Innovation, value, and cost containment in shoulder arthroplasty

Abstract

Background

The words “innovation” and “value” are generally not used in the same sentence when considering orthopedics. When used independently, however, it is evident that there has been no shortage of innovation within the field of orthopedics, especially in shoulder arthroplasty. Despite the abundance of innovations in shoulder arthroplasty prostheses and implants, it remains a challenge to identify whether there is apparent value associated with a given product. More often than not, new innovations are introduced with the promise of added value but prove to solely increase cost with no nominal outcome improvement.

Objective

This article considers shoulder arthroplasty within the contexts of innovation, value, and cost. In order to define innovation, value, and cost, we look to the work of numerous experts and scholars in the fields of business, economics, and medicine. Our goal is to identify the shortcomings of current innovations in shoulder arthroplasty. Our purpose is to hold these innovations accountable for the holistic consideration of the value equation—namely, improved outcomes and reduced costs. The ideal innovation in shoulder arthroplasty shoulder offer tangible value to all stakeholders involved in the episode of care inclusive of patients, surgeons, insurers, and vendors/industry.

Results

Under the premise that modern shoulder innovations are increasing in cost but have failed to demonstrate substantial improvements in patient treatment outcomes, we identify a number of key issues within shoulder surgery that remain: (1) better anatomical glenoid longevity, (2) better restoration of range of motion with reverse replacement, (3) reduced cost of implants, and (4) more transparency in outcomes.

Conclusion

In order to qualify an innovation in shoulder arthroplasty which creates value, it must be backed by robust evidence-based studies that demonstrate the desired improved outcomes and reduced cost without any interfering biases.