Using hospital performance data in quality improvement: the Cleveland Health Quality Choice experience

Abstract

Background: Cleveland Health Quality Choice is a regional initiative to assess hospital performance which was implemented in 1989. The project developed and validated CHOICE, a severity adjustment system that includes diagnosis-specific models for medical, surgical, and obstetrical patients which are based on clinical data abstracted from patients' medical records.

Methodology: Since 1992 Cleveland Health Quality Choice has disseminated semi-annual reports that profile hospital mortality rates, lengths of stay, and cesarean section rates using the CHOICE severity adjustment models. Hospitals receive tabular and graphical representations of hospital outcomes and electronic patient-level data files that can be used to further examine outcomes in clinical subgroups.

Results: Four case studies illustrate how outcomes data derived from the CHOICE models led to the development of successful hospital programs to decrease lengths of stay, cesarean section rates, and hospital mortality rates. Although each case study reflected a unique approach to process improvement, several common characteristics were observed: (1) establishment of interdisciplinary process improvement teams with senior physician and nursing leadership; (2) detailed review of the process of care to identify modifiable clinical practices likely to affect outcomes; (3) development of practice guidelines based on group consensus or published recommendations that were designed to affect modifiable practices; and (4) aggressive sharing of serial data with individual practitioners.

Conclusions: Although outcomes data can provide powerful insight on where to target quality improvement efforts, hospitals must identify influential and modifiable clinical practices. Such efforts are most likely to be successful if driven by interdisciplinary work groups, supported by senior clinicians and administrators, and based on locally accepted practice standards.