Reimbursing Your Digital Twin: A New Era In Patient-Specific Surgery

Patient-specific surgical planning, powered by 3D visualization, has proven transformative for surgical precision and patient outcomes. Until recently, hospitals faced a significant barrier to scaling adoption: the absence of dedicated reimbursement for the substantial time, cost, and expertise required to generate accurate 3D visualizations of patient anatomy. Consequently, hospitals were unable to fully recoup their investments in digital, volumetric patient anatomy representations.

This reimbursement gap has slowed broader adoption of patient-specific medical devices, as the segmentation of CT or MRI scans represents the foundational step toward downstream applications, such as personalized device manufacturing, targeted drug delivery, surgical mapping and navigation, robotic surgery, and mixed reality visualization.

A new billing code 

This landscape shifted in January 2025, when Medicare introduced HCPCS code C8001. This code makes eligible reimbursement for 3D anatomical segmentation imaging for preoperative planning, including data preparation and transmission, obtained from previous diagnostic imaging, per segmented region. It encompasses the software, labor, and data processing essential to producing a virtual 3D model. 

At $156.46 per segmented region, following CMS’s April update that reassigned C8001 from APC 5521 ($88.05) to APC 5721, Medicare’s reimbursement for this code may not fully offset the costs of traditional segmentation – whether outsourced or performed manually by a radiologist, technologist, or engineer. Nevertheless, it signifies a pivotal advancement, transforming 3D anatomical visualization from an unfunded clinical necessity into a (partially) reimbursable standard of care.

However, the economics remain challenging. Whether outsourced or in-house, segmentation typically costs hospitals $200 to $800 per case, accounting for labor, software licensing, and overhead. Medicare’s current C8001 reimbursement falls short of this, compelling hospitals to absorb the shortfall. To achieve financial sustainability, health systems must leverage automation to maximize throughput and build efficiencies into segmentation workflows across picture archiving and communication systems and radiology teams, without compromising quality or compliance.

AI segmentation platforms are well-positioned to close this economic gap

AI-based segmentation platforms are emerging as promising tools to help bridge this economic divide. Many of these solutions deliver precise 3D reconstructions in minutes instead of hours, substantially lowering labor costs and accelerating clinical decisions. Hospitals adopting AI-based segmentation technologies have the potential to recoup expenses and could also enhance the scalability of personalized surgical planning, aligning with value-based care principles.

Breast oncology is among the pioneering specialties reaping the benefits of AI-driven segmentation. For example, some companies are leveraging cloud-based software platform’s to automatically convert breast MRI scans into detailed 3D tumor models, precisely delineating tumor margins and critical anatomical structures. This precision empowers surgeons to enhance oncologic and cosmetic outcomes, reduce costly reoperations, and support evidence-based decision-making.

In cardiac surgery, rapid and accurate segmentation is critical, particularly in pediatric cases with complex congenital defects. Fully automated tools are available that transform cardiac CT scans into comprehensive 3D visual models in minutes. By automating segmentation and reducing manual processing time, these AI-powered solutions can potentially ease the burden on clinical personnel and support more efficient, scalable preoperative planning, especially in resource-constrained settings.

Orthopedic trauma similarly gains from AI-based segmentation, especially in intricate cases like pelvic fractures. Advanced platforms can now swiftly convert CT images into high-fidelity 3D models, offering surgeons a clearer trajectory for fixation. Peer-reviewed studies indicate that such models can reduce operating room time by up to an hour – yielding savings of thousands of dollars per procedure that far exceed the modest segmentation reimbursement.

Alignment strengthens the healthcare ecosystem and enhances patient care

To maximize this reimbursement opportunity, hospitals and device manufacturers should collaborate with operational rigor, ensuring segmentation billing adheres to Medicare guidelines. Segmentation should be documented explicitly as a standalone, medically necessary service to prevent denials due to bundling.

When partnering with external device manufacturers or technology vendors, hospitals could consider mandating that segmentation appears as a separate line item on invoices. Crucially, essential metadata should integrate into billing systems via automated reports or electronic health record (EHR) interfaces. This could streamline compliant documentation, precise charge capture, and reliable reimbursement under HCPCS C8001, while supporting audit readiness.

Timing is equally important. In accordance with Medicare’s comprehensive Ambulatory Payment Classification (APC) packaging rules 12, which may package certain ancillary services when provided on the same day as other procedures, segmentation could potentially be performed and billed as a discrete outpatient imaging encounter, ideally on a date separate from surgery if clinically appropriate. This approach may help support C8001 reimbursement eligibility in certain circumstances and aligns with common clinical workflows, given that segmentation typically precedes surgical planning. Meticulous documentation for each case, encompassing modality, anatomical region, software, and timing, is indispensable for maintaining revenue cycle integrity and withstanding payer audits.

Early adopters will lead

Beyond immediate reimbursement, adopting AI-driven segmentation could position hospitals as leaders in precision medicine and value-based care. It may pave the way for scalable personalization in surgical workflows, envisioning a future where clinicians can simply “chat with an agent” about a case to access patient-specific devices and surgical kits. Early adopters stand to gain measurable benefits: enhanced surgical accuracy, reduced complications, shorter hospital stays, and stronger institutional reputations.

Hospitals that adopt and scale AI-powered 3D segmentation can address today’s economic pressures while laying the groundwork for broader reimbursement in the future. By collecting data on measurable clinical and financial impacts – such as reduced operative times and cost savings – institutions could petition the Centers for Medicare & Medicaid Services (CMS) and commercial payers for higher payments or new codes encompassing the full scope of patient-specific modeling and 3D printing.

Deploying AI-driven segmentation at an enterprise level goes beyond operational efficiency. It signals a commitment to pioneering the next era of precision healthcare. It equips clinicians with advanced tools for deeply personalized patient outcomes, positioning institutions at the forefront of medical innovation. We envision a near-future landscape where AI-driven segmentation powers the growing of patient-specific replacement parts right at the point of care – ushering in an ambitious era of on-demand regenerative medicine that transforms reimbursement models from procedural costs to health span restoring outcomes. 

Supported by comprehensive bedside kits that integrate AI segmentation, 3D printing, and biomanufacturing, surgeons could access ready-to-use, patient-matched solutions on demand, which could help with accelerating healing and restoring lives with unprecedented speed and precision. The future is accelerating, and hospitals investing now in scalable 3D visualization infrastructure will be the ones taking a first step towards this future.

Photo: santima.studio, Getty Images

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