Healthcare Analytics

Data Governance in Healthcare

The Importance of Managing Your Valuable Data Assets

The transition to value-based reimbursement has made data governance in healthcare more critical than ever. Data is no longer just an asset; it is how hospitals and healthcare networks get paid. Businesses now pay the price for bad data since errors, inconsistencies, and gaps in data can significantly affect the bottom line.

The What, Who, and Why of Data Governance in Healthcare

What Is It?

  • Analytics and information governance is a cultural behavior of responsible information management
  • It is a formal framework that establishes accountability and executive-level stewardship of an organization's data and information assets
  • It provides the strategic vision and an accountability framework that spans the information life cycle, including the processes, roles, and resources required to support it
  • A robust data governance strategy brings together business operations, clinical, and IT stakeholders to bridge the understanding of current information systems and business needs

Who Is Involved?

  • Data and analytics are an organizational resource, not a departmental resource
  • Executive sponsors and governing committees must help communicate and maintain a consistent message about data governance across the organization
  • Governance provides multi-disciplinary ownership over analytics tools and processes—data is no longer "an IT problem"

Why Does It Matter?

  • Data governance in healthcare fosters and promotes organizational support for a successful healthcare analytics culture
  • It creates standards, establishes a streamlined data feed, oversees documentation, and maintains the report request process
  • Major enterprise data and report requests are prioritized while enabling self-service use of healthcare analytics software and encouraging data exploration

Common Barriers to Data Governance in Healthcare

  • Lack of executive-level sponsorship and involvement
  • Organizational politics and “turf wars” over data ownership and reporting
  • Insufficient infrastructure
    • Understaffing
    • Ill-defined reporting relationships
    • Limited access to information, both perceived and actual
  • Multiple, siloed databases
  • Lack of clarity about roles and responsibilities, which can lead to highly skilled analysts writing reports instead of analyzing data

Questions to Ask When Defining a Data Governance Process

Strategic Questions to ConsiderAnalytic Questions to Consider
  • What are the top three priorities in your strategic plan?
  • What are the most important currently active initiatives?
  • If you poll different leaders across your organization on the previous two bullets, would you get a consistent list?
  • What are the characteristics of competition in your area?
  • Are you managing clinical variations in care? To what degree?
  • How are you positioned for the transition from volume- to value-based care?
  • What are your three most significant challenges over the next 18 to 24 months?
  • Are your analytics programs administered at the departmental level or the enterprise level?
  • What productivity metrics do you measure today?
  • Which operational metrics do you review and how often?
  • What is the source of these metrics?
  • Do you have a standard data dictionary?
  • Do you have enough qualified analysts and data scientists?
  • Are you benchmarking internally and externally?
  • Are there corporate targets in place for key metrics?
  • What is your accountability structure?
  • Have you implemented any healthcare performance improvement initiatives in the past or are any currently running? What were the results?

Best Practices for Data Governance in Healthcare

A model of data governance in healthcare
McKesson recommends a top-down approach with significant executive support and involvement

One size does not fit all when it comes to data governance, so establish the oversight model that best fits your organization. Best practices include:

  • Establishing strong executive-level sponsorship
  • Leveraging existing strengths
  • Prioritizing and addressing gaps in communication and skills
  • Understanding that governance is an ongoing and iterative process
  • Evaluating structure periodically to meet evolving needs of the organization

Next: Learn how to identify High-value Opportunities for Improvement with Healthcare Data Analysis

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