Didn’t you used to be a nurse?

Didn’t you used to be a nurse?

I get this question more often than you might expect—and frankly, a little more often than I would expect.

I am a nurse who presently serves as the director of the National Library of Medicine. I’m the first nurse to direct the Library but not the first licensed health professional to do so. In fact, all of my predecessors have been licensed health professionals—specifically, physicians. I wonder how many of them were asked, “Didn’t you used to be a physician?”

The answer to the nurse question, by the way, is, “No.”

I am a nurse. I didn’t used to be a nurse.

I have an active license as a registered nurse. I am a member of the American Nurses Association. And though I might wait a beat to raise my hand when that call comes over the airline public address system—“Is there a health professional on board? We have an emergency.”—I sometimes do, doing what I can to help but always deferring to someone with more current clinical knowledge.

I don’t even think it’s possible to leave nursing behind. Nursing is as much a calling as a profession. The calling fuels the desire to be a professional with specialized knowledge, operating under a contract with society (Nursing’s Social Policy Statement: The Essence of the Profession, 2010).  One does not forget the knowledge, nor does one abandon the calling.

A commercial years ago used the slogan, “If caring was enough, anyone could be a nurse.” I care, but that does not make me a nurse. I’m a nurse because I possess specific, advanced knowledge about the diagnosis and treatment of the human response to disease, disability, and developmental challenges, and I apply that knowledge to caring for others.  Today, I demonstrate that caring and fulfill my contract with society as the director of the largest biomedical library in the world.

It takes 1,700 women and men to bring to society all the products and services NLM offers. But being a nurse gives me insights into and an understanding of health that help me channel their efforts in different ways. Being a nurse broadens my perspective on what constitutes relevant health information. Being a nurse drives me to connect the knowledge of how to manage a health problem with the skills needed to do it. It highlights that health is a team sport, not a solo pursuit, and that I must create the environment that lets all team members, including patients, their family, and friends, operate at the top of their skills. And as essential as trusted, quality health information is, being a nurse reminds me that information is only part of the equation. Personal motivation, a sense of self-efficacy, and the ability to act in accord with one’s values and outlook on life contribute mightily to someone’s willingness and ability to move toward health—and even how they define health.

Of course, I’m not the only nurse working outside a traditional clinical setting. Nurses do many things, but all fall under nursing’s contract with society: helping people, sick or well, by understanding their human responses to disease, disability, and development and partnering with them to move toward health informed by mutual respect and shaped by our combined talents and skills.

So, no, I didn’t used to be a nurse. I am a nurse. And my job as a nurse is to lead a library.

Come join me in my practice, add your skills and knowledge to the mix, and work with me toward the future of data-powered health.

Code-Breaking Librarians

Did you know that librarians helped crack enemy codes in support of the US war effort during World War II?

Until I read Liza Mundy’s book Code Girls: The Untold Story of the American Women Code Breakers of World War II, I was unaware, but when I found out, I was certainly not surprised.

Codes and ciphers are the tools of spies and subterfuge. Coded messages systematically replace a word, phrase, or sentence with specific alternates. In ciphers, each letter is replaced according to some formula or algorithm, making ciphers much harder to break.

The US military, caught by surprise at Pearl Harbor, realized they needed to quickly ramp up a code-breaking unit. They turned to thousands of women with classical liberal arts educations and built on those skills to assemble teams of expert code breakers. Like their counterparts working at England’s Bletchley Park, the American women’s collegiate experience reading and interpreting complex texts or wrestling with advanced mathematics prepared them well for untangling the shifting, arcane world of crypotanalysis.

Librarians brought their own skills to the teams. In addition to breaking codes, these professionals, mostly women, set the stage for their teams’ successes. They kept records. They organized vast amounts of disordered and unrelated information into logical categories. And by applying the principles of indexing and cataloging, they connected previously disjointed information and made it discoverable.

Librarians played important wartime roles outside the US as well.

Early in the war, Richard Hayes, director of the National Library of Ireland, was tapped by Irish army intelligence to help decode a cipher found on a German agent captured in Ireland. His success prompted Irish prime minister Éamon de Valera to set up a small office in Dublin for Hayes where Hayes and a small team could decode Axis messages being transmitted out of Ireland—all while Hayes continued to serve as library director. Hayes’ involvement had a significant impact on the war. His ingenuity and tenacity enabled him to unlock a notoriously difficult Nazi code, one that stumped Britain’s MI5 and the intelligence experts at Bletchley Park.

Most librarians today aren’t deciphering secret codes, but the skills behind that work—order, reason, connection, and interpretation—remain essential. We still need skilled professionals to create and maintain enduring systems to organize data, information, and knowledge and make them accessible. Unlocking the secrets of medicine and science depend upon it.

And yet, like the code-breaking librarians of World War II, today’s librarians often go unrecognized and their contributions unacknowledged. What can we do to change that?

Science and Medicine Need Women

The first woman ever to be an institute director at NIH, Dr. Ruth Kirschstein, took the helm at the National Institute of General Medical Sciences in 1974. It took 17 more years for Dr. Bernadine Healy to become the first—and so far only—female director of NIH.

Today, I am one of 10 women serving as directors across the 27 institutes and centers at NIH—the most female directors NIH has ever seen at one time. Clearly, we’ve made some important gains, but as NIH Director Dr. Francis Collins has recently said, “We have not achieved the point where women have their rightful place in leadership.”

It’s not that women aren’t interested in science or in leadership. Instead, studies are finding that far too many women who enter the field abandon their careers, whether due to hiring bias, the wage gap, or sexual or gender harassment. We’re all losing due to that loss of talent and intelligence.

Science needs women, not just as laborers, but as thinkers, innovators, and leaders. It needs our different perspectives and our thoughts on what issues are worthy of research. It needs our different ways of attacking problems, interpreting results, and considering solutions. It needs our diversity to help reduce bias and to yield findings that are more generalizable. The problems science addresses are too large, too multifaceted, and too important to tackle using the talents of only 50% of the population.

It’s a fertile and shifting time. We are becoming increasingly aware of the systemic barriers that keep society and science from benefiting from women’s full contributions, but awareness isn’t enough. We must act. We must change.

NIH is working to do that. New policies and practices are in place to address sexual harassment at NIH, at the institutions we support, and anywhere NIH research activities take place. And NIH has just completed a survey of all staff and contractors to help assess NIH workplace climate and harassment.

It’s a start.

I’m proud to be a part of a group tasked with recommending what comes next. As part of the NIH Director’s Advisory Committee Working Group on Changing the Culture to End Sexual Harassment, I have the opportunity to help redress wrongs and improve engagement. Together my colleagues and I will be looking for ways the institution can promote a safe and inclusive environment.

On a personal level, I work to effect that change by nudging my colleagues gently or, if needed, bluntly, when implicit bias, traditional thinking, or even malignant motives stand in the way of fair judgment or women’s rightful progression in science. And I try to engage all my colleagues, regardless of gender, in working toward ways to dismantle the barriers that hold women back.

Just as with scientific research itself, we need everyone’s full participation in the solution.

What guidance do you have for me about how to take up this important mantle?

Learn More
Women scientists at NLM and throughout history.

Expanding Access, Improving Health

Guest post by Kathryn Funk, program manager for NLM’s PubMed Central.

Last week, National Library Week celebrated how libraries and library workers make our communities stronger. In the spirit of building strong communities, NLM has committed to “democratiz[ing] access to the products and processes of scientific research.”

NLM delivers on that commitment by supporting the NIH Public Access Policy. This policy, passed by Congress in 2008, requires authors funded by NIH to make publicly accessible in PubMed Central (PMC) any peer-reviewed paper accepted for publication. Now, over a decade after the NIH Public Access Policy went in to effect, PMC makes more than 1 million NIH-funded papers available to the research community and the public. This volume of publicly accessible, NIH-funded papers represents a clear return on investment for the public, but numbers alone don’t provide the full story.

A quick dive into NIH Research Matters, a weekly update of NIH research highlights, offers a much richer and more personal picture of how the NIH Public Access Policy and NLM’s support of it can strengthen and empower communities. Making NIH-funded papers publicly accessible in PMC means that the public has free and direct access to research that touches on some of the most critical public health concerns facing our community, including studies that:

  • Suggest a method for detecting breast tumors earlier and more often, creating a higher chance of survival for patients (NIH Research Matters | PMC);
  • Identify treatment options for reducing the risk of death for people who’d previously had a non-fatal opioid overdose (NIH Research Matters | PMC);
  • Explore how maternal nutrition supplements can increase infant birth size and potentially improve children’s life-long health (NIH Research Matters | PMC);
  • Identify young people with suicidal thoughts by using machine learning to analyze brain images (NIH Research Matters | PMC);
  • Gauge exercise’s impact on the growth of new nerve cells in the brains of mice, which could potentially reduce memory problems in people with Alzheimer’s disease (NIH Research Matters | PMC); and
  • Develop blood tests to detect signs of eight common types of cancer (NIH Research Matters | PMC).

These examples illustrate that access, while essential, is not the Library’s end goal. Improved health is.

NLM supports public access to research outputs to accelerate scientific discovery and advance the health of individuals and our communities. It is the best way we can honor the investment made by the American people in scientific research and the surest way to make our communities stronger.

casual photo of Kathryn FunkKathryn Funk is the program manager for PubMed Central. She is responsible for PMC policy as well as PMC’s role in supporting the public access policies of numerous funding agencies, including NIH. Katie received her master’s degree in library and information science from The Catholic University of America.

Building Data Science Expertise at NLM

Guest post by the Data Science @NLM Training Program team.

Regular readers of this blog probably know that NLM staff are expanding their expertise beyond library science and computer science to embrace data science. As a result, NLM—in alignment with strategic plan Goal 3 to “build a workforce for data-driven research and health”—is taking steps to improve the entire staff’s facility and fluency with this field so critical to our future.

The Library is rolling out a new Data Science @NLM Training Program that will provide targeted training to all of NLM’s 1,700 staff members. We are also inviting staff from the National Network of Libraries of Medicine (NNLM) to participate so that everyone in the expanded NLM workforce has the opportunity badge reading "Data Science @NLM Training Kickoff" to become more aware of data science and how it is woven in to so many NLM products and services.

For some of our staff, data science is already a part of their day-to-day activities; for others, data science may be only a concept, a phrase in the strategic plan—and that’s okay. Not everyone needs to be a data scientist, but we can all become more data savvy, learning from one another along the way and preparing to play our part in NLM’s data-driven future. (See NLM in Focus for a glimpse into how seven staff members already see themselves supporting data science.)

Over the course of this year, the data science training program will help strengthen and empower our diverse and data-centric workforce. The program will provide opportunities for all staff to participate in a variety of data science training events targeted to their specific interests and needs. These events range from the all-hands session we had in late January that helped establish a common data science vocabulary among staff to an intensive, 120-hour data science fundamentals course designed to give select NLM staff the skills and tools needed to use data to answer critical research questions. a badge reading "Data Science Readiness Survey Completed" and showing a thumbs up We’re also assessing staff members’ data science skill levels and creating skill development profiles that will guide staff in taking the steps necessary to build their capacity and readiness for working with data.

At the end of this process, we’ll better understand the range of data science expertise across the Library. We’ll also have a much clearer idea of what more we can do to develop staff’s facility and fluency with data science and how to better recruit new employees with the knowledge and skills needed to advance our mission.

In August, the training program will culminate with a data science open house where staff can share their data science journey, highlight group projects from the fundamentals course, and find partners with whom they can collaborate on emerging projects throughout the Library.

But that final phase of the training initiative doesn’t mean NLM’s commitment to data science is over. In fact, it will be just the beginning.

In the coming years, staff will apply their new and evolving skills and knowledge to help NLM achieve its vision of serving as a platform for biomedical discovery and data-powered health.

How you are supporting the data science development of your staff? Let’s share ideas to keep the momentum going!


Co-authored by the Data Science @NLM Training Program team (left to right):

    • Dianne Babski, Deputy Associate Director, Library Operations
    • Peter Cooper, Strategic Communications Team Lead, National Center for Biotechnology Information
    • Lisa Federer, Data Science and Open Science Librarian, Office of Strategic Initiatives
    • Anna Ripple, Information Research Specialist, Lister Hill National Center for Biomedical Communications

National Public Health Week 2019: How NLM Brings Together Libraries and Public Health

Guest post by Derek Johnson, MLIS, Health Professionals Outreach Specialist for the National Network of Libraries of Medicine Greater Midwest Region

Recent articles in Preventing Chronic Disease and The Nation’s Health chronicle how public libraries can complement the efforts of public health workers in community outreach and engagement. Data tell us that more Americans visit public libraries in a year (1.39 billion) than they do health care providers (990 million). More so, over 40% of computer-using patrons report using libraries to search for health information. However, we also know many individuals struggle with accessing and understanding the health information they encounter every day.

This challenge begs the question, “How does the National Library of Medicine (NLM) increase access to trustworthy health information to improve the health of communities across the United States?”

It’s an important question, and, as we celebrate National Public Health Week, it gives us an opportunity to reflect on the incredible work NLM is doing through its National Network of Libraries of Medicine (NNLM) to bring libraries and public health together.

Take, for example, Richland County Public Health in Ohio. Richland County is approximately 33% rural. Many rural areas have been identified as “internet deserts.” In addition, adults in the county have lower rates of high school and college-level education compared to state averages. Seeking to address these disparities, Richland County Public Health applied for a funding award from NNLM’s Greater Midwest Region to develop an Interactive Health Information Kiosk in partnership with the county public library system.

With funding in hand, Richland County Public Health loaded select NLM resources onto specially configured iPads and installed them in the nine branches of the Richland County Libraries. A health educator trained library staff, local healthcare providers, and the public on how to use those resources to access trustworthy health information. Moving forward, librarians will be able to help patrons use the health kiosks. As a result, Richland County Public Health is helping improve health literacy among adult residents and, ultimately, enabling them to make more informed decisions about their health.

Another example of a public health and public library collaboration comes from NNLM’s Middle Atlantic Region (MAR). The Philadelphia Department of Public Health recognized the need to engage individuals in neighborhoods most vulnerable to severe weather events to increase their knowledge of disaster and emergency preparedness.

With funding from MAR, the Philadelphia Department of Public Health partnered with four branches of the Free Library of Philadelphia to train both librarians and local residents on emergency preparedness. Participants learned how to make use of the NLM Disaster Information Management Research Center and where to find local resources during weather-related emergencies.

These are just two of the many projects that NNLM helps facilitate across the country through its network of more than 7,500 library, public health, community-based, and other organizational members.

And, while NNLM continues to identify partnerships for funding public health and library projects, it also engages health educators by offering continuing education credit for Certified Health Education Specialists (CHES). CHES-certified professionals work in a variety of health care and public health settings where they help community members adopt and maintain healthy lifestyles. Health educators can earn continuing education credits by attending specially designated NNLM webinars on topics such as health statistics and evidence-based public health, with more courses in the works.

As communities continue to rely on the public health workforce to sustain and build healthy environments, know that the National Library of Medicine and its National Network of Libraries of Medicine are here to support the work they do!

headshot of Derek JohnsonDerek Johnson, MLIS is the Health Professionals Outreach Specialist for the National Network of Libraries of Medicine Greater Midwest Region. In this capacity, he conducts training and outreach to public health professionals on a variety of topics, including evidence-based public health, health disparities, and community outreach.

 

AI is coming. Are the data ready?

The artificial intelligence (AI) revolution is upon us. You can barely read the paper, watch TV, or see a movie without encountering AI and how it promises to change society. In fact, last month, the President signed an executive order directing the US government to prioritize artificial intelligence in its research and development spending to help drive economic growth and benefit the American people.

Artificial intelligence refers to a suite of computer analysis methods—including machine learning, neural networks, deep learning models, and natural language processing—that can enable machines to function as if possessing human reasoning. With AI, computer systems ingest and analyze vast amounts of data and then “learn” through high-volume repetition how to do the task better and better, “reasoning” or “self-modifying” to improve the analytics that shape the outcome.

That learning process results in some pretty amazing stuff. In the health care field alone, AI can determine the presence or absence of abnormalities in clinical images, predict which patients are at risk for rare disorders, and detect irregular heartbeats.

To make all that happen requires data, massive amounts of data.

But like the computer-era quip, “garbage in, garbage out,” the data need to be good to yield valid analyses. What does “good” mean? Two things:

  • The data are accurate, truly representing the underlying phenomena.
  • The data are unbiased, i.e., the observations reflect the complete experience and no inherent errors were introduced anywhere along the chain from data capture to coding to processing.

As much as we’d like to think otherwise, we already know data are biased. Human genetic sequences drawn from studies of white males of Northern European descent do not adequately represent the genetic diversity within women or people from other parts of the globe. Image data generated by different X-ray machines might show slight variations depending upon how the machines were calibrated. Electrical pathways collected from neurological studies conducted as recently as a decade ago do not reflect the level of resolution possible today.

So, what can we do?

It doesn’t make sense to throw out existing data and start anew, but it can be misleading to apply AI to data known to be biased. And it can be risky. Bias in underlying data can result in algorithms that propagate the same bias, leading to inaccurate findings.

That’s why NLM is working to develop computational approaches to account for bias in existing data sets and why we’re investing in this line of research. In fact, we’re actively encouraging grant applications focused on reducing or mitigating gaps and errors in health data research sets.

I have confidence that researchers will crack the puzzle, but until then, let’s look at how the business intelligence community is approaching the issue.

Concerned with reducing the effect of biases in management decision-making, business intelligence specialists have identified strategies to help uncover patterns and probabilities in data sets. They pair these patterns with AI algorithms to create calibration tools informed by human judgment while taking advantage of the algorithms’ power. That same approach might work with biomedical data.

In addition, our colleagues in business now approach data analysis in ways that help detect bias and limit its impact. They:

  • invest more human resources in interpreting the results of AI analytics, not relying exclusively on the algorithms;
  • challenge decision makers to consider plausible alternative explanations for the generated results; and
  • train decision makers to be skeptical and to anticipate aberrant findings.

There’s no reason we can’t adopt that approach in biomedical research.

So, as you read and think more about the potential of artificial intelligence, remember that AI applications are only as good as the data upon which they are trained and built. Remember, too, that the results of an AI-powered analysis should only factor in to the final decision; they should not be the final arbiter of that decision. After all, the findings may sound good, but they may not be real, just an artifact of biased, imperfect data.