Month: August 2024

One of the hardest (and most fun) parts of my job is conjuring up my crystal ball to foresee the policy needs of tomorrow. Of course, forecasting the future isn’t really that easy. Technology moves at light speed, while policy… well, policy often moves slower (we can get into that in another blog).  How can we ready the biomedical research enterprise for the future when the future itself is a moving target?  Paradoxically, to quote Taylor Swift, “lookin’ backwards might be the only way to move forward.”

As policymakers, we are continuously striving to develop policies capable of evolving alongside science and technology, taking into consideration that we know there will be unexpected twists and turns along the way. This is why we built a certain degree of flexibility into the NIH Data Management and Sharing Policy – because new tools for creating, sharing, and accessing data are being developed every day.

This is especially the case for machine learning algorithms, natural language processing, and other forms of “artificial intelligence” that are creating new research opportunities and transforming a new wave of improved health outcomes. Artificial intelligence (AI) has been around in some shape or form for some time. However, the ease in which AI approaches can be developed and deployed (similar to what CRISPR did for gene editing) has leveled the playing field for researchers around the globe to find new patterns in rich and complex datasets.

Of course, with great power comes great responsibility. At NIH, we prioritize the safe and responsible development and use of algorithms and models for research. We also implement policies and practices to ensure deposition of AI-ready datasets that are reliable, representative, and robust. To achieve these aims, NIH relies on its forward-leaning policy infrastructure to safeguard our work. While these policies might not specifically state AI in the title, the anticipated use of AI and risks from those uses is what we intended to address. To help the research community understand how these policies guide AI-related research, OSP recently released a centralized NIH policy resource illustrating the applicability of existing policies to AI, including policies related to participant protections, intellectual property, peer review, and many other topics.

An important thing to keep in mind is that NIH’s current policy landscape is well positioned to ensure the responsible use of AI technologies. However, as a policy office, we know that sometimes new policies are indeed warranted to capture new risks as science and technology progresses.  NIH is committed to monitoring the field of AI and other emerging technologies and we will continue to update this resource to make sure we are keeping pace from a policy perspective. I invite you to check out this new resource and let us know your thoughts.  Research that leverages the most promising technologies in a responsible manner will lead to better health for all.  That is a future we can all hope for.

Dr. Lyric Jorgenson has co-authored this blog with Dr. Joni Rutter (r). Dr. Rutter is the Director of the National Center for Advancing Translational Sciences (NCATS) within NIH.

Have you ever tried to put together a jigsaw puzzle without looking at the completed picture they include in the box?  How about trying to build a model airplane without consulting the step-by-step instructions?  Being able to see the end from the beginning helps guide the direction of what you are trying to build.  Without it, you are simply looking at individual pieces that might obscure the clarity of the big picture.  Just as these tools help guide assembly, case studies help guide understanding and implementation in clinical research.  This might be one of the reasons why so many people within the biomedical research community rely on (and request the development of) case studies as an educational resource to better understand policy matters.

When OSP recently announced the ENGAGE initiative, we immediately recognized an opportunity to provide practical, real-world examples of the many effective tools, strategies, and best practices of clinical research engagement being used by NIH’s Institutes and Centers. To be the most useful, a case study must present relatable problems, tangible results, and generalizable conclusions. Without these elements, the risk of your case study being seen as a “one-off” experience is magnified.  Fortunately, NIH has a wide body of experience to draw from to create case studies that would be useful to our research community. 

NIH has added a section to the ENGAGE website (PartnersinResearch.nih.gov) dedicated to presenting these tools, strategies, and best practices in case study form.  These case studies are aimed at three primary audiences:

1. Patients, Caregivers, and Advocacy Groups: These stakeholders can benefit from understanding how to effectively collaborate with researchers in the development and oversight of clinical studies. The case studies provide practical examples of successful patient engagement, illustrating how active involvement can influence research outcomes and ensure that studies address the real-world needs and concerns of patients and their families.
2. Community Members: Individuals can benefit from insights into the methods and importance of participating in clinical research. The case studies demonstrate how community involvement can enhance the relevance and cultural sensitivity of research, ensuring that the findings are more broadly applicable and beneficial to diverse populations. They also aim to empower community members to become active contributors to the research process. and
3. Researchers and Research Teams: Members of these communities can benefit by learning how to integrate engagement strategies into their work. The  case studies provide evidence-based practices and models for fostering collaborative decision-making, equitable engagement, and sustainable capacity building within research programs. By learning from these examples, researchers can improve the design and implementation of their studies, ultimately enhancing the impact and ethical integrity of their work.

The case studies also cover five broad areas associated with clinical research engagement, including:

• Collaborative Decision-Making
• Equitable Engagement
• Methods of Engagement
• Sustainable Engagement & Capacity Building
• Return of Research Value

The case studies cover a broad range of NIH resources, programs, and strategies.  For example, some of the case studies include materials to help patients and patient advocacy groups partner with scientists in the treatment development process, some describe models for integrating patients and community members into the oversight of research programs, and others describe strategies for creating institutional capacity to ensure that meaningful engagement is sustainable over time.

We encourage you to take a look!  Right now, there are 14 case studies available for use which can be accessed at https://partnersinresearch.nih.gov/case-studies/.  As the ENGAGE initiative continues, we plan to add more case studies that reflect the knowledge base we are building. NIH, through the ENGAGE Working Group, is working hard to ensure that all public voices are not only heard but meaningfully incorporated into NIH-supported clinical research. We hope you find these case studies useful as a first step in achieving that goal.