Four Critical Phases for Effective Healthcare Data Governance

Add

Editor’s Note: This article is based on the 2018 Healthcare Analytics Summit presentation by Thomas Burton, MBA (Co-founder and President, Professional Services, Health Catalyst) and Mike Noke (Senior Vice President, Professional Services, Health Catalyst) entitled, “The Data Maze Game: Navigating the Complexities of Data Governance.”

Data drives healthcare improvement and is an imperative resource for navigating a changing healthcare landscape. It allows healthcare organizations to evaluate how care is delivered and funded, how patients are engaged and educated, and how payers and providers can work together to improve value. But healthcare data is complex, and it can be difficult to leverage. A principle-based approach to data, through good data governance practices, can bridge the gap. Data governance allows organizations to maximize the value of their data to improve outcomes.

Defining Healthcare Data Governance

Healthcare data governance is the discipline of managing data as a strategic asset. It paves the way for data to support organizational priorities through the orchestration of people, processes, and technology. Through a focus on enhanced decision-making, data governance helps organizational leaders improve clinical, operational, and financial outcomes. Importantly, data governance is an ongoing, enterprise-wide, cross-functional effort to optimize data for the benefit of patients, staff, and community.

Data governance cannot serve as an end to itself—governance for governance’s sake. It will fail if approached with the “one and done” mentality of a one-time event. And it cannot be restricted to the IT department in the organizational hierarchy.

Today’s Healthcare Challenges and Data Governance

Organizations urgently need data governance to confront today’s challenges. By ensuring that people have access to the right data and information at the right time and in the right format to make clinical and business decisions, data governance shows the value of organizational investment in data. However, too often, organizations face:

  • An inability to respond to new analytic use cases and requirements.
  • Poor or unknown data quality; data that is siloed, inaccurate, inconsistent, unstandardized, etc.
  • Lengthy and inaccurate decision cycles.
  • Inconsistent analytic results from different sources attempting to answer the same question.
  • A lack of accountability or process for fixing data quality problems.

Each of these issues has negative implications for organizations and their patients. Through better data governance, organizations can overcome financial risks, operational inefficiencies, and safety concerns to return value to the entire system—patients included.

Tomorrow’s Healthcare Imperatives and Data Governance

Eventually, data governance will enable even more transformation. By maximizing the value of data through good governance practices, organizations will be able to see and use all of the factors that affect a patient’s health, such as incorporating socioeconomic and wearable data into the hospital data ecosystem.

The shift to value-based care drives the need for reliable data that measure cost of care, margin, and productivity across the continuum of care. It also necessitates the ability for leaders to evaluate the expected return on investments to improve specific healthcare outcomes. Done well, data governance is an accelerant to transformation. It allows organizations to achieve the breadth and speed of integration required by healthcare reform, powered by clinical and technical innovations, and vital for improving the cost and quality of care for patients and communities.

Four Phases of Healthcare Data Governance

Strong data governance is built from four phases, all enabling improved decision-making from the boardroom to the bedside.

Data Governance Phase One: Elevate

The first phase for effective data governance is to elevate governance in importance by developing a vision and agenda that align with organizational priorities. The goal is to shift the perception of data from an expensive technology to a valuable resource.

As with any collaborative effort, it’s best to begin with a shared understanding of the goals and to define a limited, achievable, and strategically targeted scope for the work. Using an incremental approach, so organizations can work on what matters most first, this step promotes executive buy-in by defining expected outcomes and demonstrating how they relate to the clinical and business activities central to the organization’s strategic direction.

Data Governance Phase Two: Establish

Building an organizational structure to fulfill the data governance mandate helps establish good governance practice. Governing data is an effort that impacts many groups across the system. Getting organized and bringing the right people and teams to the table is important for the success of any new initiative.

An organization’s approach to data governance must overcome substantial complexities. Often governance decisions sit at the intersection of competing priorities (financial vs. clinical vs. operational). The governance team must be curated with this cross-functionality in mind. Additionally, resources for data governance are likely limited, especially at first. Ensuring the right people in the right groups will help make the most of what’s available.

Finally, organizations have different needs and there’s no single, “one size fits all” template for structuring data governance resources. Leaders must work on establishing teams only after a high-level agenda for data governance is set. Organizing people around the work that most needs doing, rather than setting up teams that then look for work, will help guide the way.

Data Governance Phase Three: Execute

Create prioritized data governance projects, with people and resource assignments, to execute the governance mandate. The Data Governance Committee, created in phase two, should sponsor a set of discrete projects with unambiguous goals to support disciplined focus on the work. This project-portfolio approach helps break the work into manageable units that can be phased over time, and it allows for different people to be called upon to support different projects in the data governance portfolio.

Data Governance Phase Four: Extend

Ensure data investments and efforts will last and extend throughout the entire organization into the future. Establishing a set of practices to continually support and improve data governance work is important to ensure ongoing relevance and value for the data governance portfolio and to uphold the gains made early.

Data governance opportunities are seemingly endless, but people’s time, energy, and focus are limited. Looking forward, organizations can bring new energy and fresh perspectives into the governance work that help build and continue the momentum for achievement. Continued investment in data governance also helps to nurture a data-driven culture, reinforcing desired behaviors across the organization.

Principles of Effective Healthcare Data Governance

Each data governance phase must incorporate four core principles to support success, regardless of its specific structures or leadership:

Stakeholder engagement: Organizations must move beyond the IT department, and engage clinical, operational, and financial stakeholders around an awareness of data as a strategic asset—highlighting its value for supporting better decisions from the boardroom to the bedside.

Shared understanding: Promote the goals and accomplishments of data governance across the organization and nurture a data-driven culture.

Alignment: Ensure that data governance clearly supports the priorities and strategies of the organization, serves the needs of users throughout the data life cycle, and balances polarities (apparent conflicts).

Focus: Organizational leaders should think lean and do what matters most, then adjust as the project advances. It’s important to govern data to the least extent necessary to achieve the greatest common good.

Effective Healthcare Data Governance Requires Ongoing Transformation

Successful data governance is not a one-time event. These steps cannot be a one-off effort because healthcare is continuing to evolve and change; healthcare organizations must be able to react. As an ongoing and iterative process, data governance is responsive to changing circumstances, and enables leaders to re-balance priorities to manage conflict. Finally, data governance efforts must be promoted by leaders, throughout the organization, who emphasize a data-driven culture and thinking of data as an asset to the organization’s goals and improvement.

Additional Reading

Would you like to learn more about this topic? Here are some articles we suggest:

  1. Effective Healthcare Data Governance: How One Hospital System is Managing its Data Assets to Improve Outcomes
  2. Outcomes Improvement Governance: A Handbook for Success and Achieving More with Less (Executive Report)
  3. 7 Essential Practices for Data Governance in Healthcare
  4. Demystifying Healthcare Data Governance: An Executive Report
  5. 3 Phases of Healthcare Data Governance in Analytics
Loading next article...