Provider Referral Patterns and Surgical Utilization Among New Patients Seen in Spine Clinic

Retrospective chart review.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.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.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.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).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.