We can all agree that FDA has the right to set expectations for efficient and appropriate interaction with their review teams. However, FDA’s latest updates to the Q-sub guidance create more confusion than clarity and reveal frustrations at FDA with how industry is using the pre-submission program. As medical product development continues to become more complex along with the regulatory pathways we use to get them to market, FDA needs to find a balance between setting boundaries and putting up barriers to communication.
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On February 2, 2024, we finally got our long awaited Final Rule establishing the new Quality Management System Regulations (QMSR) from the FDA. The new QMSR is essentially the full implementation of ISO 13485 by incorporating the entirety of this standard by reference. With global regulatory frameworks becoming more difficult to manage in terms of compliance, harmonizing with the entirety of the ISO standard with FDA regualtions should have a net positive effect on industry.
Read MoreFDA has raised the bar on real-world evidence (RWE) studies with its new draft guidance. FDA’s expectations that RWE studies be good quality, rigorous, and conducted under a protocol and with IRB approval are not new. But the depth of data assessment, documentation, and requirements for submission outlined in this guidance have been augmented beyond what may be reasonable. A greater desire by regulators for clinical data to support medical device safety and effectiveness is here to stay, not just at FDA, but globally. While the motivations are genuinely for the benefit of public health, the reality is that clinical and observational research is expensive, complicated, time consuming, and burdensome. These costs will ultimately be passed down to payers and patients. I am not sure the incremental confidence FDA has in its regulatory decisions is worth the cost.
Read MoreA recent article published in NPJ Digital Medicine found that large language models (LLM) used in healthcare propagate racist medical advice that has largely been debunked. This is not a surprise to me, but it is an alarming finding that reminds us that the data we use to train our AI-driven devices and healthcare decision programs is horribly biased. Industry and regulators must ensure that data being used to train these devices are diverse, equitable, and representative to prevent potential patient harm.
Read MoreOne of the greatest risks to patients from use of AI in medical devices, and healthcare in general, is the presence of bias in the models used to train them. Given that we use our histories and historical data to train our AI models, we must address the inequity, racism, disparities, and disenfranchisement present in these data. This urgent issue must be taken up by industry to generate agreement on the definition of bias and how to address it. Regulators must hold industry accountable for ensuring equity in the models being used to train AI. We cannot afford to create greater public health disparities as we rush toward a future of AI in healthcare.
Read MoreA recent Northeastern University panel discussion about about the history and future of governance in artificial intelligence got me thinking about harmonization of medical device regulatory policy and wondering why it is so hard to do. As medical device technology is advancing at breakneck speed to bring AI-driven medical devices to patients and practitioners, we are seeing a rapid increase in policy implementation and legislation all intended to govern the use of AI in medical devices - but without much harmonization. Similar lack of harmonization in device classification, quality systems, and RWD/RWE governance have created barriers and costs to industry. With IMDRF being only a voluntary-based collaborative organization, what can be done to facilitate global harmonization of medical device regulation?
Read MoreAt the October NORD conference, Jeff Shuren suggested that AI-driven medical devices could be designed and evaluated using an FDA AI Assurance Lab. He suggested that this lab could house quality data on which AI algorithms could be trained. But, is this idea too good to be true? Where will the data come from? What therapeutic areas will it include? And will industry play nice in FDA’s AI Assurance Lab sandbox? Given the need for rational, consistent and transparent AI regulatory policy, this could be a great idea. But, the practical application of such a plan is unclear.
Read MoreThe desire for RWD/RWE by global regulators to demonstrate medical device safety and efficacy is growing. We exist in an era of unprecedented technological capabilities for data linking, tokenization to protect privacy, and big data analytics. However, many challenges remain with the use of these data for medicines and device evaluation. Data quality, common data models, alignment on statistical methodologies, data linkage technology, data access and privacy, stakeholder agreements for data sharing, patient input and consent, and ethical concerns persist. Regulators must collaborate such that industry can leverage these capabilities in a least burdensome way and ensure RWE is used to effectively and efficiently get innovative medicines and technologies to patients around the world.
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Regulation of artificial intelligence/machine learning (AI/ML) medical devices is heating up worldwide. While FDA has only issued one guidance document and an action plan, the EU is preparing to enact significant legislation that will govern the regulation of AI, including AI medical devices, in the EU. The EU AI Act is scheduled to go into affect in 2024. Medical Device sponsors with AI/ML devices must be ready to meet the requirements of the AI Act along with EU MDR.
Read MoreRequirements for clinical evidence of medical device safety and performance are increasing globally. Not only in Europe, but in Australia and the United States. For devices that have already been on the market in the United States, you may have an option you hadn’t thought of: a retrospective observational research study. This option is particularly useful for implants, but can be used for any medical device used by or prescribed by a clinician in the United States. Simply put, you can tap into existing medical records to gather sufficient clinical evidence of the safety and efficacy of your device. You just have to make sure you do it the right way.
Read MoreOn September 7, 2023, FDA released a new guidance document as part of the 510(k) modernization plan that sets out potential requirements for clinical data in pre-market submissions for Class II medical devices. This guidance should give pause to industry as the vague language appears to give FDA broad authority to request pre-market clinical data for Class II medical devices. The requirements appear to mimic what is happening in the EU under MDR where sufficient clinical data is necessary for a MDR CE mark of these same devices. Did the 510(k) program just get an EU MDR upgrade? If so, these new requirements will hamper innovation and delay patient access to new devices.
Read MoreWhat if we had an Attribute Library that helped create consistency across patient preference research? Would it work? While generating consistency and cross-study comparability would be beneficial to the field of patient preference, perhaps we should consider a more flexible repository of attribute knowledge. What attributes would you put in the repository if you could?
Read MoreCommenters on FDA’s recently opened docket for proposed changes to the PPI Guidance made their recommendations clear: we need more specificity in FDA’s expectations for use of PPI data in regulatory decision-making. Specifically, there is a strong desire for FDA use existing best practice recommendations; the need to increase the expectation and accountability of those conducting PPI studies to engage patients in the study design process; more transparency around how FDA will use PPI data in its decision-making; a desire to see the guidance expanded to therapeutics beyond medical devices; consistency in attribute definition and utilization; and greater clarity around FDA’s expectations for study methodology. The hope is that FDA takes these thoughtful and substantive comments into account when updating the PPI guidance.
Read MoreFDA recently requested public comment on updates to the PPI Guidance. The most common theme in the comments submitted suggested that all of us want to know what FDA really thinks about PPI studies and how they can support medical device regulatory decisions. If FDA can update the guidance with more transparency, the medical device industry will benefit and so will patients.
Read MoreIt’s been a few years since I wrote in this space. That is about to change. I’m ready to un-circle the wagons and jump back in to the discussion around patient-focused policy. Join me in moving the needle toward effective policy that gets the patient voice into regulatory decision-making.
Read MoreWhile attending a session about crowd-sourcing cures for rare disease, I was struck by the need for credible scientific information accessible by patients. But the conversation also raised questions for me about how we can get patients to trust science again and generate credible information from anecdotal patient experience. There has to be a better way for patients to get the information they need to thrive.
Read MoreIt’s the end of the year, so I thought I would reflect a bit on the highs and lows of 2019, including my journey to Saudi Arabia (high), and my diagnosis with an autoimmune disorder (more on the low side). I learned a lot about myself and I have confidence and hope in the future.
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Most people expect to be compensated for their time when they provide a service, except when that service is clearly intended to be voluntary. When it comes to engaging patients, there is a persistent question of whether those patients should be compensated for their time. This post explores the issue of whether we can achieve equitable compensation for patients who contribute to observational or clinical research, not as human subjects, but as part of the research team.
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