Five things to know:
1. The study evaluated how hospital size, geographic region and patient-specific variables affect charges associated with the primary admission period after a cervical disc replacement.
2. Data from the National Inpatient Sample database from 2016 to 2020 was processed through multivariate linear regression and machine learning algorithms.
3. The analysis included 3,772 cervical disc replacement cases, and major predictors of charges included hospital size and ownership type.
4. Large and privately owned hospitals were associated with higher charges, and the Western region of the U.S. had significantly higher charges compared to the Northeast.
5. The study concluded, “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.”
