Prediction of Primary Admission Total Charges Following Cervical Disc Arthroplasty Utilizing Machine Learning.

BACKGROUND CONTEXT: Cervical disc arthroplasty (CDA) has become an increasingly popular alternative to anterior cervical discectomy and fusion, offering benefits such as motion preservation and reduced risk of adjacent segment disease. Despite its advantages, understanding the economic implications associated with varying patient and hospital factors remains critical. PURPOSE: To evaluate how hospital size, geographic region, and patient-specific variables influence charges associated with the primary admission period following CDA. STUDY DESIGN: A retrospective analysis using machine learning models to predict and analyze charge factors associated with CDA. PATIENT SAMPLE: Data from the National Inpatient Sample (NIS) Database from 2016 to 2020 was used, focusing on patients undergoing CDA. OUTCOME MEASURES: The primary outcome was total charge associated with the primary admission for CDA, analyzed against patient demographics, hospital characteristics, and regional economic conditions. METHODS: Multivariate linear regression and machine learning algorithms including logistic regression, random forest, and gradient boosting trees were employed to assess their predictive power on charge outcomes. Statistical significance was set at the 0.003 level after applying a Bonferroni correction. RESULTS: The analysis included 3,772 eligible CDA cases. Major predictors of charge identified were hospital size and ownership type, with large and privately owned hospitals associated with higher charges (p<0.001). The Western region of the U.S. also showed significantly higher charges compared to the Northeast (p<0.001). The gradient boosting trees model showed the highest accuracy (AUC = 85.6%). Length of stay and wage index were significant charge drivers, with each additional inpatient day increasing charges significantly (p<0.001) and higher wage index regions correlating with increased charges (p<0.001). CONCLUSIONS: Hospital size, geographic region, and specific patient demographics significantly influence the charges of CDA. Machine learning models proved effective in predicting these charges, suggesting that they could be instrumental in guiding economic decision-making in spine surgery. Future efforts should aim to incorporate these models into broader clinical practice to optimize healthcare spending and enhance patient care outcomes.

Modern Internet Search Analytics: Is There a Difference in What Patients are Searching Regarding the Operative and Nonoperative Management of Scoliosis?

STUDY DESIGN: Observational Study. OBJECTIVES: This study aimed to investigate the most searched types of questions and online resources implicated in the operative and nonoperative management of scoliosis. METHODS: Six terms related to operative and nonoperative scoliosis treatment were searched on Google's People Also Ask section on October 12, 2023. The Rothwell classification was used to sort questions into fact, policy, or value categories, and associated websites were classified by type. Fischer's exact tests compared question type and websites encountered between operative and nonoperative questions. Statistical significance was set at the .05 level. RESULTS: The most common questions concerning operative and nonoperative management were fact (53.4%) and value (35.5%) questions, respectively. The most common subcategory pertaining to operative and nonoperative questions were specific activities/restrictions (21.7%) and evaluation of treatment (33.3%), respectively. Questions on indications/management (13.2% vs 31.2%, P < .001) and evaluation of treatment (10.1% vs 33.3%, P < .001) were associated with nonoperative scoliosis management. Medical practice websites were the most common website to which questions concerning operative (31.9%) and nonoperative (51.4%) management were directed to. Operative questions were more likely to be directed to academic websites (21.7% vs 10.0%, P = .037) and less likely to be directed to medical practice websites (31.9% vs 51.4%, P = .007) than nonoperative questions. CONCLUSIONS: During scoliosis consultations, spine surgeons should emphasize the postoperative recovery process and efficacy of conservative treatment modalities for the operative and nonoperative management of scoliosis, respectively. Future research should assess the impact of website encounters on patients' decision-making.

GPT-4 as a Source of Patient Information for Anterior Cervical Discectomy and Fusion: A Comparative Analysis Against Google Web Search.

STUDY DESIGN: Comparative study. OBJECTIVES: This study aims to compare Google and GPT-4 in terms of (1) question types, (2) response readability, (3) source quality, and (4) numerical response accuracy for the top 10 most frequently asked questions (FAQs) about anterior cervical discectomy and fusion (ACDF). METHODS: "Anterior cervical discectomy and fusion" was searched on Google and GPT-4 on December 18, 2023. Top 10 FAQs were classified according to the Rothwell system. Source quality was evaluated using JAMA benchmark criteria and readability was assessed using Flesch Reading Ease and Flesch-Kincaid grade level. Differences in JAMA scores, Flesch-Kincaid grade level, Flesch Reading Ease, and word count between platforms were analyzed using Student's t-tests. Statistical significance was set at the .05 level. RESULTS: Frequently asked questions from Google were varied, while GPT-4 focused on technical details and indications/management. GPT-4 showed a higher Flesch-Kincaid grade level (12.96 vs 9.28, P = .003), lower Flesch Reading Ease score (37.07 vs 54.85, P = .005), and higher JAMA scores for source quality (3.333 vs 1.800, P = .016). Numerically, 6 out of 10 responses varied between platforms, with GPT-4 providing broader recovery timelines for ACDF. CONCLUSIONS: This study demonstrates GPT-4's ability to elevate patient education by providing high-quality, diverse information tailored to those with advanced literacy levels. As AI technology evolves, refining these tools for accuracy and user-friendliness remains crucial, catering to patients' varying literacy levels and information needs in spine surgery.