Estimating annual charges for ambulatory care from limited utilization data.
OBJECTIVE
This study explores the types of utilization information needed to produce a reasonable estimate of annual charges for ambulatory care that could be used in the absence of charge or cost data as an aggregate utilization measure.


METHODS
Charge and utilization data from the RAND Health Insurance Experiment were used.


METHODS
Services provided to enrollees in the Health Insurance Experiment at each of the six sites for a one-year period were grouped into categories according to California Relative Value Studies (CRVS) codes. Using annual charges as the dependent variable, we evaluated linear regression models for their predictive accuracy, as indicated by adjusted R2-values. Categories of services were combined on the basis of clinical meaningfulness (e.g., all provider visits into one group), and predictive accuracy of models with these groupings of services examined. We examined model validity by applying the derived models to each of the 30 remaining site-years of data from the Health Insurance Experiment.


RESULTS
We were able to explain 84 percent of the variance in charges with a model containing counts of provider visits exclusive of mental health visits, mental health provider visits, days drugs were prescribed, days radiologic procedures were performed, procedural visits subdivided according to whether they were performed by a surgical or medical provider, days laboratory and/or pathology tests were performed, days a grouping of miscellaneous tests were performed, and days supplies were purchased. When applied to the validation data, this model predicted a mean of 77 percent of the variance and mean charges 102 +/- 9 percent of actual mean charges. A model with only the first four of the listed categories explained 77 percent of the variance in charges.


CONCLUSIONS
Models using only counts of several broad categories of services perform rather well in predicting annual charges for ambulatory care.
Saver
B G
BG
Department of Family Medicine, University of Washington, Seattle 98195.
Wagner
E H
EH
D000553:Ambulatory Care / Q000145:classification / Q000191:economics* / Q000706:statistics & numerical data*; D005249:Fees and Charges / Q000706:statistics & numerical data* / Q000639:trends; D005544:Forecasting; D006302:Health Services Research / Q000379:methods; D006801:Humans; D007250:Inflation, Economic / Q000706:statistics & numerical data / Q000639:trends; D007348:Insurance, Health / Q000191:economics / Q000706:statistics & numerical data; D016014:Linear Models; D008404:Massachusetts; D018803:Models, Economic; D009819:Office Visits / Q000706:statistics & numerical data; D015203:Reproducibility of Results; D013022:South Carolina; D014861:Washington