When your words count: a discriminative model to predict approval of referrals

Adol Esquivel, Kimberly Dunn, Sharon McLane, Dov T_eni, Jiajie Zhang, James Turley


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.
Design Retrospective cross-sectional study.
Setting Large public county hospital system in a southern US city.
Participants 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.
Main outcome measures 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.


outpatient referral; prediction rates; statistical model

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DOI: http://dx.doi.org/10.14236/jhi.v17i4.738


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