Data governance leads, data policy managers, and data managers across the health sector (pharmaceutical companies, healthcare providers, non-government organisations, community health, hospital networks, biotech, health tech startups)
What it’s about:
Our health data governance model looks beyond technical aspects to responsible, ethical and equitable health data management and use policies and processes can centre on data ethics and data justice principles, enable value from data, build trust, reduce resource use and generate benefits from patients to communities, commercial enterprises, society, and the environment.
Why it’s important:
Health data governance requires specialised skills around managing data securely, in compliance with regulations, and with a view to generating benefits for patients, organisations and society. Well-managed data can aid in improving population health and where people trust systems and structures to share data responsibly, data can be combined in new ways to amplify benefits for patients, organisations, society, and the environment.
At Platformable, we support the development of open digital ecosystems. A big part of that is ensuring that data is well managed: responsibly, ethically, and equitably. That means that open digital ecosystems need to have rules of engagement around how data is used. These rules of engagement are called "data governance". In the health sector, this is especially important as health data is personal and sensitive, but also because good use of data could save lives, reduce inequalities, improve life quality, prevent health risks, and optimise the resources available to spend on health care. Health data governance is needed at multiple levels: for the country or region, as well as by each stakeholder in the open digital health ecosystem.
Previous experience in Health Data Governance
My work and the work of Platformable has involved data governance across a range of sectors, in particular often focusing on health data governance. This has included:
Created population health and wellbeing data governance models for local governments in Australia and to track data on health inequalities for state government
Conducted workshops on health data governance of secondary use of health data and on the value of data across Europe
Developed a health data governance model
Analysed health data governance regulatory frameworks for three global projects, including Europe, Western Balkans, and Canada
Mentored and helped build data teams for health-based community organisations in the US and New York City, focused on HIV/AIDS and health inequalities
Built data governance software for community health organisations
Created a health data governance training course
Health Data Governance (Platformable's definition)
Data governance is the ability of organisations to use health data in a secure, safe, ethical, and equitable manner with the goal of generating personal, commercial, societal, and environmental benefits. Health data governance includes infrastructure, policies and procedures, and secure data sharing. Health data governance fosters open health data ecosystems that allow organisations to effectively collect, manage, use, and share data with other stakeholders to achieve shared outcomes.
Health Data Governance in an ecosystem context
When we look at health data governance, we consider how it impacts across the digital health ecosystem. Many health data governance models focus on the technical aspects (such as defining data models) and on legal and regulatory compliance requirements (such as labeling data so that it is easier to ensure it is managed in a way that meets data protection requirements). We take a step back and consider the wider digital health ecosystem context in which data is collected, processed, used, analysed, and shared. Our health data governance framework reflects the central role that responsible and ethical data management plays in fostering a trustworthy and effective digital health ecosystem.
Data governance is often set at a geographic, or regional level: governments introduce data protection and data sharing regulations and establish regulators to ensure that data is managed by all stakeholders appropriately.
Policies and approaches to aid the use of data, such as digital health or interoperability policies, help data flow in an ecosystem.
Each stakeholder collecting, consuming and using data may then also have health data governance frameworks and systems in place.
And a range of capacity-building stakeholders including consultants, trainers, and tools providers/software vendors help support data governance best practice implementation.
There are also components and enablers that increase the impact of data governance activities to generate benefits. For example, a good process of continuous engagement and continuous consent where health subjects can give consent to sharing their health data, understand the risks and see how sharing data creates benefits will help augment trustworthiness in a digital health ecosystem.
Documented and shared health data governance artefacts, training, tools and infrastructure (including health data research platforms where data can be analysed but not removed) can all embed health data governance processes, simplifying the adoption of health data governance best practices by ecosystem stakeholders.
In a digital health ecosystem, data governance is a context-settingcomponent that can contribute to value generation. We have identified6 key benefits that can be generated from functioning digital health ecosystems:
Benefit
Better health outcomes
Description
Increased coordination to support high-quality care throughout the patient journey
Example
Data is used to seamlessly provide care across a range of healthcare providers where needed, reduces risks of adverse reactions and contraindications from prescribed therapies, and enables personalised medicine advancements.
Benefit
Reduced Inequality
Description
Using data to better target services and disaggregate data to understand the impacts on vulnerable populations
Example
Data allows insight into differential health outcomes by gender and socioeconomic status.
Benefit
Optimised health system
Description
Achieving efficiencies in care delivery and resource allocation, decreasing the cost of care while maintaining high quality and satisfaction
Example
Data is used to reduce inefficiencies and ensure care is provided through early diagnosis when health care delivery costs are cheaper. Data can also be used to set prevention policies to reduce future strain on healthcare systems.
Benefit
Greater patient-public participation
Description
Understanding of connections and collaborations, and a sense of engagement and involvement in health decision-making by communities and all stakeholders
Example
Access to data enables deeper conversations between patients and health providers, and allows the public to participate in discussions on health research, building trust and supporting local industry growth.
Benefit
Expanded innovation
Description
New industry opportunities and deeper insights into existing systems
Example
Reuse of population health data and research data accelerates innovation, identifying high-potential opportunities for healthcare product development.
Benefit
Professional recognition & Private profits
Description
Support for researchers and other stakeholders that collect, use, and share data
Example
Academics can build careers through data reuse and recognition. For-profit businesses can leverage health data to innovate, remain profitable, and drive economic growth.
The virtuous cycle/theory of change for why Health Data Governance generates impacts
Health data governance policies and practices occur at multiple levels and is managed by multiple stakeholders.
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At the government/regulator (context-setting) level: Sets regulatory and legal frameworks for data governance, funds data governance best practice research and adoption
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At the capacity-building level: Builds guidelines, tools, digital public infrastructure and training services to support data governance adoption
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At the individual stakeholder level: Implements their own health data governance policies and processes aligned with the context-setting level and leveraging the capacity-building components
The adoption of health data governance policies and processes increases trust and encourages stakeholders to share and make us of health data responsibly and ethically. Which, in turn, amplifies the value generated across the ecosystem.
Platformable's Health Data Governance model
We position health data governance within an overall data justice framework.
Data Justice (Platformable's definition)
Data justice is a set of ideas that addresses the way in which people are represented and/or harmed through their data being made visible to others, or conversely, excluded from the public eye, sites of power, and the process of decision-making. It confronts and challenges structural biases in the ways that we think about, collect, steward, and use data. (From: Royal Roads University, https://libguides.royalroads.ca/datajustice)
There are a variety of models that offer approaches that address data justice:
Data Ethics Frameworks
FAIR and CARE
OCAP
Patient-Centred Care and Participatory Models
Each digital health ecosystem could define their data justice and data ethics principles using these or other models to ensure that as they create the various data governance framework components described below, that each aligns with these defined principles. Otherwise, there is a high risk that the collection and use of data will not build trustworthy infrastructure and institutions, which decreases the strength, capabilities and value able to be generated in the digital health ecosystem.
Within this data justice context, there are two phases of health data governance:
Phase 01
Policies and processes for robust health data governance
Phase 02
Implementation technologies and tools that automate the policies and processes
A fourth level then looks at how data quality can be maintained over time and that the data and processes continue to generate value while continuallly aligned with the defined data justice context.
Core Health Data Governance components
InPhase 1, the following policies, processes and artefacts can be created:
Stage
Data policy
Description
A documented set of guidelines that ensures your organisation's data and information assets are
managed consistently and used properly. The policy typically includes separate sections for data
quality, access, security, privacy, and usage, as well as roles and responsibilities for
implementing those policies and monitoring compliance with them.
Key questions
How do we manage data ethically and responsibly?
Deliverables
Data policies
Stage
Logic model / Theory of change / Operational model
Description
Use of a model to describe how data is collected and the intended impacts of collecting and using this data.
A Program Logic Model is a schema or visual outline that demonstrates how your program is designed. The Theory
of Change approach to program design is a well-defined methodology that explains how an intervention is expected
to result in a specific change as it relates to the pathway that occurs between causes and effects. The Operational
Model establishes a causal pathway that shows the connection between causes and effects and includes any intermediary
variables that may impact the pathway.
Key questions
What do we need to collect and why?
Deliverables
Visualisation of model
Stage
Ecosystem and persona maps
Description
Using a digital health ecosystem to map all local, relevant stakeholders in your ecosystem can help identify where
opportunities to build partnerships exist and where existing resources and approaches can be drawn on.
Persona maps can then be used to identify how a particular local stakeholder interacts with your organisation
when using data or digital products and services.
Key questions
Who will use the data and how?
Deliverables
Ecosystem model
Persona templates
Stage
Reference Library
Description
A reference library is a catalog of all Data Standards, External Datasets and Data Models used by your organisation.
Key questions
What external datasets can we use to demonstrate need and impact?
Deliverables
Standards library
Data licenses
Dataset library
Open data models
Stage
Data Dictionary
Description
A data dictionary describes an individual dataset in your data inventory and each individual data field within
that dataset. At the dataset level, metadata fields could include a description of the dataset, the methodology
used to collect the data, how often it is updated, how it should be used, any limitations and equity considerations
that should be kept in mind, when it was last updated and so on. At the data field level, a data dictionary would
list each data field, describe what that data is about, define any validation rules and formatting rules, describe
any alignments with other datasets, score the level of risk management required to manage the data, describe any
specific business rules for collecting the data, provide a sample of what the data field would look like, and so on.
Key questions
How do we standardise and consistently describe each data item we collect?
Deliverables
Data inventory for all datasets you create and manage
Metadata file for each dataset in your inventory
Data dictionary for each dataset in your inventory
Stage
Workflow
Description
Documentation for each process identified as part of your operational framework describing how data will be collected,
processed, managed, used and shared, including business process guides and workflow diagrams.
Key questions
What is the flow from collection to processing to analysis to use?
Deliverables
Workflow diagrams for each process
Business process management tools
Implementation Health Data Governance components
In Phase 2, the policies and processes can be implemented using the following tools:
Stage
Data tech and architecture
Description
Documents that describe your data architecture and choice of tools
Key questions
How do we build a tech stack that meets our current and future needs?
Deliverables
Architecture diagrams
Tools library
Internal developer portal
Wiki
Stage
Surveys
Description
Data collection instruments that align with your data dictionaries
Key questions
What are the standard data instruments/surveys we use to collect data?
Deliverables
Data spreadsheets/tables
Data surveys
Data collection instruments
Stage
Database
Description
Databases that collect, store, clean/transform and catalogue all data within the datasets that are managed by your organisation
Key questions
How do we store all of the data?
Deliverables
Databases
Stage
Integrations
Description
Workflow automation tools (including AI) that help to automate processes and reduce manual handling of data including data entry
Key questions
How do we reduce duplication and move data from survey to database to reporting?
Deliverables
Workflow integration tools
Stage
Reports
Description
Reporting and analysis form factors that allow data to be more easily accessed and understood by analysts and other users
Key questions
How do we report on the data findings to identified personas?
Deliverables
Report templates
Dashboards
Publications
Once health data is being collected, managed, analysed, used and shared, there is the need for ongoing quality review and data assurance mechanisms to be in place.
Our series on health data governance, being released in September 2025, will go into further detail on each component of this model.
21 min read
based in Barcelona
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