- Bundled payments for total knee arthroplasty (TKA) focus on costs within 90 days of the surgical procedure and are not designed to contain long-term costs
- Simulation modeling indicated that greater adoption of customized, individually made knee implants would result in substantial reductions in the cost of TKA procedures, recovery, readmissions and revision surgeries
- Modeling results also indicated that if insurance coverage of bundled payments for procedures increased to 90%, the health care system could achieve cumulative savings of $38 billion by 2026
Customized, individually made knee implants (CIM) have been adopted slowly since being introduced around 2011. Aside from the natural resistance to adopting new technology, surgeons and hospitals tend to prefer off-the-shelf implants (OTS) because of proven outcomes, familiarity, perception of lower legal risk and established contracts with manufacturers.
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One of the greatest barriers, though, is that the upfront cost for CIM is 20% to 30% higher than for OTS. Both Medicare and third-party payers typically pay hospitals a bundled payment for all costs associated with total knee arthroplasty (TKA) and the following 90 days of care, so hospitals have a profit motive to contain expenses during this period.
Potential long-term savings, such as savings from fewer revision surgeries, are not relevant to a 90-day bundled payment. This situation highlights the need for more cost-effective long-term strategies, according to Hany S. Bedair, MD, of the Department of Orthopedics at Massachusetts General Hospital, and colleagues.
The research team created a system dynamics simulation model that produced a comparative analysis for CIM versus OTS procedures, taking bundled payment programs into account. In Value in Health they report that wider adoption of CIM would reduce costs for all stakeholders, but better insurance coverage would be required.
The model explored how different factors might interact to influence the economic and patient outcomes of OTS and CIM procedures. It simulated changes from 2018 to 2026 under various what-if scenarios. The main assumptions were:
- CIM adoption rates by patients are influenced by out-of-pocket surgery costs and surgeons' recommendations
- Surgery costs for patients depend on levels of insurance coverage
- Surgeons' recommendations are mainly influenced by outcomes of previous patients, established contracts with vendors and levels of insurance coverage
Some research shows that CIMs have advantages over OTS in certain categories of patient outcomes/functionality, and those data were incorporated into the model. Established contracts between hospitals and vendors and natural resistance to new technology were considered barriers to CIM adoption in the model and changed with the changing ratio of CIM adopters to OTS users.
Readmissions and Revision Surgeries
The highest numbers of patients who were readmitted or underwent revision surgeries occurred in the current scenario (<5% market share for CIM). The lowest numbers were predicted for 2026 with 90% coverage of CIM procedures, at which time readmissions would be reduced by 62% and revision surgeries would be reduced by 39%.
The researchers analyzed the costs of the procedure (product, surgeon fees and operating room time), recovery, readmission within 90 days and revision surgery within three years. The costs of all four categories were lower with CIM than with OTS procedures, and those lower costs compensated for the higher cost of CIM implants.
The higher the insurance coverage rate for CIM, the higher the cost savings. The highest cumulative savings for all stakeholders together was approximately $38 billion, achieved in 2026 under 90% coverage for CIM procedures.
Cost savings to patients could be achieved with only 50% insurance coverage of CIM, the model showed. The savings significantly increased for higher coverage rates: $1,600 per patient for 70% and $2,200 for 90%.
The Importance of Systems Thinking
Because of the model's systematic perspective, it allows decision makers to test the effects of different policies, the researchers note. For example, insurers can consider a dynamic scenario for their coverage of CIM procedures on the basis of their initial investment and savings throughout the simulation time. They can also test the effect of time delays.
The model is flexible enough that innovative policies and interventions can be studied as new information becomes available. The authors believe their modeling approach could be used to evaluate a broad range of customized therapies in the coming era of personalized medicine.
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