When your words count: a discriminative model to predict approval of referrals.
OBJECTIVE
To develop and test a statistical model which correctly predicts the approval of outpatient referrals when reviewed by a specialty service based on nine discriminating variables.


METHODS
Retrospective cross-sectional study.


METHODS
Large public county hospital system in a southern US city.


METHODS
Written documents and associated data from 500 random adult referrals made by primary care providers to various specialty services during the course of one month.


METHODS
The resulting correct prediction rates obtained by the model.


RESULTS
The model correctly predicted 78.6% of approved referrals using all nine discriminating variables, 75.3% of approved referrals using all variables in a stepwise manner and 74.7% of approved referrals using only the referral total word count as a single discriminating variable.


CONCLUSIONS
Three iterations of the model correctly predicted at least 75% of the approved referrals in the validation set. A correct prediction of whether or not a referral will be approved can be made in three out of four cases.
Esquivel
Adol
A
Houston VA Health Services Research and Development Center of Excellence, Michael E De Bakey Veterans Affairs Medical Center and Baylor College of Medicine, 20002 Holcombe Blvd 152, Houston, TX 77030, USA.
Dunn
Kimberly
K
McLane
Sharon
S
Te'eni
Dov
D
Zhang
Jiajie
J
Turley
James P
JP
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D003142:Communication; D003266:Continuity of Patient Care; D005260:Female; D005544:Forecasting; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D012017:Referral and Consultation; D014481:United States