‘Medical device’ is defined as any instrument, apparatus, appliance, software, material, or another article, whether used alone or in combination, to be used for human beings for diagnosis, prevention, treatment, monitoring, or alleviation of disease. Medical devices differ from other health technologies in several respects:
- They often change rapidly
- Clinical outcomes often depend on the training, competence, and experience of the end-user
- Pricing is typically more dynamic than that of pharmaceuticals
- Costs often comprise both procurement costs (including the associated infrastructure) and running costs (including maintenance and consumables)
Core issues for decision-makers
Medical devices have a considerable potential to deliver economic value for the healthcare sector in increasing length and quality of life, producing savings in the use of other healthcare resources, and facilitating changes in the organization of care. But the economic evaluation of medical devices is less developed than the equivalent processes used for pharmaceuticals, and decision-makers are increasingly demanding evidence on the effectiveness and cost-effectiveness of medical technologies, given the noticeable pressures on healthcare budgets.
Specific characteristics of medical devices have been recognized that may pose challenges to economic evaluation:
Economic evaluation challenges of medical devices
An economic evaluation aims to provide a quantitative underpinning for reimbursement decisions. However, the development of economic assessment of medical devices faces various challenges stemming from external regulatory circumstances and product-specific characteristics. The current regulatory framework for medical devices, elements of the medical device industry, and market and device-related constraints to certain aspects of clinical trial design limit the availability of high-quality evidence during reimbursement decisions. Issues such as device reusability, procedural integration, and temporal changes in determinants of medical device performance challenge the calculation, validity, and generalizability of the cost-effectiveness of a medical device.
In the figure below, we can see the four exogenous factors that may jeopardize the ability to perform a thorough economic evaluation of medical devices, hindering the quality and timeliness of available data.1
1 Kirisits A, Redekop WK. The economic evaluation of medical devices: challenges ahead. Appl Health Econ Health Policy. 2013 Feb;11(1):15-26. doi: 10.1007/s40258-012-0006-9. PMID: 23329383
Other factors vary depending on the functional nature of the device, and these will translate in so many other challenges displayed in the figure below.
Medical devices often have a weak evidence base for an economic evaluation during market entry. Consequently, when evaluating a new device, a balance must be struck between data availability (and its quality) and the timeliness of an economic evaluation. The need to draw on data from observational and small or nonblinded experimental studies translates into more significant parameter uncertainty and threatens both the validity and precision of cost-effectiveness calculations. Determinants such as user skill and organizational factors can confound the generalizability of economic evaluations due to variations in these determinants between healthcare settings. A sufficiently accurate and precise cost analysis can be a complex – if not impossible – undertaking due to the number and shared character of relevant cost items associated with device use. In the case of reusable devices, the attribution of fixed costs and varying clinical effectiveness and utilization rates for different target indications must be considered. Temporal changes in the determinants of device-related outcomes and expenses mean that cost-effectiveness estimates can have a limited shelf-life.
An economic evaluation based on poor-quality clinical studies may lead to an underestimate or overestimation of the actual cost-effectiveness of a device. However, the availability of good quality data will not be sufficient if the quantity of data on a device is too small (number of patients, measurements, or clinical events); there will be substantial uncertainty about its cost-effectiveness, which in turn could result in significant uncertainty about whether to reimburse it. Two unfavorable scenarios are imaginable: an incorrect decision to return the device and a wrong decision to reject it. Both decisions will result in inefficient use of resources and possibly health loss through diverting funds away from more cost-effective interventions.