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Appropriateness of Referrals to a Urogynecology Practice

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Publisher Springer
Date 2019 Sep 4
PMID 31478077
Citations 1
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Abstract

Introduction And Hypothesis: The urogynecology subspecialty relies on appropriate referrals from their referral base. We sought to provide guidance for optimizing appropriate referrals to urogynecology by comparing pre-referral characteristics between appropriate and inappropriate referrals.

Methods: This retrospective cohort study examined predictors of appropriate urogynecology referrals. Appropriateness categorization was based upon pelvic floor disorder (PFD) symptoms and signs provided by the referring provider. Patients with both a PFD symptom and sign were considered "appropriate." Patients with neither a PFD symptom nor sign were considered "inappropriate." PFD symptoms were: vaginal bulge, voiding or defecatory dysfunction. PFD signs were: vaginal vault prolapse, urethral hypermobility, mesh/sling exposure, elevated post-void residual, positive standing stress test, abnormal urinalysis or urine culture-proven infection. Continuous and categorical data were analyzed with ANOVA and chi-square test, respectively. A logistic regression model to predict appropriateness was developed from variables identified from the bivariate analysis.

Results: Bivariate predictors of an appropriate referral for 1716 study subjects were older age, prior overactive bladder medication use, MD/DO referrer source and OBGYN, urogynecology or urology referrer specialty. Our logistic regression model correctly classified referrals as appropriate in 93.6% of cases.

Conclusions: Age, anti-cholinergic medication use, referrer source and specialty are pre-initial visit predictors of urogynecology referral appropriateness. The predictor-generated model was successful in predicting referral appropriateness. Potential bias from information transfer issues, lack of pre-referral evaluation and referring provider unfamiliarity with urogynecology are possible reasons for inappropriate referrals and potential areas for improvement.

Citing Articles

Characterizing barriers to closing cross-institutional referral loops: Workflow and information flow analysis.

McCormack C, Savoy A, Mathew A, Gilmore A, Cottingham E, Sangani A Appl Ergon. 2023; 115:104177.

PMID: 38016271 PMC: 10863302. DOI: 10.1016/j.apergo.2023.104177.

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