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

Abstract


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.

Keywords


outpatient referral; prediction rates; statistical model

Full Text:

PDF


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

Refbacks

  • There are currently no refbacks.


This is an open access journal, which means that all content is freely available without charge to the user or their institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal starting from Volume 21 without asking prior permission from the publisher or the author. This is in accordance with the BOAI definition of open accessFor permission regarding papers published in previous volumes, please contact us.

Privacy statement: The names and email addresses entered in this journal site will be used exclusively for the stated purposes of this journal and will not be made available for any other purpose or to any other party.

Online ISSN 2058-4563 - Print ISSN 2058-4555. Published by BCS, The Chartered Institute for IT