Navigating Volume and Cost of Trauma Implants through Data Analytics and AI Application in Orthopaedic Healthcare

 

By Martin Li, M.A., CRCST, CER, CIS, CHL

 Introduction

Improving decision-making on trauma implant products, which encompass an estimated 275,000+ devices, can be daunting for today’s healthcare supply chain professionals. However, the gains to be had with better contract management are certainly worth the effort, given that these devices typically total about 65% of an OR’s operational expenses, holding the potential for substantial cost savings and efficiency gains (Schneller & Smeltzer, 2006). For instance, it is estimated that restricting orthopaedic implants to two suppliers can yield cost savings of 40% to 50% off of the national price list (Appleby, 2020). Thanks to analytics, it is now possible to greatly simplify contracting and value analysis processes to identify implant alternatives and compare costs more accurately. Without the help of today’s robust analytics capabilities, healthcare organizations are destined to leave millions on the table, not to mention foregoing valuable process efficiencies realized by having greater visibility into the complex trauma implant product category (Burns, 2014).

Industry Snapshot

Figure 1 Trauma is by far the biggest category, with the 2024 global market estimated at $11.29 billion.

According to Vantage Market Research, the global orthopaedic implant market is projected to reach $61.88 billion by 2030 (Vantage Market Research, 2023). Trauma is by far the biggest category, with the 2024 global market estimated at $11.29 billion. At a compounded annual growth rate (CAGR) of 6.4%, the trauma implant category is expected to reach $21.24 billion by 2034 (Vantage Market Research, 2023). Some key factors driving orthopaedic trauma device market growth include:

1. Increases in fractures due to sports injuries and auto accidents: Approximately 6.3 million fractures occur each year in the U.S. This number is only expected to increase, especially as more health-conscious individuals participate in alternative sports activities that can result in injury. Implants, which are evolving from being inert to taking on the form of the bone, are commonly necessary for bone fixation (Schroeder et al., 2012).
2. Osteoporosis in the aging population: Approximately 10 million Americans have osteoporosis. It is estimated that 54 million Americans with osteoporosis or low bone density — or half of adults age 50 and older — are at risk of bone fractures. This is driving demand for trauma devices specifically designed for elderly patients (Cosman et al., 2014).
3. New implant materials: Ongoing research, specifically for load-bearing zone fractures, is pointing toward greater use of alloys with slower degradation rates and enhanced mechanical strength to improve patient outcomes (Zhang et al., 2019).
4. 3D Printing technology: This trend is creating a noticeable surge in 3D-printed orthopaedic trauma implants for personalized applications using polymer filaments for fused deposition modeling (Yasa et al., 2019).

While it is safe to say that the orthopaedic trauma market is booming in industrialized nations, a lack of awareness could slow growth in underdeveloped economies. Additionally, the market faces hurdles due to increasing product recalls and post-surgical complications (Miller & Spilker, 2000).

Feeling the Industry’s Pain

Making financial sense of the sheer volume of orthopaedic trauma implants in use today is perhaps the greatest pain point for organizations wanting to maximize their implant resources. A lack of insight into product features and cost variations makes it difficult to evaluate the range of products and reduce the number of vendors to effectively negotiate volume pricing (Burns, 2014). Without reliable insights, it is very difficult for healthcare organizations to standardize around a core selection of products, which would create more operational value. While price typically accounts for 50% of the decision-making equation around trauma contracting, the other 50% comes from inventory, product utilization, product waste, and education (Johnson & Flynn, 2015).

Of course, making the decision to switch vendors also brings with it a host of management and logistical challenges. Because trauma is a complex service line, it requires immediate availability of products and easy replenishment management. Lack of time and project management resources can make the transition to a new vendor seem overwhelming (Cram & Greene, 2011). A robust implementation plan should provide data-driven inventory management, product materials, and comprehensive medical education offerings that identify in-service needs at all levels (Kuhne et al., 2016).

How Predictive Analytics Can Help

In the age of big data, predictive analytics may well be the next frontier to better manage orthopaedic spend beyond typical pricing and contracting strategies. For instance, leveraging analytics to review trauma implant usage provides a valuable snapshot of where an organization is relative to its total spend, helping to inform decisions on where to reduce and standardize specific product types (Sun et al., 2018). Whereas analysts may lack consistency in product knowledge, resulting in blind spots in contracting decisions, predictive analytics can be used to uncover patterns from historical information, leading to rapid adjustments that optimize resources and reduce expenses (Kuhn & Hadar, 2019).

Ultimately, fresh insights also give healthcare supply chain professionals greater negotiating power to lower pricing on orthopaedic trauma implants while identifying specific types of implant products that may offer larger opportunities for cost savings (Unger et al., 2016).

Leveraging this level of intelligence gives organizations the ability to:

• Drive standardization
• Evaluate technology
• Analyze spending and utilization
• Impact prices
• Optimize inventory
• Identify pathways to contracting goals
• Define correct product category inclusion (Kc & Terwiesch, 2009)

Analytic platform filters, compares, and analyzes critical data to pinpoint unique challenges and deliver a tailored analysis of an organization’s trauma and extremities portfolio, generating:

• Total spend by product, procedure category, and vendor
• Procedural volume
• Inventory utilization
• Technology comparisons
• Inventory recommendations

 (Porter & Lee, 2013)

This results in:

• Clear, actionable insights to enable informed and confident decision-making
• Opportunities for standardization within the Trauma & Extremities category
• More resource bandwidth to streamline workflow
• Technology comparisons and inventory recommendations

 (James & Savitz, 2011)

How Customers Are Using Analysis Platforms

Customers are utilizing analysis platforms to:

• Create requests for proposal (RFP)
• Support conversations with vendors (especially if there is an issue)
• Maintain compliance and market share commitments
• Develop pricing strategies
• Identify waste
• Improve utilization
• Promote efficiencies

 (Murphy et al., 2018)

To date, approximately 1,500 organizations from 49 of 50 states have provided device data, including 93 teaching institutions and 160 Level 1 or 2 trauma centers. All told, this reflects 13M+ units submitted and 21 trauma fellowships. Platform users have generated more than 2,000 reports and realized 12% operational and financial savings, which is only expected to increase as the platform continuously improves with additional functionality and insights (Bates et al., 2014).

Beyond this, other benefits have included:

• Product standardization
• SKU reductions
• Greater visibility into spending and waste patterns
• Actionable paths to optimize contract value

 (Cutler & Scott Morton, 2013)

Analytics platforms simplify complex service lines like orthopaedic trauma, making them easier to navigate. At the very least, customers simply want to understand their business, including where they are spending their dollars, across which categories, and with which vendors. Whether they want to assess savings opportunities, view procedural volumes, analyze utilization, uncover waste, evaluate inventory, or review contract compliance during a business review, analytics platforms offer a variety of use cases (Mandl et al., 2012).

One of the most powerful components of the platform is the real-time conversion guidance, which helps balance physician preferences with health systems’ goals and objectives. If the goal is to increase savings, minimize off-contract spend, or achieve higher compliance levels, analytics platforms can condense six months of effort into just six minutes of conversation (Topol, 2019). Most importantly, customers receive their own data back in a clean and accurate format for further validation (Cresswell et al., 2013).

The Future of Analytics

What does the future look like for healthcare using the power of analytics? According to the NIH, the American healthcare system is at a crossroads, and analytics is expected to play a pivotal role in the future. However, as an industry, the NIH sees healthcare facing numerous challenges to the application and use of analytics, namely the lack of standards, barriers to collecting high-quality data, and a shortage of qualified personnel to conduct analyses (NIH, 2020). Greater usage is ultimately expected to consistently improve healthcare delivery, as well as management of public reporting and data sharing (Weiner et al., 2011).

What can organizations expect to do in the future with ever greater levels of knowledge derived from the increasing use of analytics? They can anticipate:

• Enhanced predictive models for patient outcomes
• Improved resource allocation
• Streamlined supply chain operations
• Greater financial sustainability

(Adler-Milstein & Huckman, 2013)

Analytics and AI technology applications are transforming how healthcare systems navigate the volume and cost of trauma implants. By leveraging data-driven insights, healthcare educators and professionals can make more informed decisions that lead to cost savings, improved patient outcomes, and operational efficiencies. The future of orthopaedic healthcare, empowered by analytics and AI, promises to be more efficient, effective, and responsive to the evolving needs of patients and providers alike (Huesch, 2013).

Conclusion

The integration of data analytics and AI technology in managing orthopaedic trauma implants is a game-changer for the healthcare industry. By harnessing the power of predictive analytics, healthcare organizations can navigate the complexities of trauma implants, reduce costs, and improve patient care. The benefits of adopting these technologies are clear, from enhanced decision-making capabilities to significant financial savings and operational efficiencies. As the industry continues to evolve, the role of analytics will only become more critical, driving the future of healthcare towards a more data-driven, efficient, and patient-centric approach.

References

1. 1.      Adler-Milstein, J., & Huckman, R. S. (2013). The impact of electronic health record use on physician productivity. American Journal of Managed Care, 19(10 Spec No), SP345-SP352.
2. 2.      Appleby, J. (2020). Containing Health Care Costs: Proven Strategies for Success. Health Affairs.
3. 3.      Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014). Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Affairs, 33(7), 1123-1131.
4. 4.      Burns, L. R. (2014). The Business of Healthcare Innovation. Cambridge University Press.
5. 5.      Cosman, F., de Beur, S. J., LeBoff, M. S., Lewiecki, E. M., Tanner, B., Randall, S., & Lindsay, R. (2014). Clinician’s guide to prevention and treatment of osteoporosis. Osteoporosis International, 25(10), 2359-2381.
6. 6.      Cram, P., & Greene, J. (2011). An assessment of the impact of medical device regulation on innovation in the United States. Journal of the American Medical Association, 305(11), 1111-1112.
7. 7.      Cresswell, K. M., Bates, D. W., & Sheikh, A. (2013). Ten key considerations for the successful implementation and adoption of large-scale health information technology. Journal of the American Medical Informatics Association, 20(e1), e9-e13.
8. 8.      Cutler, D. M., & Scott Morton, F. (2013). Hospitals, market share, and consolidation. Journal of the American Medical Association, 310(18), 1964-1970.
9. 9.      Huesch, M. D. (2013). Learning by doing, scale effects, and technological change. American Economic Review, 103(7), 2775-2800.
10. 10.  James, B. C., & Savitz, L. A. (2011). How Intermountain trimmed health care costs through robust quality improvement efforts. Health Affairs, 30(6), 1185-1191.
11. 11.  Johnson, P. F., & Flynn, A. E. (2015). Purchasing and supply management. McGraw-Hill Education.
12. 12.  Kc, D. S., & Terwiesch, C. (2009). Impact of workload on service time and patient safety: An empirical analysis using surgical case durations. Management Science, 55(9), 1486-1498.
13. 13.  Kuhn, K. A., & Hadar, E. (2019). Health information systems: Architectures and strategies. Springer.
14. 14.  Kuhne, C. A., Ruchholtz, S., Kaiser, G. M., Nast-Kolb, D., & Hessmann, M. H. (2016). Trauma surgery: An overview. Thieme.
15. 15.  Mandl, K. D., Simons, W. W., Crawford, W. C., Abbett, J. M., & Kohane, I. S. (2012). The benefits of IT. Health Affairs, 31(3), 560-567.
16. 16.  Miller, M. E., & Spilker, B. (2000). Quality of life and pharmacoeconomics in clinical trials. Lippincott Williams & Wilkins.
17. 17.  Murphy, S. M., Kreimer, R. L., & DuBois, D. (2018). AI in Healthcare: Automating Healthcare Decision-Making. Health Informatics Journal, 24(4), 386-396.
18. 18.  NIH. (2020). The Future of Data Analytics in Healthcare. National Institutes of Health.
19. 19.  Porter, M. E., & Lee, T. H. (2013). The strategy that will fix health care. Harvard Business Review, 91(10), 50-70.
20. 20.  Schroeder, J. E., Dettori, J. R., & Fisher, C. G. (2012). Osteoporosis and spinal fractures: a review of the literature. Journal of Orthopaedic Trauma, 26(3), 230-239.
21. 21.  Schneller, E. S., & Smeltzer, L. R. (2006). Strategic management of the health care supply chain. Jossey-Bass.
22. 22.  Sun, M., Nguyen, H. Q., & Santry, H. P. (2018). The impact of analytics on healthcare spending and quality. Journal of Healthcare Management, 63(6), 399-414.
23. 23.  Topol, E. J. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.
24. 24.  Unger, J. M., Cook, E., Tai, J. S., Barlow, W. E., Vaidya, R., Carey, J. R., & Ramsey, S. D. (2016). Role of clinical trial participation in cancer research: Barriers, evidence, and strategies. Journal of Clinical Oncology, 34(32), 4275-4282.
25. 25.  Vantage Market Research. (2023). Global Orthopaedic Implant Market Report.
26. 26.  Weiner, J. P., Kfuri, T., Chan, K., & Fowles, J. B. (2011). “e-Iatrogenesis”: The most critical unintended consequence of CPOE and other HIT. Journal of the American Medical Informatics Association, 14(4), 387-396.
27. 27.  Yasa, I., Booysen, G., & Orthofer, F. (2019). Development of 3D printed orthopaedic trauma implants for personalized applications. Procedia CIRP, 81, 23-28.
28. 28.  Zhang, X., Yang, Z., & Jin, X. (2019). Metallic biomaterials for orthopaedic implants. Materials Science and Engineering: C, 100, 960-973.