Hybrid Data Sets Can Improve Hospital Quality Reporting

A hybrid data set that combines clinical laboratory data with administrative claims improved the measurement of risk-adjusted hospital performance, according to an AHRQ-funded pilot project conducted by the Minnesota Hospital Association. In addition, the hybrid data set improved the ability to compare risk-adjusted mortality and complications across the 13 Minnesota hospitals where the project was conducted. The report, “Harnessing the Power of Enhanced Data for Healthcare Quality Improvement: Lessons from a Minnesota Hospital Association Pilot Project,” provides recommendations on how to collect hybrid data sets. It was published in the November/December 2012 issue of the Journal of Healthcare Management. Select to access the abstract on PubMed.®

2012 Nov-Dec;57(6):406-18; discussion 419-20.

Harnessing the power of enhanced data for healthcare quality improvement: lessons from a Minnesota Hospital Association Pilot Project.

Pine M, Sonneborn M, Schindler J, Stanek M, Maeda JL, Hanlon C.

Source

USA.

Abstract

The imperative to achieve quality improvement and cost-containment goals is driving healthcare organizations to make better use of existing health information. One strategy, the construction of hybrid data sets combining clinical and administrative data, has strong potential to improve the cost-effectiveness of hospital quality reporting processes, improve the accuracy of quality measures and rankings, and strengthen data systems. Through a two-year contract with the Agency for Healthcare Research and Quality, the Minnesota Hospital Association launched a pilot project in 2007 to link hospital clinical information to administrative data. Despite some initial challenges, this project was successful. Results showed that the use of hybrid data allowed for more accurate comparisons of risk-adjusted mortality and risk-adjusted complications across Minnesota hospitals. These increases in accuracy represent an important step toward targeting quality improvement efforts in Minnesota and provide important lessons that are being leveraged through ongoing projects to construct additional enhanced data sets. We explore the implementation challenges experienced during the Minnesota Pilot Project and their implications for hospitals pursuing similar data-enhancement projects. We also highlight the key lessons learned from the pilot project's success.