Optimizing Hospital Billing by Using Data from the Vascular Quality Initiative.
Academic Article
Overview
abstract
INTRODUCTION: Hospitals are reimbursed for inpatient admissions by Medicare based on the principal Medicare severity diagnostic related group (MS-DRG) assigned to that admission. MS-DRGs with complication or comorbidity (CC) or major complication or comorbidity (MCC) increase reimbursement to reflect the added cost of caring for complex patients. Under-coding DRG complexity has been shown to result in unrecovered reimbursement and financial losses for hospitals. We hypothesized that granular, objective data collected by trained abstractors in the Society for Vascular Surgery Vascular Quality Initiative (SVS VQI) could be used to improve MS-DRG coding accuracy by detecting CC and MCCs which might be missed in the standard coding process. METHODS: The Medicare-linked VQI Peripheral Vascular Intervention (PVI) registry was queried to identify patients who underwent admission for PVI procedures at 230 centers between 2010-19. Patients were included if the index DRG represented a PVI procedure. Cases were grouped into those "without CC/MCC" vs. "with CC/MCC." PVI registry characteristics for each group were compared. Logistic stepwise regression was used to identify variables predicting CC/MCC billing. The model was then used to calculate the variation in the expected number of admissions qualifying as CC/MCC at each center, compared to the observed number actually billed across centers. RESULTS: 40,822 admissions associated with PVI treatment were analyzed, of which 76% of which were billed with CC/MCC. This rate varied substantially across the 230 hospitals, from 48-100%. Stepwise regression identified that pre-existing congestive heart failure, diabetes mellitus, dialysis, prior amputation and dependent functional status, plus post-treatment cardiac, renal, pulmonary or access site complications, amputation, and longer length of stay were independently associated with hospitals billing an MS-DRG with CC/MCC. The model was highly accurate with a c-statistic of 0.82. The expected number of cases billed "with CC/MCC" exceeded the observed number at 89/230 centers, suggesting underbilling at 39% of centers. CONCLUSION: Data available in the VQI PVI registry accurately identified admissions billed by VQI hospitals with MS-DRGs for CC or MCC. This multivariable model could be used to prepare reports for participating hospitals to identify cases likely to justify MS-DRG coding with CC or MCC, for special attention by coders. This would not be intended to supplant existing coding systems, but rather to provide additional validation and benchmarking that could help avoid under-coding and loss of hospital revenue. This could provide added value to VQI hospitals at no additional cost, to help offset the cost of VQI participation.
