Mapping the Health Data Landscape
Data underpins the shift to value-based care underway across health systems.
The many kinds of health and health-relevant data vary in complexity, strengths and weaknesses, and are typically owned by different stakeholders. Provider-run electronic health records lie at the heart of the health data universe, yet structured payer claims data is also key to the shift to value-based care. Genomics data and digital-technology-derived data sources are proliferating.
Value-Based Care is Changing the Data Landscape
The demand for high-quality data to enable the shift to value-based care has created a vibrant data market, which has attracted a growing range of data crunchers and solution providers, both large established groups and small start-ups. There is now more data, and better data, than ever before. Interoperability is improving slowly, and efforts are underway to combine disparate sources such as clinical and claims data, thereby providing a full picture of the patient care journey. Pharma firms are tapping into multiple data types to improve R&D efficiency and optimize market access.
Challenges Facing the Data Market
Challenges facing the data market lie around data collection, quality, sharing, standards, governance, security and access, privacy, validation, application, and around the mindset shift required within provider and pharma organizations in particular, to take full advantage of the growing data-driven insights available.
Population Health Management: Using Data To Control Costs And Manage Disease
Much of the health data being collected is intended to help improve care and outcomes, be it through better, more targeted disease treatment and management, more effective prevention, and/or improved cost controls. Pharma is using better data - mostly genomic sequencing data - to develop more targeted treatments for individuals. Providers and payers are using a far broader range of data and sophisticated analytics tools to more effectively manage the health of their covered populations - known as population health management. Key to that is risk-stratification - figuring out which are the 20% of patients at high risk of adverse events. The more integrated and holistic the data available to tap into, the more accurate the risk-stratification will be - and the more appropriately interventions can be targeted to help manage or reduce that risk.
Hurdles to Population Health Management
Hurdles to population health management include variable pharmaceutical pricing; the challenge, for physicians and other providers, of actually using data to inform decision-making; implementing the shift from a treatment toward a prevention mentality; and the controversy around using social and behavioral data in a healthcare setting.
Pharma Needs Cross-Functional Data Capabilities
Pharmaceutical firms must develop broader data-focused expertise across all of their functions. Data is increasingly part of an overall, corporate-wide strategy, underpinned by significant investment and new kinds of deals to generate and access richer data - and/or make better use of existing data. A new role - that of the data scientist - has evolved: an expert in extracting insights from data and in building predictive models. Focus remains critical though, as data is only useful if it answers a specific, well-articulated need.
Data underpins the shift to value-based care underway across health systems.
The many kinds of health and health-relevant data vary in complexity, strengths and weaknesses, and are typically owned by different stakeholders. Provider-run electronic health records lie at the heart of the health data universe, yet structured payer claims data is also key to the shift to value-based care. Genomics data and digital-technology-derived data sources are proliferating.
Value-Based Care is Changing the Data Landscape
The demand for high-quality data to enable the shift to value-based care has created a vibrant data market, which has attracted a growing range of data crunchers and solution providers, both large established groups and small start-ups. There is now more data, and better data, than ever before. Interoperability is improving slowly, and efforts are underway to combine disparate sources such as clinical and claims data, thereby providing a full picture of the patient care journey. Pharma firms are tapping into multiple data types to improve R&D efficiency and optimize market access.
Challenges Facing the Data Market
Challenges facing the data market lie around data collection, quality, sharing, standards, governance, security and access, privacy, validation, application, and around the mindset shift required within provider and pharma organizations in particular, to take full advantage of the growing data-driven insights available.
Population Health Management: Using Data To Control Costs And Manage Disease
Much of the health data being collected is intended to help improve care and outcomes, be it through better, more targeted disease treatment and management, more effective prevention, and/or improved cost controls. Pharma is using better data - mostly genomic sequencing data - to develop more targeted treatments for individuals. Providers and payers are using a far broader range of data and sophisticated analytics tools to more effectively manage the health of their covered populations - known as population health management. Key to that is risk-stratification - figuring out which are the 20% of patients at high risk of adverse events. The more integrated and holistic the data available to tap into, the more accurate the risk-stratification will be - and the more appropriately interventions can be targeted to help manage or reduce that risk.
Hurdles to Population Health Management
Hurdles to population health management include variable pharmaceutical pricing; the challenge, for physicians and other providers, of actually using data to inform decision-making; implementing the shift from a treatment toward a prevention mentality; and the controversy around using social and behavioral data in a healthcare setting.
Pharma Needs Cross-Functional Data Capabilities
Pharmaceutical firms must develop broader data-focused expertise across all of their functions. Data is increasingly part of an overall, corporate-wide strategy, underpinned by significant investment and new kinds of deals to generate and access richer data - and/or make better use of existing data. A new role - that of the data scientist - has evolved: an expert in extracting insights from data and in building predictive models. Focus remains critical though, as data is only useful if it answers a specific, well-articulated need.
Table of Contents
EXECUTIVE SUMMARYMapping the health data landscape
Value-based care is changing the data landscape
Challenges facing the data market
Population health management: using data to control costs and manage disease
Hurdles to population health management
Pharma needs cross-functional data capabilities
MAPPING THE HEALTHCARE DATA LANDSCAPE
Data underpins the shift to value
A healthcare data taxonomy
Bibliography
VALUE-BASED CARE IS CHANGING THE DATA LANDSCAPE
Demand for data creates a vibrant market
Boon for data crunchers and solution providers
Bibliography
CHALLENGES FACING THE DATA MARKET
Data collection
Data quality
Data sharing
Data standards
Data governance
Data security and access
Data privacy
Data validation
Data application and the culture shift
Bibliography
POPULATION HEALTH MANAGEMENT: USING DATA TO CONTROL COSTS AND MANAGE DISEASE
Risk-stratifying patients to better manage costs and outcomes
Hurdles to population health management
Bibliography
PHARMA NEEDS CROSS-FUNCTIONAL DATA CAPABILITIES
Sharing data across the organization
Growing the “data scientist”
Data dealmaking
Asking the right questions
Data-driven shift in healthcare is underway
Bibliography
APPENDIX
About the author
Scope
Methodology
List of Figures
Figure 1: Roles of different healthcare stakeholders and players within the data landscape
List of Tables
Table 1: Categories of healthcare data
Table 2: Selected data-focused start-ups supporting healthcare stakeholders
Table 3: Challenges facing the data market
Table 4: Hurdles to population health management
