Drivers of Medicare Reimbursement for Thoracolumbar Fusion
| Autor: | Benjamin Zmistowski, Michael J. Howley, Eric M. Padegimas, Krishn Khanna, Kushagra Verma |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Data Analysis
medicine.medical_specialty media_common.quotation_subject Medicare Centers for Medicare and Medicaid Services U.S Thoracic Vertebrae Young Adult 03 medical and health sciences 0302 clinical medicine Health care Humans Medicine Orthopedics and Sports Medicine Diagnosis-Related Groups Reimbursement Aged Retrospective Studies media_common 030222 orthopedics Lumbar Vertebrae Medicaid business.industry Rural health Retrospective cohort study Diagnosis-related group Evidence-based medicine Payment United States Spinal Fusion Family medicine Insurance Health Reimbursement Emergency medicine Spinal Diseases Neurology (clinical) Health Expenditures business 030217 neurology & neurosurgery |
| Zdroj: | Spine. 42:1648-1656 |
| ISSN: | 1528-1159 0362-2436 |
| DOI: | 10.1097/brs.0000000000002171 |
| Popis: | STUDY DESIGN A retrospective observational study. OBJECTIVE The purpose of this study is to examine the variation in thoracolumbar fusion (TLF) payment and determine the drivers of this variation. SUMMARY OF BACKGROUND DATA As health care spending continues to increase, variation in surgical procedures reimbursements has come under more scrutiny. TLF is an example of a high-cost, proven-benefit procedure that is often the focus of Centers for Medicare and Medicaid Services (CMS) administrators. There is a wide variation in TLF charges, but the drivers for this variation are not clear. METHODS Claims for TLF were identified in the CMS data by analyzing Diagnosis Related Group (DRG) number 460 ("Spinal Fusion Except Cervical without Major Complications or Comorbidities"). Data on factors that may impact cost of care were collected from four sources: the United States Census Bureau, CMS, the Dartmouth Atlas, and WWAMI Rural Health Research Center. These were then grouped into seven categories: quality, supply, demand, substitute treatment availability, patient characteristics, competitive factors, and provider characteristics. Predictive reimbursement models were created from the data using multivariate linear regression to understand the factors that influence TLF reimbursement. RESULTS There was significant geographic variability in reimbursement. The largest contribution to reimbursement variation came from variables in the demand (ΔR = 13.4%, P |
| Databáze: | OpenAIRE |
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