Computerized decision support for concurrent utilization review using the HELP system.
OBJECTIVE
Development and evaluation of computerized concurrent utilization review (UR) support taking advantage of a clinically rich computerized patient database.


METHODS
The Automated Support System for Utilization Review (ASSURE) applies the Appropriateness Evaluation Protocol (AEP) Day of Care criteria to computerized patient data in the HELP hospital information system. This paper reports the development, verification, and validation of ASSURE.


METHODS
Implementation correctness was verified by measuring agreement with a nurse reviewer, using separate sample sets for all 20 criteria for a total of 560 current inpatients. Usefulness in detecting inappropriate days of care was validated by two nurse reviewers who were crossed with manual and computer-assisted review methods in a blocked design for 168 current inpatients. Agreement with reviewers, sensitivity, specificity, positive predictive value, and negative predictive value were measured.


RESULTS
Agreement was very good for satisfaction of criteria, and good for appropriateness of day of care. A patient day identified by ASSURE as potentially inappropriate would be twice as likely to be judged inappropriate by a reviewer as a randomly selected patient day. Review of the 10% of patient days identified as potentially inappropriate by ASSURE would identify approximately 21% of the inappropriate days of care.


CONCLUSIONS
ASSURE is a clinically useful tool for screening adult acute care patients for inappropriate days of care, and promises to make a major contribution to reducing health care costs. The prognosis for successful routine clinical use is good.
Nelson
B D
BD
Medical Informatics Department, University of Utah, Salt Lake City, USA.
Gardner
R M
RM
Hedrick
G
G
Gould
P
P
D001185:Artificial Intelligence; D003211:Concurrent Review / Q000379:methods*; D003661:Decision Support Techniques; D006751:Hospital Information Systems; D006761:Hospitals / Q000706:statistics & numerical data; D006801:Humans; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012986:Software Validation; D014589:Utah