open innovation whitepaper

Business white paper

Transform the Healthcare and Life Sciences industry through open innovationWhy open innovation matters to Healthcare and Life Sciences and how technology can enable IT for innovation

About the AuthorBhuvaneashwar Subramanian is a program manager and subject-matter expert with the HP Life Sciences Market and Sales Intelligence Practice of Global AnalyticsmdashCorporate 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 masterrsquos 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 RampD environments to open RampD 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 RampD manufacturing distribution and marketing operating in a siloed environment has served well for over a century of the industryrsquos 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 Young12 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 Financingmdashthe path to universal coverage 2010

2 ldquoBeyond BordersmdashGlobal Biotechnology Reportrdquo 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 eraIn 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 ldquoOpen Innovation State of the Art and Future Perspectivesrdquo Technovation HuizinghE2010

4 ldquoCrowdsourcing Pharma Drug DevelopmentrdquoLife Sciences Leader 2012

5 ldquoExperiments in Open Innovation at Harvard Medical Schoolrdquo MIT Sloan Management ReviewGuinanE Boudreau KJLakhaniK2013

6 ldquoKnowledge Networks and Markets in Life Sciencesrdquo OECD 2012

7 ldquoSharing the Idea The Emergence of Open Innovation Networksrdquo 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 changesmegatrends 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

bull Creation of disruptive business segments based on converging diagnostic therapeutic and computational technologies

bull New forms of equity through co-creation investment pools

bull IT specific implications Opportunities in driving transformative knowledge management and stakeholder connectivity solutions

bull Emergence of multiple revenue streamsbull Increased bargaining power for large pharmaceutical

companies while targeting emerging and under-developed markets

bull Leverage crowdsourcing to build product portfoliosbull IT specific implications Building converged

infrastructure cloud and tracking solutions for information exchange and analytics to make better informed decisions

bull Emergence of profit sharing models across commonly addressed disease segments

bull Potential for creation of technology commercialization units with equity by iP creators

bull IT specific implication Increased opportunity for outsourcing key IT processes and development of IT enabled diagnostics and drug delivery systems

bull 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 industrySignificant 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 biology68


8 ldquoThe New Science of Personalized Medicinerdquo 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

bull General Implication Potential to impact realization of personalized and translational medicine in high growth areas

bull IT specific implication Potential to increase demand for knowledge management and collaboration solutions

7


Engaging in collaboration to improve pipelinesThe realization of tailoring medicines to the patientrsquos 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 efficiency1112

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


9 ldquoOpen Innovation in the Pharma Industryrdquo Genetic Engineering and Biotechnology News 2012

10 ldquoValue Based Pricing for Pharmaceuticals Implications of the shift from volume to valuerdquo Deloitte 2012

11 ldquoThe Health of Innovation Why Open Business Models Can Benefit The Healthcare Sectorrdquo Irish Journal of Management Davey SM et al 2010

12 ldquoSocial Media Likes Healthcare From Marketing to Social Businessrdquo Price WaterHouse Coopers 2012

13 ldquoCreative Destruction of Medicinerdquo Eric Topol

bull Siloed RampD environments to open RampD environments

bull Unidirectional marketing to co-creative marketing

bull Clinical diagnosis as a predictive approach

bull Suppliers as partners in innovation

bull Buyers as partners in the creative process

bull Substitute technologies and new entrants as value enabling assets

bull Regulators as participant gatekeepers in the development process

bull Precompetitive partnerships

bull Open source information

bull 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

bull 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

ndash High cost of infrastructure design and skilled manpower

ndash Complex knowledge flows coupled with targeted specificity

ndash 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 RampD 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 pointsmdashactivities that carry the opportunity to embrace an open innovation model for the healthcare business

1 Siloed RampD environments to open RampD environments The transformation of siloed RampD environments into open RampD 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

bull Sharing clinical and research data

bull 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

bull Engagement of patient pools interested in research to contribute ideas and participate in the drug design and development process

bull Idea evaluation and technology licensing from universities and smaller biomedical companies

An intuitive technology based approach to facilitate the transition towards open RampD 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 patientrsquos 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 ldquopushrdquo 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 sequencesmdasha direct implication for gene therapy coupled with techniques to map the individualrsquos 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

bull Core disease diagnosismdashMapping 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

bull Defining treatment optionsmdashUnlike 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 DisordersrdquoThe New England Journal of Medicine YangY 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 incumbentsBased on the identification of key elements that can be driven through the open innovation model it is crucial to design relationships with the organizationrsquos incumbents to make the innovation environment functional

bull 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

bull 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

bull 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

Precompetitive knowledge

brokering modelm

anag

ement model

management model

Knowledge

Open

sour

ce knowledge

Figure 5 Knowledge networks supporting open innovation

bull 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

bull Works with a hybrid cloud model that facilitates delineation of tacit and codified knowledge forms driven by appropriate security measures

bull Ideal for co-creation environments

bull Leverage hybrid cloud and mobility platforms to facilitate data and solution contributions on a secure platform

bull Driven by Government mandates and publicprivate partnerships

bull 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


bull 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 innovationThe 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 objectivesKnowledge 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 divisionof labour in business models

Novelclinical trial meth-odology design

Accessibility of electronic health recordfor research

IT e

nabl

emen

t opp

ortu

niti

es

Figure 6 Mapping open innovation objectives in Healthcare and Life Sciences to IT enablement opportunities



Predictive drug disposition

Knowledge flows Project management

Clinical trial design

Access to EHR

RampD 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 timeinformation map-ping of molecular biology to patient healthtreatment

Cloud Converged cloud solutions to enable real time collabora-tion among RampD 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 RampD 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


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 transfermdashThe 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

bull Expand precompetitive research basemdashPrecompetitive 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

bull Promote innovation networks across stakeholdersmdashInnovation 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 futureThe 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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copy Copyright 2014 Hewlett-Packard Development Company LP The information contained herein is subject to change without notice The only warranties for HP products and services are set forth in the express warranty statements accompanying such products and services Nothing herein 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


bull Lower the cost of therapeutic development and caremdashThe 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

bull Improve the quality of caremdashUltimately 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

About the AuthorBhuvaneashwar Subramanian is a program manager and subject-matter expert with the HP Life Sciences Market and Sales Intelligence Practice of Global AnalyticsmdashCorporate 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 masterrsquos degree in molecular genetics from Banaras Hindu University in India He has an MBA in international business from Edith Cowan University in Australia


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Access to EHR

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