A Peek into the Inner Workings of NLM’s Health Information Services

Guest post by Dianne Babski, Associate Director for Library Operations at NLM

How does an organization like NLM build and deploy 21st century products and services to support a global user audience? I’d like to give you a behind the scenes glimpse into NLM’s ever-evolving operations, and how we continue to develop the health information resources that you know and love, such as MEDLINE/PubMed, Medical Subject Headings (MeSH), and MedlinePlus.

Agile Product Development

NLM continues to move towards agile product development and digital unification. Where we used to release enhancements and features once or twice a year, we now develop incrementally and release product enhancements frequently. NLM supports innovation in our workforce by empowering product owners to make data-driven decisions through usability reviews and analytics of features, page views, and user requests to inform future actions.

We encourage staff to ask, “Are we meeting users’ needs—now and into the future?”

We have seen the success of this approach in the rollout of DOCLINE, our interlibrary loan request routing system, and the redesign of PubMed. We are in the planning phase of modernizing our flagship clinical trials registry and repository, ClinicalTrials.gov, to deliver an improved user experience on an updated platform to accommodate growth and enhance efficiency. We also embarked on the recommendations of several studies to increase the automation of MEDLINE Indexing. This involves incorporating machine learning and computational algorithms to apply MeSH terms to PubMed citations. As a result, the time for MEDLINE citations to be searched as indexed with MeSH in PubMed will be dramatically reduced, and, more importantly, will better leverage NLM staff expertise around chemical and gene names to enhance discoverability.

Data-Driven and Data-Informed

NLM uses data to balance our portfolio of products and offerings. I like to use the analogy of thinning garden beds to make room for healthier and stronger plants.  We created evaluation measures to review our products and services, which allow us to make data-driven and data-informed decisions to streamline, simplify, and optimize NLM’s portfolio of offerings.

NLM Herb Garden

One key principle is to consolidate information into fewer platforms for improved user experience, discoverability, and efficiency. Pruning our garden allows us to focus on products that are unique, high-quality, and trusted resources. I think we can all agree that it’s more difficult to find what you need when information is scattered and disparate. This has informed the retirement of some products that are no longer sustainable or have a succession plan, or low or declining usage. And while a product may no longer exist as a stand-alone product, we have ensured that data and information from those products are integrated into others, made available for download, or both. For example, by integrating Genetics Home Reference and GeneEd data, we enhanced and made MedlinePlus more robust.

Other agencies or organizations sometimes have equally sufficient information and resources available that duplicate efforts. For example, this is true for the resources held in our Disaster Information Management Research Center (DIMRC), which we have begun retiring by limiting updates to select resources, such as Disaster Lit. This resource is currently only updated with COVID-19-related information as the product (or data) transitions ownership to other organizations. Meanwhile, much of the grey literature from Disaster Lit will remain available in the Digital Collections or the NLM Bookshelf.

To help users navigate NLM collections, we are upgrading our Integrated Library System infrastructure with a cloud-based library services platform. The new platform will allow for better systems integration, collaborative functionality, and community features to keep pace with the data demands of a digital ecosystem and enable better distribution to libraries worldwide. Stay tuned for a new and improved Catalog!

A Common Data Language

As a standards organization, NLM designs and integrates products to make information Findable, Accessible, Interoperable, and Reusable (FAIR). Following the FAIR data principles, an interconnected ecosystem of biomedical data, tools and software enables faster research conclusions and resulting publication(s).

NLM’s goal is to link different but related digital research objects, such as articles, data sets, visualization tools, and predictive models, to advance discovery within our vast collection and resources beyond NLM. For example, in response to the global COVID-19 pandemic, we quickly processed provisional out-of-cycle codes and terms from terminology sources in UMLS, RxNorm, SNOMED CT, and VSAC, added new MeSH and supplemental concept records, and new COVID-19-related Common Data Elements (CDEs) in the NIH CDE Repository. NLM also convened a trans-NIH team to identify NIH-endorsed data elements. We are extremely proud of the role we played in accelerating the interoperability and discoverability of critical COVID-19-related information to help solve a global health crisis.

Looking ahead to January 2023, NIH will adopt a new NIH Policy for Data Management and Sharing, requiring NIH-funded researchers to prospectively submit a plan outlining how scientific data from their research will be managed and shared. In response, NLM developed the Dataset Metadata Model (DATMM), designed to describe biomedical research datasets to drive discoverability and re-use of shared research data.

Serving Society

NLM connects globally to a large and diverse mix of stakeholders both in public and private sectors. Our products and services—no matter how agile, digital, or interconnected—would be nothing without our valued users.

We intentionally aggregate diverse data and analytical tools into our collections to advance research on factors such as biological, genomic, social, behavioral, and environmental impacts on health, and characteristics such as sex, gender, age, race and ethnicity. Working with other standards development groups, we are actively involved in efforts to represent sex, gender, race, and social determinants of health in their resources. We develop reliable health information in visual ways that are accessible to broad audiences, including users with low literacy. For example, MedlinePlus offers a series of brief videos (in English and Spanish) covering several popular health topics, and maintains a Health Information in Multiple Languages Collection featuring more than 60 languages to support the information needs of a global audience.

In its 2021-26 funding cycle, the NLM-supported Network of the National Library of Medicine has a new goal to “advance health equity through information”, and will focus on serving underrepresented populations. NLM remains committed to addressing the challenge of health disparities and seeks new ways to provide understandable and trusted health information resources in a variety of ways to support a broad spectrum of users.

I hope this peek inside of NLM gives you a sense of the ways that our dedicated staff are striving to meet the digital demands of the 21st century. Using our strategic plan as a roadmap, we continue to evaluate and develop products with our diverse user base in mind, and recognize that sometimes we need to rethink, rebuild, and reduce our presentation structures.

We’d love to hear how you are reimagining your services. Until next time, may your garden of health and knowledge blossom this spring!

Dianne Babski is responsible for the overall management of one of NLM’s largest divisions, Library Operations, with more than 450 staff providing health information services to a global audience of health care professionals, researchers, administrators, students, historians, patients, and the public. She oversees budget, facilities, administration, and operations, including of a national network of more than 8,000 academic health science libraries, hospital and public libraries, and community organizations to improve access to health information.

NIH Strategically, and Ethically, Building a Bridge to AI (Bridge2AI)

This piece was authored by staff across NIH that serve on the working group for the NIH Common Fund’s Bridge2AI program—a new trans-NIH effort to harness the power of AI to propel biomedical and behavioral research forward.

The evolving field of Artificial Intelligence (AI) has the potential to revolutionize scientific discovery from bench to bedside. The understanding of human health and disease has vastly expanded as a result of research supported by the National Institutes of Health (NIH) and others. Every discovery and advance in contemporary medicine comes with a deluge of data. These large quantities of data, however, still result in restricted, incomplete views into the natural processes underlying human health and disease. These complex processes occur across the “health-disease” spectrum over temporal scales – sub-seconds to years – and biological scales – atomic, molecular, cellular, organ systems, individual to population. AI provides the computational and analytical tools that have the potential to connect the dots across these scales to drive discovery and clinical utility from all of the available evidence.

A new NIH Common Fund program, Bridge to Artificial Intelligence (Bridge2AI), will tap into the power of AI to lead the way toward insights that can ultimately inform clinical decisions and individualize care. AI, which encompasses many methods, including modern machine learning (ML), offers potential solutions to many challenges in biomedical and behavioral research.

AI emerged in the 1960s and has evolved substantially in the past two decades in terms of its utility for biomedical research. The impact of AI for biomedical and behavioral research and clinical care derives from its ability to use computer algorithms to quickly find connections from within large data sets and predict future outcomes. AI is already used to improve diagnostic accuracy, increase efficiency in workflow and clinical operations, and facilitate disease and therapeutic monitoring, to name a few applications. To date, the FDA has approved more than 100 AI-based medical products.

AI-assisted learning and discovery is only as good as the data used to train it. 

The use of AI/ML modeling in biomedical and behavioral research is limited by the availability of well-defined data to “train” AI algorithms to learn how to recognize patterns within the data. Existing biomedical and behavioral data sets rarely include all necessary information as they are collected on relatively small samples and lack the diversity of the U.S. population. Data from a variety of sources are necessary to characterize human health, such as those from -omics, imaging, behavior, and clinical indicators, electronic health records, wearable sensors, and population health summaries. The data generation process itself involves human assumptions, inferences, and biases that must be considered in developing ethical principles surrounding data collection and use. Standardizing collection processes is challenging and requires new approaches and methods. Comprehensive, systematically generated and carefully collected data is critical to build AI models that provide actionable information and predictive power. Data generation remains among the greatest challenges that must be resolved for AI to have a real-world impact on medicine.

Bridge2AI is a bold new initiative at the National Institutes of Health designed to propel research forward by accelerating AI/ML solutions to complex biomedical and behavioral health challenges whose resolution lies far beyond human intuition. Bridge2AI will support the generation of new biomedically relevant data sets amenable to AI/ML analysis at scale; development of standards across multiple data sources and types; production of tools to accelerate the creation of FAIR (Findable, Accessible, Interoperable, Reusable) AI/ML-ready data; design of skills and workforce development materials and activities; and promotion of a culture of diversity and ethical inquiry throughout the data generation process.

Bridge2AI plans to support several Data Generation Projects and an Integration, Dissemination and Evaluation (BRIDGE) Center to develop best practices for the use of AI/ML in biomedical and behavioral research. For additional information, see NOT-OD-21-021 and NOT-OD-21-022. Keep up with the latest news by visiting the Bridge2AI website regularly and subscribing to the Bridge2AI listserv.

Top Row (left to right):
Patricia Flatley Brennan, RN, PhD, Director, National Library of Medicine
Michael F. Chiang, MD, Director, National Eye Institute
Eric Green, MD, PhD, Director, National Human Genome Research Institute
 
Bottom Row (left to right):
Helene Langevin, MD, Director, National Center for Complementary and Integrative Health
Bruce J. Tromberg, PhD, Director, National Institute of Biomedical Imaging and Bioengineering

One Year of Rapid Acceleration of Diagnostics, and Anticipating New Challenges

This piece was authored in collaboration with leadership across NIH and represents a unified effort to meet the testing-related challenges presented by the COVID-19 pandemic with excellence and innovation.

Over the past year, our team of NIH leaders has used this blog to report on an initiative called Rapid Acceleration of Diagnostics – or RADxSM for short. The RADx initiative includes five key components designed to address the coronavirus (COVID-19) pandemic by ensuring that companies make and distribute tests to detect SARS-CoV-2, the virus that causes COVID-19; develop ways to deliver those tests and results directly to people—independent of their age, race, ethnicity, disability, financial status, or where they live; and invest in innovative approaches to detect emerging and spreading infections. NIH has also added a new component to RADx – to find ways to understand and address the concerns of people worried about testing, vaccine safety, and efficacy. The RADx components are described below.

RADx Underserved Populations (RADx-UP) is a significant investment to bring testing to traditionally underserved communities. Last fall, we launched a nationwide program, involving more than 60 research teams and a Coordination and Data Collection Center, to better understand the needs of people in a wide range of communities, and to ensure that underserved communities have adequate access to COVID-19-testing, and return results in ways that are actionable to promote health. We estimate that up to 500,000 people will participate in the study in more than 33 states, the District of Columbia, and Puerto Rico—representing a broad spectrum of communities of color and socially vulnerable populations. RADx-UP is collaborating with the NIH Community Engagement Alliance (CEAL) Against COVID-19 Disparities to further amplify the NIH’s focus on communities hardest hit. 

Through two programs within the RADx initiative — RADx Tech and RADx ATP (Advanced Technology Platforms)—researchers aim to accelerate evaluation, validation, and scale up of promising COVID-19 testing technologies for laboratory, point-of-care and at-home settings, and provide guidance on when to test. More than 700 applications were submitted to the programs’ unique “innovation funnel” review process over a three-month period. To date, 29 projects have progressed through multiple phases of review to receive contracts for expansion of manufacturing and clinical studies. RADx Tech and RADx-ATP-supported companies have increased COVID-19 testing capacity across the United States by more than 150 million tests and compressed the typical multi-year tech commercialization process into approximately six months.

RADx Radical (RADx-rad) is designed to support innovative research programs focused on developing novel and potentially “radical” ways to detect infectious disease from SARS-CoV-2 or other agents, and evaluate community spread. Unlike the RADx Tech and RADx-ATP programs, which focus on developing technologies that can be delivered in the near term, projects within RADx-rad may require additional time for development. While some of these projects may not be available in the near term to respond to the current COVID-19 pandemic, they could be potentially applicable to deploy quickly for future pandemics. Currently, RADx-rad projects involve a broad array of activities that range from analysis of wastewater for infectious agents, like SARS-CoV-2, all the way to the development of artificial sensory devices to detect volatile organic compounds that uniquely emanate from individuals carrying an infection. Most notably, RADx-rad provides a mechanism for giving radical ideas a chance to demonstrate efficacy and promise.

A central focus of the RADx data management strategy is the safe management of data that is collected, standardized, and harmonized as a result of the implementation of new and novel testing methodologies. The close collaboration with RADx data and coordinating centers to develop and implement common data elements and models is important to the success of this strategy; along with the facilitation of harmonized data sharing on a secure cloud-based data platform. This platform, the RADx data hub, will provide a research data repository of curated and de-identified RADx COVID-19 data—allowing researchers to find, aggregate, and perform data analysis. The data hub will also enable researchers to share results of their analyses (citing relevant data) with collaborators and the external community; and provide a portal where researchers can find additional data and information from other NIH-supported COVID-19 resources.

Although programs such as RADx have helped create COVID-19 tests and make them more available to the public, our work and your work is not done.

Vaccines will go a long way in bringing protections to society and researchers are still learning how well the vaccine prevents people from spreading the virus. Public health measures, such as wearing face masks and frequent testing, continue to be important in efforts to contain this pandemic and address its consequences on society. Testing resources and places to get tested have become more accessible, but still need to be more widely available, affordable, and convenient. Even once people are vaccinated, testing for the presence of the SARS-CoV-2 virus in the nasal passage or in saliva needs to continue. This will help detect and identify new variants, discover asymptomatic infections, and help reduce community spread. As case rates decrease, these strategies will be complemented by the expansion of contact tracing to control the pandemic.

As vaccines help reduce the overall national prevalence of COVID-19, it’s important to pay attention to local trends in the percent of people who test positive and continue to test accordingly. Baseline testing should be adjusted to match regional and community needs and to prevent surges in community transmission. As the prevalence of positive tests decreases in a population, it will become cost-effective to test pooled samples from multiple donors by highly sensitive molecular tests, followed by testing of individual samples from any pools that are positive. Access to inexpensive rapid antigen tests authorized by FDA for self-testing and serial screening will continue to expand. Finally, tests that are designed to detect the presence of specific SARS-CoV-2 variants will become available. Ultimately, we’ll need to have baseline testing platforms and protocols in place to identify future outbreaks, detect other pathogens, and leverage these advances for accessible testing and treatment of other diseases.

The three W’s will remain an important part of society for some time:

  1. Wash your hands often and for at least 20 seconds.
  2. Wear your mask correctly for maximum protection.
  3. Watch your distance and avoid indoor gatherings without masks.

People need to be aware of and encouraged to sign up and use the exposure notification apps created by public health authorities and available on iPhone and Android devices. This secure electronic effort complements contact tracing and appears to be effective at saving lives by alerting people if they have been exposed to COVID-19 and providing guidance for further action.

Our response to COVID-19 is built not only on lessons learned over the past year, but also on the sustained investment in biomedical research of the past decades. We are proud of our agency and researchers for their efforts to mobilize and tackle this destructive pandemic. We are also very grateful to our research participants in communities around the country.

We’re interested in hearing how we could better serve the public.

Top Row (left to right):
Diana W. Bianchi, MD, Director, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Patricia Flatley Brennan, RN, PhD, Director, National Library of Medicine
Noni Byrnes, PhD, Director, Center for Scientific Review
Gary H. Gibbons, MD, Director, National Heart, Lung, and Blood Institute

Second Row (left to right):
Joshua Gordon, MD, PhD, Director, National Institute of Mental Health
Susan Gregurick, PhD, Associate Director for Data Science and Director, Office of Data Science Strategy
Richard J. Hodes, MD, Director, National Institute on Aging
Helene Langevin, MD, Director, National Center for Complementary and Integrative Health

Third Row (left to right):
Jon R. Lorsch, PhD, Director, National Institute of General Medical Sciences
George A. Mensah, MD, Division Director, National Heart, Lung, and Blood Institute
Eliseo J. Perez-Stable, MD, Director, National Institute on Minority Health and Health Disparities
William Riley, PhD, Director, NIH Office of Behavioral and Social Sciences Research

Bottom Row (left to right):
Tara A. Schwetz, PhD, Associate Deputy Director, National Institutes of Health
Bruce J. Tromberg, PhD, Director, National Institute of Biomedical Imaging and Bioengineering
Nora D. Volkow, MD, Director, National Institute on Drug Abuse
Richard (Rick) P. Woychik, PhD, Director, National Institute of Environmental Health Sciences

NNLM and COVID-19: Adapting to a New Normal

Guest post by Martha Meacham, MA, MLIS, NNLM Project Director

The NLM’s Network of the National Library of Medicine (NNLM) has a long, successful history of promoting access to and education about high quality health information, improving the health and health literacy of all. The COVID-19 pandemic may have changed how we approach our work, but our goals and successes have not changed. Adaptability, without sacrificing the quality and impact of our programs, is at our core. We’ve discovered new possibilities and engaged communities in new ways. These are just a few stories from across NNLM.

NNLM will operate throughout seven regions across the United States beginning in May 2021.

Even before the onset of the COVID-19 pandemic, NNLM had expertise conducting virtual programming due to the hundreds of events and classes offered each year. NNLM’s expertise was leveraged to expand our programming by transforming in-person events to virtual experiences, including virtual trainings, book clubs, and online symposiums addressing misinformation about COVID-19 — all with the goal of continuing to engage with communities.

Kiri Burcat, a Data and Evaluation Coordinator with the NNLM says, “NNLM was well-equipped for the COVID-19 work environment. With our regional/national collaborative model, we were used to video conferencing, long-distance collaboration, and online learning. While I hope to see my colleagues across the country in-person again soon, I also hope that this experience will lead us to experiment with different ways to keep online learning fresh and engaging.”

An attendee from one of NNLM’s virtual book club events shared how adapting to a virtual format provided different opportunities for the community, “I was at the Hood River Public Library and saw a book that caught my eye being given out at the COVID-safe space in the lobby. I learned about the upcoming author talk and the NNLM’s role in this effort. I was so impressed. I sent out a notice to my friends and encouraged them to join me in sharing the information on the book’s availability and the January 14th livestream event on Facebook and other social media to help reach more people. How inspirational! Thank you!”

Even with the restrictions of COVID-19, NNLM enabled its partners to continue their community outreach and engagement efforts. Cara Burton, System Director, Eastern Shore Public Library, Accomac, VA, highlighted, “The free print materials are very helpful in our outreach to poor, rural areas. For example, [NNLM-selected precision medicine] materials were distributed during COVID-19 in library packets at the public school free-meal pick-up sites. The NNLM staff did great outreach.”

NNLM coordinators across the country work closely with partners and member organizations to create and maintain high-quality work. “I have been incredibly inspired by the tenacity and innovation of our members who had to reinvent their organizations and services all at once to provide for their communities — and did so with excellence,” says Network Engagement Coordinator, Nancy Patterson.

The COVID-19 pandemic revealed opportunities for education on new topics and brought attention to needed skills. For example, a group within the NNLM organized a webinar series, Identifying and Combating Health Misinformation, featuring expert speakers who discussed various aspects of online health misinformation, how to identify it, and how to help curb its spread. After attending an event, one participant noted, “All of it was helpful. It will assist in better educating others on vaccines, the importance, and ways to know what is and what is not misleading information.” Another attendee noted that a benefit of the class was, “Learning about various ways misinformation can be spread. and learning about ways to stop the spread of misinformation.”

In another example of unique and timely programming, Liz Waltman, NNLM Outreach, Education, and Communications Coordinator notes, “The NNLM has had the opportunity to highlight the work our members are doing at this time. In particular, the webinar about evaluating information during COVID-19 was well attended and received.”

NNLM maintains its commitment to providing high quality educational and engagement opportunities for medical librarians and other professionals. Miso Lee, a data analyst with the University of Texas Medical Branch in Galveston, TX writes, “I am really grateful for [the] professional development award that allowed me to get the training I need. Second, I like informational webinars, particularly those related to COVID-19. I learned several creative ideas from other organizations.”  

The resilience and adaptability of NNLM, founded on its unique expertise and experience, enabled this network of more than 8,000 academic health science libraries, hospital and public libraries, and community organizations to stretch, grow, and keep NLM relevant to communities, including medically underserved communities. Looking forward, NNLM will nurture the partnerships and approaches it has gained from this experience as it continues to expand and deepen NLM’s presence in communities across the country.

COVID-19 certainly has brought about changes and challenges, but through the great efforts of the NNLM staff and the wonderful work of its members and community partners, we remain strong and dedicated in these times.

Check out this upcoming series exploring the impact of COVID-19 and sign up here.

Martha Meacham is the Project Director of NNLM. Martha is a passionate advocate for improving the health of all through access to and understanding of health information.

Walking in Each Other’s Shoes: Fostering Interdisciplinary Research Collaboration

Guest post by Teresa Przytycka, PhD, Senior Investigator, Computational and Systems Biology section of the Computational Biology Branch at the National Library of Medicine’s National Center for Biotechnology Information, National Institutes of Health

If you pose the question – “What is the difference between computational biology and bioinformatics?”– you will get many contradictory answers. Terminology aside, most researchers would agree that the space between traditional biology and traditional computer science is wide enough to accommodate many different models of collaborations between these two groups of researchers.  

Bioinformatics analysis, which involves the analysis of biological data such as DNA, RNA, and protein sequences, has become a standard step required after many types of now routine experiments. For example, after performing an experiment measuring gene expression in different conditions, bioinformatics analysis is likely to be used to compare gene expression between these conditions. In this setting, while experimental and computational components are necessary for the success of the project, only limited interaction between the experimentalist (the user of the tool) and the computational expert (the producer of the tool) is required.

However, given the richness of biomedical data and the complexity of the relations between various bimolecular entities, such as genes and proteins, researchers can be challenged to ask questions that cannot be answered through traditional means. In such cases, the user-producer model of collaboration is increasingly replaced by a different model of collaboration where biologists and computer scientists work side-by-side to both formulate and answer questions. Such collaborations across disciplines can introduce new perspectives and approaches to spur innovation and open the door to addressing new challenging questions.

Recognizing the need to foster interdisciplinary science, NIH formed an interdisciplinary committee to explore the development of a systems biology center at NIH in 2008. The driving idea behind such a center was to create a space where people of different backgrounds can mix, exchange ideas, and through these exchanges come to solutions to open biomedical questions enabled by interdisciplinary approaches. While this effort did not result in the establishment of a physical center, per se, it did give rise to a network of interdisciplinary collaborations. Interestingly, two of the collaborations NLM started at the time still continue today:  the collaboration with the Center for Cancer Research at the National Cancer Institute focusing on conformational dynamics of DNA structure, and the collaboration with the Laboratory of Cellular and Developmental Biology’s Developmental Genomics Section at the National Institute of Diabetes and Digestive and Kidney Diseases studying various aspects of gene regulation.  

What made these collaborations successful and long-lasting?  

Perhaps most importantly, the process of bringing experimental and computational groups together calls for a willingness to learn each other’s languages, thought processes, and cultures — or the proverbial walking in someone else’s shoes. 

Over the years, we learned to think together, work together, and publish many papers together. For example, in one of our joint projects, our experimental partners collected data that helped the computational group construct the gene regulatory network for a fly that can later be utilized by other researchers studying this model organism. In addition to the joy of advancing discovery together, these collaborations opened doors to foster synergies among young computational and experimental researchers from the collaborating groups. These interactions have enriched their NIH experience in important ways, giving them skills that they are likely to find very useful in the future — whether they go on to build their own research groups or follow other career choices.

I am confident that NLM’s support for interdisciplinary research through scientific collaborations will continue to spur innovation and discovery. It will also help to train a generation of researchers who can seamlessly work with people of difference scientific backgrounds.    

What do you value in your collaborations?

Teresa M. Przytycka, PhD, leads the Algorithmic Methods in Computational and Systems Biology section at the National Center for Biotechnology Information. Dr. Przytycka is particularly interested in the dynamical properties of biological systems, including spatial, temporal and contextual variations, and exploring how such variations impact gene expression, the functioning of biological pathways, and the phenotype of the organism.