Challenges and gaps in healthcare worldwide can seem daunting.
Tackling them will require a paradigm shift in how we think and approach information.
Human Data Science incorporates human ingenuity, breakthroughs in science, and disruptive technology to power future healthcare advances and improve health outcomes for individuals and populations globally.
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Case Study
Applying Human Data Science
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Setting the Stage to Improve Human Health
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Advancing Disease Prevention and Treatment
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Delivering Human Health Services
Optimizing Health System Performance: Guiding Strategies to Reduce Healthcare Waste
Human Data Science in Action
Outcome
Case Study
Analysis of Part A and B Medicare claims showed that although 17% of Medicare beneficiaries were considered at highest-risk of future costs and were significant drivers of overall cost, they accounted for only 27% of low-value service. A data-driven approach is necessary to dig into the root cause of the waste and a view of overall patient wellness guides a change in healthcare policy.
Human Data Science in Action
A reduction of low-value treatments for all patients would better reduce healthcare waste than just reducing low-value treatments in the high-risk population. Targeting only high-risk patients may miss opportunities for waste reduction.
Outcome
01
Setting the Stage to Improve Human Health
Accelerating Clinical Development: Clarifying the Risk-Benefit Profile of Innovative Therapies Through New Trial Designs
Human Data Science provides supportive evidence where conventional, randomized controlled trials are not feasible. The use of real world data can increase the pool of patients available for study, such as in male breast cancer, which accounts for less than 1% of all cases.
A supplemental approval for Pfizer’s palbociclib for the treatment HR+/HER2- breast cancer in male patients was based in part on real world use of palbociclib. Data included prescription and medical claims data from IQVIA, Flatiron Health’s Breast Cancer database and Pfizer’s global safety database.
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Advancing Disease Prevention and Treatment
Furthering Patient-Centric Health Services: Proving the Value of Coordinated Maternity Care
Analysis of bundled episode-based payments in maternity care provides evidence for the value of coordinated care programs. Data collected from these programs can be analyzed to determine if quality goals are being met and can demonstrate cost savings.
Results from an episode-based payments program in Arkansas led to a 3.8% decline in total episode spending. The drop was predominantly driven by declines in intrapartum facility spending during the hospitalization for childbirth, which dropped 6.6% relative to surrounding states and accounted for 80% of overall savings.
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Delivering Human Health Services
We have the opportunity to improve outcomes, impact cost structure, and positively change the healthcare system. Through a community and systems approach we can ensure that this all works together.
Michael Kleinrock
Research Director, IQVIA Institute for Human Data Science
Human Science Meets Data Science
The Makeup
The Drivers
The Impact on Stakeholders
The Makeup
SCIENCE
data
human
data
human
Human Data
Incorporating human interactions with health systems
Emphasizing human behavioral, social, and environmental information
Understanding patients outside of the health system
human
Human Science
Incorporating genomics and proteomics
Investing in diagnostics and therapeutic interventions
Focusing on wellness and disease prevention
SCIENCE
Wellness, disease prevention
SCIENCE
Data Science
Using artificial intelligence, machine learning, and predictive analytics
Having data access, linkage, and management capabilities in accordance with patient privacy and transparency
Enabling shared access to data and insights
data
Supportive policy and regulations
Ensure the availability and proper use of data to support and promote data interoperability
The Drivers
Investment in basic research and translational science
Expand understanding of human biology and behavior to innovate in healthcare and analytic research
Technology to enable artificial intelligence and machine learning
Invest in data management strategies and IT infrastructure and support transparency and intellectual property protection
Patient privacy and data security
Set policies and guidelines to ensure patient privacy and security with use of new data types including social determinants and special category data
Big data availability and data science methodologies
Standardize, share, and link data to drive alignment between stakeholders and reduce bias in the performance and reporting of research
Human expertise
Promote human-centricity in the use of data and apply a deep level of understanding of clinical care, human science, data, and the healthcare environment
Click through the icons to learn the six key drivers and call-to-actions to realize the true potential of Human Data Science.
Technology to enable artificial intelligence and machine learning
Payers
Patients
Physicians
Researchers
Real time access to real world data and linked datasets
An integrated approach can help improve scientific research, enable stakeholder alignment, facilitate better clinical decision-making, and enhance health system performance.
Click through the icons to learn how Human Data Science can positively enable six stakeholders across the healthcare space.
The Impact on Stakeholders
Domain knowledge of disease biology and treatment dynamics
Real time access to data, insights and clinical decision-tools
Access to coordinated care collaboration
Coordinated and seamless care for people with chronic conditions
Access to real time health and wellness information tools and guidance
Access real world data and linked datasets
Validation of outcomes-based and risk-sharing arrangements
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Advances in healthcare are accelerating as our understanding of human science grows. New therapeutic approaches are being incorporated into clinical practice alongside new digital technologies, all with the goal to improve human health. Still, major gaps in health and disease understanding exist. Tackling these gaps requires an innovative approach. An approach that integrates human data, human science, and data science.
A NEW APPROACH TO IMPROVING HUMAN HEALTH OUTCOMES
Advancing Human
Data Science
Murray Aitken
Executive Director, IQVIA Institute for Human Data Science
How do we link advances in human science with advances in data science? How do we bring them together to find new, better ways to close knowledge gaps and improve human health and wellness? That unique intersection is Human Data Science.
See EXamples
Human Data Science —
with breakthroughs in data science and technology to advance our understanding of human health and enable healthcare stakeholders to make better, more insightful, decisions. Decisions that will drive better health outcomes and control the rise in healthcare costs.
a discipline integrating the life sciences
A Lens on the State of Healthcare
Healthcare is in the midst of a positive transformation.
Scientific advances are accelerating, a record number of new active substances (NAS) were launched in the United States in 2018 bringing 59 new treatment options to patients.
There is growing investment in healthcare innovation, 1,308 life science venture capital deals closed in 2018, with a total value of over $23 billion.
The use of technology is becoming more mainstream, information technology is being incorporated into clinical practice to manage costs and improve care.
Health and wellness are being prioritized, payers and employers are supporting programs to reduce obesity or substance abuse and wellness apps account for the majority of available health apps.
Despite the increased output and acceleration in transformation and growth, challenges in healthcare remain. Challenges that suggest a better approach to health-related issues is needed.
Gaps in disease understanding across therapy areas slow the development of life-saving treatments.
Disparities in healthcare related to an individual’s environment and other social determinants can negatively impact care, outcomes, and life expectancies.
Despite existing policies and healthcare incentives intended to improve wellness, the number of patients with chronic diseases are rising globally.
Gaps in data collection, quality, and bias are impeding the full potential for advanced analytics to solve healthcare problems.
However, major gaps in healthcare exist.
EXamples
Alzheimer’s Disease
Infectious Disease Outbreaks
Despite vaccines being one of the greatest public health tools in the modern era, vaccination rates across communities and geographies vary. Misinformation, lack of access, and affordability—these can all lead to people choosing to not use vaccines. These reasons are usually driven by social and environmental factors and have led to new outbreaks of these diseases that could have been contained sooner. See page 5 in the report for the research.
Infectious Disease Outbreaks
A clear understanding of the underlying causes of certain diseases still eludes scientists and has hindered the development of breakthrough treatments for a disease that effects > 50 million humans globally.
Since 1992, only five medications have received regulatory approval for the treatment of Alzheimer’s disease. In the past 16 years, 137 Alzheimer’s development projects were discontinued, while only one medicine — a combination of two previously approved therapies — received regulatory approval globally. See page 4 in the report for the research.
Alzheimer’s Disease
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Life Science Companies
Innovation and clinical trial productivity
Access to real world evidence and linked datasets
Policy Makers
Access comparative datasets and benchmarks
Evidence-based guidance and validation of policy changes
Investment in basic research and translational science
Supportive policy and regulations
Patient privacy and data security
Big data availability and data science methodologies
Human expertise
Policy Makers
Payers
Life Science Companies
Patients
Physicians
Researchers
Click through to learn how stakeholders will need to come together to maximize the value that can be derived from Human Data Science
Click through to learn how stakeholders will need to come together to maximize the value that can be derived from Human Data Science
Click through to learn how stakeholders will need to come together to maximize the value that can be derived from Human Data Science
Click through to learn how stakeholders will need to come together to maximize the value that can be derived from Human Data Science
Click through to learn how stakeholders will need to come together to maximize the value that can be derived from Human Data Science
Click through the icons to learn how Human Data Science can positively enable six stakeholders across the healthcare space.
There are six key drivers influencing the relevance, confidence, and applicability of Human Data Science.
Click through the icons to learn the six key drivers and call-to-actions to realize the true potential of Human Data Science.
About the artwork
The Algorithmic Art featured on this page was generated using data about per capita antibiotic use and the number of antibiotic prescriptions written in the U.S. in 2017 and 2018.
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2018—2019, there were declared measles outbreaks across North and South America, Africa, Europe and Asia
January—July 2019, over 300,000 measles cases were reported to the WHO
As of July 2019, Ebola had re-emerged in the Democratic Republic of Congo and Uganda with over 2,500 confirmed cases and 1,700 deaths
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Challenges and gaps in healthcare worldwide can seem daunting. The emerging discipline of Human Data Science is an opportunity to tackle those challenges to advance healthcare.
The Elements of Human Data Science
Three elements make up the discipline of Human Data Science.
Click through the green dots to learn about the three elements, characteristics of each, and their impact together.
The three elements come together to provide a complete picture of factors influencing human health—allowing for a unique integration to generate innovative solutions to healthcare problems.
Experience with advanced analytical techniques
Leverage latest computing technology (genomics, artificial intelligence, machine learning, natural language processing)
Incentives to deliver value through outcomes-based payment models
Best practice sharing and benchmarking
Incentives to improve wellness and health
Access to high-value, affordable care
Understanding of natural history and biology of disease
Regulatory and health policy insights and stakeholder influence
Incentives to improve wellness, population health, disease interception and reduce costs
Evidence-driven insights on policy change
Leverage outcomes-based analytics and contracts
Options to change population health and social determinants
Advancing Disease Prevention and Treatment
Proving the value of coordinated care
About the artwork
Understanding the natural history of disease
Human Data Science
Understanding the natural history of disease
Human Data Science
Learn more about Algorithmic Art
Learn more about Algorithmic Art
Explore how Human Data Science unveiled new ways to look at data and make decisions across three domains.
Background
Background
Waste in the healthcare system occurs when money is spent on services that do not improve outcomes or quality of care. These unnecessary services drive up costs of healthcare and can expose patients to unnecessary risks and stress.
Case Study
Human Data Science in Action
Outcome
Background
Background
Big data gathered in real world healthcare settings has become more prevalent, robust, and more skillfully curated. As such, there has been increasing acceptance of the use of real world evidence and real world data in clinical development programs.
Case Study
Human Data Science in Action
Outcome
Case Study
Human Data Science in Action
Outcome
Background
Background
Patient-centered coordinated care programs facilitate the delivery of healthcare services and can improve clinical practice and patient outcomes, particularly in maternal care, where there are missed opportunities to support patients and mitigate health risk.
Case Study
Human Data Science in Action
Outcome
Background
Background
Waste in the healthcare system occurs when money is spent on services that do not improve outcomes or quality of care. These unnecessary services drive up costs of healthcare and can expose patients to unnecessary risks and stress.
Case Study
Human Data Science in Action
Outcome
Background
Big data gathered in real world healthcare settings has become more prevalent, robust, and more skillfully curated. As such, there has been increasing acceptance of the use of real world evidence and real world data in clinical development programs.
Case Study
Human Data Science in Action
Background
Outcome
Case Study
Human Data Science in Action
Background
Outcome
Case Study
Human Data Science in Action
Outcome
Background
Patient-centered coordinated care programs facilitate the delivery of healthcare services and can improve clinical practice and patient outcomes, particularly in maternal care, where there are missed opportunities to support patients and mitigate health risk.
