Solo Librarians as Information Servers

Guest post by Louise McLaughlin, MSLS, Information Specialist at Woman’s Hospital in Baton Rouge, Louisiana.

As information flows from the data collection pipeline to research, curation, and publication, hospital librarians, especially those who practice closely with health care providers, become the human face of information servers. And like those data processing units that serve numerous users, these librarians, many of whom work alone as solo librarians, must be prepared to fill requests from all quarters.

Consider, for example, the following vignettes:

The Chief Operating Officer is launching the next phase of a project to reduce perinatal mortality and preterm births. The librarian continually provides the physicians, nurses, and social workers on the project committee with research articles on emerging causes, new treatments, and community-health approaches to improving outcomes.

A pre-op nurse talks with a colleague about a practice difference they have in monitoring a patient. She wants to know what the evidence says.

A nurse educator asks for help proofreading an article about a successful quality improvement project and confirming the proper citation format for the references.

A physician teaching medical students in Mongolia about the latest updates in women’s health asks, “Can you gather research articles that would address their population on this list of topics?”

The marketing department is updating the hospital’s website. They want to know where they can find consumer-friendly health care definitions.

Sometimes, that can all happen in one day!

But answering questions is not all we do.

A 2016 survey of solo librarians garnered responses from 383 professionals who reported on job duties. Using a pick list, respondents identified an average of nine different job duties for which they were responsible, from a high of 17 to a low of five. (To the best of the authors’ knowledge, no data exists regarding the total number of solo librarians in health care, so the survey results are limited.)

Judging by their selections, a job description that fairly represents a solo librarian’s qualifications might include strong literature search skills across multiple databases, managing electronic resources, experience instructing clinicians, fluency with medical terminology, and advanced budget skills. Working with researchers and rounding with clinical staff may also be required, as might serving on hospital committees, including the Institutional Review Board. Strong outreach practices are encouraged.

Even with such a diverse skill set, many of these librarians lack job security. While many solos are regarded as valuable members of their health care teams, they also know their jobs may not survive the next hospital merger or budget crisis. In fact, listserv news of hospital library closures, anticipated or unexpected, can turn that fear into an ever-present companion.

Yet being resilient may be a solo librarian’s strongest quality. We always have an eye toward future trends, both in our hospitals and in the information arena. Listen to our conversations, and you will hear us talking about ways to use data to demonstrate to our administrators our daily contributions to patient safety, improved outcomes, case management, and the hospital’s overall return on investment.

And though we call ourselves “solo librarians”—and might be managing a hospital’s library services alone or with a skeleton staff of part-timers or volunteers—we know we do not work in isolation.

Our colleagues in academia and at the National Network of Libraries of Medicine nourish us with webinars about the basics of electronic medical record data, innovative instructional methods, consumer health resources, and best uses for a variety of NLM databases. Many of them are our professional best friends, supporting us when we need clarity on best practices in running our library or offering support with a perplexing situation.

We also rely upon the National Library of Medicine, both for its resources and its vision. We view NLM’s 10-Year Strategic Plan as a roadmap to where we are headed. Solo librarians are well-prepared to support Goals 2 and 3 (PDF) of the plan, whether with skills we already have or others we need to develop. Supporting biomedical and health information access and dissemination is already part of our lives; learning to identify and appreciate the capabilities of new digital products is on our must-do list. With training and guidance, we can be the link that facilitates data science proficiency within our institutions and healthy living within our communities.

But like information servers, solo librarians are most valuable when we are kept updated, valued, and used. For this, we count on those higher up the knowledge-creation ladder to share their wisdom with us, value our expertise in local health dynamics, and remind others to use us as resource partners.

casual headshot of Louise McLaughlinLouise McLaughlin, MSLS, stepped into the role of Information Specialist at Woman’s Hospital in Baton Rouge, Louisiana, when her predecessor retired, and her job as assistant librarian was eliminated. She has reached out to friends in similar settings and established a monthly Solo Chat and worked as co-convener of the Medical Library Association’s Solo Special Interest Group. Louise has authored or co-authored several articles on solo librarianship for the Journal of Hospital Librarianship, the National Network, and other association publications.

NLM Delivers for Health Services Research

How are we as a country doing in our delivery of health care?

Even if you’re happy with your access to medical care and the work of your doctor’s office or local hospital, I bet you would also acknowledge there’s significant room for improvement in the system overall.

Studying health care systems and policies and finding ways to improve them falls within the field known as health services research. Exceedingly multidisciplinary, health services research draws from medicine, public health, and an array of social sciences, including economics, political science, and psychology, to name but a few. This unique scientific community leverages the findings from basic and clinical research studies to uncover new ways to invest in and support health care—to make it safer and improve quality.

I’ve spent the last few days with members of this community at the  AcademyHealth Annual Research Meeting, and it has been a tremendous experience.

Given the multifaceted and interdisciplinary nature of health services research, conference sessions have run the gamut from Medicaid dental benefits to physician work experience, from patient safety to health economics. Other sessions—such as those on patient-centered care, electronic health records, health IT, and big data—have offered fresh perspectives on issues I’ve been wrestling with for a while.

Regardless of the topic, all the sessions I’ve attended—along with those I’ve been following virtually on Twitter (#ARM18)—have underscored for me the importance of a robust and responsive NLM to health services research.

NLM’s work to accelerate scientific discovery through data, while not targeting health services researchers specifically, will certainly help their efforts. But beyond those broad initiatives, we also have a few things just for them.

Tops on the list: the National Center for Health Services Research and Health Care Technology (NICHSR), which is housed here at NLM. Created by Congress in 1993, NICHSR (pronounced “nik-H-S-R”) is tasked with improving the collection, storage, analysis, retrieval, and dissemination of health services research. Its website and databases deliver curated information on key topics related to health services research, along with health statistics, community resources, and training opportunities.

As part of encouraging the next generation of health services researchers, NICHSR recently hosted the second annual HSRProj Research Competition. This competition invites students to use data from the Health Services Research Projects in Progress (HSRProj) database, in conjunction with other sources, to identify research gaps in health services and systems research. The winners —Julia Burgdorf, Sarah Gensheimer, and Zachary Predmore, all of the Johns Hopkins Bloomberg School of Public Health—were acknowledged at the AcademyHealth meeting.

NLM also supports research training in biomedical informatics and data science at sixteen universities across the US. These graduate and postdoctoral programs cover a range of specialties that include health care and public health informatics, training researchers to tackle some of the biggest challenges within the health care system.

We’re going to need them.

The health care system is changing, moving—we hope—toward something that is safer, more effective, more efficient, and more accessible for all. NLM is here to support that.

What would most help you in your health services research?

From Collection to Commitment

The future of scientific communication and health information is shining brightly these days. New models of communication—videos and data sets and Jupyter Notebooks, to name a few—bring the excitement of discovery, the potential of in-the-moment reproducibility, and an accelerated pace to scholarly discourse.

But what is NLM’s role in an era of memes, data warehouses, and the cloud?

To look forward, we must first look back.

NLM’s original legislative mandate emerged when paper was the dominant medium for scientific communication and articles the primary unit of scientific contribution. The Library’s work reflected that.

Long a physical repository of “library materials pertinent to medicine,” NLM acquired and organized stacks and stacks (and stacks!) of books, journals, manuscripts, dissertations, pamphlets, prints, and photographs. We published the Indexus Medicus and Index-Catalogue. Though not a lending library per se, we made our materials available through an on-site reading room, interlibrary loan, and judicious sharing with peer institutions. And we helped people find what they need within the collection through reference and research assistance.

But anyone who has used the Library in the last 50-plus years knows that each of those functions has evolved to increasingly leverage technological advances. The move to electronic journals has left us with little to purchase and even less to store. PubMed provides an online index to the medical literature. Digitized content has simplified access, streamlined interlibrary loan, and given people the ability to help themselves.

But for all these advancements, the human role remains essential.

After all, collections don’t materialize on their own. They are the result of systematic and informed choices by Library staff guided by the members of the Literature Selection Technical Review Committee and by policy.

Collections don’t describe and organize themselves. Staff in our indexing and cataloging branches add their human expertise to automated strategies to make the literature discoverable.

Collections don’t share themselves. While citation records let people know what we have, our outreach and reference staff get word out about the Library’s collections and services through a range of strategies and interactions, building awareness, research skills, and information literacy.

Collectively, that human role is becoming more active, more engaging, more purposeful, making NLM less a custodian and more a curator of health information. As scientific communication changes and evolves—and its associated content and data explode in size and multiply in demand—I expect that curatorial responsibility to grow, potentially encompassing responsibilities such as establishing quality criteria and tracking data’s provenance and versioning.

Strictly speaking, the collection will not give way, but by becoming less physical, less tangible, it will morph into a commitment—to findability, to accessibility, to usefulness.

In essence, we will shift our focus to ensuring those “library materials pertinent to medicine” remain discoverable and available in perpetuity and across the miles, whether they’re physically within these walls or not, whether they’re physical things or not. In that way, we’ll help ensure that the new models of communication deliver on their promise to accelerate scientific discovery and improve health.

Training for Lifelong Learning

To say biomedical informatics is a rapidly changing field might be an understatement. Or a truism. Probably both.

Given its interdisciplinary nature and the myriad ways each of those disciplines is changing, it’s no wonder. From advances in molecular biology to the gigantic leaps we’re making in artificial intelligence and pattern recognition, the fields that feed in to biomedical informatics are speeding forward, so we shouldn’t be surprised they’re driving biomedical informatics forward as well.

Dr. George Hripcsak’s post from last week made this point in the context of biomedical informatics training. Our trainees must be prepared to master what will likely be a never-ending series of new topics and skills, and our training programs must evolve to keep up with them. And while we can’t anticipate every twist or turn, we can prepare our trainees for the road ahead by giving them the skills to navigate change.

NLM is trying to do that.

NLM supports university-based training in biomedical informatics and data science at 16 institutions around the country. That translates into over 200 trainees supported annually.

While the university programs share common elements, in the end each is unique.  They vary in focus, with some emphasizing the informatics related to biological phenomena and others addressing clinical informatics. They also require different levels of course work. But in general, both pre- and postdoctoral trainees in these programs attend classes, participate in research projects, and are mentored to become independent researchers, earning a PhD or a Master’s degree upon completion.

Annually, the predoctoral students, postdoctoral fellows, and the faculty from the 16 university programs NLM supports get together for a two-day meeting. It’s both an honored tradition and a much-valued component of the training process—kind of a networking event crossed with an extended family reunion. In a good way.

The meeting gives trainees the opportunity to develop career-shaping networks, learn about different concentrations in biomedical informatics, and, perhaps most importantly, present posters and podium talks that both hone their scientific communications skills and promote their research. Meanwhile, the training directors and faculty get together to share best practices, discuss curriculum, and offer NLM guidance regarding future training directions and support.

This year’s training meeting—hosted just last week by Vanderbilt University—emerged for the first time from the trainees and fellows themselves.  That is, the Vanderbilt students planned the meeting (with a bit of guidance from their faculty). This shift put the meeting’s structure and content in the hands of those most likely to benefit from them—but also most likely to know what they and their colleagues need to hear.

The outcome exceeded expectations.

The opening student-only social event kicked things off, and the pace never relented. In a good way.

Podium presentations of completed research joined poster presentations of works in progress, 3-3 lightening talks (three slides, three minutes), and small group “birds of a feather” discussions around themes such as interoperability, user experience, and curation.

Regardless of what was happening though, conversations abounded. The social mixing that sometimes took a full day to occur was evident in the first few hours, making those rooms loud! In a good way.

Clearly, peer-directed learning involves a lot of conversation.

When I had the chance to address the group, I pointed out how all that conversation paralleled the careers that lie before them. That is, in such a rapidly changing field, never-ending curiosity and unrelenting inquiry are absolutely essential. Trainees and fellows must be prepared for an ever-changing world and embrace the idea that their current training programs are launch pads, not tool belts. Content mastery will get them only so far.

To respect the public investment in their careers, they must always learn, always question, always engage.

They can’t rush it. Or consider it done. Careers take a lifetime.

And NLM is committed to preparing them for that lifetime of contribution and discovery. After all, those working in a field that is ever-changing must be ever-changing themselves. In a good way.

The Evolution of Data Science Training in Biomedical Informatics

Guest post by Dr. George Hripcsak, Vivian Beaumont Allen Professor and Chair of Columbia University’s Department of Biomedical Informatics and Director of Medical Informatics Services for New York-Presbyterian Hospital/Columbia Campus.

Biomedical informatics is an exciting field that addresses information in biomedicine. At over half a century, it is older than many realize. Looking back, I am struck that in one sense, its areas of interest have remained stable. As a trainee in the 1980s, I published on artificial neural networks, clinical information systems, and clinical information standards. In 2018, I published on deep learning (neural networks), electronic health records (clinical information systems), and terminology standards. I believe this stability reflects the maturity of the field and the difficult problems we have taken on.

On the other hand, we have made enormous progress. In the 1980s we dreamed of adopting electronic health records and the widespread use of decision support fueled by computational techniques. Nowadays we celebrate and bemoan the widespread adoption of electronic health records, although we still look forward to more widespread decision support.

Data science has filled the media lately, and it has been part of biomedical informatics throughout its life. Progress here has been especially notable.

Take the Observational Health Data Sciences and Informatics (OHDSI) project as an example: a billion patient records from about 400 million unique patients, with 200 researchers from 25 countries. This scale would not have been possible in the 1980s. A combination of improved health record adoption, improved clinical data standards, more computing power and data storage, advanced data science methods (regularized regression, Bayesian approaches), and advanced communications have made it possible. For example, you can now look up any side effect on any drug on the world market, review a 17,000-hypotheses study (publication forthcoming) comparing the side effects caused by different treatments for depression, and study how three chronic diseases are actually treated around the world.

How we teach data science in biomedical informatics has also evolved. Take as an example Columbia University’s Department of Biomedical Informatics training program, which has been funded by the National Library of Medicine for about three decades. It initially focused on clinical information systems under its founding chair, Paul Clayton, and while researchers individually worked on what today would be called data science, the curriculum focused heavily on techniques related to clinical information systems. For the first decade, our data science methods were largely pulled in from computer science and statistics courses, with the department focusing on the application of those techniques. During that time, I filled a gap in my own data science knowledge by obtaining a master’s degree in biostatistics.

In the second decade, as presented well by Ted Shortliffe and Stephen Johnson in the 2002 IMIA Yearbook of Medical Informatics, the department shifted to take on a greater responsibility for teaching its own methods, including data science. Our core courses focused on data representation, information systems, formal models, information presentation, decision making, evaluation, and specialization in application tracks. The Methods in Medical Informatics course focused mainly on how to represent knowledge (using Sowa’s 1999 Knowledge Representation textbook), but it also included numeric data science components like Bayesian inference, Markov models, and machine learning algorithms, with the choice between symbolic and statistical approaches to solving problems as a recurring theme. We also relied on computer science and statistics faculty to teach data management, software engineering, and basic statistics.

In the most recent decade, the department expanded its internal focus on data science and made it more explicit, with the content from the original methods course split among three courses: computational methods, symbolic methods, and research methods. The computational methods course covered the numerical methods commonly associated with data science, and the symbolic methods course included the representational structures that support the data.

This expansion into data science continued four years ago when Noemie Elhadad created a data science track  (with supplemental funding from the National Library of Medicine) that encouraged interested students to dive more deeply into data science through additional departmental and external courses. At present, all students get a foundation in data science through the computational methods class and required seminars, and those with additional interest can engage as deeply as any computer science or statistics trainee.

We encourage our students not just to apply data science methods but to develop new methods, including supplying the theoretical foundation for the work. While this may not be for every informatics trainee, we believe that our field must be as rigorous as the methodological fields we pull from. Examples include work on deep hierarchical families by Ranganath, Blei, and colleagues, and remaking survival analysis with Perotte and Elhadad.

To survive, a department must look forward. Our department invested heavily in data science and in electronic health record research in 2007. A decade later, what is on the horizon?

I believe informatics will come full circle, returning at least in part to its physiological modeling origins that predated our department. As we reach the limits of what our noisy and sparse data can provide for deep learning, we will learn to exploit pre-existing biomedical knowledge in different forms of mechanistic models. I believe these hybrid empirical-mechanistic methods can produce patient-specific recommendations and realize the dream of precision medicine. And we have begun to teach our trainees how to do it.

formal headshot of Dr. HripcsakGeorge Hripcsak, MD, MS, is Vivian Beaumont Allen Professor and Chair of Columbia University’s Department of Biomedical Informatics and Director of Medical Informatics Services for New York-Presbyterian Hospital/Columbia Campus. He has more than 25 years of experience in biomedical informatics with a special interest in the clinical information stored in electronic health records and the development of next-generation health record systems. He is an elected member of the Institute of Medicine and an elected fellow of the American College of Medical Informatics and the New York Academy of Medicine. He has published more than 250 papers and previously chaired NLM’s Biomedical Library and Informatics Review Committee.