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.
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.
Authors: Michael L Barnett; Ateev Mehrotra; Joseph P Frolkis; Melissa Spinks; Casey Steiger; Brandon Hehir; Jeffrey O Greenberg; Hardeep Singh Journal: J Gen Intern Med Date: 2016-03 Impact factor: 5.128