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USING DATA TO ENHANCE THE EXPERT PANEL PROCESS

Rating Indications of Alcohol-related Problems in Older Adults

Published online by Cambridge University Press:  25 May 2001

Sabine M. Oishi
Affiliation:
Health Services Research Center, UCLA
Sally C. Morton
Affiliation:
RAND
Alison A. Moore
Affiliation:
UCLA
John C. Beck
Affiliation:
UCLA
Ron D. Hays
Affiliation:
UCLA
Karen L. Spritzer
Affiliation:
UCLA
Jennifer M. Partridge
Affiliation:
UCLA
Arlene Fink
Affiliation:
UCLA

Abstract

Objective: To enhance the validity of a well-known expert panel process, we used data from patient surveys to identify and correct rating errors.

Methods: We used the two-round RAND/UCLA panel method to rate indications of harmful (presence of problems), hazardous (at risk for problems), and nonhazardous (no known risks) drinking in older adults. Results from the panel provided guidelines for classifying older individuals as harmful, hazardous, or nonhazardous drinkers, using a survey. The classifications yielded unexpectedly high numbers of harmful and hazardous drinkers. We hypothesized possible misclassifications of drinking risks and used the survey data to identify indications that may have led to invalid ratings. We modified problematic indications and asked three clinician panelists to evaluate the clinical usefulness of the modifications in a third panel round. We revised the indications based on panelist response and reexamined drinking classifications.

Results: Using the original indications, 48% of drinkers in the sample were classified as harmful, 31% as hazardous, and 21% as nonhazardous. A review of the indications revealed framing bias in the original rating task and vague definitions of certain symptoms and conditions. The modified indications resulted in classifications of 22% harmful, 47% hazardous, and 31% nonhazardous drinkers.

Conclusions: Analysis of survey data led to identification and correction of specific errors occurring during the panel-rating process. The validity of the RAND/UCLA method can be enhanced using data-driven modifications.

Type
Research Article
Copyright
© 2001 Cambridge University Press

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