Ending the HIV Epidemic: Equitable Access, Everyone’s Voice

A guest post by Amanda J. Wilson, Chief, Office of Engagement and Training; Leigh Samsel, NLM Planning and Evaluation Officer, Office of Strategic Initiatives; and Elizabeth A. Mullen, Manager of Web Development and Social Media, History of Medicine Division, National Library of Medicine at the National Institutes of Health.

This year’s theme for World AIDS Day is Ending the HIV Epidemic: Equitable Access, Everyone’s Voice. As the world’s largest biomedical library, NLM has a long history of supporting NIH’s efforts to end the HIV epidemic by providing equitable access to trusted biomedical information, supporting biomedical research, and highlighting the historical, social, and cultural context of this research. Expedient, reliable, free public access to NLM’s trusted biomedical information resources and literature collections advances the knowledge and treatment of HIV/AIDS worldwide, helps progress research to end the HIV epidemic and improves the health of people living with HIV.

Improving HIV/AIDS Health Information Access

NLM integrates multiple types of HIV literature, sequence, testing, and clinical trials data into a portfolio of resources that enables researchers to easily find related information and robustly supports global research to end the HIV epidemic. Free public access to citations to the scholarly literature is provided through NLM’s PubMed and Bookshelf, while free access to full-text is provided through PubMed Central and NLM Digital CollectionsGenBank and the Sequence Read Archive allow researchers to deposit and access publicly available DNA sequences, including HIV experimental and clinical genome sequences. NCBI Virus makes HIV and other virus sequences from RefSeq, GenBank, and other NLM repositories available. The HIV-1 Human Interaction Database includes information gleaned from literature about the interaction between human and HIV-1 genes and proteins.

NLM also provides a rich resource of easy-to-understand online health and wellness information available in English and Spanish via MedlinePlus. Among its wealth of content, MedlinePlus contains several HIV/AIDS topical pages. NLM supports an equitable distribution of information by providing resources and support to strengthen skills and literacies needed to access and use biomedical information for those affected by HIV/AIDS. The Network of the National Library of Medicine provides free health information training, from webinars to instructor-led classes to on-demand tutorials. Additional trainings on a variety of topics are added weekly.

A Long History of Support

NLM’s engagement and collections activities strive to capture the many voices in and around the HIV epidemic. A key historical partnership for engagement with the NIH Office of AIDS Research was the HIV/AIDS Community Information Outreach Program; beginning in 1994 as a resource for community-based organizations to improve the knowledge, skills, and technical means to access and provide the latest authoritative prevention, treatment, and research information electronically. The current iteration of the program concludes this month with 200 plus organizations conducting more than 350  projects in 38 states providing training, communications campaigns, and enhanced products and apps to raise awareness of HIV/AIDS information and discoveries over the 40-year history.

NLM has been collecting publications and archival materials related to HIV/AIDS since the first Morbidity and Mortality Weekly Report on the topic was issued in June 1981. The NLM HIV/AIDS Web Archive offers more than 150 websites documenting the biomedical, clinical, cultural, and social aspects of HIV/AIDS in the early 21st century. This month you can look for new additions and resources.

World AIDS Day 2021

Screen shot of NLM's History of Medicine Division's exhibit entitled "AIDS, Posters and Stories of Public Health"

This month, to help mark World AIDS Day, NLM will launch a new online exhibition—A People’s History of Pandemic: AIDS, Posters, and Stories of Public Health. The NLM exhibition will cover the archive of public health posters about AIDS rooted in the cultural output of artists, activists, and community workers. Their work, specifically the use of personal narrative as a visual-art strategy, along with language and the collective process of creating AIDS posters, continues to broadcast the message that, 40 years after the crisis began, attention to AIDS has not diminished. In mid-December, NLM’s Profiles in Science, will release a curated collection of digitized primary source materials related to the United States National Commission on AIDS, dating from the mid-1980s through the early 1990s. Learn more about the diversity of NLM’s historical collections related to HIV/AIDS on Circulating Now.

As we mark this year’s World AIDS Day, NLM is proud to continue its efforts to provide global access to trusted resources; share the voices of those affected by HIV; and provide the foundation for researchers, clinicians, patients, and families to engage and answer the call to end the HIV epidemic.

Photo of Amanda Wilson

Amanda J. Wilson is Chief of the NLM Office of Engagement and Training (OET), bringing together general engagement, training, and outreach staff from across NLM to focus on NLM’s presence across the U.S. and internationally. OET is also home to the Environmental Health Information Partnership for NLM and coordinates the Network of the National Library of Medicine.

Photo of Leigh Samsel

Leigh Samsel, MS, is responsible for formal reporting of NLM activities and for providing staff leadership to strategic planning activities. Leigh is currently serving as NLM’s AIDS Coordinator to the NIH Office of AIDS Research.

Photo of Elizabeth Mullen

Elizabeth A. Mullen, MS, is Manager of Web Development and Social Media for the History of Medicine Division (HMD) and Managing Editor of Circulating Now, a blog featuring new research, curatorial insights, and news about NLM’s historical collections.

Please Join Me in Thanking our NLM Veterans

Every year at this time, I take advantage of Musings from the Mezzanine to share with you some of the things for which I am thankful. In my 2020 blog, I reflected on how far we’ve come together since I joined the NLM in 2016. In my 2019 blog, I mused about the people, professionals, and personnel for whom I give thanks. This year, I want to give thanks for all veterans in the United States, but particularly for those NLM staff members who are also veterans.

There’s an official legal definition of a veteran – according to Title 38 United States Code, a veteran is a person who served in the active military, naval, or air service, and who was discharged or released under conditions other than dishonorable. Also included as veterans under certain circumstances are National Guard members and members of the uniformed services such as the Public Health Service.

Left to right: My grandfather, Michael Flatley, and my father, Thomas Michael Flatley.

I come from a strong veteran family – my dad, my uncles Bill and Ed (who were military chaplains in WWII and Vietnam, respectively), my cousin Joey, and my nephew Chris.

At NLM, we are fortunate to count many veterans among our numbers. Some of our staff are not only veterans of active-duty service, but they also continue to serve through the reserves or through membership in the National Guard.

It’s good for NLM to have veterans among our workforce. Veterans bring well-developed skills that can effectively be applied to our operations and research enterprise. While each veteran is unique, and entered uniformed service for very personal reasons, veterans bring a commitment to the country refined through their assignments. And veterans strengthen NLM’s commitment to serve the public through government service.

I think that working at NLM is also good for our veterans. NLM allows them to continue in public service and provides them with a world class enterprise environment that makes effective use of their talents and skills honed through previous service. And working at NLM enjoins the efforts of these veterans with the remaining 1,600 plus people who work every day to bring information to the public, make genomic information safely and securely available for science and public health, and help reach communities across the country with trusted health information.

I am pleased and proud to honor these select members of our outstanding workforce. Thank you for your military service and thank you for your continued service at NLM!

Clockwise from top left:  Dianna Adams (U.S. Army), Alvin Stockdale (U.S. Army), Velvet Abercrumbie (U.S. Navy), Ken Koyle (U.S. Army)
Clockwise from top left: Dianne Babski (U.S. Army), Kevin Gates (U.S. Air Force), Bryant Pegram (U.S. Army)
Left to right: Todd Danielson (U.S. Army) and Peter Seibert (U.S. Army)

Turning Talent into Treasure

One of NLM’s greatest assets is its talented, creative workforce. Last year, NIH called on its 27 Institutes and Centers to step up to mount an effective response to COVID-19. Supported by Congress, NIH invested more than $2 billion to ensure rapid access to COVID-19 testing for everyone in the United States — funding research to accelerate access to vaccines and therapeutics and leveraging existing clinical trials and electronic health record data to characterize, monitor, and treat the long-term sequalae of COVID-19 infections.

How is NLM supporting NIH’s COVID-19 response? Well, not surprisingly, our literature and genomic repositories are key to inspiring new research and providing the reference annotated genomes used to evaluate the SARS-CoV-2 virus and help discern its variants. Our Network of the National Library of Medicine (NNLM) gives NLM a face in communities across the United States, providing trustable, community-specific health information and increasing community engagement in NIH research programs. Our researchers are developing new analytic tools to more efficiently interpret medical images and refine the taxonomy of viruses so the properties of related viruses can be better understood. All of these activities draw on the talents of our almost 1,700 staff and the extensive partnerships we have with collaborators within the government and across the country. But it’s our special knowledge of data science, library science, and informatics that is making it possible for NIH to set up many new research programs with systematic attention to data coordination, data reuse, and data integration.

I want to highlight the talents of people working diligently across NIH. When NIH receives congressional funding for new programs or innovative research, a lot of work happens behind the scenes before these funds are awarded to investigators. Program announcements are written, solicitations offered, proposals received and reviewed, and awards made. Each of these steps requires an enormous amount of human effort. NIH has staff engaged in all of these activities for our typical programs and standard research mechanisms. To date, NIH received almost $4.9 billion to fight COVID, which is about 8.8% of the NIH’s total budget of nearly $43 billion for fiscal year 2021. NIH efforts to address COVID required a legion of staff members to refocus their regular priorities to participate in this emergency response. The contributions of NLM staff in this effort were amazing, with nearly 50 people from NLM stepping up to help write funding announcements, participate in reviews, and/or managing the awards process.

In particular, I want to elevate the work of three of our NLM staff who have made significant contributions to this effort. Yanli Wang, PhD, is a program officer in our Division of Extramural Programs. Because of her expertise in data science and training in chemistry, Dr. Wang was detailed to the RADx Radical (RADx-rad) program. RADx-rad is supporting innovative approaches, including rapid detection devices and home-based testing technologies, that will address current gaps in COVID-19 testing and extend existing approaches to make them more usable, accessible, or accurate. Dr. Wang serves as the program officer for the Discoveries and Data Coordinating Center and is working to provide programmatic stewardship and make sure that data across all studies is collected in a systematic manner that fosters data integration and data reuse. A critical aspect of Dr. Wang’s work is fostering the uses of common data elements across the projects and over time.

Two NLM staff members support NIH’s Researching COVID to Enhance Recovery or RECOVER Initiative. RECOVER is studying the post-acute experiences of the estimated 10% to 30% of people who contract COVID-19 and continue to experience a range of symptoms. Amanda J. Wilson, Chief of NLM’s Office of Engagement and Training, is our representative to the RECOVER Initiative executive and coordinating committee. In this role she helps prepare the many funding announcements that stimulate research or reuse of clinical data to best understand this complex problem. Ms. Wilson leverages the extensive resource of the NNLM in support of community-based education and support of the COVID-19 crisis.

Another NLM staffer supporting the RECOVER Initiative is Paul Fontelo. In addition to his roles in training and research in NLM’s Intramural Research Program, Dr. Fontelo is a pathologist by training. He provides specialized expertise to the Autopsy Cohort Studies to identify tissue injury due to SARS-COV-2 infection, delivers technical direction to awardees, and approves certain deliverables and reports as required. He also participates in the application reviews of the Autopsy Cohort and the Mobile/Digital Health platform and is a member of the Post-Acute Sequelae of SARS-CoV-2 Executive Coordination Committee.

I’m grateful to these colleagues, and many more across NLM, who are going above and beyond their usual job responsibilities to help NIH step up to the challenges of the COVID-19 pandemic! Join me in thanking them for their efforts and using the talents of the NLM to create invaluable treasures for NIH!

Artificial Intelligence, Imaging, and the Promising Future of Image-Based Medicine

In mid-October I gave the NLM Research in Trustable, Transparent AI for Decision Support keynote speech to the 50th Institute of Electrical and Electronics Engineers (IEEE) Applied Imagery Pattern Recognition conference in Washington, D.C. (virtually, for me). The IEEE continues to advance new topics in applied image and visual understanding, and the focus this year was to explore artificial intelligence (AI) in medicine, health care, and neuroscience.

To prepare for my talk, I reviewed our extramural research portfolio so I could highlight current research on these topics. NLM’s brilliant investigators are using a range of machine learning and AI strategies to analyze diverse image types. Some of the work fosters biomedical discovery; other work is focused on creating novel decision support or quality improvement strategies for clinical care. As I did with the audience at IEEE, I’d like to introduce you to a few of these investigators and their projects.

Hagit Shatkay and her colleagues from the University of Delaware direct a project titled Incorporating Image-based Features into Biomedical Document Classification. This research aims to support and accelerate the search for biomedical literature by leveraging images within articles that are rich and essential indicators for relevance-based searches. This project will build robust tools for harvesting images from PDF articles and segment compound figures into individual image panels, identify and investigate features for biomedical image-representation and categorization of biomedical images, and create an effective representation of documents using text and images grounded in the integration of text-based and image-based classifiers.

Hailing from the University of Michigan, Jenna Wiens leads a project called Leveraging Clinical Time Series to Learn Optimal Treatment of Acute Dyspnea. Managing patients with acute dyspnea is a challenge, sometimes requiring minute-to-minute changes in care approaches. This team will develop a novel clinician-in-the-loop reinforcement learning (RL) framework that analyzes electronic health record (EHR) clinical time-series data to support physician decision-making. RL differs from the more traditional classification-based supervised learning approach to prediction; RL “learns” from evaluating multiple pathways to many different solution states. Wiens’ team will create a shareable, de-identified EHR time-series dataset of 35,000 patients with acute dyspnea and develop techniques for exploiting invariances (different approaches to the same outcome) in tasks involving clinical time-series data. Finally, the team will develop and evaluate an RL-based framework for learning optimal treatment policies and validating the learned treatment model prospectively.

Quynh Nguyen from the University of Maryland leads a project called Neighborhood Looking Glass: 360 Degree Automated Characterization of the Built Environment for Neighborhood Effects Research. Using geographic information systems and images to assemble a national collection of all road intersections and street segments in the United States, this team is developing informatics algorithms to capture neighborhood characteristics to assess the potential impact on health.

Corey Lester from the University of Michigan leads a multidisciplinary team using machine intelligence in a project titled Preventing Medication Dispensing Errors in Pharmacy Practice with Interpretable Machine Intelligence. Machine intelligence is a branch of AI distinguished by its reliance on deductive logic, and the ability to make continuous modifications based in part on its ability to detect patterns and trends in data. The team is designing interpretable machine intelligence to double-check dispensed medication images in real-time, evaluate changes in pharmacy staff trust, and determine the effect of interpretable machine intelligence on long-term pharmacy staff performance. More than 50,000 images are captured and put through an automated check process to predict the shape, color, and National Drug Code of the medication product. This use of interpretable machine intelligence methods in the context of medication dispensing is designed to provide pharmacists with confirmatory information about prescription accuracy in a way that reduces cognitive demand while promoting patient safety.

Alan McMillan from the University of Wisconsin-Madison and his team are examining how image interpretation can improve noisy data in a project called Can Machines be Trusted? Robustification of Deep Learning for Medical Imaging. Noisy data is information that cannot be understood and interpreted correctly by machines (such as unstructured text). While deep learning approaches (methods that automatically extract high-level features from input data to discern relationships) to image interpretation is gaining acceptance, these algorithms can fail when the images themselves include small errors arising from problems with the image capture or slight movements (e.g., chest excursion in the breathing of the patient). The project team will probe the limits of deep learning when presented with noisy data with the ultimate goal of making the deep learning algorithms more robust for clinical use.

In the work of Joshua Campbell’s team at Boston University, the images emerge at the end of the process to allow for visualization of large-scale datasets of single-cell data. The project, titled Integrative Clustering of Cells and Samples Using Multi-Modal Single-Cell Datauses a Bayesian hierarchical model developed by the team to perform bi-clustering of genes into modules and cells into subpopulations. The team is developing innovative models that cluster cells into subpopulations using multiple data types and cluster patients into subgroups using both single-cell data and patient-level characteristics. This approach offers improvements over discrete Bayesian hierarchical models for classification in that it will support multi-modal and multilevel clustering of data.

Several things struck me as I reviewed these research projects. The first was a sense of excitement over the engagement of so many smart young people at the intersection of analytics, biomedicine, and technology. The second was the variety of types of images considered within each project. While one study explores radiological images, another study examines how image data types vary from figures in journal papers to pictures of the built environment and images of workflows in a pharmacy. Two of these studies use AI techniques to analyze the impact of the physical environment to better understand its influence on patient health and safety, and one study uses images as a visualization tool to better support inference of large-scale biomedical research projects. Images appear at all points of the research process, and their effective use heralds an era of image-based medicine. Let’s see what lies ahead!

Leveraging the Value of Biomedical Informatics Across NIH

The American Medical Informatics Association (AMIA) 2021 Annual Symposium is coming to a close today, and I was honored to moderate NLM’s Annual Update Panel. This was an opportunity to talk about NLM’s contributions to NIH data science and tools, common data elements and clinical informatics. Over the last 40 years I’ve proudly served in many roles in AMIA and its predecessor organizations, including president, member of the Board of Directors, associate editor of the Journal of the American Medical Informatics Association, and numerous committee assignments; all of which fostered the advancement of health at the intersection of informatics, clinical, and biomedical knowledge. I’ve made many friends, been mentored by some of the greatest minds in the field, mentored others, and am grateful for the intellectual leadership and personal support provided by attendees at this meeting.

This year I am leading a completely new effort; for the first time in its 134-year history, NIH has multiple leaders across its Institutes and Centers who also are leaders in biomedical informatics. Together, with Michael F. Chiang, MD, Director of the National Eye Institute; Joshua Denny, MD, MS, CEO of NIH’s All of Us Program; Zhiyong Lu, PhD, FACMI, Senior Investigator in NLM’s National Center for Biotechnology Information; and Clement McDonald, MD, Chief, Health Data Standards Officer at NLM, I had the pleasure of leading a panel discussion about how informatics is accelerating efforts at NIH in support of biomedical discovery and the public health response to the COVID-19 pandemic.

NIH recognizes that the future of biomedical discovery rests, in part, on being able to leverage the knowledge embodied in clinical health records. For example:

  • As we learned throughout the pandemic, the insights we glean from clinical health records and from understanding the natural history of COVID-19 inform best practices for addressing its spread. More importantly, the ability to quickly and efficiently access clinical information provides an opportunity to titrate clinical trials in response to the ‘in-the-moment’ understanding of the course of an illness and clinical care for patients.
  • An improved understanding of the long-term course of COVID-19 and its clinical sequalae rests on being able to follow patients across time. Clarifying the impact of novel vaccines or clinical therapeutics would be enhanced by the ability to integrate participant information across any and every study in which the participant is represented.
  • NIH is engaged in exploring the value of contemporary and emerging informatics innovations, such as cloud-based reusable platforms and datasets, common data models, and the effective development and use of artificial intelligence and machine learning approaches to biomedical research and clinical practice.

Each of these requires effective deployment of informatics innovations into the research process.

AMIA’s Clinical Research Informatics community was foundational to the enhancement of the integration of biomedical informatics concepts into the research process. Much progress has been made to structure information for clinical and translational research and common data elements. Incremental progress must be praised, but to engage the operations of the world’s largest biomedical enterprise requires multiple touch points. Specifically, expanding the critical mass of leadership with expertise in biomedical informatics is essential for instituting enterprise-wide change.

Perhaps, as evidence of the importance of biomedical informatics in the research enterprise, NIH now has directors of three institutes and/or major operations who stand as leaders in the biomedical informatics community. The number of American College of Medical Informatics fellows among the NIH staff is expanding. Not only does this allow a ‘community of conversation,’ but embedding informatics expertise across NIH has accelerated the acceptance of, and the valuing of, biomedical informatics in the biomedical research enterprise. Together we are stimulating new biomedical informatics methods, processes, and the application of biomedical informatics innovation to science and health. I invite you to come join us!

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