How to Structure Data Governance in Healthcare; Data Governance in Healthcare, Part 1

Healthcare, not unlike other industries, has experienced a digital transformation. With modern enterprise resource planning (ERP) and electronic health record (EHR) platforms, healthcare organizations can capture more operations and patient data than ever before.

However, these platforms create natural silos of data, rather than improve visibility into the relationship between financial outcomes and quality. That’s detrimental to hospitals and health systems, which use quality analytics as leading performance indicators, as adverse events can create financial repercussions such as cost-of-care increases and reduced reimbursement.

There is a clear need for actionable insights from data; however, not all hospitals and health systems are equally equipped to leverage data as a strategic asset. In Syntellis Performance Solutions’ 2022 Annual Healthcare CFO Outlook Survey, 85% of CFOs agreed they should be doing more to leverage financial, clinical, and operational data to guide and inform their planning decisions.

This combination of processes, roles, and policies is called data governance. Data governance includes guidance around the people, processes, and technologies involved in collecting and using data. But why is data governance in healthcare so difficult?

The HFMA Financial Analytics Council, sponsored by Syntellis Performance Solutions since its inception in 2018, provides a valuable forum for its members to engage in collaborative discussion and share best practices related to healthcare financial management topics.

In a recent survey, council members identified the top two data governance challenges as executive attention and resources and establishing a clear vision for the end result.

Based on recent council discussions, participating finance leaders agree that breaking down the walls around data and analytics — in other words, implementing a data governance function — is a top priority.  This three-part blog series will explore what makes an organization high-performing in its use of data and analytics. Data governance in healthcare requires three components, starting with effective internal data governance structures that encourage executive buy-in and sponsorship:

• Adopting a leadership structure and best practices for data and analysis
• Collecting the right data and getting it into the hands of the right users (part 2)
• Measuring the return on data and analytics investment (part 3)

Organizational Structures That Work

There is no single healthcare data governance model that is considered best practice; organizations achieve success with a variety of team structures and reporting relationships. However, three primary themes emerged in high-performing organizations:

1. The Rise of the Chief Analytics Officer

In healthcare organizations, analysts often fall into the same siloes as data sources: financial, quality, revenue cycle, etc., with a separate team dedicated to each area. While this model can and does work for some, it creates challenges around how organizations share, integrate, structure, define, and present data in a consistent manner.

More advanced organizations bring those teams together to report to a chief analytics officer. “[Introducing a chief analytics officer role] is one step toward an organization recognizing the importance of analytics, the role that it plays, and some of the business imperatives around that,” says Randy Albert, Vice President of Finance over Operations and Analytics for Northern Light Health.

With a dedicated team and visibility at the C-suite level, this reporting structure provides significant benefits. First, it legitimizes the strategic importance of data to the organization and provides a forum to discuss and address resource constraints, sensitive data access, and ownership. Second, it gives visibility into the broader portfolio of current strategic initiatives and tactical performance improvement activities and areas where the data and analytics team may be able to add value. 

2. Introducing the Analytics Consultant Role

Many high-performing organizations cite the importance of repositioning the role of the traditional analyst. Hospitals and health systems often employ analysts within each area of the organization (e.g., financial, decision support, quality) who are responsible for a variety of administrative and reporting functions. While important, these responsibilities can be repetitive and routine. 

An analytics consultant takes on a broader role, supporting operational and clinical leaders as a partner and problem solver. In this collaborative role, analytics consultants deliver more than just data outputs; they also provide interpretation and insights.

“We used the term consultant to articulate that the role is more than just an analyst delivering reports,” explains Caroline Gay, former Senior Vice President and Chief Analytics Officer at Lakeland Regional Health – Florida. “Our analytics management consultants are positioned to partner with our management team to teach them and guide them.”

Albert adds, “We’re starting to think more broadly in terms of analytics in a consulting role. You might have a person who is a subject matter expert in quality, finance, decision support, or other clinical spaces, but you’re broadening the role. They’re helping you take data out of your source systems, and tell stories with your data to clinicians, operations, finance, etc.”

3. Importance of Executive Sponsorship

Engaging executives as active sponsors of data and analytics can also boost your chances of success. To do that, create a quarterly meeting steering committee that brings together executives from financial, operational, and clinical domains with IT. Especially in the early stages of a healthcare data governance initiative, it is easy to get stuck on the politics of where data lives and who has access to that data. Forums that promote transparency around the answers to those questions can build trust and gain advocacy from the executive team.

We have an information governance council that consists of senior executives, including four members of our enterprise leadership team,” explains Richard Pro, Chief Data and Analytics Officer at Cone Health. “They weigh in on a monthly basis regarding our more resource-intensive and time-intensive analytics project requests and help set priorities.”

Organizations with mature data governance and analytics functions may alternatively choose to form an analytics improvement council that meets on a quarterly basis. Meetings should actively engage leaders and promote discussion on how analytics tools, processes and measures need to evolve in response to new growth strategies, acquisitions, and performance improvement initiatives. This forum gives the executive leadership team a voice into an important feedback loop that informs the evolution of the organization’s data and analytics function and builds momentum around those efforts.

To unlock the power of data through a deliberate data governance and analytics function, healthcare organizations must encourage collaboration rather than siloed work. By prioritizing C-suite engagement, hospitals and health systems can realize the value of data as a strategic asset and use it to inform decision-making across financial and clinical domains. The next blog post in this series will discuss how organizations can measure what matters and get that data into the right hands. 

Syntellis’ Axiom Enterprise Decision Support empowers healthcare organizations to drive performance and decision-making with a single, trusted source of financial and clinical performance measures. Equipped with the right data, healthcare leaders can reduce costs, optimize revenue, and improve clinical quality.