RESUMO
STUDY DESIGN: Retrospective chart review. OBJECTIVE: The objective of this study was to elucidate the demographics of patient referrals from different sources and identify factors that affect a patient's likelihood of undergoing surgery. SUMMARY OF BACKGROUND DATA: Despite baseline factors for surgical consideration, such as attempting conservative management, surgeons encounter many patients who are not surgically indicated. Overreferrals, that is, a patient referred to a surgeon that does not need surgery, can result in long wait times, delayed care, worse outcomes, and resource waste. MATERIALS AND METHODS: All new patients at a single academic institution seen in the clinic by eight spine surgeons between January 1, 2018, and January 1, 2022, were analyzed. Referral types included self-referral, musculoskeletal (MSK), and non-MSK provider referral. Patient demographics included age, body mass index (BMI), zip code as a proxy for socioeconomic status, sex, insurance type, and surgical procedures undergone within 1.5 years postclinic visit. Analysis of variance and a Kruskal-Wallis test was used to compare means among normally and non-normally disturbed referral groups, respectively. Multivariable logistic regressions were run to assess demographic variables associated with undergoing surgery. RESULTS: From 9356 patients, 84% (7834) were self-referred, 3% (319) were non-MSK, and 13% (1203) were MSK. A statistically significant association with ultimately undergoing surgery was observed with MSK referral type compared with non-MSK referral [odds ratio (OR)=1.37, CI: 1.04-1.82, P =0.0246]. Additional independent variables observed to be associated with patients undergoing surgery included older age (OR=1.004, CI: 1.002-1.007, P =0.0018), higher BMI (OR=1.02, CI: 1.011-1.029, P <0.0001), high-income quartile (OR=1.343, CI: 1.177-1.533, P <0.0001), and male sex (OR=1.189, CI: 1.085-1.302, P =0.0002). CONCLUSIONS: A statistically significant association with undergoing surgery was observed with a referral by an MSK provider, older age, male sex, high BMI, and a high-income quartile home zip code. Understanding these factors and patterns is critical for optimizing practice efficiency and reducing the burdens of inappropriate referrals.