ideas for a program driven by
the healthcare or life sciences organization. In this particular case, organizations may leverage
existing hybrid cloud platforms or community clouds to facilitate idea collation. Given that
the nature of content in such an environment would typically entail significant knowledge
capture from the customer or co-creator, as opposed to inputs from the organization,
the establishment of such a platform would call for systematic capture of structured and
unstructured data which may be suitably imported in the internal database of the organization.
Furthermore, engaging in the knowledge brokering model would require accessibility across
mobile devices and a multilayered security feature that would facilitate wide and secure access
of the knowledge platform.
Open innovation objectives
Knowledge
creation through
information
management
Expand
precompetitive
research base
Promotion
of open
innovation
networks
Lower cost
of product
development
Improve
healthcare
quality
Interoperable
biomedical
databases
● ●
Transparency
and access
to databases
● ● ● ●
Predictive drug
disposition ● ● ● ●
Improve
knowledge
flows across
organizations
● ● ● ●
Refine division
of labour
in business
models
● ●
Novel
clinical trial meth-
odology design
● ● ●
Accessibility
of electronic
health record
for research
● ● ● ●
ITenablementopportunities
Figure 6: Mapping open innovation objectives in Healthcare and Life Sciences to IT enablement opportunities

Interoperable
biomedical
databases
Transparency and
access to
databases
Predictive drug
disposition
Knowledge flows Project
management
Clinical trial
design
Access to EHR
RD H H H H H L H
Trials H H H H H H H
SCM M L M H M L L
Manufact. L L L H H L L
Sales M L L H M L L
Care H H H H H H H
Information
optimization
Subject oriented
relational mapping to
distill most relevant
biomedical informa-
tion and patient
localization
Pharmacogenetic
evaluation of disease
population and linkage
to patient susceptibility
Linkage of biomo-
lecular data, drug
prescription trends,
supply chain, and pric-
ing models to identify
new therapeutics and
building market share
Linkage of work-
flows to manage
SKUs, patent, and
seasonality
Linking pharma-
cogenetic data to
patient data in driv-
ing trial design
Enabling bench
to bedside access
through real time
information map-
ping of molecular
biology to patient
health/treatment
Cloud Converged cloud
solutions to enable
real time collabora-
tion among RD
teams
Collaborative cloud
architectures
Cloud based predictive
analytics solutions
Collaborative cloud
peer communication
platforms
Collaborative peer
cloud communi-
cation and LIMS
systems
Integrated stake-
holder environ-
ments linking care
and RD units
Collaborative se-
cure cloud environ-
ments to facilitate
health record ac-
cess to authorized
stakeholders
Mobility Mobile applications
for information
management and
collaborative
communication that
connect stakeholders
Tablets and
applications that are
linked to biomedical
databases in a secure
manner
Apps to link patient and
research data
Sales apps that link
knowledge base to
sales
Inventory
management
Apps to manage
trial recruits and
progress
Telemedicine appli-
cations for patient
monitoring and
translational
medicine
Security Secure encrypted
login solutions tied
to cloud infrastruc-
tures
Tiered access to
databases across
relevant stakeholders
Secure knowledge
management capability
Secure communi-
cation systems to
facilitate knowledge
access and transfer
Secure data
management
solutions
Secure regulatory
and data manage-
ment solutions
Secure knowledge
management
capability
Figure 7: Enabling open innovation in Healthcare and Life Sciences through IT
15
Taken together, identifying the right kind of knowledge management model would facilitate
significant benefits for the healthcare and life sciences industry around:
Knowledge creation and transfer—The creation and capture of knowledge resident in
biomedical environments would be made possible through development of interoperable
biomedical databases that integrate information from the research labs with patient medical
records to create relational data sets that map patient disease symptoms to real-time research
data on relevant biomolecules.
• Expand precompetitive research base—Precompetitive partnerships would be facilitated
through a cloud based collaborative environment that would facilitate uniform access
and interoperability across key participant biomedical databases. Further with the help of
collaborative environments and analytics platforms, linkage of data around research on
pipeline, regulatory requirements, marketing forecasts, and supply chain can be collectively
utilized to predict the potential success of a therapeutic approach and leverage the right
resources for the development of suitable therapies.
• Promote innovation networks across stakeholders—Innovation across the participating
stakeholders would be made possible through the creation of multiple avenues to share
clinical and research data particularly across mobile, social, and knowledge brokering
platforms. Furthermore, the cloud based collaborative environment aided with a robust
analytics platform would facilitate project management across participating sites and
optimize the division of core activities by competencies of participating stakeholders.

Enabling the patient centric innovation ecosystem:
A vision for the future
The net outcome of implementing an open innovation model in the healthcare and life
sciences industry is the evolution of a patient centric innovation ecosystem. The patient centric
innovation ecosystem is an agile information driven environment that facilitates collaborative
engagements between providers, payers, and life sciences organizations to accelerate the
delivery of personalized therapeutics, care, and wellness. Engaging the new nexus of IT
enablers such as cloud, analytics, and mobility, the open innovation environment would
facilitate collaborative engagements across the provider-payer-life sciences network towards
empowering information driven therapy design and patient care models that are enriched with
personalized treatment and care management plans, even as cost structures are optimized
towards enabling a wider reach of therapeutics and efficiencies in care delivery.
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© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. The only
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should be construed as constituting an additional warranty. HP shall not be liable for technical or editorial errors or omissions contained herein.
Trademark acknowledgements if needed.
4AA4-xxxxENW, April 2014
• Lower the cost of therapeutic development and care—The open innovation model would
particularly reduce the cost of therapeutic development through open data sharing models
driven by an agile cloud based information ecosystem. This would be further bolstered by a
collaborative IT environment that facilitates communication across providers, payers, and
pharmaceutical organizations towards designing clinical trials and employs an analytics
based evaluation of patient and biomolecular data to facilitate molecular design that can be
customized towards therapy development for specific disease populations.
• Improve the quality of care—Ultimately, the goal of an open innovation environment driven
by a strong knowledge network is to drive improvements in the quality of care delivery. While
collaborative cloud based environments would enable acceleration of bench to bedside
therapies, adopting an analytics based approach combining pharmacogenomics profiles with
patient health records would facilitate predictive diagnosis and wellness management and
leverage mobility platforms to enable remote care. A second outcome would be to increase
accessibility of care through a common interoperable cloud based database of health records
and knowledge sharing collaborative portals driven across mobile platforms to facilitate
primary and secondary care across areas with poor health facilities.

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