The value of the Uniform Clinical Data Set System (UCDSS) in a hospital setting.

BACKGROUND

The Health Care Financing Administration designed the Uniform Clinical Data Set System (UCDSS) to help peer review organizations (PROs) identify problems with patient care. The system currently is being piloted in five states and may be used for the PRO review of Medicare patients from all states by January 1995. This study tested whether UCDSS could be used with modifications for effective internal hospital quality review.

METHODS

The UCDSS includes a computer program for collecting information from the medical record and 359 computerized algorithms that evaluate quality of care. For this study, 2,313 randomly chosen medical records from a tertiary care teaching hospital were abstracted with the UCDSS at the time of discharge. Cases flagged by the UCDSS algorithms as having potential quality-of-care problems were referred to a clinical reviewer to evaluate whether the flag was a true positive and to identify reasons for false-positive flags. The algorithms were modified based on this hospital's experience by adding the reasons for false-positive flags as exceptions to the algorithm rules.

RESULTS

To abstract the data with the UCDSS required a median time of 45 minutes and a mean time of 55 minutes per medical record. The percentages of algorithm flags that were confirmed upon physician review to have a quality problem were estimated to be 21% for the UCDSS and 43% for the modified UCDSS. The confirmed problem rate varied substantially by algorithm. Confirmed problems were the source of numerous departmental and individual discussions and led to changes in five departmental procedures.

CONCLUSIONS

Although the results of this study are preliminary and require further verification, they suggest that with modifications and careful attention to implementation, the UCDSS may be an expensive but potentially useful tool for in-hospital quality review. In-hospital employment of the UCDSS offers more opportunities for practical use of algorithms for continuous quality improvement rather than the sometimes punitive use of system findings by PROs.