Safely Introducing New Surgical Devices in Minimally Invasive Gynecologic Surgery: An Argument for Use of the IDEAL Framework.

OBJECTIVE: To review the Food and Drug Administration (FDA) premarket regulatory and postmarket surveillance processes for 2 minimally invasive gynecologic surgery case studies-the laparoscopic power morcellator and the Essure permanent birth control device-and to introduce the IDEAL (Idea, Development, Exploration, Assessment, Long-term) framework for safely introducing and monitoring minimally invasive gynecologic surgery devices. DATA SOURCES: News media publications, agency websites, legal articles, and scientific papers. METHODS OF STUDY SELECTION: Articles and papers were selected that described events leading to FDA approval of these devices and subsequent complications. TABULATION, INTEGRATION, AND RESULTS: Based on current FDA premarket regulatory processes, 67% of devices do not currently require premarket review and only 1% are subject to clinical trial requirements. The IDEAL framework delineates 5 phases for safe introduction of medical devices: idea, development, exploration, assessment, and long-term study. CONCLUSION: The laparoscopic power morcellator and Essure cases illustrate possible shortcomings in our device approval system that could be improved upon using the IDEAL framework, which limits device use until safety and efficacy data have been established and recommends postmarket surveillance.

Data Requirement for Animal-Derived Wound Care Devices: Limitations of the 510(k) Regulatory Pathway.

BACKGROUND: Device classification and preclinical data requirements for animal-derived wound care products were recently reviewed by the FDA. Given the possible performance differences for these products, we evaluated the FDA data requirements as well as the published literature for all animal-derived wound care products ever cleared through the FDA. STUDY DESIGN: The publicly available online database was queried for all animal-derived wound products; premarket data requirements for each product were recorded. A PubMed search was conducted to determine the number of published clinical studies for each product, and manufacturer websites were accessed to obtain the price for each product. RESULTS: A total of 132 animal-derived wound products have been cleared by the FDA since the Center for Devices and Radiological Health was established in 1976. Of these, 114 had a publicly available clearance statement online. Preclinical biocompatibility testing was performed in 85 products (74.6%) and referenced in 10 (8.8%). Preclinical animal wound healing testing took place in 17 (14.9%). Only 9 products (7.9%) had clinical safety testing, and no products had clinical effectiveness data. We found no published peer-reviewed clinical data for 97 products (73%). Cost was infrequently available but ranged from $4.79 to $2,178 per unit. CONCLUSIONS: Although the current pathway is appropriate for efficiently clearing new wound care products, clinical effectiveness is not included in the regulatory review process. Wound care products are primarily evaluated by the FDA for safety and biocompatibility. Thus, any claims of clinical effectiveness require independent validation, which is often lacking.

[Introduction and Reflection on Novel Medical Device Regulatory Science Tool MDDT].

In recent years, emerging technology medical devices have developed rapidly. How to more scientifically and more efficiently regulate these novel medical devices so as to improve access to advanced medical technology while ensuring safety and effectiveness is a new challenge faced by regulatory authorities, and is also the core topic of regulatory science. New tools, new standards and new methods are important means to achieve regulatory science. "Medical Device Development Tool" proposed by the U.S. FDA is a novel medical device regulatory science tool, which can help medical device developers to predict and evaluate product performance more efficiently. It is also helpful for regulatory authorities to make regulatory decisions more efficiently. This study introduces the concept, qualification process, role of MDDT in medical device regulation and MDDT examples, and makes some discussion on the device evaluation from the perspective of reliability and validity. MDDT can facilitate the developing of novel medical device.

Aligning US Agency Policies for Cardiovascular Devices Through the Breakthrough Devices Program.

Importance: The US Food and Drug Administration (FDA) and Centers for Medicare & Medicaid Services (CMS) have different statutory authorities; FDA evaluates safety and effectiveness for market authorization of medical devices while CMS determines whether coverage is "reasonable and necessary" for its beneficiaries. CMS has recently enacted policies automatically providing supplemental reimbursement for new, costly devices authorized after designation in FDA's Breakthrough Devices Program (BDP) and in June 2023 issued notice for a new Transitional Coverage for Emerging Technologies pathway, accelerating coverage for Breakthrough devices. Observations: Aiming to incentivize innovation, FDA awards Breakthrough designations early in device development to expedite market authorization and can accept greater uncertainty in benefit and risk, contingent on postmarket evidence generation. Since 2020, Breakthrough designation has effectively automatically qualified devices to receive supplemental Medicare reimbursement after CMS waived a long-standing requirement that devices demonstrate "substantial clinical improvement" for beneficiaries. Using publicly available information, 3 examples of cardiovascular devices illustrate that the BDP may allow for FDA authorization based on less rigorous evidence, such as single-arm trials focused on surrogate end points with short-term follow-up whose participants are often not representative of Medicare beneficiaries. In 1 case, Breakthrough designation allowed a 30% decrease in enrollment of a trial used to support approval. Initial positive findings for some devices have remained unverified, and in 1 case even partially nullified, by postmarket studies. Manufacturers have also used Breakthrough designations to set the price of devices to facilitate additional pass-through payments, leading to higher short-term and long-term costs to CMS and health care systems. Conclusions and Relevance: The BDP may qualify new, costly devices for higher and automatic Medicare reimbursement despite evidence not being representative of CMS beneficiaries and persistent uncertainty of benefit and risk. To ensure the best evidence is generated to inform clinical care, FDA could apply more selectivity to BDP eligibility, specify objective criteria for revoking Breakthrough designation when appropriate, and ensure timely postmarket evidence generation, whereas CMS could independently review clinical evidence, advise manufacturers about standards for coverage review, and make supplemental payments and long-term device reimbursement contingent on clinical outcome benefit and postmarket evidence generation.

Analysis of Devices Authorized by the FDA for Clinical Decision Support in Critical Care.

This case series study examines the clinical evidence cited for US Food and Drug Administration­approved clinical decision support devices for use in the critical care setting.

An FDA Guide on Indications for Use and Device Reporting of Artificial Intelligence-Enabled Devices: Significance for Pediatric Use.

Radiology has been a pioneer in adopting artificial intelligence (AI)-enabled devices into the clinic. However, initial clinical experience has identified concerns of inconsistent device performance across different patient populations. Medical devices, including those using AI, are cleared by the FDA for their specific indications for use (IFUs). IFU describes the disease or condition the device will diagnose or treat, including a description of the intended patient population. Performance data evaluated during the premarket submission support the IFU and include the intended patient population. Understanding the IFUs of a given device is thus critical to ensuring that the device is used properly and performs as expected. When devices do not perform as expected or malfunction, medical device reporting is an important way to provide feedback about the device to the manufacturer, the FDA, and other users. This article describes the ways to retrieve the IFU and performance data information as well as the FDA medical device reporting systems for unexpected performance discrepancy. It is crucial that imaging professionals, including radiologists, know how to access and use these tools to improve the informed use of medical devices for patients of all ages.

Assessment of US Food and Drug Administration-Approved Digital Medical Devices for Just-in-Time Interventions: A Systematic Review.

Importance: Just-in-time interventions (JITIs) are a type of digital therapeutic intervention that combines remote monitoring tools and algorithms to personalize the delivery of specific interventions at the right time. The US Food and Drug Administration (FDA) regulatory approval documents are often the only available source of information on the effectiveness of therapeutic interventions based on these devices. Objective: To systematically review the publicly available information from the FDA on all recently approved medical devices used in JITIs to (1) assess how they operate to deliver JITIs and (2) appraise the evidence supporting their performance and clinical effectiveness. Evidence Review: Two reviewers systematically searched the Premarket Notifications (510(k)), Premarket Approvals, De Novo, and Humanitarian Device Exemption databases from January 2019 to December 2021 for all entries associated with devices that monitored patients' data over time to personalize the delivery of interventions to treat, prevent, or mitigate health conditions or events. They assessed whether the product summaries (1) enabled an understanding of how the device operated to deliver a JITI (eg, the nature, type, and frequency of the monitoring, the nature of the decision algorithm, and the nature and intended receiver of the intervention); (2) informed about the performance and effectiveness of the JITI; and (3) included information on data security and ownership. Findings: In total, 38 devices were included in this review. These were mainly intended for cardiac conditions (12 [31.6%]), diabetes (10 [26.3%]), and neurological diseases (4 [10.5%]). Monitoring devices ranged from wearable (18 of 28 [64.4%]; eg, smartwatches) to implanted sensors (6 of 28 [21.4%]; eg, inserted electrocardiographic sensors). Only 10 of 38 product summaries (26.3%) allowed a full understanding of how the device operated to deliver a JITI. Similarly, only 12 of 28 (42.9%), 12 of 36 (33.3%), and 5 of 38 (13.2%) reported the assessment of the performance of the monitoring device, assessment of the decision algorithm, and results of clinical studies assessing the effectiveness of the JITI, respectively. Finally, 14 of 36 product summaries (38.9%) included some information on data security, but none included information on data ownership. Conclusion and Relevance: The results of this systematic review suggest that the information publicly available in the FDA databases on the performance and effectiveness of digital medical devices used in JITIs is heterogeneous.

Marketing and US Food and Drug Administration Clearance of Artificial Intelligence and Machine Learning Enabled Software in and as Medical Devices: A Systematic Review.

Importance: The marketing of health care devices enabled for use with artificial intelligence (AI) or machine learning (ML) is regulated in the US by the US Food and Drug Administration (FDA), which is responsible for approving and regulating medical devices. Currently, there are no uniform guidelines set by the FDA to regulate AI- or ML-enabled medical devices, and discrepancies between FDA-approved indications for use and device marketing require articulation. Objective: To explore any discrepancy between marketing and 510(k) clearance of AI- or ML-enabled medical devices. Evidence Review: This systematic review was a manually conducted survey of 510(k) approval summaries and accompanying marketing materials of devices approved between November 2021 and March 2022, conducted between March and November 2022, following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. Analysis focused on the prevalence of discrepancies between marketing and certification material for AI/ML enabled medical devices. Findings: A total of 119 FDA 510(k) clearance summaries were analyzed in tandem with their respective marketing materials. The devices were taxonomized into 3 individual categories of adherent, contentious, and discrepant devices. A total of 15 devices (12.61%) were considered discrepant, 8 devices (6.72%) were considered contentious, and 96 devices (84.03%) were consistent between marketing and FDA 510(k) clearance summaries. Most devices were from the radiological approval committees (75 devices [82.35%]), with 62 of these devices (82.67%) adherent, 3 (4.00%) contentious, and 10 (13.33%) discrepant; followed by the cardiovascular device approval committee (23 devices [19.33%]), with 19 of these devices (82.61%) considered adherent, 2 contentious (8.70%) and 2 discrepant (8.70%). The difference between these 3 categories in cardiovascular and radiological devices was statistically significant (P < .001). Conclusions and Relevance: In this systematic review, low adherence rates within committees were observed most often in committees with few AI- or ML-enabled devices. and discrepancies between clearance documentation and marketing material were present in one-fifth of devices surveyed.

Availability of Unique Device Identifiers for Class I Medical Device Recalls From 2018 to 2022.

This cross-sectional study describes the inclusion of unique device identifier in recall notices for moderate- and high-risk medical devices in the US.

Time to publication of cost-effectiveness analyses for medical devices.

OBJECTIVES: Academic researchers and physicians have called for greater use of cost-effectiveness analyses in informing treatment and reimbursement decisions. This study examines the availability of cost-effectiveness analyses for medical devices, in terms of both the number of studies and when studies are published. STUDY DESIGN: Analysis of the number of years between FDA approval/clearance and publication for cost-effectiveness analyses of medical devices in the United States published between 2002 and 2020 (n = 86). METHODS: Cost-effectiveness analyses of medical devices were identified using the Tufts University Cost-Effectiveness Analysis Registry. Studies in which the model and manufacturer of the medical device used in the intervention were identifiable were linked to FDA databases. Years between FDA approval/clearance and publication of cost-effectiveness analyses were calculated. RESULTS: A total of 218 cost-effectiveness analyses of medical devices in the United States published between 2002 and 2020 were identified. Of these studies, 86 (39.4%) were linked to FDA databases. Studies examining devices approved via premarket approval were published a mean of 6.0 years after the device received FDA approval (median, 4 years), whereas studies examining devices that were cleared via the 510(k) process were published a mean of 6.5 years after the device received FDA clearance (median, 5 years). CONCLUSIONS: There are few studies describing the cost-effectiveness of medical devices. Most of these studies' findings are not published until several years after the studied devices received FDA approval/clearance, meaning that decision makers will likely not have evidence of cost-effectiveness when making initial decisions related to newly available medical devices.

More than algorithms: an analysis of safety events involving ML-enabled medical devices reported to the FDA.

OBJECTIVE: To examine the real-world safety problems involving machine learning (ML)-enabled medical devices. MATERIALS AND METHODS: We analyzed 266 safety events involving approved ML medical devices reported to the US FDA's MAUDE program between 2015 and October 2021. Events were reviewed against an existing framework for safety problems with Health IT to identify whether a reported problem was due to the ML device (device problem) or its use, and key contributors to the problem. Consequences of events were also classified. RESULTS: Events described hazards with potential to harm (66%), actual harm (16%), consequences for healthcare delivery (9%), near misses that would have led to harm if not for intervention (4%), no harm or consequences (3%), and complaints (2%). While most events involved device problems (93%), use problems (7%) were 4 times more likely to harm (relative risk 4.2; 95% CI 2.5-7). Problems with data input to ML devices were the top contributor to events (82%). DISCUSSION: Much of what is known about ML safety comes from case studies and the theoretical limitations of ML. We contribute a systematic analysis of ML safety problems captured as part of the FDA's routine post-market surveillance. Most problems involved devices and concerned the acquisition of data for processing by algorithms. However, problems with the use of devices were more likely to harm. CONCLUSIONS: Safety problems with ML devices involve more than algorithms, highlighting the need for a whole-of-system approach to safe implementation with a special focus on how users interact with devices.

Identification of predicate creep under the 510(k) process: A case study of a robotic surgical device.

The FDA's 510(k) process for medical devices is based on "substantial equivalence" to devices clearedpre-1976 or legally marketed thereafter, known as predicate devices. In the last decade, several high-profile device recalls have drawn attention to this regulatory clearance process and researchers have raised questions about the validity of the 510(k) process as a broad clearance mechanism. One of the issues raised is the risk of predicate creep, a cycle of technology change through repeated clearance of devices based on predicates with slightly different technological characteristics, such as materials and power sources, or have indications for different anatomical sites. This paper proposes a new way to identify potential "predicate creep" through the use of product codes and regulatory classifications. We test this method by applying it to a case study of a Robotic Assisted Surgery (RAS) device, the Intuitive Surgical Da Vinci Si Surgical System. We find that there is evidence of predicate creep using our method, and discuss implications of this method for research and policy.

Evolution of China regulatory guidance on the clinical evaluation of medical devices and its implication on pre-market and post-approval clinical evaluation strategies.

INTRODUCTION: The rapid growth of the medical device industry has driven the evolution of regulatory guidance on medical device clinical evaluation (MDCE) of the China National Medical Products Administration (NMPA), which consequently influences pre-market and post-approval clinical evaluation (CE) strategies. AREAS COVERED: We aimed to investigate the 3-stage evolution of NMPA regulatory guidance on MDCE (1. The era before specific CE guidance, 2. 2015 CE Guidance, and 3. 2021 CE Guidance Series), analyze the gaps between each stage, and assess the evolution's impact on pre-market and post-approval CE strategies. EXPERT OPINION: The fundamental principles of the NMPA 2021 CE Guidance Series were transformed from the 2019 International Medical Device Regulatory Forum documents. Compared with the 2015 guidance, the 2021 CE Guidance Series further clarifies the CE definition by emphasizing the ongoing activity of CE through an entire product lifecycle and the use of scientifically sound methods for CE and narrows the pre-market CE pathways into the equivalent device and clinical trial pathways. The 2021 CE Guidance Series simplifies the process for selecting the pre-market CE strategy but does not specify post-approval CE update cadency and general requirements for post-market clinical follow-up.

Global Medical Device Clinical Trials Involving Both the United States and Japan: Key Considerations for Development, Regulatory Approval, and Conduct.

As medical device development becomes increasingly global, the opportunities and potential advantages offered by international clinical trial and regulatory approval strategies are also growing. In particular, medical device clinical trials involving sites in both the United States and Japan and intended to support marketing in both countries may warrant particular consideration, given the similarities in their regulatory systems, patients and clinical practice patterns, and market sizes. Since 2003, the US-Japan Harmonization By Doing (HBD) initiative has been focused on identifying and addressing clinical and regulatory barriers to medical devices access in both countries via collaboration between governmental, academic, and industry stakeholders. Through the efforts of HBD participants, US-Japanese clinical trials have been conducted and the resulting data have supported regulatory approval for marketing in both countries. Based on these experiences, this paper outlines some of the key factors to consider when developing a global clinical trial involving US and Japanese participation. These considerations include the mechanisms for consultation with regulatory authorities on clinical trial strategies, the regulatory framework for clinical trial notification and approval, recruitment and conduct of clinical sites, and lessons learned from specific US-Japanese clinical trial experiences. The goal of this paper is to promote global access to promising medical technologies by assisting potential clinical trial sponsors in understanding when an international strategy may be appropriate and successful.