Determining Prior Authorization Approval for Lumbar Stenosis Surgery With Machine Learning

Abstract

Study Design Medical vignettes. Objectives Lumbar spinal stenosis (LSS) is a degenerative condition with a high prevalence in the elderly population, that is associated with a significant economic burden and often requires spinal surgery. Prior authorization of surgical candidates is required before patients can be covered by a health plan and must be approved by medical directors (MDs), which is often subjective and clinician specific. In this study, we hypothesized that the prediction accuracy of machine learning (ML) methods regarding surgical candidates is comparable to that of a panel of MDs. Methods Based on patient demographic factors, previous therapeutic history, symptoms and physical examinations and imaging findings, we propose an ML which computes the probability of spinal surgical recommendations for LSS. The model implements a random forest model trained from medical vignette data reviewed by MDs. Sets of 400 and 100 medical vignettes reviewed by MDs were used for training and testing. Results The predictive accuracy of the machine learning model was with a root mean square error (RMSE) between model predictions and ground truth of .1123, while the average RMSE between individual MD’s recommendations and ground truth was .2661. For binary classification, the AUROC and Cohen’s kappa were .959 and .801, while the corresponding average metrics based on individual MD’s recommendations were .844 and .564, respectively. Conclusions Our results suggest that ML can be used to automate prior authorization approval of surgery for LSS with performance comparable to a panel of MDs.

Current Applications of Artificial Intelligence in Billing Practices and Clinical Plastic Surgery

“…De Barros et al discovered that the machine learning model had superior predictive accuracy, assessed by root mean square error, relative to recommendations made by individual medical directors, with root mean square error values of 0.1123 and 0.2661, respectively. 32 The AUROC and Cohen’s kappa for the machine learning model were 0.959 and 0.801, respectively, compared with the individual medical directors’ recommendations of 0.844 and 0.564, respectively. These results suggest that AI can be effectively applied to prior authorization approvals for lumbar spinal stenosis surgery.…”

“…These results suggest that AI can be effectively applied to prior authorization approvals for lumbar spinal stenosis surgery. 32 Broadly, AI can be extremely valuable for billing procedures in a plastic surgery practice by way of streamlining CPT codes in billing, reducing coding errors, and assisting with prior authorization approvals. However, the incorporation of AI into billing practices can lead to competing goals for providers and payers.…”

“…The overall impact significantly reduced administrative burden and cost on Blue Cross Blue Shield of Massachusetts by reducing prior authorization approval time from an average of 9 days to an average of less than 1 day. 31 Furthermore, a multidisciplinary physician group in France, De Barros et al 32 created a machine learning model to assess the eligibility of spinal surgical candidates for lumbar spinal stenosis compared with the standard prior authorization process, which must be approved by medical directors. Based on 66 variables, including patient demographics, medical history, clinical symptoms and physical examination findings, and imaging, they created 500 medical vignettes to encompass a wide range of probabilities for surgical recommendations to train and test the model.…”

“…These results suggest that AI can be effectively applied to prior authorization approvals for lumbar spinal stenosis surgery. 32 …”

Machine Learning Predicts Decompression Levels for Lumbar Spinal Stenosis Using Canal Radiomic Features from Computed Tomography Myelography

“…In outcome predictions for LSS, researchers have also reported several ML studies such as patient-reported outcome measures [ 16 , 17 ], clinical outcome predictions [ 18 , 19 , 20 , 21 ], patient-specific outcomes (such as patient resource utilization [ 22 ], non-home discharge placement prediction [ 23 ], prolonged opioid prescriptions [ 24 ], and prolonged length of hospital stay [ 25 ]). Other ML studies, such as surgical candidacy prediction [ 26 ] and prior authorization approval prediction [ 27 ], have been also reported.…”