Integrating three key principles of data governance—stakeholder engagement, shared understanding, and strategic alignment— enhances healthcare delivery when it's couched within an improvement framework. Health Catalyst's Tom Burton, MBA, and Aimee Gomlak Capelli, MBA, FACHE, explain why.
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Editor’s Note: This article is based on insights from the Healthcare Analytics Summit 2024 (HAS 24) educational breakout session titled, Effective Governance: The Foundation for Driving Massive Improvements, delivered by Thomas D. Burton, MBA, Co-founder and Strategic Advisor at Health Catalyst, and Aimee Gomlak Capelli, MBA, FACHE, Senior Vice President and Strategic Account Executive, also at Health Catalyst. Learn more about the seven principles mentioned in this article in Health Catalyst’s governance handbook.
Data governance in healthcare involves systematically managing data assets to ensure reliability, accountability, and quality standards in patient care and organizational processes.
However, a frequent misstep made by healthcare organizations is initiating data governance without the foundational support of improvement governance or framework, which addresses essential questions:
During the Healthcare Analytics Summit (HAS) in February, Tom Burton and Aimee Gomlak Capelli of Health Catalyst emphasized this strategic approach, explaining how it ensures that data governance is purposefully linked to enhancing operational and clinical outcomes.
The following recounts their three key data governance principles to drive massive healthcare delivery improvements.
Effective data governance begins with stakeholder engagement at the executive level. This ensures initiatives receive the resources and attention necessary to be effective and widely adopted.
When forming a data governance leadership team, consider the following:
1) Which stakeholders control key resources and funding?
2) Who has specific domain knowledge and is the most qualified to make key tradeoff decisions?
3) Who can influence others to adopt change?
4) Who understands the root causes of poor outcomes and can redesign more effective processes?
Extend a call to action to these stakeholders to create a team equipped to guide, oversee, and make decisions about data governance initiatives that consider the interests of all stakeholders.
Before launching data governance initiatives, assess preparedness. This assessment should evaluate existing data governance maturity, identify capability gaps, and determine the resources and training required to bridge these gaps. A thorough readiness assessment provides a solid foundation for deploying effective data governance and healthcare analytics solutions.
The second key principle is developing a shared understanding across all levels of an organization, which includes a thorough understanding of organizational opportunities, needs, capabilities, and readiness to embrace new processes. Engage leaders and stakeholders around a common vision that aligns data governance efforts with the organization’s goals.
Pinpoint high-level data governance opportunities that align with strategic goals, such as enhancing predictive analytics capabilities and meaningfully leveraging augmented intelligence (AI) in healthcare.
Prioritize objectives that promote data quality, access, utilization, and alignment with strategic goals — areas that will yield the greatest benefits and enhance healthcare outcomes.
By focusing on high-level opportunities, organizations can significantly improve data quality, accessibility, and compliance, ultimately leading to enhanced patient care and operational efficiency.
To drive engagement and commitment across the board, align incentives with data governance objectives. This includes incorporating shared data governance goals into performance metrics, job descriptions, and vendor contracts, creating a culture that values and rewards data governance efforts and actively participates in its initiatives.
The third principle is adopting a uniform improvement methodology across the organization, aligning incentives with data governance objectives, and managing priorities effectively.
The methodology should answer the following seven questions:
An improvement methodology that answers these core questions ensures every element of the organization is synchronized, enhancing the efficacy of data governance strategies, leading to improved patient care and operational performance.
It’s worth noting that certain healthcare polarities, such as data protection and data sharing or cost containment and care delivery, represent dual positives requiring careful management. These are not challenges to be solved but balances to be maintained.
Effective data governance policies and guidelines are crucial in achieving this balance, ensuring that all sides are considered while maintaining the organization’s goals and meeting regulatory requirements.
Leaders can initiate their data governance program once an organization allocates the necessary resources and establishes prioritized teams. Then, as data informs care and leads to measurable results, the organization can scale improvements and maintain gains.
Effective data governance in healthcare transcends basic data management, using strategic principles to drive significant organizational improvements. As Burton and Capelli stressed in their presentation, anchoring data governance within an improvement framework helps ensure transformative and sustained outcomes.
By integrating stakeholder engagement, shared understanding, and alignment to strategic objectives, healthcare organizations can enhance patient care, improve operational efficiency, and ensure compliance while fostering a culture of data-driven excellence. This cohesive and multi-pronged approach empowers organizations to adapt to changes and thrive, making data a cornerstone of healthcare excellence.
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