Centralizing data management in multistate hospital systems


The partner is a health care system comprised of 80+ hospitals in over 15 states. It is one of the largest U.S. health care systems in number of sites, employees, number of beds, and the net patient revenue (NPR).

Over the past fifteen years, the health care system grew through an aggressive acquisition strategy. Rapid growth resulted in noted redundancies and opportunities for efficiencies that triggered interest in a centralized approach to their clinical data management. In addition to the efficiencies, quality leaders within the organization had also noted data variances across facilities that resulted in compliance complications. In 2018, the system partnered with Q-Centrix to centralize the core measures data management for more than 100 of its sites.

After evaluating several different centralization strategies including both internal resources and external vendors, the system decided the best approach was to partner with Q-Centrix on all core measures, developing a centralization plan that would be implemented in one year.


Stakeholder buy-In: Executives were hesitant to lose oversight of staff and data procurement process

Data management: refers to the process of procuring, storing, analyzing, and utilizing health care data

  • Each site adopted unique data integrity standards leading to variations in data quality
  • Variation in data quality resulted in mistrust and; ultimately, unusable for PI

Employee management: variety of procedures and strategies to maximize contribution to company based on talents, skills, and needs of employees

  • Employee expertise and skills varied from site to site
  • Many sites had a single employee devoted to registries and feared disruption would stall work
  • Employees were not exclusively dedicated to clinical data management. The multi-tasking lead to inefficiency.
  • Difficulty recruiting qualified candidates for open positions
  • No consistent training in data procurement

Size: hospital sites were scattered across the country and had different needs dependent on technology

  • With hospitals in different states, the partner was largely dispersed throughout the country with different technology interfaces
  • The implementation plan was defined by region. However, this approach added unintentional complexity.


Improved staffing model:

  • Partnered with third-party vendor that provided highly experienced and trained data experts to work on data procurement
  • Third-party vendor worked with the health system to hire and train abstractors to ensure consistently high standards and to ease the fear of layoffs

Improved data procurement model:

  • Abstractors hired by third-party vendor handled data procurement from beginning to end, bringing extra scrutiny to the Inter- Rater Reliability (IRR) process
  • Third-party vendor monitored progress, engaged leaders, and discussed solutions

A revised technology-based rollout:

  • Amended initial rollout strategy to a technology-based strategy utilizing each site’s technology platform


  • Estimated cost savings ROI of 21%



Data integrity and quality
  • Improved CMS compliance 33%
  • Increase in CMS compliance for the Inpatient Psychiatric Facility Quality Reporting (IPFQR) core measure after only 3 months of partnership
  • Centralized a data procurement and clinical data governance plan
  • Multi-disciplinary engagement: data integrity sparked discussions on how to use and implement policies based on data to improve patient care quality across disciplines
Continued Centralization
  • Sparked centralization efforts in areas such as the cardiovascular registries
  • Cut centralization rollout timeline by 50%
  • Centralization rollout plan was six months shorter than planned


Optimized resource allocation
  • Minimized potential for qualified staffing shortages
  • More working hours and employees dedicated to other patient-focused areas of work