Objective: To evaluate the impact of regulatory scenarios on the financial viability of medical device companies.
Design: We developed a model to calculate the expected net present value of a hypothetical product throughout preclinical development, clinical testing, regulatory approval, and postmarketing. We tested 3 scenarios: (1) the current regulatory environment; (2) a scenario in which medical devices are subject to the same evidence standards required for pharmaceuticals; and (3) a scenario consistent with the Coverage with Evidence Development: Coverage with Study Participation (CSP) policy proposed by the Centers for Medicare and Medicaid Services, whereby Medicare will pay for beneficiaries to receive new devices that are not currently determined to be "reasonable and necessary" if the patients participate in clinical studies or registries.
Measurements And Main Results: When applying assumptions consistent with the implantable cardioverter-defibrillator market, the net present value at the start of development was an estimated $553 million in the current regulatory environment, $322 million in the pharmaceutical scenario, and $403 million in the CSP scenario. Sensitivity analyses showed that the device industry would likely be profitable in all 3 scenarios over a range of assumptions.
Conclusions: The environment in which the medical device industry operates is financially attractive. Furthermore, when compared with the alternative of applying the same evidence standards for pharmaceuticals to medical devices, the CSP policy offers improved financial incentives for medical device companies.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2150632 | PMC |
http://dx.doi.org/10.1007/s11606-007-0246-9 | DOI Listing |
J Clin Orthod
November 2024
Division of Orthodontics, Department of Craniofacial Sciences; Division of Orthodontics, University of Connecticut School of Dental Medicine, Farmington, CT.
Int J Comput Assist Radiol Surg
January 2025
Advanced Medical Devices Laboratory, Kyushu University, Nishi-ku, Fukuoka, 819-0382, Japan.
Purpose: This paper presents a deep learning approach to recognize and predict surgical activity in robot-assisted minimally invasive surgery (RAMIS). Our primary objective is to deploy the developed model for implementing a real-time surgical risk monitoring system within the realm of RAMIS.
Methods: We propose a modified Transformer model with the architecture comprising no positional encoding, 5 fully connected layers, 1 encoder, and 3 decoders.
Am J Sports Med
January 2025
Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, New York, USA.
BMC Oral Health
January 2025
4th Division, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, People's Republic of China.
Background: The stability of soft and hard tissues surrounding the implant is not only a matter of aesthetics, but also affects the long-term stability of the implant. The present study was to explore the influence of buccal mucosa width/height (W/H) ratio, emergence profile and buccal bone width on peri-implant soft and hard tissue changes in the posterior region.
Methods: Fifty-eight posterior implant restoration cases were recruited in this study.
BMC Oral Health
January 2025
Sub-Institute of Public Safety Standardization, China National Institute of Standardization, No.4 Zhichun Road, Haidian District, Beijing, 100191, PR China.
Background: This study aimed to establish a model for predicting the difficulty of mandibular third molar extraction based on a Bayesian network to meet following requirements: (1) analyse the interaction of the primary risk factors; (2) output quantitative difficulty-evaluation results based on the patient's personal situation; and (3) identify key surgical points and propose surgical protocols to decrease complications.
Methods: Relevant articles were searched to identify risk factors. Clinical knowledge and experience were used to analyse the risk factors to establish the Bayesian network.
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