1、FEBRUARY202 2Improving Decision-Making through Connected IntelligenceLEVERAGING NEW CAPABILITIES TO HELP LIFE SCIENCES COMPANIES ADVANCE HEALTHCARELife sciences companies key contributors to the healthcare ecosystem are challenged to evolve continually by obtaining new skills,building new technology

2、 systems,and delivering more value to patients and other stakeholders,all while acting more efficiently and rapidly.Every few years,changes in the healthcare ecosystem drive a new set of needs.The impact of advancements in human science,technology,and data science,combined with the impact of the pan

3、demic,have increased the urgency of this transformation and have made a new set of competencies that of connected intelligence essential.For life sciences companies,building connected intelligence enables them to make better decisions,share insights with stakeholders collaboratively,and act agilely

4、in an increasingly competitive environment.However,building connected intelligence will impact the data they use,the talent they hire,and the technology they build.This report examines the fundamental elements of connected intelligence that feed into decision-making at life sciences companies:buildi

5、ng meaningful connections in data and technology,elevating skills to generate insights,and shaping the organizational structure and culture to share,link and utilize those insights.With an eye to what lies ahead for life sciences organizations,the report examines how connected intelligence can gener

6、ate better insights to guide decision-making at various levels,including at critical points along a molecules lifecycle and at the enterprise level,and can enable evidence-sharing and dissemination in support of multi-stakeholder decisions.Finally,it puts forth a framework of diagnostic questions to

7、 assess a companys performance in this area.The study was produced independently by the IQVIA Institute for Human Data Science as a public service,without industry or government funding.The contributions to this report of Phil Coggshall,Jessica Cunningham,Luke Dunlap,Lucas Glass,Andrea Morton-Morys,

8、Tanveer Nasir,Prashant Parab,Andrew Ploszay,Elizabeth Powers,Avinob Roy,Cara Willoughby,David Wolter,Yilian Yuan,and dozens of others at IQVIA are gratefully acknowledged.Find Out MoreIf you wish to receive future reports from the IQVIA Institute for Human Data Science or join our mailing list,visit

9、 iqviainstitute.org.MURRAY AITKENExecutive Director IQVIA Institute for Human Data Science2022 IQVIA and its affiliates.All reproduction rights,quotations,broadcasting,publications reserved.No part of this publication may be reproduced or transmitted in any form or by any means,electronic or mechani

10、cal,including photocopy,recording,or any information storage and retrieval system,without express written consent of IQVIA and the IQVIA Institute.IntroductionImproving Decision-Making through Connected Intelligencec Table of ContentsOverview 1Understanding the need for speed and agility in life sci

11、ences companies 4A changing healthcare ecosystem 4Leveraging new capabilities to enable better and faster decision-making 11Drawing value from data 11Providing improved insights through AI&ML 12Realizing value through technology platforms and tools 14Applying connected intelligence to transform deci

12、sion-making 15Defining connected intelligence 15Elements needed to support connected intelligence 16Critical business decisions in action with connected intelligence 22Critical points along a molecules lifecycle 23CASE STUDY:Data-informed trial design assessment helps sponsor avoid substantial amend

13、ment 24CASE STUDY:Patient pathway mapping and disease detection 27CASE STUDY:Addressing complexity with an enterprise-level long-term revenue forecasting system 28CASE STUDY:Platform to rapidly evaluate cost,timing,risk and net present value of different clinical development plans 30CASE STUDY:Synth

14、etic control arms and regulatory approvals 33Assessing a companys connected intelligence IQ 36References 45About the authors 47About the Institute 48iqviainstitute.org|12|Improving Decision-Making through Connected IntelligenceSignificant changes within the healthcare ecosystem occur every few years

15、 and lead to shifts in the needs of various healthcare stakeholders.Currently,as life sciences companies work toward serving patients unmet needs in an interconnected,multi-stakeholder health system,they are faced with increasing complexity,heightened competitiveness,and greater interconnectedness e

16、ven as they grapple with changes resulting from the pandemic.Each of these developments must be well understood so that life sciences companies can appropriately respond to them.Despite unprecedented progress in scientific innovation,unmet patient needs remain significant,and there is a greater focu

17、s on issues of healthcare disparity across patient sub-populations and the ability for all to benefit from human science advances.Additionally,complexity in health systems has grown over time with an increasing number of prescribers and influencers,such as payers and patient advocacy groups,which is

18、 impacting care decisions.This complexity requires customization and orchestration of engagement approaches and can make commercial decision-making more challenging.Increasing competitiveness in the healthcare space has also made it critical for companies to make faster,more informed decisions and a

19、dapt to change more agilely.The healthcare ecosystem is also becoming progressively more interconnected and focused on the value of health interventions.Such developments require stakeholders to build systems to share and compare real-world evidence,creating opportunities for improvements in patient

20、 outcomes,population health,and system sustainability.Finally,the COVID-19 pandemic disrupted traditional healthcare and clinical development approaches,in many cases shifting these to offsite or virtual formats supported by digital technologies,thereby changing the way life sciences companies engag

21、e with their customers.These dynamics in the healthcare ecosystem require life sciences companies to consider internal transformations that can help them adapt and be innovative in their approaches to delivering patient care and outcomes and achieve commercial success.These transformations must leve

22、rage new capabilities to enable better and faster decision-making.Advancements in human science,technology and data science are already changing the way healthcare and life sciences organizations conduct business.Stakeholders now have access to more diverse sources of data that can guide them at a r

23、ange of critical decision-points in healthcare both for medical and commercial decisions.Improved ability to integrate data from disparate sources,including documents with natural spoken or written language using natural language processing(NLP),is expanding opportunities to apply real-world data.Ho

24、wever,since data sources are not always clean or reliable,skills to integrate,manage quality control,impute and project missing data,build data models,and ensure data privacy and security are essential.Artificial intelligence and machine learning(AI&ML)have contributed to the value life sciences com

25、panies can now draw from large datasets,helping to yield new and improved insights,predictions,and forecasts.The emergence of healthcare-dedicated cloud software An evolving capacity known as connected intelligence offers to yield new insights,drive smarter decision-making and enable insight-sharing

26、 and partnerships among stakeholders.2|Improving Decision-Making through Connected IntelligenceOverviewplatforms to serve the analytic needs of this space has placed high-level analytic capabilities in the hands of small,emerging,and large companies alike.New technology platforms can help create one

27、 source of truth for an organization and aid in information sharing across teams who are performing multiple functions within life sciences companies.Advances in apps and tools can provide value in decision-making by bringing insights to end-users at multiple points along their workflow and creating

28、 intuitive and user-friendly interfaces that improve the user experience.Given the evolution in data,technologies and overall platforms,companies are increasingly challenged to build new capabilities and achieve efficiencies by harnessing these developments to succeed in the changing healthcare ecos

29、ystem.An evolving capacity known as connected intelligence offers to yield new insights,drive smarter decision-making and enable insight-sharing and partnerships among stakeholders.This capability can help bring together and drive synergies across new stakeholders where historical data and technolog

30、y silos may not have enabled or necessitated different groups to work together.Connected intelligence consists of five key elements:data and technology,which form the analytic basis;the skills and capabilities to make the analytics meaningful in the healthcare space;the generation and dissemination

31、of insights;and an organizational structure and culture that supports the development and evolution of connected systems.Creating intelligent connections between internal capabilities and the healthcare ecosystem enables life sciences companies to be more agile,accelerate results,and deliver the ful

32、l value of medicines to the patients who will benefit from them.Connected intelligence can improve critical business decisions made at specific points across a molecules lifecycle,at the enterprise level,and provide evidence to guide collaboration with external stakeholders.At critical points along

33、a molecules lifecycle,connected intelligence can aid in discovery research and target identification,clinical development planning and execution,medical affairs,and marketing and sales.At the enterprise level,connected intelligence can impact portfolio strategy and forecasting,cost and risk manageme

34、nt,and resource utilization.Connected intelligence can also help with decision-making in relation to multiple stakeholders,such as patients and advocacy groups,healthcare providers,payers/integrated health systems,big tech and digital,and regulatory agencies.Connected intelligence can help life scie

35、nces companies continuously adapt to the significant changes taking place across the healthcare system and deliver patient outcomes and care in an efficient manner,all while optimizing commercial objectives.Connected intelligence can help with decision-making in relation to multiple stakeholders,suc

36、h as patients and advocacy groups,healthcare providers,payers/integrated health systems,big tech and digital,and regulatory agencies.iqviainstitute.org|34|Improving Decision-Making through Connected IntelligenceUnderstanding the need for speed and agility in life sciences companies +Changes within t

37、he healthcare ecosystem are shifting the needs of stakeholders and leading to a series of internal transformations within life sciences companies to help them adapt and deliver.+Even as scientific innovation progresses at an unprecedented pace,unmet patient needs remain significant,and heightened at

38、tention is being focused on issues of disparity across patient sub-populations and the ability for all to benefit from human science advances.+Complexity in health systems has grown over time with an increasing number of prescribers and influencers impacting care decisions,including payers,and requi

39、ring customization and orchestration of engagement approaches and making commercial action,and the decision-making that informs it,more challenging.+Heightened competitiveness in the healthcare space has made it critical for life sciences companies to make faster and better decisions and to adapt to

40、 change more agilely.+An increasingly connected multi-stakeholder healthcare ecosystem focused on the value of health interventions is requiring stakeholders to build systems to share and compare real-world evidence,creating opportunities for improvements in patient outcomes,population health,and sy

41、stem sustainability.+The COVID-19 pandemic disrupted traditional healthcare and clinical development approaches,in many cases shifting these to offsite or virtual formats supported by digital technologies,and thereby changing the way life sciences companies engage their customers.A CHANGING HEALTHCA

42、RE ECOSYSTEM Every few years,significant changes within the healthcare ecosystem cause the downstream needs of various stakeholders to change and compel a series of internal changes within life sciences companies to help them adapt and deliver.Life sciences companies are currently experiencing such

43、a shift.As they continue their work to serve the unmet needs of patients in an interconnected,multi-stakeholder health system,companies are faced with increasing complexity and heightened competitiveness even as they grapple with changes resulting from the pandemic(see Exhibit 1).To meet the challen

44、ges of this evolution while maintaining or achieving a competitive edge will require an organization to demonstrate speed and agility,along with new skills and capabilities that support interactions with external stakeholders.To meet changes in the healthcare ecosystem,organizations will need to dem

45、onstrate speed and agility,along with new skills and capabilities that support interactions with internal and external stakeholders.iqviainstitute.org|5Unmet patient needs During the past few years and particularly during the pandemic heightened attention has been placed on the need for new approach

46、es to ensure equitable health benefits are available to all social strata and races within the United States and beyond.This means that life sciences companies continue to be challenged to be innovative in their approaches and contributions to delivering patient care and improving outcomes.Even as s

47、cientific innovation proceeds to unprecedented levels,with the launch of record numbers of new active substances and the emergence of new medicine classes such as next-generation therapeutics,many patients fail to benefit from innovation due to healthcare disparities and other factors.Exhibit 1:Driv

48、ers of internal change within life sciences companiesUnmet patient needs Continued high burden of disease Failure of all eligible patients to benefit from innovation due to healthcare disparities Mortality and outcomes differences tied to social determinants Missed opportunities for improved health

49、through self-reported outcomes Opportunities along the patient journey to improve outcomes and experience Delayed and discontinued treatment High patient dropout and burnout rate in clinical trialsComplexity in health systems More prescribers and influencers,including payers No one-size-fits-all app

50、roach to engaging customers and stakeholders Reduced ROI from R&D and life sciences sales forces Increased demands for evidence of value from HCPs&other stakeholders Clearer standards from regulators about RWE quality and transparency Increased regulation of medical devices Increased need to engage

51、and partner across stakeholdersHeightened competitiveness Increased drug launches crowding the space Narrow target patient populations making it harder to find patients Tech companies with strengths in AI shifting into healthcare Threats from new domains such as digital care requiring assessment Fin

52、ancially constrained health systems Competition for payer dollars leading to risk sharing requirements and performance guaranteesMore interconnected healthcare ecosystem Health system stakeholders more co-interdependent Shared focus on assessing patient outcomes to select among care options RWE exch

53、ange between stakeholders with building of evidence systems and tech Innovative risk sharing approaches growing Partnerships with innovators and patient advocacy orgs increasingly critical Patient-centricity challenging companies to use digital tech to track patient experience and PROsImpact of the

54、pandemic Disruption to conventional care approaches and the patient journey Reduced on-premise access to HCPs and shift to digital engagement Patient care avoidance Reduced patient visits to clinical trial sites through hybrid/decentralized trials Increased use of home research nurses and phlebotomi

55、sts for trials Shift of patients to online pharmacy purchasing Emergence of PASC/Long-COVID as a new disease stateLife sciences companies are challenged to be innovative in their approaches and contributions to delivering patient care and improving outcomes.Source:IQVIA Institute,Jan 2022.6|Improvin

56、g Decision-Making through Connected IntelligenceSource:Top chart:Global Medicine and Spending Trends Outlook to 2025.Report by the IQVIA Institute for Human Data Science with data from IHME Global Burden of Disease 2019,accessed Feb 2021 and The World Bank Country Income Bands.Middle chart:American

57、Cancer Society,Cancer Facts&Figures 2021,American Cancer Society.Atlanta:2021.Bottom chart:Seer data created by https:/seer.cancer.gov/explorer on Sep 10,2021 from US Mortality Files.National Center for Health Statistics,CDC.Notes:Bottom chart shows data for all stages and all sites of cancer by rac

58、e/ethnicity for both sexes and across all ages.Rates are per 100,000 and are age adjusted to the 2000 US standard population.Black includes Hispanic.White excludes Hispanic.Rates for American Indians/Alaskan Natives only include cases that are in a Purchased/Referred Care Delivery Area(PRCDA).In mid

59、dle chart white and black include Hispanic.Exhibit 2:Continued health outcome differences by geography and race 68 91 64 21 21 92 68 48 77 84 63 82 57 14 18 67 50 41 64 63 DALYs per capitaGlobalDevelopedPharmergingLow&lower middle incomeWhiteBlack2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 201

60、930025020015010050Age-adjusted mortality rates per 100,000 with trend line2000 2002 2004 2006 2008 2010 2012 2014 2015 20185-year relative cancer survival rates(%)by race and location,U.S.,201020164.58 1.93 1.36 1.09 3.21 1.82 1.37 1.03 Black mortality rate Black trend line White mortality rate Whit

61、e trend line American Indian/Alaska Native mortality rate American Indian/Alaska Native trend line Hispanic(any race)mortality rate Hispanic(any race)trend line Asian/Pacific Islander mortality rate Asian/Pacific Islander trend line All sites Breast (female)Colon Esophagus Lung&bronchus Melanoma of

62、the skin Oral cavity&pharynx Ovary Urinary bladder Uterine corpus Progress in addressing the global burden of disease has also been underwhelming.1 While progress has been made in lower income countries where the disability adjusted life years lost to disease(DALYs)declined 30%over 10 years(see Exhi

63、bit 2,top chart)by successfully addressing several diseases through medicine use,philanthropy,and global health outreach by wealthier countries this has not been the case in developed and pharmerging countries.Instead,DALYs per capita have remained somewhat steady,shifting only from 1.361.37 in deve

64、loped nations.Further,there are disparities in outcomes in the United States tied to race and class,for example,that are clearly illustrated by differences in five-year survival and mortality rates for cancers(see Exhibit 2,middle and bottom charts).2 Although mortality rates have been improving sin

65、ce 2000,mortality remains higher in blacks than whites,and the five-year relative survival overall and across a number of cancers is higher in whites.Part of this disparity ties to the fact that blacks are less likely to be diagnosed in the more survivable local stage.3 Finally,there is a continued

66、need to intervene in the patient journey to improve health outcomes and the patient experience.Social determinants data and digital technologies such as wearables and smartphone apps,which have expanded opportunities for patient self-monitoring,can shed light on the patient experience.However,contin

67、uous remote monitoring of various digital biomarkers of health and self-reported outcomes data are still underutilized,preventing lessons about unmet patient needs to be learned and applied.Issues such as delayed and discontinued treatment(i.e.,persistence/adherence),while not new,remain an issue pr

68、eventing patients from receiving the full benefit of healthcare,and life sciences companies are increasingly iqviainstitute.org|7assessing new digital strategies and patient care programs that can potentially shift behavior.4 Finally,similar to adherence issues,there remains high patient dropout and

69、 burnout rates in clinical trials that need to be addressed to ensure that trials become an increasingly feasible option for larger segments of the population,providing them earlier access to life-saving innovation.Complexity in health systems Complexity in health systems has grown over time,increas

70、ing the number of decisions life sciences companies must face,and making those decisions more challenging.In addition to those able to prescribe,such as physicians,nurse practitioners and physician assistants,an increasing number of influencers impact care decisions,including payers,provider systems

71、 and individuals in purchasing and quality roles.Each stakeholder type has distinct needs that require different skillsets to serve them,so life sciences companies have needed to expand the range of skills among staff.This expansion includes building clinical knowledge and business acumen more widel

72、y across roles to enable staff to engage and partner with this greater number and diversity of stakeholders.Further,as no one-size-fits-all approach leads to success engaging with even a single stakeholder type(such as might have been the case in the use of decile targeting to guide sales force enga

73、gement in the past),the need for customization and orchestration of approaches leveraging behavioral insights has become increasingly critical.The information each stakeholder is demanding and the quality of that information is also changing,compelling life sciences companies to react and shift thei

74、r processes.Physicians are increasingly demanding medical and scientific engagement,shifting relationships with medical affairs teams,while all stakeholders are increasingly demanding evidence of value in the forms of efficacy and outcomes data based on real-world data that require increasing eviden

75、ce generation and reporting capabilities.Finally,regulatory changes have also increased complexity for life sciences companies in some cases.For example,medical devices and technologies face increased regulation in the European Union through the Medical Device Regulations(EU MDR),which require addit

76、ional evidence generation.This has resulted in increasing complexities and pressures for medical device manufacturers.Heightened competitiveness Heightened competitiveness between life sciences companies has made the ability to make faster and better decisions,and the ability to adapt to changes,eve

77、r more critical.As these companies seek to drive revenue growth in financially constrained health systems,proving the value of their treatments through data and evidence-sharing with stakeholders has become increasingly critical.Data is needed to demonstrate economic benefits and patient outcomes ac

78、ross the intervention or treatment pathway,prove reduced complications and the need for secondary interventions,and reduce length of stay and costs.Additionally,companies are being asked more often by payers to share risk and issue performance guarantees.All stakeholders are increasingly demanding e

79、vidence of value in the forms of efficacy and outcomes data based on real-world data that require increasing evidence generation and reporting capabilities.8|Improving Decision-Making through Connected IntelligenceThe surge in the number of innovative therapeutics launched into the market has intens

80、ified competition over the past several years5,while a shift to developing drugs that support narrow patient populations has made it harder to find and compete for the fewer patients that may be eligible for both clinical trials and treatment.These challenges have led to changes in the need for pred

81、ictive insights and analytics to guide company strategy and better understand likely safety profiles,the use of data to find patients with specific biomarkers or eligibility criteria,and the use of RWE to support novel trial designs in rare diseases.The field for innovation in healthcare is also bec

82、oming more crowded and disrupted as technology companies with strengths in healthcare AI shift into the$8.3 trillion healthcare space6,disrupting established players.Companies must be agile to identify where to engage and where seeming threats are minimal.In the past years,Facebook,Apple,Microsoft,G

83、oogle,and Amazon have all moved to find a place for themselves in healthcare,offering to disrupt established players in new ways.For instance,Amazon launched their online pharmacy,Amazon Pharmacy,at the end of 20207,marking a significant push into the healthcare space that may disrupt the retail pha

84、rmacy sector,while others such as Apple,with its homegrown development of health devices,and Google,with its acquisition of Fitbit,are moving more deeply into the health wearables space for remote patient monitoring and have made investments to validate the efficacy of these technologies.8 Apple,for

85、 instance,has made recent investments in the Apple Watch that incorporate ECG capabilities,potentially useful for atrial fibrillation detection9-11 and a blood oxygen saturation sensor.Both tech and non-tech players are also disrupting and increasing competition in the clinical research space.Verily

86、,an Alphabet company,continues to acquire clinical trial management tools and systems12,and participates in digital care and remote monitoring through wearables.Non-tech players such as Walmart are also opening healthcare“supercenters”that include clinical laboratory services,helping to connect pati

87、ents to clinical trials,and have further offered Medicare health plans as an insurance service.13,14 Other technology players,including Microsoft,appear to be provider and hospital system focused.And Facebook has long built and hosted patient support communities.15 These changes in the digital world

88、 are influencing the way life sciences companies build medical devices,with connectedness becoming a priority and putting pressure on pharmaceutical companies to compete on a new playing field where digital tools can be used alongside medicines and in clinical development processes to track and impr

89、ove health outcomes.It is further part of a broader trend toward providing tailored support for customers and patients through wrap-around services and digital engagement,where life sciences companies will compete with a variety of other types of companies.Obtaining the skills necessary to meet this

90、 new competition has led to challenges in staff retention and a talent war.16 An interconnected,co-interdependent,multi-stakeholder healthcare ecosystem The healthcare ecosystem is also increasingly interconnected,where data and technology hold the key to the language of value for multiple stakehold

91、ers.United in efforts to assess patient outcomes and make the right choices among medicines and care options,The field for innovation in healthcare is becoming more crowded and disrupted as technology companies with strengths in healthcare AI shift into the$8.3 trillion healthcare space.iqviainstitu

92、te.org|9stakeholders are increasingly building,sharing,and comparing evidence(often supported by real-world data and technology systems to guide decision-making).For instance,payers now use innovative contracts and risk-sharing approaches that leverage such evidence17,including a growing,but still l

93、imited,use of value-based contracts where performance is measured by data.For life sciences companies,this has required a shift to leveraging technology and data in novel ways both to improve health and to prove that patient health is improving.Building partnerships with new innovators has thereby b

94、ecome critical in this changing space,whether to secure access to AI&ML technologies that facilitate insight generation or to build applications and approaches that support clinical development of next generation therapeutics and mRNA technologies.Agreements and partnerships are now the cornerstone

95、of a new economy in innovation,requiring life sciences companies to build skills in alliance management that stretch across stakeholders.Finally,a trend toward patient-centricity has meant that the attention paid by stakeholders to patient reported outcomes is also building,bringing the patient expe

96、rience and voice increasingly to the development process for therapies and their adoption once on the market.In addition to building relationships with patient advocacy organizations,this shift is also challenging companies to develop both digital health apps to collect self-reported data and digita

97、l biomarkers to track patient experience more continually.Impact of the pandemic The long-term legacy of the COVID-19 pandemic includes impact on traditional healthcare approaches and the patient journey.It has shifted how patients interact with physicians,such as driving day-to-day preventative and

98、 routine consults to telemedicine and reducing elective visits and care.Constrained access to care during pandemic peaks when facilities were overloaded,along with increased barriers to care such as COVID-19 testing requirements for inpatient treatment,further made care even less accessible or desir

99、able to some individuals.However,it also expanded the sites where care is delivered,including an increase in care provision and lab work at home through home health nurses.For patients participating in medical research,the pandemic shifted how often these patients visit clinical trial sites,as the u

100、se of remote and hybrid study approaches has grown,supported by a growing number of home research nurses and phlebotomists.This shift in clinical development toward hybrid trials has further driven the need for technological tools that can take in and use new sources of data,such as connected and we

101、arable devices or PROs from smartphones.During the pandemic,patients increasingly used and interacted with digital health apps and connected devices and wearables to maintain their health.This will likely strengthen the ability of the health system to provide remote patient monitoring but also chall

102、enges life sciences companies to build new capabilities to make use of these technologies.For life sciences companies,the pandemic not only disrupted their ability to conduct clinical trials and launch new drugs,but with reduced onsite access to HCPs,has also changed how they communicate with care p

103、roviders and if/when sales representatives are able to enter doctors offices.In 2020,companies mostly shifted to reaching physicians virtually for sales calls and other forms of digital engagement,providing education,product demos,KOL programs,and product support through those channels.In the future

104、,life sciences companies During the pandemic,patients increasingly used and interacted with digital health apps and connected devices and wearables to maintain their health.10|Improving Decision-Making through Connected Intelligencewill likely need to provide a hybrid form of engagement combining vi

105、rtual and in-person interactions.The flux of a world where COVID-19 disrupted both care and communication on an ongoing basis has created an imperative for life sciences companies to embed insights and analytics in business decision-making,guiding customer engagement and scaling and adapting to chan

106、ges in customer needs.As physicians have become more familiar and comfortable with virtual engagement via technology,opening a new route to communication,it has changed expectations of how life sciences companies will communicate with them and has resulted in an increased need for digital channels.H

107、CPs have also become increasingly interested in choosing what information is presented to them,as they increasingly seek higher-level medical and scientific engagement from medical affairs staff supported by timely information;being able to personalize information tailored to the needs and interests

108、 of stakeholders is therefore increasingly critical.This similarly applies to patients,with whom companies have increasingly engaged directly,developing digital content and customer-facing applications,building or partnering with patient community portals,engaging in DTC messaging,and continuing to

109、deploy patient support programs.Finally,post-pandemic,multiple stakeholders now have a more pointed focus on ensuring social equality and diversity in care.For instance,life sciences companies have begun to scrutinize the recruitment process in clinical trials based on certain diversity and inclusio

110、n(D&I)criteria.Finally,the emergence of post-COVID-19 conditions as a new disease state poses new challenges to healthcare stakeholders,including understanding and gaining insights about the set of conditions,determining the best care for these conditions,and developing new therapeutics.18As physici

111、ans have become more familiar and comfortable with virtual engagement via technology,it has changed expectations of how life sciences companies will communicate with them.iqviainstitute.org|11Leveraging new capabilities to enable better and faster decision-making+Advancements in human science,techno

112、logy and data science are already transforming the way healthcare and life sciences organizations conduct business.+Stakeholders now have access to more diverse sources of data that can help guide them at a range of critical decision-points in healthcare both for medical and commercial decisions.+Im

113、proved ability to integrate data from disparate sources,including documents with natural spoken or written language using NLP,is expanding opportunities to utilize real-world data.+However,since data sources are not always clean or reliable,application of skills to integrate,manage quality control,i

114、mpute,and project missing data,build data models,and ensure data privacy and security is essential.+Artificial intelligence and machine learning (AI&ML)have contributed to the value life sciences companies can now draw from large datasets,helping to yield new and improved insights,predictions,and fo

115、recasts.+The emergence of healthcare-dedicated cloud software platforms to serve the analytic needs of this space has placed high-level analytic capabilities in the hands of small,emerging,and large companies alike.+New tech platforms help create one source of truth for an organization and aid in in

116、formation sharing across teams performing multiple functions within life sciences companies.+Advances in apps and tools provide value in decision-making by bringing insights to end users at multiple points along their workflow and creating intuitive and user-friendly interfaces that improve the user

117、 experience.The impact of advancements in human science,technology and data science combined with a changing healthcare-ecosystem have increased the urgency of transformation and made a new set of competencies essential.While life sciences companies have long had access to data and technology,meetin

118、g the needs of a changing healthcare ecosystem now requires connecting and integrating those data sources and systems in new ways and infusing insights into workflows using advanced analytic technologies.These advancements are already transforming the way healthcare and life sciences organizations c

119、onduct business.From the way data is used to the way that technology platforms,AI&ML,and other advanced analytics can now draw new value from big data,companies are leveraging these advancements to improve decisions and help teams make them faster while reducing costs and improving internal organiza

120、tional alignment.DRAWING VALUE FROM DATAThe evolution of data sources and their use in the healthcare industry continues to transform how businesses across the spectrum operate.Stakeholders now have access to more diverse sources of data that can help guide them at a range of critical decision point

121、s in healthcare both for medical and commercial decisions and they are using the expanded access to such data to further transform their analytic processes internally and impact patient care and the experience externally.Among the data sources available to advance understanding in healthcare are:non

122、-identified patient-level data sources that can provide information about health outcomes in response to care;data on drug molecular structures that can clarify if drug candidates are likely to impact disease;data from apps and consumer wearables that can shed light on patient health status and expe

123、rience;and data on social determinants of health that can improve the ability of the health system to tackle inequality.12|Improving Decision-Making through Connected IntelligenceThe automation of how data flows into systems is also changing,with some data streams updating in real-time through strea

124、ming data pipelines.In the research and development(R&D)space,the automation of data flows from clinical trial sites,offering to reduce or eliminate a major and costly issue for life sciences companies developing new molecules that of missing data.Clinical trial primary efficacy and safety data can

125、now flow directly into the trial database from connected devices rather than having to be written down manually and/or entered manually by data managers or investigators,thereby reducing errors and missing data.This direct flow into the trial database enables sites to know when data is missing in re

126、al-time so it can be corrected before a patient leaves the clinic.However,data is not always clean and reliable.To make data meaningful and ensure it can best guide decision-making and is of high enough quality to disseminate to stakeholders as evidence requires not only the right selection of data

127、sources,but the skill to draw value from those data sources.Such mechanisms include applying de-identification techniques to patient data that will be used beyond patient care,ensuring and validating the quality of the data,imputing and applying projection methodologies to fill in missing data,trans

128、forming the data,building appropriate data models,storing data securely,and linking data in such a way as to maximize its value and privacy.Further,since the value of data has a short lifespan for most critical business decisions,new advancements in high-performance data transmission,along with data

129、 views that dynamically update within analytic tools,help to make this information available to guide action.Two critical skills that have increased the value that can be derived from data are the improved ability to integrate data from multiple sources and the ability to apply AI&ML techniques to d

130、ata to enable natural language processing(NLP).For instance,integration has enabled a more complete picture of healthcare needs and delivery.Specifically,non-identified patient data on medicine use can be linked to other valuable data on health outcomes and genetic sequencing to shed light on geneti

131、c drivers of patient response to therapy.And NLP has enabled messy,unstructured data sources such as social media data to be interpreted providing a better understanding of the patient experience and customer perceptions on therapeutic options and has shifted the processing of information included i

132、n written documents from a manual process to an automated one.With data streams flowing into life sciences companies faster and from more sources,companies are being challenged to refresh and use their data more rapidly and gain access to needed data and skills to make the data meaningful and useful

133、.As the health ecosystem changes,they are also being challenged to augment those data sources to power insights to their teams.This includes ensuring there is a single source of truth derived from the multiple datasets,such as through integration in data hubs,lakes and warehouses,as fragmented data

134、can frustrate decision-making and impede the attainment of key business goals.Such data repositories can also support more use cases for the organization.Advances have also been made in the way data is viewed or presented to users,who notably face challenges in interpreting data presented with compl

135、exity and without context.Indeed,as the amount of information that users receive has increased,the complexity of some dashboards and business intelligence tools can make them challenging to use.Advances in this space are not only in user interfaces and the surfacing of information within workflows,b

136、ut also push for global and/or regional consistency in the data and analytics disseminated across the enterprise,and contextualization of these trends to help the user understand what they are seeing.These changes have been driven by the urgency for rapid action,which can be hindered by global and r

137、egional teams having different views without consistency and little context with which to interpret a trend.PROVIDING IMPROVED INSIGHTS THROUGH AI&MLArtificial intelligence and machine learning have also contributed to the value life sciences companies can now draw from big data.These capabilities c

138、an enable systems to recognize patterns within the data and train models to intelligently learn,helping iqviainstitute.org|13to yield new and improved insights,predictions and forecasts(see Exhibit 3).19 Applying this approach,life sciences companies are gradually shifting to systems leveraging AI&M

139、L that can anticipate likely outcomes,identify the best action or decisions to take,and make recommendations that can guide customer interactions.22 AI&ML algorithms can handle large data and complex data structure or unstructured data much more effectively than traditional models.AI&ML can also aut

140、omate manual tasks,forecast likely outcomes based on analysis of historical data,and create alerts surfacing important information.There are a number of examples of how AI&ML are gradually transforming processes for life sciences companies.Through AI&ML,companies have been able to select investigato

141、rs and countries for their clinical trials and predict their performance,find HCPs treating patients at risk of disease or undiagnosed,leverage natural language processing(NLP)that enables valuable information to be synthesized from documents such as translating clinical trial documents that support

142、 regulatory approval,alert trial investigators to outlier laboratory tests and vitals values to act on,guide commercial engagement and strategy,and optimize investments and manage costs,among other applications.This is done through multiple advanced machine learning techniques(see Exhibit 3)and othe

143、r non-AI&ML statistical approaches to develop robust and reliable predictive models.However,multiple aspects need to be kept in mind for life sciences companies to successfully develop and deploy AI&ML.These include the importance of feeding the AI&ML model with quality data,the appropriate selectio

144、n and function of the model(as in some cases inappropriate selection will fail to produce results),and the need to run simulations on live data to understand outputs before finalizing algorithm rules.From an organizational perspective,analyzing the target business Exhibit 3:Artificial intelligence a

145、nd machine learning algorithms trained with real-world data uncover deep and actionable insights Source:IQVIA Institute,Dec 2021;IQIVA,Aug 2021.DATA SOURCESProprietary and public datasets are integrated in a data lake and underly a variety of insight-generating technology systemsAdvanced InsightsDee

146、per insights,predictions,forecasts and evidence-based recommendationsClinicaltrial dataMoleculeand drugtarget dataPrescriptiondataDe-identifiedpatient level dataClaimsdataEMRdataSocial andsentimentdataMedicalliteratureApps andwearablesdataPharmacydataReferencedataPhenotypedataGeneticsequencingdataOm

147、icsdataAdvanced AnalyticsThe examination of information using sophisticated techniques and tools to discover deeper insightsFEEDSYIELDSINFORMSMachineLearningA subset of AI that enables computers to improve attasks with experience.Artificial IntelligenceAny technique that enables computers to mimic h

148、uman intelligence.Deep LearningA subset of machine learningthat permits softwareto train itself to perform tasks by exposing it tovast amounts of data.Un/SupervisedLearningNeuralNetworksNaturalLanguageProcessing(NLP)A subset of AI that enablescomputers to understandhuman languageasit is spoken orwri

149、tten.Source:IQVIA Institute,Dec 2021;IQVIA,Aug 2021.14|Improving Decision-Making through Connected Intelligenceprocess is also a first necessary step in recruiting the right decision support resources such as AI&ML to support analytics that will have the most impact.Finally,it is important to ensure

150、 a repeatable and scalable process for applying AI&ML to business workflows,such as by having a configurable system.While AI&ML are very powerful tools that can achieve speed,precision and scale,life sciences companies are still finding it difficult to bring their AI&ML projects to fruition in a tim

151、ely fashion,identify the right use cases for such investments20,and prove the business value and ROI from their AI&ML investments.This is partly due to that fact that even when intelligence is embedded into workflow to ensure a workable,easy-to-consume user experience,user adoption and scaling acros

152、s an organization can be a challenge.For this reason,establishing the KPIs to measure the effectiveness of AI&ML implementations and define success is therefore critical,as is ensuring algorithm settings can be updated at any point in the deployment journey and even post-launch.REALIZING VALUE THROU

153、GH TECHNOLOGY PLATFORMS AND TOOLSWith growing sources of data to interpret and more stakeholders to engage,advancements in technology platforms are increasingly critical and available to help life sciences companies improve the speed and productivity of decision-making.These new platforms use advanc

154、ed techniques such as NLP,AI&ML or other algorithms,predictive analytics,models,and data visualization to support the delivery of insights to stakeholders in the life sciences industry about markets,trends,and opportunities.The emergence of healthcare-dedicated cloud software to serve the analytic n

155、eeds of this space,with built-in compliance and security,has placed high-level analytic capabilities in the hands of small and emerging companies,in addition to large companies that have built their own systems.These advanced platforms often provide cloud-based access to vast quantities of data,whet

156、her from a companys data lake or external or federated sources of data.They also embed anticipatory intelligence into standardized workflows to provide insights and recommendations,thereby ensuring key information can be brought to user attention at the right time(such as via alerts).Built-in key pe

157、rformance indicators(KPIs)also provide a robust early warning system of risks and opportunities,thereby mitigating risk in users daily work and decision-making.These technology platforms can deliver an improved user experience and have also become more critical as the cost of non-compliance has incr

158、eased.These technology platforms also have the benefit of breaking down silos within an organization perpetuated by non-integrated systems,processes,and data.Unlike former times when individual teams would create their own dashboards whenever they faced an analytic problem,enterprise technology plat

159、forms help create one source of the truth for the organization and aid in information sharing across teams.Compatible automated systems connecting big data platforms across multiple functions now exist within life sciences companies,and are used across clinical trial design and conduct,safety and re

160、gulatory,quality and compliance,real-world evidence of product outcomes,and patient and customer engagement.By establishing an enterprise-wide view of data with multi-language capabilities,global and cross-functional teams can present consistent metrics to executives.Such platforms also tend to incl

161、ude integration with legacy enterprise systems and applications through APIs.As an example,orchestrated multichannel marketing systems can enable a single customer view to be shared across the organization connecting sales,marketing,and medical science liaisons to balance touchpoints and create a be

162、tter customer experience.Finally,advances in the use of apps and tools linked to these platforms are further providing value in decision-making by bringing insights to end-users at multiple points along their workflow and creating intuitive and user-friendly interfaces that improve the user experien

163、ce.This,along with providing access to underlying contextual data,tends to accelerate adoption of new systems and insights,as tools with poor user interfaces can be cumbersome to use,and lack of transparency about the data presented and feedback can erode trust.AI-powered precision insightsiqviainst

164、itute.org|15Applying connected intelligence to transform decision-making +Companies are increasingly challenged to build new capabilities and achieve efficiencies by leveraging new technologies to succeed in the changing healthcare ecosystem.+An evolving capacity known as“connected intelligence”offe

165、rs to yield new insights,drive smarter decision-making,and enable insight-sharing and partnerships among stakeholders.+The five key elements of connected intelligence include:analytic inputs,including data and technology;the skills and capabilities to make these inputs meaningful in the healthcare s

166、pace;the generation and dissemination of insights;and an organizational structure and culture that support the development and evolution of connected systems.+Creating intelligent connections between internal capabilities and the healthcare ecosystem enables life sciences companies to be more agile,

167、accelerate results and deliver the full value of medicines to the patients who will benefit from them.DEFINING CONNECTED INTELLIGENCELife sciences companies are continually looking for ways to be more agile,accelerate results and improve patient outcomes.To meet recent changes in the healthcare ecos

168、ystem,companies are increasingly combining capabilities in data and analytic technology systems with the application of healthcare expertise to build intelligent connections within their organization and with other stakeholders in the healthcare ecosystem.This evolving capacity known as connected in

169、telligence offers to yield new insights,drive smarter decision-making,and enable insight-sharing and partnerships among stakeholders.It also promises to better deliver the full value of scientific innovation to the patients who will benefit from them,as well as offer a combination of increased reven

170、ue,reduced risk,productivity gains,and cost savings to life sciences companies.Connected intelligence is generated through the combination of its five key elements:analytic inputs,including data and technology;the skills and capabilities to make these inputs meaningful in the healthcare space;the ge

171、neration and dissemination of insights;and an organizational structure and culture that supports the development and evolution of connected systems (see Exhibit 4).More specifically,successful application of connected intelligence requires:Access to a variety of data types to support the business ac

172、ross its various functions Analytic technologies to yield high-volume,high-quality scientific and evidence-based insights that guide decision-making and action The skills and capabilities to handle data,build technology platforms and ensure they make sense for healthcare The actions a company takes

173、to disseminate those insights effectively throughout the company and to external stakeholders The organizational culture and structure to support alignment across the enterprise,ensure that action is taken based on insights generated,and ensure the company is ready for dynamic change over time.Conne

174、cted intelligence promises to deliver the full value of scientific innovation to the patients who will benefit from them.16|Improving Decision-Making through Connected IntelligenceELEMENTS NEEDED TO SUPPORT CONNECTED INTELLIGENCEData Underlying the ability for an organization to demonstrate connecte

175、d intelligence is having access to coherent raw data across a variety of data types and being able to break down internal data silos between these data sources.These might include commercial/sales data,medical data,genomic data,reference data,clinical trial data,and real-world data from sources such

176、 as claims,EMR,social media,lab,and wearable and device collected data.The data that is sourced should include best-in-class external data sources as well as all internal sources to be able to support the business across its various functions.Some companies even make major investments in privileged

177、access to data assets such as repositories of real-world data via acquisitions,partnerships,or joint ventures,or leverage internally collected data to provide competitive advantage.The data should also exist in an encrypted and centralized data lake(or other centralized data management platform),ser

178、ving the entire enterprise rather than only one functional team/domain.In this way,it can serve a variety of purposes,including guiding company investments,accelerating drug discovery and optimization,making the R&D process more efficient and innovative,shedding light on patient needs and heterogene

179、ity,reaching and communicating with stakeholders,supporting brand commercialization,improving patient outcomes,and demonstrating an impact on total cost of care.The key foundational components of connected intelligence in a company include making the most efficient and innovative uses of these data,

180、having the right balance of broad data and deep/granular data to provide clinical richness within therapy areas or big picture insights,and making appropriate connections between datasets to enable advanced insight generation.Exhibit 4:Elements of a connected intelligence systemSkills andcapabilitie

181、s TechnologyInsightgenerationanddissemination DataOrganizational structure and cultureConnectedIntelligenceSource:IQVIA Institute,Jan 2022.iqviainstitute.org|17Linkage of core data including non-identified patient records to other valuable data(like genomic data,social determinants data,device data,

182、clinical or patient reported outcome data,etc.)determines the value that can be gleaned by various parts of the organization.It is also critical to provide the 360-degree views of the care provided to patients and a comprehensive understanding of patient needs and outcomes that are necessary to prov

183、e value in the new environment,while ensuring this data remains privacy protected and secured in an encrypted data lake.It also determines whether the right sources of data are available to answer business questions.Further,having data streams that are automated updated frequently or rapidly in real

184、-time as needed to provide actionable information that enable users to quickly course-correct will determine the value an organization can derive from them.So too will data validation and quality checks that are automated.Normalizing and standardizing the data as well as enriching the data attribute

185、s through machine learning algorithms is critical to address the messiness of the data and gaps in data(missingness)to be able to layer analytics on top to extract insights.It is also critical in order to progress the data to regulatory grade.Finally,the way data is sourced can determine whether the

186、 companys data needs can be easily scaled for the future;in particular,whether data is syndicated or individually developed,is available as data on-demand where a company can rent and pay by-use,or involves integrated datasets owned/held in-house or in the cloud.Technology Enterprise technology is a

187、t once the product of connected intelligence and the enabling foundation that accelerates the use of connected intelligence throughout the organization.The quality of a companys technology,particularly its analytic platforms,is what enables it to bring value from data to the organization through ins

188、ight generation.Building such insight generation platforms and refining their operation requires all elements of connected intelligence including data,technology,skills and capabilities,and organizational support to achieve optimal information flows across the enterprise.It also critically requires

189、connectedness of teams throughout the organization to gather specs to build what is truly needed.Technology systems may bring value to the business by applying AI&ML and deep learning to large datasets to draw insights from unstructured data in the healthcare arena as well as enable natural language

190、 processing(NLP)to make sense of human language and enable more and deeper insights.Whether such systems and their outputs are being built to provide actors across the company the access they need to high-volume,high-quality scientific and evidence-based insights defines this aspect of connected int

191、elligence.When achieved,the technology uses the right data and has the right structure to serve the business across multiple functions.Insights are generated at the right level(e.g.,predictive or prescriptive analytics)and surfaced at the right time(at the point-of-decision)not just to confidently i

192、nform decision-making but also to guide action,enable users to quickly course-correct,and improve ROI through evidence-based recommendations.In marketing,the ability to engage external customers by leveraging all available channels of communication through omnichannel technology systems is a key com

193、mercial capability,enabling life sciences companies to reach customers and stakeholders in the most effective way to improve relationships.This includes The quality of a companys technology,particularly its analytic platforms,is what enables it to bring value from data to the organization.18|Improvi

194、ng Decision-Making through Connected Intelligencedigital connection to external stakeholders which,in the current and future environment post-COVID,will be critical,making it essential for organizations to build the connected intelligence to communicate through digital routes.Further,technological p

195、latforms and systems should be structured to allow for the evolution of analytics and capabilities over time,rapidly being able to add data sources.Ideally these systems can also use expanded and more diverse sources of data,with the ability to conduct federated analytics on rich offsite data source

196、s.Such flexibility is a critical component of connected intelligence to ensure companies are agile enough to adjust based on changing needs and opportunities.Systems that are scalable or modular to accommodate a mix of vendor-and custom-built solutions,have an open architecture to connect to other s

197、ystems through APIs,and can support multiple therapeutic area verticals,define this agility.An insight-generation platform that is customizable through self-service apps and allows users to make changes to algorithms and rules without coding,further serves to enable the platform to be adapted to dif

198、ferent brands with different strategies.The connectedness of such integrated systems can help break down silos within an organization and thereby enable consistent metrics to be shared with executives.Finally,whether a company builds their own technology,outsources,buys or rents cloud software,or a

199、mixture of these,is also likely to determine the longevity of its analytic solutions,and whether it is agile and prepared to adapt as needs change in the future.Systems that enable and can leverage a bi-directional flow of data or create a feedback loop for learning further strengthen a companys abi

200、lity to gain insights and quickly course correct.For example,a life sciences company can leverage data records of its proprietary past experiences such as the clinical trials it ran and the protocol amendments or other design adjustments that were required and apply AI&ML-enabled systems to learn ho

201、w to design better trials in the future.Similarly,learning systems can take data collected from providers or patients(with permission)to shed light on their perspectives or responses and use them to improve the life sciences companys interactions with their customers,improve patient retention in fut

202、ure studies,or leverage them across other parts of the business.Finally,ensuring that standardized tools with aligned views and harmonized assumptions are deployed across the enterprise can help aid the downstream adoption of its outputs.Having technology systems that transparently supply contextual

203、 factors about data-based insights can help ensure internal alignment and avoid user challenges in interpreting them.This,along with change management,will determine whether insights and insight-sharing are likely to have downstream impact on action across relevant teams and whether decision-making

204、for the organization can be substantively transformed.Skills and capabilities An organizations skills and capabilities strongly feed into its ability to apply and benefit from connected intelligence.A company needs to tap into key talent across a variety of roles in order to know what to do with the

205、 data and ensure it makes sense for healthcare,select the right data for the right purpose,put needed technology systems in place,and appropriately generate and disseminate insights across the full organization and to stakeholders.This includes,for example,individuals with experience across data sci

206、ence,statistics,Scalable and agile technologiesAn organizations skills and capabilities strongly feed into its ability to apply and benefit from connected intelligence.iqviainstitute.org|19bioinformatics,medical/clinical expertise,epidemiology,and people who know how to extract insights from data th

207、rough a variety of AI approaches and other analytics.Some critical roles include RWE strategists and data scientists with a strong therapeutic area focus.To achieve connected intelligence,companies may therefore need to reconsider the roles and responsibilities required or change their organizationa

208、l models to accommodate needed new business goals.For instance,as real-world data became more readily available for analysis over the past years,many organizations created real-world evidence centers of excellence so they could extract the most value out of this new data and the capabilities it prov

209、ides.From a data perspective,critical enablers of building and maintaining connected intelligence include the ability to de-identify,prepare,integrate and transform data,ensure its quality and integrity apply imputation and projection methodologies,maintain healthcare reference data,build optimized

210、data models and data warehouses,design and train algorithms specifically for healthcare applications,apply intelligent machine learning,and ensure data security,compliance and governance are in place.The skills to ensure the privacy of data while using and re-using existing data(privacy stewardship)

211、are also critical.A company then further needs people with healthcare expertise to apply the right data science methodologies to the right health data and apply local knowledge to generate insights and operationalize those into the business workflow,both globally and locally.To enhance the outputs o

212、f insight generation platforms and ensure they align with business needs requires the ability to apply“decision intelligence”a requirements-collection process that helps identify the key performance indicators(KPIs)that teams need,along with the required data,AI&ML application,and techniques to trac

213、k those KPIs.This process is guided by behavioral research into decision-making and the mapping of workflows and decision contexts before deploying technology systems.23 However,an organization does not need to have all of these skills internally but can obtain them through partnerships with data,an

214、alytics,and technology vendors.Therefore,staff fluency to know what the company needs to own in-house versus outsource,and the ability to manage vendors and partners,are also essential skills.Other critical skills in non-data domains focus on building relationships with other stakeholders and meetin

215、g their needs.These include expert understanding of the requirements of key stakeholders such as payers,patients,and providers,as well as scientific and therapeutic area expertise,and global and local regulatory and payer expertise,in addition to cultural support.One skill growing in importance for

216、building connected intelligence is alliance management,where individuals across multiple roles build connections with and between stakeholders,such as alliances with payers and other life sciences companies or partnerships with vendors or clinical sites.Insight generation and dissemination Once a co

217、mpany has invested to get the most value from its data via technology,it can gain a competitive edge through the approaches and actions it takes to operationalize that value internally and externally by structuring those insights and disseminating them throughout the company and beyond to stakeholde

218、rs.Connected intelligence means ensuring To achieve connected intelligence,companies may need to reconsider the roles and responsibilities required or change their organizational models to accommodate needed new business goals.20|Improving Decision-Making through Connected Intelligenceeveryone throu

219、ghout the company,as well as external stakeholders,are getting access to the right types of outputs and insights they need,when they need them,to provide the best possible outcomes in the competitive and scientific landscape in key therapy areas.A major issue facing most users is that the informatio

220、n they receive does not prioritize the key things they need to know,and they dont have time to synthesize findings from it.Therefore,a critical aspect of connected intelligence is connecting people to the narrowly focused essential information they need to do their jobs better without overburdening

221、them with too much information.This includes providing appropriate nudges to execute on the insights developed through a connected ecosystem.Actionable insights may be delivered as highly user-friendly apps and tools rather than basic reports to facilitate understanding.Getting users to adopt the re

222、commendations presented by insight systems is a key challenge,with end-users ignoring these much of the time.The timing and placement of a single recommendation(“next-best action”)at the time a decision is made can increase adoption,as can an elegance and simplicity in design and the ability to acce

223、ss information on the business rules applied.Further,the context in which the decision or alert was generated should ideally be explained to users to increase conviction that action should be taken for instance,not just alerting the user to an alarming trend but letting a user know that the trend is

224、 tracking more slowly/poorly than would be expected by AI&ML predictions.Another feature of connected intelligence is the ability to enable sharing of insights across teams with different functions.Rather than having a technology system yield a“one-off”insight,installing a mechanism to capture this

225、information and flow it across the organization from one area of the business to another as needed can bring added value.For instance,if a serious adverse event is detected in a trial,ensuring the downstream implications are conveyed to teams involved in trial design,medical education,and even comme

226、rcial strategy and launch in addition to meeting regulatory reporting requirements.For the external healthcare ecosystem,connected intelligence means the company is enabled by these insights to engage effectively and disseminate insights outside the organization in new and collaborative ways.Externa

227、l stakeholder engagement includes contacts with KOLs,regulatory agencies,payers,and public advocacy groups,as well as patients and physicians.To approach such external engagement with connected intelligence means to share evidence and insights many of which are powered by real-world data across the

228、life sciences ecosystem.And by doing so,stakeholders can speak the same language of evidence and insights,enabling innovative approaches and collaborative/joint decision-making,as well as helping build multi-stakeholder relationships and action.Such insight-sharing and transparency enables greater i

229、ntimacy and relevance for life sciences companies with their customers and partners.For instance,in the new paradigm where leaders across the healthcare ecosystem leverage real-world data and insights to determine value,the generation of cost-effectiveness insights through analytic RWE systems can h

230、elp engagement with payers.Key measures of connected intelligence would include a companys external communications being evidence-and insights-driven,and external engagement having the intended impact.77%of organizationsstruggle withbusiness adoptionof analyticsLow adoption minimizesbusiness impacti

231、qviainstitute.org|21Organizational structure and culture Effective action within an organization requires internal alignment and connection both to aggressively identify where change,insights and action are needed in response to the new connected intelligence ecosystem,and to ensure that action is t

232、aken based on the insights generated.To do so,a company must first understand the problems that they are trying to solve by connecting across the organization to identify pain points and understand the needs of employees and external stakeholders.Only then can they build to meet those needs identify

233、ing the parties within the organization with the skills to help or seeking solutions to better communicate and serve those internally and externally.Internally,to achieve connected intelligence and enable coordinated action,an organization needs to enable users globally to share a consistent view of

234、 the truth through enterprise-wide data use and insight dissemination.This need for alignment and efficiency calls for organizational transformation and the breaking down of traditional silos that lead to fragmented and local decision-making.For instance,ensuring that teams have aligned KPIs globall

235、y and are working in a coordinated fashion toward a common goal without duplicated effort is a key measure of connected intelligence within this domain.Funding models are also a critical enabler to realize such efficiencies across the enterprise.Centralized funding or hub and spoke models with clear

236、 role mapping that balance global,regional,and local teams can provide the necessary access to aligned data and analytics while also promoting efficiency.Organizations may need to re-think business functions and adjust roles and responsibilities that align with new business goals in order to realize

237、 the full benefits of connected intelligence.Positioning key roles within an organization to have global and cross-functional impact and influence is critical,lest isolated pockets of excellence emerge while the rest of the organization is left behind.For instance,where the head of the RWE center of

238、 excellence sits can impact the ability of the organization to deploy evidence effectively across functions.Expanding the influence of innovation departments and centers of excellence by rotating personnel through these teams can also help achieve the cultural change that may be needed.Change manage

239、ment initiatives can also be useful to help users trust the new insights flowing in through new systems and convince them to act on their recommendations.Finally,where a company places its investments is also likely to determine success.For example,pressure placed on the need to own data and technol

240、ogy in-house versus in-or out-sourcing can lead to inefficiencies when building connected intelligence and thereby negatively impact a business total cost of ownership.Funding models are a critical enabler to realize efficiencies across the enterprise.22|Improving Decision-Making through Connected I

241、ntelligenceCritical business decisions in action with connected intelligence +Connected intelligence can improve critical business decisions made at specific points across a molecules lifecycle,at the enterprise level,and can provide evidence to guide collaboration with external stakeholders.+At cri

242、tical points along a molecules lifecycle,connected intelligence can aid in discovery research and target identification,clinical development planning and execution,medical affairs,and marketing and sales.+At the enterprise level,connected Intelligence can impact portfolio strategy and forecasting,co

243、st and risk management and resource utilization.+Connected intelligence can also help with decision-making in relation to multiple stakeholders,such as patients and advocacy groups,healthcare providers,payers/integrated health systems,big tech and digital,and regulatory agencies.The five building bl

244、ocks of connected intelligence yield their value by improving and accelerating critical business decisions at various levels within a life sciences organization.They can be harnessed to guide decisions at specific points across a molecules lifecycle,at the enterprise level,and can provide evidence t

245、o guide collaboration with external stakeholders.Exhibit 5:Applications of connected intelligence to transform decision-making within life sciences companies Discovery research and target identification Target mining Molecule generation Drug-repurposing Biomarker identification/utilization De novo m

246、olecule optimizationClinical development planning and execution Optimizing asset value Site identification Patient screening Predicting clinical trial success Enrollment planning and prediction Site risk assessmentMedical affairs AE tracking KOL mapping and trackingMarketing and sales Patient pathwa

247、y mapping Disease detection Patient services Promotion activity levels and mix Brand performance management HCP interaction and education Salesforce managementCritical point decisions along a molecules lifecyclePortfolio strategy and forecasting Business portfolio priorities and planning Therapy are

248、a and mechanism priorities Geographic market priorities Mergers and acquisitions/business developmentCost and risk management R&D risk portfolio Commercial risk assessments External environment risk factorsResource utilization Headcount management and organization Investment allocation Performance t

249、arget-setting Cost structure optimizationEnterprise-level decisionsPatients and advocacy groups Clinical Research As A Care Option Education and awareness programs Diagnostic/screening initiativesHealthcare providers Guideline development and interpretation Patient care programs Optimizing site of c

250、are Patient monitoring and outcomes measuresPayers Value-based payments Horizon-scanning Formulary decisions/P&T Health technology assessmentsBig tech and digital device developers Collaboration efforts Guardrails for patient privacy and data security Integration into health systemsRegulatory agenci

251、es Safety monitoring Use of RWE External comparators/synthetic trial armsMulti-stakeholder decisionsSource:IQVIA Institute,Jan 2022iqviainstitute.org|23CRITICAL POINTS ALONG A MOLECULES LIFECYCLEA molecules lifecycle stretches from the discovery phase where targets are identified,through periods of

252、clinical development,regulatory approval,and commercialization.In each of these stages,there are critical point solutions and decisions that can be transformed by applying connected intelligence.Discovery research and target identificationTo accelerate the discovery of new drugs,new targets,or new i

253、ndications for existing drugs,various data sources can be combined with cutting edge AI&ML approaches and technology platforms.By connecting various sources of data next generation sequencing data(NGS)and assay data,genotype and phenotype data,etc.and leveraging deep learning and NLP to identify and

254、 extract meaningful signals from biomedical literature,life sciences companies can understand the complex system of interactions and associations between genes,proteins,pathways,and diseases to speed drug discovery and identify targets.Similarly,automatic molecular generation algorithms applied to d

255、rug binding,toxicity,chemoinformatic and molecule structural information(e.g.,molecule graphs and fingerprints such as SMILES)among others,can help predict the properties of new drug candidates and their molecular interactions and structures to determine if they are chemically meaningful,and to help

256、 select more desirable drug properties.The application of AI&ML drug and protein sequence data can also aid in identifying drugs that could be repurposed for other diseases.This helps to optimize the value existing molecules can deliver to patients,as well as reduce risk in development projects.Conn

257、ecting non-identified patient data with genetic biomarker data can help researchers determine the size of patient subgroups with a predictive biomarker.This can help guide their clinical development approach,select enrollment criteria,and further the development of tailored therapies.Clinical develo

258、pment planning and executionPerhaps the greatest new area of application for connected intelligence in recent years has been in clinical development planning and execution,where data,analytics and technology are finding new applications.As trial designs and trial decision-making face greater complex

259、ity,tools that help with study planning and improve study efficiency have grown increasingly important.Through novel application of AI&ML and other analytic approaches to non-identified data,this has led to more rapid identification of best-performing investigator sites,more accurate prediction of h

260、ow trial design impacts enrollment rates and startup times and has facilitated more diverse patient recruitment in terms of race,ethnicity,and age.It has also guided assessments of trial feasibility and design risk within protocol reviews,thereby reducing uncertainty.New trial monitoring systems hav

261、e also been used to help sponsors,investigators and CROs know how a trial is progressing and react more rapidly to issues.By consolidating disparate data on adverse events,protocol deviations,enrollment rates,and other metrics,they can provide transparency and up-to-date information on a trials prog

262、ress presented visually,such as through a user-friendly dashboard,that facilitates action in response.Novel trials designs are also being enabled by new technology systems.In decentralized trials,which became more prevalent during the pandemic,connected intelligence is helping study participant asse

263、ssments to be conducted and their completeness monitored through the use of technology,speeding data collection and reducing missing data.And companies are also using computer models of an investigational therapy and its deployment in a trial population to create insilico Clinical development Decent

264、ralized trials Faster patient recruitment Precision site identification Efficient protocol assessments24|Improving Decision-Making through Connected Intelligencetrials that model the performance of a product and offer to reduce the size and the duration of clinical trials through better design.22 In

265、sights on patient and caregiver preferences gathered through social listening technologies that leverage NLP have also served to reduce trial burden and make trials more successful.Finally,as companies increasingly engage in direct-to-patient recruitment,these systems can pre-screen patients and ele

266、ctronically capture data on race and ethnicity as well as predict the probability that a patient is a match for a trial.Connected intelligence can also help optimize an in-development molecule or assets value and ROI over its lifecycle by leveraging AI&ML to help prioritize what indications to pursu

267、e and in what sequence,predict technical and regulatory success(PTRS),and forecast budget investment.By modelling the potential development pathways,the associated asset valuation in each indication,and the development timelines for each scenario,the best lifecycle plan can be selected.These include

268、 patient,country,and therapeutic models as well as molecule-to-market analytic models,and the application of ML-based decision engines.Efficient protocol assessmentsPrecision site identificationDecentralized trialsFaster patient recruitmentCASE STUDY:Data-informed trial design assessment helps spons

269、or avoid substantial amendment An emerging biopharma company finalizing a protocol for a Phase III hematology study sought to validate their design decisions and identify potential study risks prior to protocol approval.In general,life sciences companies face significant risk to the smooth progress

270、of their trials,with 57%of protocols across all phases facing at least one substantial global amendment and the median cost of these ranging up to$535,000 for Phase III amendments.Further,nearly half of all substantial amendments have been deemed avoidable23,making it critical to rapidly evaluate pr

271、otocol quality before it is finalized or approved.Advances in such protocol reviews are being enabled by connected intelligence by leveraging various data sources and analytic systems guided by medical,operational,and analytical expertise.The current design analytics process undertaken at IQVIA,call

272、ed Data-Informed Protocol Assessment(DIPA),connects insights from historical and real-world,non-identified patient data,proprietary and public databases of design characteristics,and survey-data-derived algorithms on trial participant burdens and preferences.It also synthesizes information from mult

273、iple analytic systems,one of which rapidly provides evidence-based insights about protocol consistency and mitigates risk by using visual cues that alert users to issues.By pressure-testing protocols across a number of domains design consistency between activities and endpoints,burden on patients an

274、d sites,missing or extraneous data collection through study procedures,comparison to competitor trials,and the impact of eligibility criteria the process can determine if there are areas of increased risk within a sponsors protocol,highlight areas for optimization,and predict the impact of various a

275、spects of the protocol on execution.Alternately,it can confirm they are in line with best practices or the design decisions of competitors.For instance,analysis of real-world,non-identified patient data can predict the impact of key inclusion and exclusion criteria on patient availability,revealing

276、potential screen failure risks and recruitment challenges.By increasing clarity on iqviainstitute.org|25CASE STUDY continuedstudy design,the risk of protocol amendments can ultimately be reduced,and patient and site burden can be minimized,along with gaining potential efficiencies in cost and execut

277、ion by avoiding costly non-core procedures.For one specific client,21 risk areas were discovered within the sponsors protocol through the DIPA process.Among the issues identified,10 were related to design consistency and costly non-core procedures,one to the trial design itself,and 10 to patient and

278、 site burden that could have caused recruitment to become a challenge.For instance,some safety labs and a planned trial cross over period were not included in the schedule of assessments,several procedure schedules were unclear,and several key eligibility requirements were missing.The EQ-5D patient

279、reported outcome(PRO)evaluation needed as a required secondary endpoint was similarly absent from the schedule of assessments.As a result of the DIPA assessment,the sponsor was able to revise their protocol and study design,mitigating risk of a potential amendment and its associated costs estimated

280、at more than$500,000 and avoiding a three-month delay.25 The client was able to add the missing procedures tied to trial endpoints,thereby ensuring needed endpoint data were not lacking.Similarly,by building in the needed safety labs and adding a safety physical examination,it improved patient safet

281、y monitoring,and by adding a missing eligibility requirement,it helped better identify the target patient population.Further,patient burden was reduced by enabling the end of study visit to be conducted by telephone.In the future,application of AI&ML to historical trial data will allow comparisons o

282、f current protocols being assessed to similar past studies to better understand outcomes and likelihood of success,and the conversion of trial documents to digitized ones with NLP will further improve this analytic data system and the ability to conduct these assessments.Medical affairs Building con

283、nected intelligence can help life sciences companies track and report adverse events(AEs)within and after clinical development,as well as better understand how to serve and communicate with the providers and the key opinion leaders(KOLs)they need to engage as new products launch.As underreporting of

284、 adverse events is a common issue and every monitor of clinical trials must determine whether all AEs were reported to the sponsor,applied machine learning models and other advanced analytics can help to identify signals or predict probabilities of delayed,missed,or under-reporting of adverse events

285、.They can also help identify sites that are high-risk for these.Such statistical key risk indicators(KRIs)can then be built into systems to generate various alerts that will indicate where additional risk monitoring may be required and prevent safety and quality issues.This can help study teams prio

286、ritize monitoring efforts and mitigate potential issues that can lead to concerns in an inspection.At the participant level,outlier data from subjects can be detected using certain unique algorithms that review lab analytics and vital signs data to automatically detect patients that are dissimilar a

287、nd therefore allow quick Advanced analytics can help to identify signals or predict probabilities of delayed,missed,or under-reporting of adverse events.26|Improving Decision-Making through Connected Intelligenceidentification of safety signals.Similarly,predictive analytics built into connected int

288、elligence systems can ensure patient well-being by determining which patients are likely to have an adverse event and enable the investigator to reach out.By building such intelligent safety mechanisms into clinical trials,higher-risk trials such as those in rare cancers may be able to proceed.Furth

289、er,as companies launch medicines for new indications,they need to build a network of KOLs to represent the risk/benefit profiles to the public.By connecting sources of data,including online social listening data that may clarify the relationship of that KOL in the disease space and offline interview

290、 or other profile data that clarifies their location and influence in terms of a relationship network,a comprehensive list of KOL candidates can be generated for outreach and engagement based on ratings and rankings along various dimensions determined by algorithmic scoring systems.Marketing and sal

291、es A prime example of connected intelligence and perhaps the most established one is in the commercial space where sophisticated cloud-based omnichannel marketing and sales systems have emerged and are helping to guide marketing and sales efforts.These systems provide the ability to engage external

292、customers by leveraging all available channels of communication,enabling life sciences companies to reach prescribers and stakeholders in the most effective way to improve relationships and even offer wraparound patient services.They also provide recommendations on next-best action and other embedde

293、d insights that guide and speed user action and can help coordinate action across the organization by connecting sales,marketing,and other functions to balance their touchpoints with the same customer.Connected intelligence can be used to adapt key performance indicators and incentive plans as organ

294、izations adjust their approach to measuring goals and achievements for example,as outcomes-based contracts become more frequently used.Other commercial applications of connected intelligence offer to help optimize promotional activity levels and mix,measure and manage brand performance,and guide sal

295、esforce behavior and set incentives.As companies look to reach the target audience for their product,they are also being aided by connected intelligence.In a changing healthcare ecosystem where life sciences companies are focused on precision medicine and rare diseases,they are able to find opportun

296、ities through a greater understanding of the patient journey.In the case of rare diseases,knowing where to find patients(or yet-to-be diagnosed patients)to treat can be a challenge in itself.Through the improved use of real-world data and AI capabilities such as advanced machine learning models,conn

297、ected intelligence is enabling companies to find where that patient is being seen and identify the high value that physicians treating these patients and then,later in the commercial phase,help drive awareness that a medicine is available for that niche patient type,and monitor de-identified patient

298、s longitudinally to determine outcomes on the therapy.Enterprise-level decisions At the enterprise level,applying connected intelligence to decision-making can help life sciences companies make more informed investment decisions,set development priorities,and manage risk across their multiple assets

299、,such as molecules or scientific platforms,to achieve success.The insights from connected intelligence can also help companies measure performance against targets and structure costs across their current portfolio of activities.Connected intelligence can be used to adapt key performance indicators a

300、nd incentive plans as organizations adjust their approach to measuring goals and achievements.iqviainstitute.org|27Analytics-driven brand strategyOmnichannel optimizationCASE STUDY:Patient pathway mapping and disease detection Currently,it takes almost five years on average to diagnose a patient wit

301、h a rare disease and many patients with cancer are diagnosed too late for meaningful medical intervention.Diagnosing patients earlier and identifying the likelihood of progression can lead to improvements in overall patient health outcomes.However,anticipating the overall medical journey of a patien

302、t with a rare disease can be challenging.Often a“rules based”approach to assess possibility of diagnosis or progression is utilized.However,such an approach may not always be optimal and can lead to delays in care.Artificial intelligence and machine learning based approaches can be used to help addr

303、ess these issues as they can identify complex patterns of symptomatology,treatments,etc.,with important temporal associations.Such approaches have been used by IQVIA to support the health system with identifying patients in a timely manner.Using de-identified patient data,IQVIA has partnered with li

304、fe sciences companies to identify providers that may have patients likely to progress or be under-diagnosed,thereby allowing companies to educate providers and support them in delivering the optimal form of care.For example,a client was looking for an innovative solution to identify rare disease pat

305、ients across multiple brands and support providers in delivering the needed care efficiently.IQVIA developed a machine learning algorithm to identify potentially undiagnosed patients.This model was able to predict currently undiagnosed patients who were likely to be diagnosed in the future.By utiliz

306、ing de-identified patient records,HCPs were linked to patients that were ready for therapy and bi-weekly alerts were created to inform the client in a timely manner.Finally,access was provided to IQVIAs Artificial Intelligence and Machine Learning platform with three web-based apps:Interactive patie

307、nt journey,precision targeting and physician triggers.Data from these apps was used to develop programs to identify providers at the right time.During the model validation phase and through a one-year historical comparison analysis,the predictive model outperformed the rules-based approach that was

308、previously used by the client.The predictive model identified a few to-be-diagnosed patients,while the rule-based approach did not find any patients in the same timeframe.The model has had a strong performance in the four months since trigger program launched.Through the bi-weekly alerts,it has succ

309、essfully allowed the client to connect with HCPs who were linked to 20 patients that were eventually diagnosed.The use of AI and predictive analytics holds the potential to deliver value for stakeholders across the healthcare ecosystem,with patients and providers benefiting from timely diagnosis and

310、 treatment.28|Improving Decision-Making through Connected IntelligencePortfolio strategy For senior executives who are responsible for company strategy,including allocating capital and making investments,changes in the external ecosystem over the past five years have increased the complexity of thes

311、e decisions.As innovation has increased rapidly,especially in oncology,choices have become more complicated.Although it has always been the prime focus of portfolio strategy to determine where to invest money across the vast landscape of innovation and how to balance risk across assets of varying ri

312、sk-levels,the speed of decision-making has become more crucial due to heightened competition for available innovative assets,whether internally discovered and developed,in-licensed or otherwise acquired.As more money flows into the biopharmaceutical sector from venture capital and other sources,24,2

313、5 a rising number of smaller EBP companies most of them no longer focused on primary care areas but rather on oncology,neurology and infectious disease and immunology(I&I)have emerged,and at the same time traditional large life sciences companies have increased their investments in R&D and expanded

314、their research programs.The amount of information flowing in to help guide decisions has also increased,leading to challenges to the portfolio planning process of data overload.Finally,the constant change from a continually shifting competitive environment where the readout of a competitors trials c

315、an suddenly make a companys drug development program less viable such that it needs to change plan on future trials or indications has also increased complexity for forecasting and other predictive techniques.While building financial data models helps answer business questions,new modeling tools wil

316、l provide assistance with the challenges of new complexity and increased data.These newer tools have the advantages of being web-based and supporting multiple simultaneous users,providing more storage capabilities for scenarios and processing calculations faster.Connected intelligence is enabling su

317、ch data platforms that can integrate multiple custom sources of data and technology tools and models to support portfolio strategy decisions.CASE STUDY:Addressing complexity with an enterprise-level long-term revenue forecasting system With the increase in complexity facing investment decisions,the

318、revenue forecasting process has become much more complex,making it challenging to use spreadsheet programs and other tools for modeling.Companies deciding whether and where to invest,or those who want to understand the risk to their products by competing with emerging therapies or biosimilars,need t

319、o balance an increasing number of factors and inputs,including details of the patient journey and impact of competitive launches.In the current competitive environment,to do so requires obtaining better patient estimates than before,collaboration between global and regional teams,and outputs that fa

320、cilitate comparisons across the portfolio.However,current models that are often created in spreadsheets may struggle with this complexity or with using large amounts of data to their full potential both of which are common for life sciences forecasts and do not facilitate linking to multiple/externa

321、l data sources.Excel-based models are also error prone,as it is easy to break or change formulas,and hard to quality control,requiring cell-by-cell checks.They also pose barriers to collaboration,with challenges in mailing large files,as well as version control.Just as tools in other areas have move

322、d to the web and cloud to create best-in-class and fit-for-purpose capabilities,applying connected intelligence to the forecasting process is yielding the next generation of forecasting tools capable of utilizing multiple data sources and surfacing insights within technology platforms.Continued on t

323、he next page.iqviainstitute.org|29Risk management As companies struggle to select among the landscape of innovation to build and evolve their portfolios,they need to balance their investments in assets with higher and lower scientific and commercial risks.Companies carefully examine their internal a

324、ssets and assess potential acquisitions across four fundamental areas:development costs,development timing,development risk,and commercial potential.They seek data,information,and tools to make these assessments evidence-based,efficient,and robust.To determine the right development pathway for their

325、 molecules and assess the probability of success across their various assets,they examine these elements,along with the impact of external environment risk factors.To this end,connected intelligence is helping companies innovate with great clarity of associated risks,using purpose-built insight-plat

326、forms for scenario planning that leverages real-world data.Connected intelligence is helping companies innovate with great clarity of associated risks,using purpose-built insight-platforms for scenario planning that leverages real-world data.CASE STUDY:Addressing complexity with an enterprise-level

327、long-term revenue forecasting system Continued.To account for complexity across a number of domains and to improve the accuracy of forecasting,these new tools include automated data inputs to leverage multiple sources of data,including real-world data.Continuous data updates also equip these with th

328、e latest information in real-time to keep up with quickly evolving markets.They also include detailed segmentation,analytics,dashboards,and forecast engines to yield better and clearer insights.Among these,IQVIAs Forecast Horizon,an enterprise-level long-term forecasting system,applies connected int

329、elligence by enabling linkage to proprietary and external data sets that can be updated in real-time.It further enables collaborative forecasting across countries and teams through communication and sharing tools such as simultaneous editing,making the forecast accessible by all groups.In addition t

330、o fostering collaboration,by having virtually unlimited storage in the cloud,it enables companies to compare more scenarios to generate insights(without being hindered by the size limitations of Excel files sent via email)and examines the impact of forecast assumption changes(new market share and si

331、ze)on revenue and volume.The application of connected intelligence within the tool also enables users to segment the market at higher granularities,apply flexible methodologies,and incorporate calculations of patient flow between lines of therapy and adherence/persistence over time,as well as conduc

332、t uncertainty analyses and revenue scenario comparisons.Finally,the tools interface includes user-friendly outputs and features to help forecasters translate their assumptions into strategic insights.Such dashboards can be used real-time during team meetings,allowing on-the-fly adjustments and addit

333、ional scenarios to be played out in person.Ultimately,as more data sources are able to feed into these types of tools in the future whether from a life sciences companys data lake or external data sources such tools will put even richer intelligence in the hands of forecaster.30|Improving Decision-Making through Connected IntelligenceCASE STUDY:Platform to rapidly evaluate cost,timing,risk and net