Financial Impact of Imaging Examination Site of Service in the Medicare Population.

PURPOSE: The financial sustainability of the US healthcare system is a growing concern in an environment of declining reimbursement and rising costs. Variable Centers for Medicare and Medicaid (CMS) reimbursement and denial rates for specific imaging examinations exist across sites of service, adding complexity to financial planning for healthcare organizations. Understanding the financial implications of site of service in existing CMS reimbursement for imaging may be of strategic importance for organizations going forward. MATERIALS AND METHODS: Current Procedural Terminology (CPT) codes were obtained for common cross-sectional imaging examinations using the 2022 CMS Medicare Physician Fee Schedule. Using reimbursement rates with historical volumes and denial rates, a simulation was created to estimate the overall reimbursement of paired hospital outpatient departments (HOPD) and free-standing office (FSO) sites. A baseline simulation was performed with random allocation of imaging examinations between sites of service, and an optimized simulation was performed to estimate the maximum financial impact of targeted allocation between sites. These simulations were performed for paired CT and MR scanners separately. RESULTS: For CT, the baseline simulation estimated annual average reimbursement for combined HOPD and FSO was $3.25M. Reimbursement increased to $3.51M after optimized reallocation of studies between sites of service, resulting in an expected gain of $260,162 for a set of paired HOPD and FSO scanners. For MR, the same approach resulted in baseline reimbursement of $2.51M, increasing to $2.60M upon reallocation between sites for an expected gain of $87,532. Assuming a stable cost of service delivery, this approach would result in improved margins of 8% for CT and 3.5% for MR. There were 28 CT and 19 MRI daily patient imaging appointments at each respective HOPD and FSO scanners, unchanged between baseline and optimized cases. Differences in reimbursement rates between sites were the dominant driver of increased margins at low denial rates, although denial rates became dominant at values greater than 50%. CONCLUSION: Given CMS payment and denial rate variability, optimally allocating imaging studies between sites of service may improve reimbursement for the same services delivered. Although financial incentives exist for site allocation, such decisions should require physician input to assess safety and appropriate level of care. This work contributes to an understanding of financial incentives of existing reimbursement policy and may guide future policy design towards high value care.

A Model for Evaluating Total Costs of Care and Cost Savings of Specialty Condition-Based Care for Hip and Knee Osteoarthritis in an Integrated Practice Unit.

OBJECTIVES: The viability of specialty condition-based care via integrated practice units (IPUs) requires a comprehensive understanding of total costs of care. Our primary objective was to introduce a model to evaluate costs and potential costs savings using time-driven activity-based costing comparing IPU-based nonoperative management with traditional nonoperative management and IPU-based operative management with traditional operative management for hip and knee osteoarthritis (OA). Secondarily, we assess drivers of incremental cost differences between IPU-based care and traditional care. Finally, we model potential cost savings through diverting patients from traditional operative management to IPU-based nonoperative management. METHODS: We developed a model to evaluate costs using time-driven activity-based costing for hip and knee OA care pathways within a musculoskeletal IPU compared with traditional care. We identified differences in costs and drivers of cost differences and developed a model to demonstrate potential cost savings through diverting patients from operative intervention. RESULTS: Weighted average costs of IPU-based nonoperative management were lower than traditional nonoperative management and lower in IPU-based operative management than traditional operative management. Key drivers of incremental cost savings included care led by surgeons in partnership with associate providers, modified physical therapy programs with self-management, and judicious use of intra-articular injections. Substantial savings were modeled by diverting patients toward IPU-based nonoperative management. CONCLUSIONS: Costing models involving musculoskeletal IPUs demonstrate favorable costs and cost savings compared with traditional management of hip or knee OA. More effective team-based care and utilization of evidence-based nonoperative strategies can drive the financial viability of these innovative care models.

Editorial Commentary: The Value of Time-Driven, Activity-Based Costing in Health Care Delivery.

Time-driven activity-based costing (TDABC) provides a powerful approach to more targeted cost accounting based on resources actually used by patients during a cycle of care. Since its introduction in 2004 by Kaplan and Anderson, TDABC has gained increasing popularity in defining the actual costs of care for various orthopaedic processes and pathways. TDABC may demonstrate lower costs of care compared with traditional cost accounting methods, including ratio of costs to charges and relative value units. Weaknesses of traditional methods include approaching costs through the lens of charges, revenue, processes and procedures, adopting a "top-down" approach, and potentially overestimating costs. In contrast, TDABC builds costs from the individual level, taking a front-line, condition-focused, and patient-centered view. Existing organizational decision-making is oriented around revenue metrics (relative value units and ratio of costs to charges) rather than cost metrics, yet alternative payment models are shifting toward fixed revenues for certain conditions or procedures. The variability, including both financial upside and loss, will primarily be a function of the cost of care-a number that is profoundly opaque in most health care settings. We view TDABC as an approach that sheds light on variation, offers a more granular differentiation of costs compared with traditional approaches, mitigates risk, and sparks opportunities for increasing operational efficiency and waste reduction. The goal is to identify and provide the greatest-value orthopaedic care.

Cost-minimization Analysis of the Management of Acute Achilles Tendon Rupture.

BACKGROUND: Outcomes of nonsurgical management of acute Achilles tendon rupture have been demonstrated to be noninferior to those of surgical management. We performed a cost-minimization analysis of surgical and nonsurgical management of acute Achilles tendon rupture. METHODS: We used a claims database to identify patients who underwent surgical (n = 1,979) and nonsurgical (n = 3,065) management of acute Achilles tendon rupture and compared overall costs of treatment (surgical procedure, follow-up care, physical therapy, and management of complications). Complication rates were also calculated. Patients were followed for 1 year after injury. RESULTS: Average treatment costs in the year after initial diagnosis were higher for patients who underwent initial surgical treatment than for patients who underwent nonsurgical treatment ($4,292 for surgical treatment versus $2,432 for nonsurgical treatment; P < 0.001). However, surgical treatment required fewer office visits (4.52 versus 10.98; P < 0.001) and less spending on physical therapy ($595 versus $928; P < 0.001). Rates of rerupture requiring subsequent treatment (2.1% versus 2.4%; P = 0.34) and additional costs ($2,950 versus $2,515; P = 0.34) were not significantly different regardless whether initial treatment was surgical or nonsurgical. In both cohorts, management of complications contributed to approximately 5% of the total cost. CONCLUSION: From the payer's perspective, the overall costs of nonsurgical management of acute Achilles tendon rupture were significantly lower than the overall costs of surgical management. LEVEL OF EVIDENCE: III, Economic Decision Analysis.