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