The Sport that Made Me a Better Leader

Late last month, I dined with the NLM/Association of Academic Health Sciences Libraries (AAHSL) Leadership Fellows on the final day of their program and got more than just lunch. I ended up with an interesting realization about how a sport I enjoy has helped shape how I lead.

The curriculum for the year-long NLM/AAHSL fellowship program includes such topics as power and influence, managing a workforce, and diversity and inclusion. The program inevitably sparks self-reflection for both fellows and their mentors, and though I spent only a short time with the group, it got me thinking, too, about issues related to leadership, personal awareness, and growth. Since then, I’ve been musing over what has helped me be an effective leader at NLM.

Certainly, having a terrific staff and the support of NIH leadership makes the whole process easier. I’m fortunate to have both.

I’ve also received words of wisdom from experienced colleagues, gleaned key insights from books, and recalled valuable lessons from management courses I’ve attended over the years.

But, believe it or not, nothing has prepared me more for senior leadership than squash—the sport, not the food.

For those of you who don’t know the game, Wikipedia explains it this way:

A ball sport played by two or four players in a four-walled court with a small, hollow rubber ball. The players must alternate in striking the ball with their racket and hit the ball onto the playable surfaces of the four walls of the court.

The game’s high speed calls for quick movements and even faster thinking. You have to predict the ball’s angle of return based on the point of contact with the racket and the velocity of the hit. You have to plan your shot based on your opponent’s position and your own. And you have to do all this while avoiding the other player as you both navigate your way around a very small space. (The entire court is about 7 feet by 10 feet.)

The situational awareness and mental agility needed to pull all this off build important skills for management and those high-pressure, think-on-your-feet moments, but I think no skill is more important than timing.

Precision timing—striking the ball at just the right moment—is key to success in squash. Precision timing is also important for leadership.

One must determine how long to let a conversation proceed before weighing in, or how many emails in a chain should pass before making a statement. One must decide if one has entered an argument on its first round or if it is an old engagement rehashed many times. And importantly, one must be prompt with feedback, good and bad, both to reveal one’s values to the organization and to expose one’s preferences and pleasures.

But squash has taught me more than getting the timing right.

Power shots in squash send a 2” ball rocketing through the air. And when that ball hits you unexpectedly, on the arm, leg, or even face, you quickly realize that, even though it is very, very small and you are very, very big, it can really hurt. A lot.

Translating this to management, I’ve seen how a small, seemingly inconsequential statement can serve as the prelude to a major problem, or, on the flip side, how well-timed praise, delivered when the recipient is ready to hear it, can have profound positive impact.

Squash has also taught me to play nice with my partners and observe the rules of engagement on the court. Management situations bring their own rules of engagement, sometimes defined, sometimes not. The unwritten rules can be the trickiest, with rules sometimes changing based on where the engagement occurs. Understanding and respecting protocol, positions at the table, and the tenor of discourse as it varies from office to hall way to conference room are skills worth developing.

I’ve learned through squash that I can tussle with an opponent over controlling the T-zone (the prime spot from which it is easiest to reach most shots) and still enjoy a cool drink and camaraderie afterward. Managerially, this means that conflict is part of the game, and going toe-to-toe over important issues doesn’t—and shouldn’t—stand in the way of collegiality. (Another key take-away: It always helps to share a cool drink afterward!)

And ultimately, in squash as in management, there are always opportunities to improve.

What has influenced your leadership? What makes you a good manager? What makes your manager a good manager?

Addressing Health Disparities to the Benefit of All

Guest post by Lisa Lang, head of NLM’s National Information Center on Health Services Research and Health Care Technology

Singer-actress Selena Gomez shocked her fans this past September with the announcement that she had received a kidney transplant to combat organ damage caused by lupus.

Lupus, an autoimmune condition, strikes women much more than men, with minority women especially vulnerable. Not only is lupus two to three times more common in African American women than in Caucasian women, but recent studies funded by the CDC suggest that, like Ms. Gomez, Hispanic and non-Hispanic Asian women are more likely to have lupus-related kidney disease (lupus nephritis)—a potentially fatal complication.

Documenting such health disparities is crucial to understanding and addressing them. Significantly, the studies mentioned above are the first registries in the United States with sufficient Asians and Hispanics involved to measure the number of people diagnosed with lupus within these populations.

Investment in research examining potential solutions for health care disparities is essential.

In 2014, The Lancet featured a study that examined patterns, gaps, and directions of health disparity and equity research. Jointly conducted by the American Academy of Medical Colleges and AcademyHealth, a non-profit dedicated to enhancing and promoting health services research and a long-time NLM partner, the study examined changes in US investments in health equities and disparities research over time. Using abstracts in the NLM database HSRProj (Health Services Research Projects in Progress), the researchers found an overall shift in disparities-focused projects. From 2007 to 2011, health services research studies seeking to document specific disparities gave way to studies examining how best to alleviate such disparities. In fact, over half of the disparities-focused health services research funded in 2011 “aimed to reduce or eliminate a documented inequity.” The researchers also found significant differences in the attention given to particular conditions, groups, and outcomes. An update by AcademyHealth (publication forthcoming) found these differences continue in more recently funded HSR projects.

A more nuanced appreciation of affected groups is also critical to addressing health disparities. For example, the designation “Hispanic” is an over-simplification, an umbrella construct that obscures potentially important cultural, environmental, and even genetic differences we must acknowledge and appreciate if we are to maximize the benefits promised by personalized medicine. Reviews such as “Hispanic health in the USA: a scoping review of the literature” and “Controversies and evidence for cardiovascular disease in the diverse Hispanic population” highlight questions and conditions that would be informed by richer, more granular, data.

Lupus is one such condition. Research into this disease’s prevalence and impact among Hispanics is underway, but more attention may be warranted. There are almost 100 active clinical studies in the US targeting lupus currently listed in ClinicalTrials.gov and, of these, 15 address lupus nephritis. And while about 5% of ongoing or recently completed projects in the HSRProj database explicitly focus on Hispanic populations, only one, funded by the Patient-Centered Outcomes Research Institute, specifically addresses lupus. (You can see this study’s baseline measures and results on ClinicalTrials.gov.)

Perhaps a celebrity like Ms. Gomez publicly discussing her experience with lupus will spark more attention from both researchers and the public seeking to contribute to knowledge and cures.

After all, we are all both fundamentally unique and alike. Reducing—or better yet, eliminating—health disparities benefits us all.

Guest blogger Lisa Lang is Assistant Director for Health Services Research Information and also Head of NLM’s National Information Center on Health Services Research and Health Care Technology (NICHSR).

Photo credit (The Scales of Justice, top): Darius Norvilas [Flickr (CC BY-NC 2.0)] | altered background

It Takes a Whole Library to Create a World of Data-powered Health

Data-powered health heralds a revolution in medical research and health care.

Data-powered health relies upon knowing more—more input in the moment, more details across systems, more people (and their data) contributing to the overall picture.

Data-powered health ushers in a new biomedical research paradigm in which patient-generated data complements clinical, observational, and experimental data to create a boundless pool we can explore. New tools based in text mining, deep learning, and artificial intelligence will allow researchers to probe that vast data pool to isolate patterns, determine trends, and predict outcomes, all while preserving patient privacy.

As a result, data-powered health promises personalized health care at a level never before seen. It signals a time when tracking one’s own health data becomes the foundation of personal health management, with sensors—coupled with something like a smartphone—delivering tailored, up-to-the-moment health coaching.

The National Library of Medicine will play an important role in the future of data-powered health. Each of our divisions has something to contribute. NCBI’s identity and access management systems will ensure a solid core for the NIH gateway to data sharing. Researchers in the Lister Hill Center can apply machine learning, computational linguistics, and natural language processing to make sense out of large, diverse data sources, whether that’s the text within medical records or large numbers of X-rays. Library Operations staff will manage the extensive terminologies that support the necessary interoperability. Specialized Information Services’ experience with disaster information management will help us ensure data remains available even with limited or no internet access. And the National Network of Libraries of Medicine will continue to partner with libraries across the country to support the public as they join this strange, new world.

Together these and other areas of excellence give NLM a solid foundation, but NLM itself must grow and develop to become the NIH hub for data science. We must develop data management skills and knowledge among the Library’s workforce. We must also partner with the other NIH institutes and centers, and with scientists around the country, to complement, not duplicate, data science efforts; to build the technical infrastructure for finding and linking data sets stored in the cloud; to shape best practices for curating data; and to craft policies that support exploration and inquiry while preserving patient privacy.

The ultimate goal is for NLM to do for data what we have already done for the literature—formulating sound, systematic approaches to acquiring and curating data sets, devising the technical platforms to ensure the data’s permanence, and creating human and computer-targeted interfaces that deliver these data sets to those around the world who need them.

We continue to discuss how best to create an organizational home for data science at NLM, and I welcome your ideas. How would you establish a visible, accessible, and stable home for data science at NLM while building upon our expertise and our tradition of collaboration?

One Library, Many Worlds

Back in January, I wrote about One NLM, an idea that acknowledges the particular contributions of each division within the Library while supporting greater engagement across our programs, all aligned toward a common vision.

I wrote that post primarily for NLM staff, but in the intervening nine months, I’ve discovered I need to take the message of One NLM to those outside the Library as much as to those within it.

As I attend conferences and meet members of the many groups NLM serves, I’ve learned the role of the Library is in the eye of the beholder. Librarians see bibliographic resources. Scientists see tools for discovery, clinicians tools for diagnosis and care. Potential post-docs see opportunities for training, and teachers see resources for learning. Even though we are one NLM, we are viewed from those various perspectives more as parts than a whole.

That’s not necessarily a bad thing, but I am working to make our stakeholders aware of both the parts they don’t naturally see and the single purpose that unites those parts.

Our core services are undeniably diverse. We acquire and preserve health and biomedical knowledge across disciplines and across the ages, and then devise platforms and processes to make this knowledge available to clinicians, researchers, and patients. We conduct research to develop more efficient ways to search the literature and to apply computational approaches, such as machine learning and natural language processing, to clinical data and published works to extract specific information. We also take advantage of our own genomic and other sequence data bases to discover the structure and functions of various genes and to create models of functional domains in proteins.

Given that diversity, it makes sense that those who use the Library might focus on one or a few of those services more than others, but for me and for the 1,700 women and men who work here, these services all contribute to one single vision: NLM as a platform for discovery.

Sometimes discovery comes by exploring PubMed’s literature citations to ground a new research program, other times by extracting gene sequences and their respective phenotypes from dbGaP, and yet other times by finding the perfect exercise to supplement a lesson plan.

In the end perhaps the lesson for all of us is that NLM is ultimately both its parts and its whole.

And my role is to help our many audiences better understand their favorite parts while learning more about the totality of who we are and how we serve society.

The Rise of Computational Linguistics Geeks

Guest post by Dina Demner-Fushman, MD, PhD, staff scientist at NLM.

“So, what do you do for a living?”

It’s a natural dinner party question, but my answer can prompt that glazed-over look we all dread.

I am a computational linguist, also known (arguably) as a specialist in natural language processing (NLP), and I work at the National Library of Medicine.

If I strike the right tone of excitement and intrigue, I might buy myself a few minutes to explain.

My work combines computer science and linguistics, and since I focus on biomedical and clinical texts, it also requires adding some biological, medical, and clinical know-how to the mix.

I work specifically in biomedical natural language processing (BioNLP). The definition of BioNLP has varied over the years, with the spotlight shifting from one task to another—from text mining to literature-based discovery to pharmacovigilance, for example—but the core purpose has remained essentially unchanged: training computers to automatically understand natural language to speed discovery, whether in service of research, patient care, or public health.

The field has been around for a while. In 1969 NIH researchers Pratt and Pacak described the early hope for what we now call BioNLP in the paper, “Automated processing of medical English,” which they presented at a computational linguistics conference:

The development of a methodology for machine encoding of diagnostic statements into a file, and the capability to retrieve information meaningfully from [a] data file with a high degree of accuracy and completeness, is the first phase towards the objective of processing general medical text.

NLM became involved in the field shortly thereafter, first with the Unified Medical Language System (UMLS) and later with tools to support text processing, such as MetaMap and TextTool, all of which we’ve improved and refined over the years. The more recent Indexing Initiative combines these tools with other machine learning methods to automatically apply MeSH terms to PubMed journal articles. (A human checks the computer’s work, revising as needed.)

These and NLM’s other NLP developments help improve the Library’s services, but they are also freely shared with the world, broadening our impact but more importantly, helping to handle the global proliferation of scientific and clinical text.

It’s that last piece that makes NLP so hot right now.

NLP, we’re finding, can take in large numbers of documents and locate relevant content, summarize text, apply appropriate descriptors, and even answer questions.

It’s every librarian’s—and every geek’s—dream.

But how can we use it?

Imagine, for example, the ever-expanding volume of health information around patients’ adverse reactions to medications. At least four different—and prolific—content streams feed into that pool of information:

  • the reactions reported in the literature, frequently in pre-market research (e.g., in the results of clinical trials);
  • the labeled reactions, i.e., the reactions described in the official drug labels provided by manufacturers;
  • the reactions noted in electronic health records and clinical progress notes; and
  • the reactions described by patients in social media.

NLM’s work in NLP—and its funding of extramural research in NLP—is helping develop approaches and resources for extracting and synthesizing adverse drug reactions from all four streams, giving a more complete picture of how people across the spectrum are responding to medications.

It’s a challenging task. Researchers must address different vocabularies and language structures to extract the information, but NLP, and my fellow computational linguists, will, I predict, prove up to it.

Now imagine parents seeking health information regarding their sick child.

NLP can answer their question, first by understanding key elements in the incoming question and then by providing a response, either by drawing upon a database of known answers (e.g., FAQs maintained by the NIH institutes) or by summarizing relevant PubMed or MedlinePlus articles. Such quick access to accurate and trustworthy health information has the potential to save time and to save lives.

We’re not fully there yet, but as our research continues, we get closer.

Maybe it’s time I reconsider how I answer that perennial dinner party question: “I’m a computational linguist, and I help improve health.”

headshot of Dr. Demner-FushmanDina  Demner-Fushman, MD, PhD is a staff scientist in NLM’s Lister Hill National Center for Biomedical Communications. She leads research in information retrieval and natural language processing focused on clinical decision-making, answering clinical and consumer health questions, and extracting information from clinical text.