Assessing your total clinical quality data investment:

How The Ohio State University Wexner Medical Center realigned its program after an enterprise assessment

Introduction

One of America’s top-ranked academic health centers, the Ohio State University Wexner Medical Center (OSUWMC) develops and provides new treatments for patients across the globe, continuously setting the standard for world-class care.

Participating in a multitude of national registries, many of which require extensive reporting, OSUWMC sought to continue streamlining operational processes and measure the effectiveness of their involvement. While they were already centralizing registry governance to enable greater value as a system, the organization quickly realized that reliably consolidating such a vast amount of clinical quality data programs and ensuring strong operational efficiencies was a significant challenge.

Championing this endeavor, OSUWMC’s director of quality and operations improvement, Anne VanBuren, MPH, partnered with Q-Centrix to develop a customized assessment to help guide the organization. Tailored to address the organization’s needs, OSUWMC’s assessment measured department-level efficiency and the effect of registry participation to answer two key questions:

  1. Were OSUWMC’s clinical data abstraction practices efficient and accurate?
  2. Was the data used to engage clinicians and influence patient care decisions?

The resulting strategic insights led to developments across the enterprise that unlocked new potential and continued success for the system.

"I was trying to put the pieces of the puzzle together, and through this assessment, the Q-Centrix team brought the resources and expertise we needed to more quickly and efficiently find solutions.”

- Anne VanBuren, OSUWMC director of quality and operations improvement.

Goals

OSUWMC’s assessment strategy targeted the following:

  • Identify the total financial, resource, and staffing costs associated with their clinical registry participation
  • Audit and validate the accuracy of their clinical data
  • Guide registry data collection and centralization efforts
  • Discover ways to optimize the organizational structure to provide a foundation for future strategic growth

The assessment process

Q-Centrix initiated a cross-departmental engagement process for the custom assessment, involving:

  • A kick-off meeting to outline the scope of the assessment and lay out the business case to key stakeholders
  • Weekly meetings with stakeholders to thoroughly explore each department’s processes, efficiencies, and data accuracy
  • Customized surveys sent to stakeholders for each registry
  • Identification of annual case volumes and current backlogs from registries
  • Extensive measurement and analytical benchmarking of time expenditures on registry data collection by internal team members
  • Early establishment of an open and direct line of communication to ensure regular discussions on case-level details and optimal assessment success
  • Usage of salary and cost information to discover total investment and potential gaps
  • External clinical data validation

Assessment findings

Q-Centrix initiated a cross-departmental engagement process for the custom assessment, involving:

Opportunities were identified for workforce development and redefining staff roles and responsibilities to ensure resources were aligned with higher value activities

Staffing did not allow for cross coverage or inter-rater reliability to validate the accuracy of the data to ensure the system was meeting registry standards

Opportunity costs were calculated for each registry to evaluate various options to outsource or adjust other levers to improve the return on investment

An outcomes summary highlighted the need for standardized reports and need for statistical support to identify opportunities, prioritize efforts, and measure impact

Redundant and “extra” data collection resulted in duplication of effort and inefficiency

Outcomes

For registries needing immediate support, OSUWMC established an external partnership for abstraction and technology services, leading to:

• Stable workforce (cross coverage, less worry about turnover, and time off)

• Flexible staffing based on volume / need

• Inter-rater reliability

The assessment established operational cost transparency and neutrality, leading to:

• Improved internal production and value

• Re-prioritized workforce allocation

• High-value external partnerships

• Cost neutral

OSUWMC leadership also made internal staffing model changes to increase efficiency

A clear roadmap for future improvements was designed to:

• Build stronger analytic and QI impact

• Increase capacity for engaging physicians

• Decrease organizational risk to market and staffing fluctuations

"While the assessment provides immense value to health systems looking for greater clarity and understanding of operational opportunities and current clinical quality data investments, Anne’s leadership in aligning and centralizing governance enabled OSU to be much more responsive to capitalizing on best practices and pivoting to maximize value. She saw the big picture and diligently led OSU to recognize a significant return on their clinical quality data investments.”
- Doug McGill, lead consultant of enterprise services at Q-Centrix.

Registry roles and responsibilities were also clearly delineated:

  • Data abstraction and registry/project management were segmented into two different functions
    • Built-in cross-coverage and validation process for internal data abstraction
    • Designated quality manager resources for supporting vendor partnership, registry oversight, case selection, performance improvement, ad hoc requests and questions, and coordinating communication between the vendor and our team members

Quality manager

  • Ensures correct identification of patients
  • Ensures valid and reliable data collection
  • Improves use of standardized documentation and data automation
  • Works with providers to correct and obtain the information in the medical record
  • Facilitates QI projects

Data abstraction

  • Data collection and entry
  • Evaluates the documentation and identifies missing data elements
  • Accurately validates peer data entry

Data manager

  • Manages vendor relationships, including coordination of system upgrades
  • Utilizes clinical registry and other data sources to generate reports to outcomes, processes and impact of improvement efforts
  • Presents analysis and findings to teams to be used in process improvement and outcomes monitoring

Operational / clinical lead

  • Provides funding for the registry and vendor fees
  • Leads quality initiatives with support from quality manager
  • Develops action plan and implements changes

Quality lead

  • Provides support, training, and conferences
  • Performance evaluations
  • Prioritization and support for quality initiatives
  • Vendor relationships and contracting (purchasing/legal)
  • Develops cross coverage and validation process

Outcomes cont.

  • A rapid cycle performance improvement model was implemented based on enterprise goals/priorities
  • Standardized reports and leveraged registry data analytics to spend less time on scorecard creation and more time on data analysis
  • Reduced duplicate data entry and “extra” data collection to increase value and efficiency
  • Increased data automation to spend less time collecting data
  • Prioritized initiatives to spend less time on ad hoc data requests and low-priority projects
  • Discontinued participation registries that are not required or have minimal impact

Conclusion

Assessments often reveal opportunities for improvement in operational efficiency and financial alignment. In the case of OSUWMC, the assessment revealed that while they were doing many things right, there were definite opportunities to “change their mindset” and improve both efficiency and data integrity. Having a champion within the health care system to guide the assessments process was key, and the team’s willingness to hear feedback and implement changes quickly led to better engagement with clinical data, enhanced efficiency for feedback in care delivery, and improved overall administrative management.