A Platform for Biomedical 

Discovery and 

Data-Powered Health

Strategic Plan 2017–2027
For more information about the National Library of Medicine, see:

 http://www.nlm.nih.gov
A Platform for 

Biomedical Discovery and 

Data-Powered Health

National Library of Medicine 

Strategic Plan 2017–2027

Report of the NLM Board of Regents

December 2017

U.S. Department of Health and Human Services

National Institutes if Health
National Library of Medicine Cataloging-in-Publication Data

Names: National Library of Medicine (U.S.). Board of Regents, author. 

Title: A platform for biomedical discovery and data-powered health : National Library of Medicine 

strategic plan 2017-2027 / report of the NLM Board of Regents.

Other titles: National Library of Medicine strategic plan 2017-2027.

Description: [Bethesda, Md.] : U.S. Dept. of Health and Human Services, Public Health Service, 

National Institutes of Health, National Library of Medicine, 2018. | Series: NIH publication | "December 

2017." | Includes bibliographical references and index.

Subjects: MESH: Libraries, Medical. | Data Curation. | Medical Informatics Applications. 

| Biomedical Research. | Research Support as Topic. | Training Support.

Classification: NLM Z 675.M4
Strategic Plan 2017–2027 

Contents

From the Director ............................................................... 1

NLM Today .................................................................... 3

Executive Summary ............................................................. 4

Strategic Plan 2017–2027 ...................................................... 5

Introduction .................................................................... 6

GOAL 1: Accelerate discovery and advance health by providing the tools 

for data-driven research. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Objective 1.1: Connect the resources of a digital research enterprise .............. 9

Objective 1.2: Advance research and development in biomedical informatics 

and data science ......................................... 11

Objective 1.3: Foster open science policies and practices ...................... 12

Objective 1.4: Create a sustainable institutional, physical, 

and computational infrastructure .............................. 13

GOAL 2: Reach more people in more ways through enhanced dissemination 

and engagement pathways ............................................ 14

Objective 2.1: Know NLM users and engage with persistence ................... 15

Objective 2.2: Foster distinctiveness of NLM as a reliable, trustable source 

of health information and biomedical data ....................... 16

Objective 2.3: Support research in biomedical and health information access 

methods and information dissemination strategies ................ 16

Objective 2.4: Enhance information delivery ................................. 17

GOAL 3: Build a workforce for data-driven research and health .................... 18

Objective 3.1: Expand and enhance research training for biomedical informatics 

and data science ........................................... 19

Objective 3.2: Assure data science and open science proficiency ................ 20

Objective 3.3: Increase workforce diversity ................................. 21

Objective 3.4: Engage the next generation and promote data literacy ............. 22

Appendices ................................................................... 23

A. References and Notes ..................................................... 23

B. NLM Authorizing Legislation and Executive Branch Assignments ................... 23

C. NLM Strategic Plan Planning Process ......................................... 25

D. Working Group Participants ................................................. 27
1

From the Director

I am pleased to present to you the NLM Strategic Plan for the period 2017-2027. Over 750 NLM 

staff members, hundreds of national and international librarians, informatics professionals, 

biomedical scientists, data scientists, clinicians, public health specialists, and other 

stakeholders advised us as we charted a pathway towards our third century, and for this I am 

grateful. We consider the environment around us: 

• First and foremost is the promise of data-driven discovery. Data-driven

discovery requires sophisticated library and information science to open the

door to thrilling new prospects for improving the public health, as well as

informatics and data science to deliver insights and solutions.

• Second, clinical care is rapidly migrating from hospital to home. This

migration challenges NLM to reach into these places where health occurs, not

just where care is delivered, which are less formal and less well controlled.

• Third, the spirit of open science suffuses the discovery landscape.

Governmental and scientific forces are aligning under a philosophy that

innovation is accelerated if data flow freely, that the results of governmentsponsored research should be open to the public as quickly as possible, and

that linking scientists, citizens, and industry yields social benefits.

• Fourth, the very nature of libraries is changing. Libraries continue to be

essential places for knowledge repositories and community gathering, yet

the advent of self-directed search, e-publishing and consolidation of hospital

library services challenges librarians and libraries to devise new services

and solutions.

This strategic plan positions NLM to address existing and emerging challenges in biomedical 

research and public health. We will achieve this by creating a vibrant workforce; building 

on our core functions of acquiring, collecting, and disseminating the world’s biomedical 

literature; and extending these skills and developing new ones to make data findable, 

accessible, interoperable, and reusable (the FAIR principles). We will continue to lead, 

conduct, and support research in biomedical information science, informatics, and data 

science to ensure that robust terminologies provide systematic characterization of complex 

health phenomena from cells to society and to devise new methodologies that uncover the 

knowledge held in data. We will expand our training programs to incorporate data science 
2

and maintain our commitment to outreach excellence and support of a diverse workforce. 

And we will do this through our valued partnerships across biomedical research, the NIH, 

libraries, and the public.

The National Library of Medicine takes seriously the enormous public trust awarded us 

worldwide for the quality and integrity of our data and information. Our commitment is to 

preserve and protect this public trust while accelerating the yield from data-driven discovery. 

We invite you to be our partner in the adventure.

Patricia Flatley Brennan, RN, PhD, Director
3

NLM Today

Every day more than four million people use NLM resources; every hour, a 

petabyte of data moves in or out of our computing systems. 

Our MedlinePlus Connect links electronic health records to trusted consumer 

health information. We foster rigor and reproducibility by linking results to the 

more than 250,000 clinical trials registered in our ClinicalTrials.gov database, and 

we advance biomedical communication and capacity in sub-Saharan Africa. 

Our Database of Genotypes and Phenotypes (dbGaP) and the ClinVar database 

of genetic variation, serves both scientists and the public health by helping identify 

the genetic components of disease and rapidly identify foodborne pathogens. 

Our role as the coordinator of terminology standards within the Department of 

Health and Human Services enables interoperability among electronic health 

records. 

NLM preserves over 10 centuries of precious medical manuscripts and other 

artifacts contributing thousands of items from its premier historical collections 

as a partner in the Medical Heritage Library to digitize treasures from the world’s 

leading medical libraries. 

We educate the next generation of innovators in biomedical informatics and 

data science, and our researchers develop new gene editing techniques, devise 

creative ways to locate and analyze medical images, and apply advanced 

analytics to decode clinical narratives and to map the spread of disease across 

communities. 

We are the world’s largest biomedical library and so much more.
Executive Summary 

The National Library of Medicine (NLM) envisions a future in which 

data and information transform and accelerate biomedical discovery 

and improve health and health care. 

Achieving this vision will require NLM to refocus and enhance its research, development, 

training, and information services to make more biomedical data findable, accessible, 

interoperable, and reusable, to invent the tools and services to turn data and information into 

knowledge and insight, and to develop the workforce capable of doing so. It will require new 

forms of partnership and engagement with stakeholders in the public and private sectors, 

including researchers, librarians, health professionals, entrepreneurs and innovators, 

underserved communities, and the public. 

This strategic plan outlines NLM’s priorities for enabling a future of biomedical discovery and 

data-powered health. It heralds a new research paradigm in which data-driven science complements experimental and observational approaches, care is characterized by high levels of 

personalization, and personally collected data become a foundation of self-knowledge and 

personal health management. 

To become a platform for biomedical discovery and data-powered health, NLM must collect 

and integrate an expanding set of information resources and enable them to be analyzed by 

tools emerging from the informatics and data science research front. It must establish pathways for dissemination and engagement across a broad range of users to drive information to 

the right place at the right time. It must expand training initiatives for informatics and data 

science research, and for technical support that will drive and shape the future of biomedical 

discovery. It must accelerate its investments in research in informatics and data science and 

align its research, development, and resource priorities toward achieving three goals, with an 

organizational structure that optimizes implementation .

GOAL 1 GOAL 2 GOAL 3

Accelerate discovery and 

advance health through 

data-driven research

1.1 Connect the resources of a 

digital research enterprise

Reach more people in more 

ways through enhanced 

dissemination and engagement

2.1 Know NLM users and engage 

with persistence

Build a workforce for data-driven 

research and health

3.1 Expand and enhance research 

training for biomedical 

informatics and data science

1.2 Advance research and 2.2 Foster distinctiveness of 3.2 Assure data science and 

development in biomedical 

informatics and data science

1.3 Foster open science policies 

and practices

1.4 Create a sustainable 

institutional, physical, and 

computational infrastructure

NLM as a reliable, trustablesource of health information 

and biomedical data 

2.3 Support research in 

biomedical and health 

information access methods 

and information dissemination 

strategies

open science proficiency

3.3 Increase workforce diversity

3.4 Engage the next generation 

and promote data literacy

2.4 Enhance information delivery

4
Strategic Plan 2017–2027 
6

The National Library of Medicine (NLM) is a global 

leader and trusted agent in the collection, organization 

and dissemination of biomedical information. Tracing 

its roots to the library of the U.S. Army Surgeon 

General in 1836, its statutory mission is “...to assist 

with the advancement of medical and related sciences 

and to aid in the dissemination and exchange of 

scientific and other information important to the 

progress of medicine and to the public health.” 

NLM fulfills this mission by collecting, organizing, and providing access to the 

biomedical literature, as well as the growing volumes of molecular biology and 

clinical research data; engaging with users to discern and meet their information 

needs; advancing research and development in biomedical informatics and data 

science; and serving as the primary supporter of pre- and post-doctoral research 

training in biomedical informatics and data science in the United States. 

As one of the 27 Institutes and Centers of the National Institutes of Health (NIH), 

NLM supports NIH priorities and NIH’s 2016-2020 Strategic Plan objectives 

addressing biomedical research opportunities, priority-setting for innovation, 

enhancing scientific stewardship, and managing for results. NLM will support 

the NIH mission of transforming discovery into health by providing a dynamic 

information platform that accelerates biomedical discovery and translation into 

clinical care, public health practices, and personal wellness. It will also work to 

achieve the recommendations of the NIH Advisory Committee to the Director 

that “NLM should lead efforts to support and catalyze open science” and “be the 

intellectual and programmatic epicenter for data science at NIH.” 

NLM’s vision for the coming decade is to unleash the potential of data and 

information to accelerate and transform discovery and improve health and 

Introduction
7

health care. Central to this vision is the idea of data-powered health, in which 

holistic knowledge of the person is enriched with greater awareness of biological 

processes, environmental exposures, and human connection. With data-powered 

health, extant data complements experimental and observational approaches, 

care processes are characterized by high levels of personalization, and personal 

data collection becomes a foundation of self-knowledge and personal health 

management. 

Achieving this vision will require NLM to: 

1 Create and link an expanding collection of integrated and 

interoperable information resources, powered by analytic tools 

from the informatics and data science research front. This will 

require increased investments in informatics and data science and 

an organizational structure that optimizes implementation.

2 Deepen and establish new forms of engagement across a broad 

range of users to drive information to the right place at the right time.

3 Expand training initiatives for informatics and data science research 

to build a workforce to drive the future of biomedicine. 
GOAL ONE: 

Accelerate discovery 

and advance health by 

providing the tools for 

data-driven research

Scientific investigations now yield not only amazing advances but also spawn 

large and potentially valuable data sets that can be exploited to achieve significant 

additional advances and even greater efficiencies to the progress of research. 

NLM will invest in novel approaches to bring its collections together—literature, 

images, biological data, environmental exposures, to name a few—and create a 

rich integrative platform for data-driven discovery and data-powered health. 

NLM will create computable libraries of data, models, and literature and devise 

an interlocked environment that enables discovery; this effort will be guided by 

principled decisions in collections management and curation, and propelled 

by practices that foster open science and scholarship. It will also build upon 

research advances in biomedical informatics and data science. Partnerships with 

domain scientists, health professionals, information professionals, and the public 

will guide the design of discovery and analysis tools to improve the insight borne 

of data, information, and knowledge. Organizational infrastructure will also be 

modified to support new science and services.

Goals and Objectives

8
9

Objective 1.1: 

Connect the resources of a digital research enterprise

NLM will enhance its efforts to collect, organize, 

and disseminate well-understood (literature) and 

non-traditional research objects, notably, but 

not only, data. These collections of information, 

whether physical or digital, are fuel for ideas, 

knowledge, insight, and progress. The value of 

individual collections can be multiplied when collections of different, but related, objects can be 

searched, interrogated, and analyzed, and their 

insights integrated, aggregated, and shared.

NLM will lead this process by creating, collecting, 

and curating digital research objects such as 

articles, data sets, analytic strategies, and visualization tools, as well as executable objects such 

as workflows, analytic pipelines, simulations, 

and predictive models. Systems will be designed 

around the FAIR principles of making information Findable, Accessible, Interoperable, and 

Re-usable. Modern approaches to attribution will 

be devised. 

NLM will modernize NLM collections and 

services, and enhance the integration and 

interoperability of existing collections. In so 

doing, it will ensure the responsiveness of collections to a broad range of information needs. 

NLM will ensure the operational and curatorial 

efficiency of collection management. This may 

include developing new methods of automated 

indexing and designing innovative approaches 

to presenting information that promotes interoperability among resources and, importantly, 

facilitates linking to resources not housed in 

NLM.

NLM will develop new collections to reflect 

scientific progress and meet new scientific 

needs. Collection development and retention 

decisions will be based on program priorities 

and informed via partnerships with stakeholders. 

While some new resources will be incorporated through acquisition by NLM, others will be 

integrated through federation, coordination, or 

other connections with the integrated platform. 

Recognizing the importance of global knowledge resources, NLM will form partnerships for 

connecting with these resources across and 

outside NIH, and around the world. 

NLM recognizes the potential for addressing the 

continuing challenge of health disparities through 

integrated and aggregated collections of diverse 

data and analytic tools. In creating a platform 

for discovery, NLM is committed to offering a 

resource that is useful for advancing basic and 

translational research on health disparities. 

Factors important for such research include a 

range of biological, genomic, social, behavioral, 

and environmental determinants of health, as 

well as population characteristics such as sex, 

gender, age, race, and ethnicity. In addition to 

incorporating relevant content in the information 

collections, NLM will promote the development 

and use of standards for data collection and 

reporting that will enable aggregation of data and 

identification of subpopulations for analysis. In 

unique ways like these, and more, NLM contributes to the Trans-NIH Minority Health and Health 

Disparities Strategic Plan.1

NLM will augment its literature and genomics 

collections to create a platform for science and 

health from new sources, such as:

Data generated from scientific research: 

New investigations generate not only hypotheses resolution but also data as a significant 

scientific product. The number of data repositories for research datasets is increasing, 

along with the volume of data and the variety 

of data. NLM will lead in developing mechanisms to make data repositories discoverable 

NLM will enhance its efforts to 

collect, organize, and disseminate 

well-understood (literature) and 

non-traditional research objects, 

notably, but not only, data.

GOAL

1
10

and set the policies to guide the selection of 

repositories and their investment. NLM will 

also develop approaches and infrastructure 

for accommodating new classes of research 

data, in addition to enabling access to externally-hosted resources. In addition, NLM will 

engage with the entire NIH to determine how 

to accommodate data generated in large 

NIH research initiatives such as the BRAIN 

Initiative, All of Us, and the Cancer Moonshot.

Curation and standards: NLM develops, 

houses, supports, and stewards many health 

data standards that promote interoperability. 

These sets of standards include: terminologies and vocabularies, data formats, value 

sets, common data elements, and coding 

systems related to clinical phenomena. There 

remain many other types of data for which 

standard representations must be developed, 

including biological, environmental, behavioral, and interactional. NLM will devise new 

methods of generating standards, maintaining version control, and applying them at 

the appropriate point in the data life cycle. 

An important research direction will develop 

strategies for curation at scale, and those 

that accelerate the standards development 

and application process. In partnership with 

community stakeholders, NLM will consider 

how to use its collections and their interconnectivity to facilitate application of standards, 

as well as how to help user communities 

increase the uniformity and the interoperability 

of standards.

Data science tools and other executables: NLM will play a role in creating new 

methodologies and new ways of organizing 

collections of data science tools. Examples 

include: libraries of mathematical models; 

indexed visualization tools; reusable data 

analytic models for health applications; open 

source software and algorithms; research 

workflows; statistical, diagnostic, or predictive 

models; or collections of machine-executable knowledge. These will not replace, but 

will be integrated with, resources that reside 

within NIH Institutes and Centers, their grantees’ institutions, or other public and private 

organizations. NLM will work with stakeholders to identify tools for dissemination and to 

develop policies to guide decisions for incorporating them within the NLM platform.

Clinical data: NLM must create the controls 

for effective stewardship of data generated 

in the course of clinical care as well as from 

clinical investigations. NLM will work with EHR 

vendors and clinical care institutions to foster 

linkage and reuse, as well as to house special 

collections. Aggregated collections of such 

data, properly curated, will enable analyses 

of subpopulations based on aspects of their 

medical care and on demographic characteristics such as gender, age, race, and ethnicity. 

NLM engagement arises from strong commitment to facilitate research in minority health 

and health disparities.

NLM will enrich its flagship clinical trial registry and repository, ClinicalTrials.gov, through 

improved front-end and back-end design. 

Enhancements to expand interoperability with 

data and information in other resources, such 

as individual-level data collected in the course 

of the trials supported by NIH, will be considered, along with improvements in operational 

efficiency in producing, curating, and ensuring 

long-term preservation of this resource. 

Status indicators of the health of people 

and communities: Growing recognition 

of the importance of social, behavioral, and 

environmental aspects of health will lead NLM 

to convene stakeholders to develop data 

standards and processes for accessing information and accelerating standards for these 

indicators. Collections relevant to the community span microbial colonies to behaviors 

to disease outbreaks to broad based environmental indicators. NLM must anticipate future 

needs for data through effective partnerships 

and implement mechanisms for the data lifecycle and the value proposition at each state. 

Scientific communication: NLM will stimulate new forms of scientific communication 

and become the library of the future, one of 

connections between and among literature, 

GOAL

1
11

data, models, and analytical tools. Creating 

efficient ways to link the literature with associated datasets enables knowledge generation 

and discovery. Initial approaches to connect 

datasets associated with papers in PubMed 

Central will be enriched with a complex environment interlocking data, literature reports, 

science profiles, and research proposals. 

NLM will anticipate developments such as 

preprints and novel alternatives to the journal issue as a mode of organizing articles. 

This includes the concept of an “executable 

article,” in which human-readable text can 

be combined with computer-interpretable 

representations of the evidence and guidance 

contained in a publication. Decomposing 

articles into components including interactive 

visualizations, decision analyses, executable 

models and simulations (e.g., of physiologic 

or molecular processes), and predictive and 

decision-analytic inference systems will 

enhance the value and extension of science.

Health information for the public: NLM 

provides portals to authoritative health 

information from NIH and other government 

agencies, as well as private sources. In the 

future, the goal is to provide that information in a way that makes it more actionable. 

In addition, there is a need to complement 

existing information by providing linkages 

to relevant data from multiple sources, such 

as environmental contaminant or exposure 

data and disease outbreak data, to enable 

an integrated presentation of information in 

understandable formats such as customizable 

dashboards. NLM will engage with commercial and non-commercial web search and 

health information providers to preserve its 

responsibility as an authoritative provider of 

health information. 

Objective 1.2: 

Advance research and development in biomedical 

informatics and data science 

As a platform for discovery, NLM plays a significant role throughout the entire research 

lifecycle, from initial inspiration through scholarly 

communication. NLM must anticipate research 

directions of the biomedical and clinical health 

science, prepare the tools necessary to accelerate discovery, and enable efficiencies in 

communication and dissemination. NLM has the 

responsibility to foster problem-inspired research 

across the various health science disciplines 

while accelerating the development of domain-independent, reusable approaches to discovery, 

curation, analytics, and collections. 

Biomedical scientists of today tell of a future 

where food itself becomes medicine, where 

understanding the context of health extends far 

beyond the intracellular environment to the built 

environment of homes and cities, where the data 

flows needed to characterize health phenomena 

move at speed of light and are far too dense to 

be captured by standard databases. NLM, in 

close partnerships with the biomedical and clinical research disciplines, will build the discovery 

and knowledge resources and tools needed while 

tailoring its investments to leverage the best of 

the disciplines. 

NLM must anticipate research 

directions of the biomedical and 

clinical health science, prepare 

the tools necessary to accelerate 

discovery, and enable efficiencies in 

communication and dissemination. 

GOAL

1
12

First among these resources and tools will be the 

devising of a system of digital research objects 

– articles, datasets, computational pathways,

analytical models, visualization tools, reference

standards, and the like, each of which will have

a unique identifier and could be coupled in novel

modes to accelerate discovery in a broad array

of disciplinary explorations. NLM will provide

the accelerators and the repositories of future

discoveries, and enable newer and more efficient

dissemination mechanisms.

Connecting collections of digital objects so that 

the whole is greater than the sum of its parts 

presents big computational and scientific challenges. Surmounting these challenges will require 

data science and informatics research and 

development, including innovation in curation, 

analytics, visualization, modeling, knowledge generation and next-generation mining 

approaches. These innovations, in turn, depend 

on advances in the science and mathematics 

underpinning such approaches. This research 

agenda will require more investment in intramural 

and extramural research support and pursuing 

partners whose participation will augment these 

efforts. 

Below are examples that illustrate how such 

research is integrally related to the mission and 

future of NLM and NIH.

• With the science and health sectors producing more digital research objects—whether

research-generated datasets, electronic

health records, or computational models—

value can be added through curation. A key

area for research is how curation at scale 

might be accomplished for hundreds of millions of such objects in a way that would be 

workable across biomedical domains, health 

care sectors, public health, and consumer 

health. Automatic, autonomous curation 

strategies will allow for operational efficiency 

as well as accelerate the speed of discovery.

• Analytics beyond statistics, data presentation

beyond visualization, and “data mining using

meaning” across heterogeneous data sources

are important areas for advancing data science. Research and development of artificial

intelligence in its many forms, including natural language processing and deep learning,

require stimulation, demonstration, testing,

and curation.

• Computable biomedical knowledge—such as

diagnostic, predictive, and decision-analytic

models, or practice guidelines represented

as coded digital objects—is an increasingly

important complement to human-readable

knowledge represented in books and journals.

• Define the structure and functions of “executable articles” that form an interactive library

where the resources “talk to each other” and

enable movement from data to knowledge

and knowledge to action.

• A rich set of data resources presents the

opportunity to pursue basic research on

data-driven questions in biomedical domains.

NLM will partner with scientists across NIH

to generate research questions and identify

value solutions.

Objective 1.3: 

Foster open science policies and practices

NLM will become an advocate for open science, 

and thereby democratize access to the products and processes of scientific research—that 

is, making them widely accessible. NLM will 

continue to develop tools that support open 

science, including information and data systems 

that preserve and make publications, data, and 

other research outputs discoverable and accessible to the broadest possible audiences. It will 

continue to develop technologies and systems 

that provide necessary protections and access 

limitations to such materials, where necessary, 

GOAL

1
13

e.g., to protect privacy, confidentiality, security, 

and certain proprietary rights. 

Beyond its systems and services, NLM has an 

essential role to play in helping develop and 

implement policies and practices that facilitate 

open science and data-driven research, while 

addressing the complex challenges of investigator freedom and patient privacy. These include 

policies related to data sharing, privacy protection, and informed consent that can motivate 

open science practices consistent with legal 

and ethical considerations. With productive 

relationships across NIH and the Department of 

Health and Human Services, NLM is positioned 

to participate in the reformulations necessary to 

allow open science to flourish, and can bring to 

these policy debates the practical experience in 

system building that can ensure effective policy 

and implementation.

Objective 1.4: 

Create a sustainable institutional, physical, 

and computational infrastructure

For NLM to become the hub for data science 

and open science activities at NIH, it must also 

make an organizational commitment to creating an institutional home, which will provide 

for organizational stability, fiscal accountability 

for data science efforts, and coordination of 

resources, investments, and communication. 

In devising the best structure to accomplish its 

data science mission, NLM must consider the 

key activities to be accomplished; how to best 

leverage existing data science efforts; and the 

possibility of a hybrid structure in which a permanent, clearly identified institutional locus for data 

science and open science exists in concert with 

the distributed nature of data science talent and 

investment.

NLM will make creative use of a constrained 

physical space while advocating for a larger 

and more appropriate physical environment. 

Sufficient space is needed for the current 

staff, which is distributed across several sites. 

Additionally, fit-for-purpose space is needed 

to house and support its physical and digital 

collections to guarantee long-term access. As a 

national biomedical library, NLM takes seriously 

the responsibility to ensure the preservation of its 

unique collections of books and medical heritage 

embodied in information artifacts. 

NLM’s mission is dependent on the viability of 

its data centers and the computation platforms 

therein. It needs to ensure adequate capacity 

and reliability in the electrical, mechanical, and 

structural infrastructure that supports NLM data 

centers to meet future computational demands. 

It must experiment with innovative computer 

and network solution strategies. Technical and 

policy solutions must accompany curation and 

collection management activities. Key technical 

challenges include authentication and authorization, assuring compliance with permissions 

required for data access and re-use, and security 

of data sets and session permission. 

NLM will become an advocate 

for open science, and thereby 

democratize access to the products 

and processes of scientific 

research—that is, making them 

widely accessible.

GOAL

1
14

GOAL 2: 

Reach more people 

in more ways through 

enhanced dissemination 

and engagement pathways 

NLM must understand and meet the needs of the stakeholders it serves: scholarly, 

clinical and community. A future of data-powered health will be built upon new 

models for interacting with information and data in innovative ways that can 

accelerate discovery and lead to knowledge that informs decisions and action. 

Advancing science and improving health is tied to potential users being aware 

of NLM resources, having access to them, and understanding their use. NLM 

continually strives to understand user needs and how they vary over time and 

across different categories of users, and to create new and better ways to deliver 

the right information, at the right level of sophistication, and at the right time.

In designing its dissemination initiatives, NLM is committed to developing 

approaches to innovation and outreach in which information access can 

reduce health disparities and support underserved communities that bear a 

disproportionate burden of disease.

Goals and Objectives
15

Objective 2.1: 

Know NLM users and engage with persistence

As new resources and services bring new users 

with new information needs, NLM will need to 

create pathways to reach them with relevant 

information at times the information is needed. 

Users will range from librarians to researchers, 

policy-makers to teens, clinicians to parents, 

pharma to public health laboratories. Each of 

these user groups will require unique patterns of 

engagement. User engagement encompasses 

promoting awareness of information resources, 

understanding information needs, facilitating 

access, and ensuring ability to use information 

resources. 

NLM will develop strategies to increase its visibility among intended audiences globally and 

nationally. Modern human factors and human 

computer design strategies will guide public-facing utilities redesign that feature the National 

Library of Medicine and National Institutes of 

Health brand. Attention to ethnographic design 

principles that draw from user experience are 

needed. Partnerships with NIH Institutes will also 

be systemically cultivated and highlighted. 

Recognizing that people live in communities, 

NLM will employ person-centric and community-conscious design strategies. This requires 

an investment in research to better understand 

health information needs and how to address 

them. It will prioritize increasing awareness of 

NLM services among low resource or low literacy communities and among underrepresented 

groups, in keeping with NLM’s commitment to 

employ information interventions to reduce health 

disparities. 

NLM will employ rapid cycle design and delivery approaches that reflect knowledge both of 

what the user seeks from its resources as well 

as why they are seeking it. It is also important 

to understand user needs in the moment. 

Approaches to dissemination must reflect different motivations for seeking information as well as 

disparities in health and data literacy among NLM 

constituencies.

Human networks remain important and will be 

leveraged to expand outreach across the U.S. 

NLM will continue to utilize the 6,500 member 

libraries of the National Network of Libraries of 

Medicine (NNLM) to serve in a trusted, visible 

manner for engaging and reaching out to the 

wide range of NLM users. With the NNLM, NLM 

will emphasize expanding partnerships with 

public libraries and community groups to improve 

awareness of NLM’s resources and to obtain 

feedback for improvement. The NNLM is partnering with the NIH All of Us Research Program to 

support community engagement efforts by public 

libraries and raise awareness about the program 

for populations unrepresented in biomedical 

research. The partnership also aims to increase 

the capacity of public library staff to improve 

health literacy.

User engagement encompasses 

promoting awareness of information 

resources, understanding information 

needs, facilitating access, and 

ensuring ability to use information 

resources. 

GOAL

2
16

Objective 2.2: 

Foster distinctiveness of NLM as a reliable, trustable 

source of health information and biomedical data

The information and data resources provided 

by NLM are becoming ever-more essential to 

biomedical discovery and contemporary health 

care while also becoming increasingly invisible as 

this information and data become embedded in 

the processes and products of our users. NLM 

is essential to public health authorities, who 

query its genomic data banks daily to discern the 

nature of a foodborne pathogen; essential to hospitals for demonstrating quality of care through 

using a MedlinePlus Connect embedded link to 

clinical systems to provide patients with tailored, 

trusted health instructions; essential to the over 

four million users who daily access its literature 

and biological databases. New devices found 

along the Internet-of-Things open an entirely 

new dissemination outlet for NLM resources. As 

NLM resources become embedded in search 

engines and informational instructions around the 

world, NLM must find ways to demonstrate its 

oversight and promote the chain of trust in health 

information. 

Those who recognize the NLM brand—libraries, 

research scientists, health professionals and 

the lay pubic—know and trust its information 

resources. Even though millions of people use 

NLM’s information services every day, there 

remains a persistent lack of name recognition 

and underappreciation for the vast number and 

variety of services provided by the NLM. NLM 

must make itself accessible and known to those 

who do not yet recognize its brand nor know 

of its services. Public awareness campaigns, 

better branding of the NLM-provided resources, 

and educating scientists and the public in best 

practices for health information and health data 

management will extend the chain of trust and 

improve discovery and care through greater reliance on trusted health information. 

NLM will aim to become recognized widely as the 

pre-eminent source of trusted information for the 

nation’s health. It will increase public awareness 

and the public’s trust in NLM-provided materials 

not only by devising ways to convey the NLM 

imprimatur on materials, but also through creative 

engagement that raises the general awareness of 

NLM as a place to turn to with health questions 

or biomedical data needs. Envision a campaign 

“NLM: your most trusted partner in health”. 

Objective 2.3: 

Support research in biomedical and health 

information access methods and information 

dissemination strategies

NLM will support programs of research in areas 

such as rapid cycle information processing, 

and will undertake new research to bring new 

information products to new users. It will focus 

on understanding how searches are initiated, 

how information is used, and how questions are 

NLM will aim to become recognized 

widely as the pre-eminent source of 

trusted information for the nation’s 

health. 

GOAL

2
17

posed and answered. The Library will provide 

important advances in delivering guidance for 

action or decision making and provide insight 

through advanced visualizations of complex 

data. NLM will continue supporting and undertaking informatics and data science research to 

design advanced interfaces and query systems 

that facilitate access to information systems 

in new ways and reflect the information needs 

and behaviors of individuals. This will include 

investigating the best methods to deliver effective information to support team science, 

family caregiving, and care coordination. Some 

examples of research areas that contribute to 

innovation in information access and delivery 

include human-computer interaction, natural 

language processing, virtual and augmented 

reality for system interaction, probabilistic inference mechanisms for sense-making from data 

visualization, and predictive analytics. To meet 

the needs of a diverse audience for information 

services, NLM must also conduct research to 

address matters of literacy, numeracy, and making information actionable in the moment.

Objective 2.4: Enhance information delivery 

NLM will provide access to information on 

multiple delivery platforms and through multiple 

user-centric interface formats, including ways 

that are very different from today’s formats. 

Strategies will be developed to provide better 

interlinking of resources to improve discovery.

In addition to designing fresh approaches for 

querying databases on demand, NLM will 

consider implementing push models that will 

anticipate user needs, operate continuously 

to search for new information, and engage 

them with results in a just in time manner. New 

approaches to input and output formats will be 

explored from the research front, including voice, 

music, image, and virtual reality applications. 

Partnerships with other NIH Institutes that are 

addressing information delivery challenges may 

lead to innovations in information delivery as well 

as new platforms.

To promote ease of access to NLM resources, 

interfaces that are more uniform across NLM 

platforms will be developed. It will explore voice, 

gesture, and behavioral interfaces, while attempting to ensure a predictable experience. Use 

of a common interface will not only improve 

accessibility of resources, it will complement the 

enhancements in interoperability and findability 

of related resources. Through test bed experimentation with new ways to improve search 

quality and usability, NLM will offer a better user 

experience for finding information faster, with a 

clean modern interface, and with better support 

for mobile devices.

Given the range of uses of NLM’s resources, 

NLM will continue to offer multiple delivery 

platforms, including application programming 

interfaces (APIs), mobile, social media, webbased, and stand-alone applications that can 

operate without internet access. New presentation modes that do not require the user to read, 

but still allow user control, will be explored. 

Fundamental to these efforts is recognizing that 

the fastest growing group of NLM users are actually digital devices that engage with the systems 

automatically.

To promote ease of access to NLM 

resources, interfaces that are more 

uniform across NLM platforms will 

be developed. It will explore voice, 

gesture, and behavioral interfaces, 

while attempting to ensure a 

predictable experience. 

GOAL

2
18

GOAL 3: 

Build a workforce for 

data-driven research 

and health 

To assure a future of data-driven discovery and health, it is essential to have 

a biomedical informatics and data science workforce prepared to make 

conceptual and methodological advances in analytics, visualization, mining, and 

other methods needed to use data for discoveries and to make it interoperable 

with existing knowledge. A new generation of librarians who are as capable of 

collecting and disseminating data resources as they are with more familiar forms 

of scholarly communications is also needed. The roles of the informationist and 

the librarian must expand to support a world where massive industrial search 

engines provide immediate access to vast stores of knowledge, while threatening 

to overwhelm the scholar, patient, and clinician with unfettered lists of potentially 

relevant resources. 

NLM must also play a role in creating a broader biomedical sciences workforce 

capable of data-driven discovery. New approaches to investigation will exist sideby-side with familiar experimental and exploratory methodologies. Training must 

foster the development of scholars with expertise in both biomedical informatics or 

data science and expertise in a biomedical or clinical domain. Both are needed in 

order to develop and deploy usable and useful applications harnessing the power 

of data to advance knowledge and improve health.

Goals and Objectives
19

Workforce development is a cornerstone of NLM’s mission. Training initiatives 

that prepare researchers for the evolving challenges of data-driven research 

will complement training that provides rapid, just-in-time and just-as-needed 

preparation for scientists and others. Current programs will be expanded and 

revised to seize scientific opportunities, form new partnerships, build capacity, and 

ensure a robust workforce for scientific discovery in the coming decades. Novel 

models of training, from three-minute videos to immersive, group-based problemsolving challenges, will engender team science skills and interdisciplinary learning. 

Initiatives to increase the size and expertise of the workforce will include a focus 

on strategies to achieve greater demographic diversity in biomedical informatics 

and data science. NLM will also engage in efforts to inspire the next generation in 

the power of data and information for progress in science and better health. 

Objective 3.1: 

Expand and enhance research training for biomedical 

informatics and data science

NLM has primary responsibility for ensuring an 

adequate supply of advanced research scientists 

to conduct research in biomedical informatics and data science and to serve as mentors 

and trainers of the next generation. Important 

skills for all researchers include being prepared 

to “compute in context;” extract meaning and 

insight from aggregations of data; and create 

new ways to analyze, visualize, mine, and integrate data and information. Specialists must 

be developed to meet the challenge of enabling 

dynamic, real-time curation at the scale and 

complexity that the future of biomedical data and 

information holds. 

NLM will train a new generation of data scientists 

to complement its current doctoral training for 

translational research, bioinformatics, consumer 

health informatics, and clinical and public health 

informatics. The emphasis on data science and 

management of large and complex biomedical 

big data will be expanded in NLM’s existing university-based training programs. This will include 

infusing data science with security and privacy 

management concepts in addition to next-generation curation, dissemination, and analytic 

approaches within data science. This increased 

focus will produce cadres of scientists who can 

develop a new array of tools and approaches for 

data, moving analytics beyond statistics, presentation beyond visualization, and more. NLM 

is strongly committed to ensuring an enduring 

professoriate for biomedical informatics, library 

and information science, and data science. 

Specialists must be developed to 

meet the challenge of enabling 

dynamic, real-time curation at the 

scale and complexity that the future 

of biomedical data and information 

holds. 

GOAL

3
20

Intramural training at NLM will be expanded 

through full post-doctoral fellowships in bioinformatics and computational biology, educational 

rotations in biomedical informatics, and 

post-graduate fellowships in library and information science. Leveraging an intramural research 

program on advanced analytics, NLM will offer 

short-term and extended training engagements 

involving interdisciplinary teams of scholars to 

craft the next generation artificial intelligence 

and machine learning methodologies. Particular 

emphasis will be placed on generating innovative 

methods to manage and disseminate the vast 

electronic resources of the NLM. 

Objective 3.2: 

Assure data science and open science proficiency 

Data science—accelerating insights from data 

through purposeful methodologies—and open 

science—a commitment to making all aspects of 

the research process from design to data accessible to scientists, professionals, and society 

at large—combine to create a new paradigm 

for training. Preparing a workforce to address 

biomedical data science problems will involve 

enhancing the computational and statistical skills 

of researchers with biomedical knowledge, and 

training computer scientists to apply their work 

to biomedical problems. In developing training approaches, NLM will combine these two 

cross-training goals to support the emerging field 

of biomedical data science. To expand its training 

initiatives, NLM will partner with other agencies, 

universities, and online education providers. 

Prepare clinicians and biomedical researchers to utilize data resources and interpret 

data science-based research findings. NLM 

will collaborate across NIH to develop a core 

curriculum for data science training embedded 

in institute specific training programs. It will 

also help ensure that domain specific university 

training and mid-career training awards meet 

proficiencies for data science. Working with 

partners, NLM will identify strategies to facilitate 

data science instruction in innovative ways such 

as modular approaches or self-directed learning, encompassing such topics as advanced 

analytics, data standards, data management, 

reproducibility, and data sharing.

Training for stewardship. Data stewardship is 

fundamental to open science and must become 

a fundamental component of every research 

project and program. The commitment to open 

science is expanding globally, with data sharing and reuse becoming the norm across many 

biomedical research disciplines. Investigators 

need assistance to ensure that data generated 

in research projects will be findable, accessible, interoperable, and reusable (FAIR) and will 

be effectively managed and curated across the 

research life cycle. As NIH develops new policies 

that guide data management and data sharing, 

NLM will develop tools and resources that help 

research teams understand and comply with 

policies. 

Preparing a workforce to address 

biomedical data science problems 

will involve enhancing the 

computational and statistical skills 

of researchers with biomedical 

knowledge, and training computer 

scientists to apply their work to 

biomedical problems.

GOAL

3
21

Promote competencies in open science and 

data science for information professionals.

As librarians and other information professionals 

meet the evolving needs of scientific research 

communities, they play a particularly important 

role in fostering open science, data management, 

data sharing, and development of repositories. 

Programs that enable such roles in promoting 

data and open science support—provided by 

NLM, through NNLM, or in partnerships with 

professional associations—ensure the availability 

of a trained workforce to support data science, 

data reuse, and responsible data stewardship. 

With the growing participation in citizen science, 

public librarians require special support and 

information on data management and interpretation of data-driven discovery. 

NLM will share responsibility with educational 

institutions, industry, and other federal agencies to ensure the development of a technical 

workforce capable of supporting data-driven 

discovery and advanced data management. 

Post-baccalaureate training programs in data 

management, medical library science, and 

clinical research data support are three such 

opportunities. 

Objective 3.3: 

Increase workforce diversity 

NLM is committed to developing a diverse workforce, inclusive of a broad range of racial and 

ethnic groups, individuals with disabilities, and 

individuals from economically or educationally 

disadvantaged backgrounds. Beyond workforce 

participation, NLM’s commitment to diversity 

extends to advocating for diversity of thought 

and plurality of methods. A commitment to diversity is grounded in the belief that participation of 

a diverse workforce improves team performance 

and engenders more robust knowledge representations and more culturally-competent means 

of supporting investigations and delivering health 

information. 

The responsibility of NLM for meeting its commitments to enhancing diversity will be met in 

several ways. First and foremost is a commitment to ensuring curricular integrity in its training 

programs, which requires explicit consideration 

of the completeness and robustness of knowledge representation and literature selection 

and organization. Second, NLM must increase 

the representativeness and participation of 

individuals from underrepresented groups in 

its university-based training programs. NLM 

will devise new incentives to recruit and retain 

underrepresented minorities within the programs 

it sponsors, and NLM will work with stakeholders 

to consider multi-focused strategies for increasing visibility and interest in these fields among 

underrepresented populations. 

NLM will partner with NIH agencies and professional societies to support programs that 

help high school and college students consider 

careers in health information technology, medical 

librarianship, and data science. NLM will leverage 

existing partnerships, such as the consortium 

of minority serving institutions that are members of NLM’s Environmental Health Information 

Partnership (EnHIP). 

Beyond workforce participation, 

NLM’s commitment to diversity 

extends to advocating for diversity of 

thought and plurality of methods. 

GOAL 

3
22

Objective 3.4: 

Engage the next generation and promote data literacy

Generations who grew up in a technologically-rich world bring a certain skill set to health 

sciences and biomedical services. Developing 

a workforce that is well prepared to meet the 

challenges of 21st century science still requires 

initiatives to develop an enthusiasm for science 

and data at an early age. NLM will cultivate 

and inspire the next generation of biomedical 

informatics researchers, data scientists, and 

information professionals. In collaboration with 

public and private partners, NLM will develop 

initiatives to spark students’ interest in science, 

technology, engineering, and mathematics 

(STEM). These initiatives will include ways to 

identify skills that will be needed and design 

targeted educational resources for learners from 

elementary school to college. 

Engagement with data is not limited to those 

who work in formal research or clinical roles. 

The public needs a basic level of scientific and 

data literacy. NLM will work with organizations, 

educational institutions, and public libraries to 

contribute to increasing public understanding 

and appreciation of science. 

Developing a workforce that is well 

prepared to meet the challenges of 

21st century science still requires 

initiatives to develop an enthusiasm 

for science and data at an early age. 

GOAL

3
23

Appendix A

References and Notes

National Institutes of Health (2015) 

NIH-Wide Strategic Plan, Fiscal Years 2016-2020: Turning Discovery Into Health. [online] 

Bethesda, MD: National Institutes of Health. 

Available at: https://www.nih.gov/sites/default/files/about-nih/strategic-plan-fy2016-2020-508.pdf

[Accessed 23 Jan 2018].

National Institutes of Health

Advisory Committee to the Director, National Library of Medicine Working Group (2015). 

Final Report—June 11, 2015. [online] 

Bethesda, MD: National Institutes of Health. 

Available at: https://acd.od.nih.gov/documents/reports/Report-NLM-06112015-ACD.pdf

[Accessed 23 Jan 2018].

National Institutes of Health, Data and Informatics Working Group (2012). 

Draft Report to The Advisory Committee to the Director. [online] 

Available at: https://acd.od.nih.gov/documents/reports/DataandInformaticsWorkingGroupReport.pdf

[Accessed 23 Jan 2018].

1 NLM contributes to the Trans-NIH Minority Health and Health Disparities Strategic Plan and complies with 

Section 2031(c)(B) of the 21st Century Cures Act.

Appendix B

NLM Authorizing Legislation and Executive 

Branch Assignments

1956 The National Library of Medicine Act of 1956 (Public law 84-941) established the National Library of Medicine (NLM) by renaming the Armed Forces 

Medical Library and transferring the Library to the Public Health Service in the 

Department of Health, Education, and Welfare (DHEW). The Act defines NLM’s 

purpose “... to assist the advancement of medical and related sciences and to 

aid the dissemination and exchange of scientific and other information important to the progress of medicine and to the public health.” Its basic functions 

are to acquire, preserve, and make available materials pertinent to medicine, 

broadly defined; to prepare and make available indexes, catalogs, and bibliographies of the materials; and to provide reference and research assistance. The 

Act also established the NLM Board of Regents to advise, consult with, and 

make recommendations to the Secretary on matters of Library policy, including the acquisition of materials, the scope, content, and organization of Library 

services, and the rules under which its materials, publications, facilities, and 

services are made available to users. 

Appendices
24

1965 The Medical Library Assistance Act of 1965 (Public Law 89-241) authorized 

the NLM to make extramural grants for research, training, and development of 

information resources and to establish Regional Medical Libraries, the origin of 

what is now called the National Network of Libraries of Medicine. Subsequent 

reauthorizations broadened the scope of the NLM’s grant authority.

1967 In response to the expressed interest by the Special Subcommittee on 

Investigation of DHEW of the House Committee on Interstate and Foreign 

Commerce, the Surgeon General transferred the Public Health Service 

Audiovisual Facility to NLM from the Center for Disease Control and renamed it 

the National Medical Audiovisual Center (NMAC). 

In response to a Presidential request based on recommendations made in the 

1966 report of the President's Science Advisory Committee, entitled "Handling 

of Toxicological Information," DHEW assigned NLM the responsibility to build a 

Toxicology Information Program (TIP). 

1968 The NLM was transferred administratively to the National Institutes of Health as 

part of a broader reorganization of the DHEW.

The Lister Hill National Center for Biomedical Communications (LHNCBC) was 

established at NLM via Senate Joint Resolution 193.

1988 The Health Omnibus Programs Extension Act established the National Center 

for Biotechnology Information (NCBI) at NLM to focus and expand the digital 

collection, storage, retrieval, and dissemination of the results of biotechnology 

research, and to support and enhance the development of new information 

technologies to aid in the understanding of the molecular processes that control 

health and disease.

1989 The amendment to the Public Health Service Act that established the Agency 

for Health Care Policy and Research (AHCPR) required the new agency to work 

with--and to transfer funds to--NLM to develop and enhance information services in the field of health services research, encompassing health technology 

assessment and the development of practice guidelines. 

1993 The National Institutes of Health Revitalization Act of 1993 established the 

National Information Center for Health Services Research and Health Care 

Technology (NICHSR) at NLM to collect, store, analyze, retrieve, and disseminate information on health services research, clinical practice guidelines, and 

health care technology. It also expanded NLM’s basic functions to include publicizing NLM services and promoting use of health information technology. 

1997 The FDA Modernization Act directed NIH to develop a registry of selected 

clinical trials of FDA-regulated drugs for serious and life-threatening conditions. The Director, NIH assigned responsibility for creating the registry to NLM, 

resulting in the release of ClinicalTrials.gov in 2000.

2004 The Secretary of Health and Human Services (HHS) designated NLM as the 

HHS central coordinating body for clinical terminology standards.

2007 The FDA Amendments Act required expansion of ClinicalTrials.gov to serve as a 

registry of clinical trials of a broad set of FDA-regulated drugs and devices and 

to collect summary results data from registered trials.
25

2008 The Consolidated Appropriations Act required NIH-funded investigators to submit final peer-reviewed journal manuscripts to NLM’s PubMed Central, making 

NIH’s previously voluntary public access policy mandatory. This requirement 

was made permanent in the Omnibus Appropriations Act, 2009. 

2013 The President’s Office of Science and Technology Policy issued a directive 

requiring all Federal agencies with more than $100 million per year in R&D 

expenditures to establish and implement public access policies similar to NIH’s 

and to develop policies for increasing access to digital data. As of 2018, all HHS 

agencies and five other Federal departments and agencies had elected to use 

PubMed Central to implement their policies. 

Appendix C

NLM Strategic Plan Planning Process

Overall development of the 2017-2027 strategic plan was guided by the Board of 

Regents Strategic Planning Subcommittee, which was appointed at the September 

2016 meeting of the Board of Regents. Members are Daniel R. Masys (co-chair), Jill 

Taylor (co-chair), Robert A. Greenes, Eric J. Horvitz, James L. Olds, and Sandra I. 

Martin. Discussions with the Board were held during its meetings in 2017, with final 

approval at the February 2018 meeting. 

Working in conjunction with NLM Planning staff, the Strategic Planning Subcommittee 

of the Board of Regents identified four themes as a framework for considering NLM’s 

priorities and future directions, and around which public input was solicited. 

These themes were: 

• Advancing data science, open science, and biomedical informatics

• Advancing biomedical discovery and translational science

• Supporting the public's health: clinical systems, public health systems

and services, and personal health

• Building collections to support discovery and health in the 21st century

In addition, the following topics were considered across the four themes: partnerships, 

user communities, user engagement and educational outreach, international engagement, health disparities, standards, infrastructure, workforce development, research 

needs and funding.

NLM engaged the broader community and its own staff in the strategic planning 

process through several approaches: Request for Information (RFI), Expert Working 

Groups, Institutional Training Site Visits, Staff Survey, and NLM Town Hall meetings.

Request for Information 

On November 8, 2016, NLM issued an RFI (https://grants.nih.gov/grants/guide/noticefiles/NOT-LM-17-002.html) based on the four themes to solicit input from its broad 

stakeholder community on goals and priorities for NLM's next strategic plan. The RFI 
26

was active through January 23, 2017. NLM received 111 responses from a wide range 

of stakeholders including librarians, researchers, historians, first responders, nurses, 

informaticians, associations, and the public. 

In addition, more than 600 responses were reviewed in response to the 2015 RFI 

(https://grants.nih.gov/grants/guide/notice-files/NOT-OD-15-067.html) issued by NIH on 

behalf of the NLM Working Group of the Advisory Committee to the NIH Director (ACD); 

that report was designed to obtain input on a vision for the future of NLM in the context 

of NLM's leadership transition and emerging NIH data science priorities. The subsequent report of the ACD Working Group report issued in June 2015 also informed our 

planning process (http://acd.od.nih.gov/reports/Report-NLM-06112015-ACD.pdf), as did 

the ACD Data and Informatics Working Group Report of 2012 (https://acd.od.nih.gov/

documents/reports/DataandInformaticsWorkingGroupReport.pdf), and the NIH-Wide 

Strategic Plan, Fiscal Years 2016-2020: Turning Discovery Into Health (https://www.nih.

gov/sites/default/files/about-nih/strategic-plan-fy2016-2020-508.pdf).

Expert working groups

Working groups of outside experts were convened around each of the four themes, with 

meetings held from March to May 2017. In these working groups, more than 100 people 

met to consider ways to propel the NLM forward, strategically and meaningfully. A fifth 

internal working group of NLM staff met to consider the reports from the four external 

groups, generate additional ideas, and synthesize and prioritize recommendations.

Training Site Visits

NLM leadership made a series of visits to NLM’s university-based training programs for 

research careers in biomedical informatics and data science. The purpose of the visits 

was to discuss the scope and characteristics of NLM’s training programs, particularly 

local perceptions about the benefits and challenges being faced and their strategies for 

incorporating data science into their programs. Separate meetings were held with three 

groups at each location: faculty, program administrators, and trainees. Ten programs 

were visited between October 2016 and June 2017, including five visits to private institutions and 5 to public institutions. 

NLM Staff Input

A staff survey soliciting input about NLM operations was available online or in paper 

format, which enabled anonymous responses to be placed in secure suggestion boxes 

in each of the office buildings where NLM staff work. The NLM planning team reviewed 

the 112 survey responses. 

Town Hall meetings open to all Federal employees and contractors at NLM were held 

in February 2017 and August 2017. The NLM leadership team shared information and 

responded to questions about the planning process and the needs of NLM moving 

into the next decade. The first meeting brought together 500 staff members, and 351 

staff attended the second meeting. Staff questions covered a broad range of topics, 

including data science, collections, media, global health, partnerships at NIH and in the 

community, reorganization, communication, workforce development, facilities, and NLM 

culture. 
27

Appendix D

Working Group Participants

Role of NLM in Advancing Biomedical Discovery and 

Translational Science

Chair:

Arthur S. Levine, MD

Senior Vice Chancellor for the Health Sciences

John and Gertrude Petersen Dean, School of Medicine

Professor of Medicine and Molecular Genetics

University of Pittsburgh

Members:

Todd Anderson, PhD

Professor and Chair

Department of Environmental Toxicology

Institute of Environmental and Human Health 

Texas Tech University

Gregory L. Armstrong, MD

Director, Office of Advanced Molecular Detection

U.S. Centers for Disease Control and Prevention

National Center for Emerging and Zoonotic 

Infectious Diseases 

Rob Califf, MD, MACC

Professor of Medicine

Duke University School of Medicine

Joshua C. Denny, MD, MS, FACMI

Professor, Biomedical Informatics & Medicine

Director, Vanderbilt Center for Precision Medicine

Vice President for Personalized Medicine

Vanderbilt University Medical Center

Filipa Godoy-Vitorino, PhD

Associate Professor

Department of Natural Sciences

Inter American University of Puerto Rico, 

Metropolitan Campus

Kristi Holmes, PhD 

Director, Galter Health Sciences Library

Director of Evaluation, Northwestern University 

Clinical and Translational Sciences Institute 

(NUCATS)

Associate Professor, Preventive Medicine 

(Health and Biomedical Informatics) and Medical 

Education 

Northwestern University Feinberg School of 

Medicine 

George Hripcsak, MD, MS

Professor and Chair of Biomedical Informatics

Columbia University

Maricel G. Kann, PhD

Associate Professor

Department of Biological Sciences

University of Maryland, Baltimore County

Sean Mooney, PhD 

Professor, Biomedical Informatics and Medical 

Education

Chief Research Information Officer (CRIO)

UW Medicine

University of Washington

Sheila Prindiville, MD, MPH 

Director, Coordinating Center for Clinical Trials 

National Cancer Institute, NIH

Richard J. Roberts, PhD 

Chief Scientific Officer

New England Biolabs
28

Julie Segre, PhD

Senior Investigator

National Human Genome Research Institute,

NIH

Christine E. Seidman, PhD

Thomas W. Smith Professor of Medicine and 

Genetics, and Investigator, HHMI

Harvard Medical School, Brigham & Women’s 

Hospital, Howard Hughes Medical Institute

Jacquelyn Taylor, PhD, PNP-BC, RN, 

FAHA, FAAN

Associate Professor and Associate Dean 

of Diversity and Inclusion

Yale School of Nursing

Jill Taylor, PhD

Director, Wadsworth Center

Faculty, Wadsworth School of Laboratory 

Sciences

New York State Department of Health

Consuelo H. Wilkins, MD, MSCI 

Executive Director, Meharry-Vanderbilt Alliance

Associate Professor of Medicine, 

Vanderbilt University Medical Center and 

Meharry Medical College

Vanderbilt University School of Medicine

Role of NLM in Supporting the Public’s Health: Clinical Systems, 

Public Health Systems and Services, and Personal Health

Chair:

Suzanne Bakken, PhD, RN, FAAN, FACMI 

The Alumni Professor of Nursing and Professor of Biomedical Informatics 

Columbia University

Members:

Julia Adler-Milstein, PhD 

Associate Professor

Schools of Information and Public Health

University of Michigan

Catherine Arnott-Smith, PhD, AMLS, MSIS 

Associate Professor, School of Library and 

Information Studies

University of Wisconsin-Madison

Discovery Fellow, Living Environments 

Laboratory

Wisconsin Institute for Discovery

Renae E. Barger, MLIS

Associate Director, Research, Instruction and 

Clinical Information Services

Program Lead, National Network of Libraries of 

Medicine, Middle Atlantic Region

University of Pittsburgh, Health Sciences Library 

System

Theresa Cullen, MD, MS 

Associate Director, Global Health Informatics 

Program

Regenstrief Institute, Inc.

Visiting Associate Professor 

Department of Family and Community Medicine

Indiana University 

Marcela Gaitán, MPH, MA 

Senior Director for External Relations

National Alliance for Hispanic Health

Graciela Gonzalez-Hernandez, MS, PhD 

Associate Professor of Informatics, Department 

of Epidemiology, Biostatistics and Informatics

Perelman School of Medicine

University of Pennsylvania

Robert Greenes, MD, PhD 

Professor and Ira A. Fulton Chair in Biomedical 

Informatics

Arizona State University
29

Shannon D. Jones, MLS, M.Ed., AHIP 

Director of Libraries & Associate Professor

Medical University of South Carolina Libraries

Marty LaVenture, PhD, MPH, FACMI 

Director

Office of Health IT, e-Health and Informatics

Minnesota Department of Health

Cynthia A Lindquist, PhD, MPA

President

Cankdeska Cikana (Little Hoop) Community 

College (CCCC)

Thomas S. Nesbitt, MD, MPH 

Associate Vice Chancellor, Strategic 

Technologies and Alliances 

Director, Center for Health & Technology

Professor, Department of Family and Community 

Medicine

UC Davis Health

Casey Overby, PhD

Assistant Professor, Divisions of General Internal 

Medicine and Health Sciences Informatics

Department of Medicine

Johns Hopkins University

Wanda Pratt, PhD

Professor, Information School

Adjunct, Division of Biomedical & Health 

Informatics, Medical School

University of Washington

Eric C. Schneider, MD, MSc, FACP

Senior Vice President for Policy and Research

The Commonwealth Fund

William W. Stead, MD 

Chief Strategy Officer

McKesson Foundation Professor of Biomedical 

Informatics

Professor of Medicine

Vanderbilt University Medical Center

Paul Tang, MD, MS

Vice President 

IBM Watson Health

Jill Taylor, PhD

Director, Wadsworth Center

Faculty, Wadsworth School of Laboratory 

Sciences

New York State Department of Health

Andrew (Andy) M. Wiesenthal, MD, SM 

Managing Director 

Health Care Practice 

Deloitte Consulting, LLP

Role of NLM in Building Collections to Support Discovery 

and Health in the 21st Century

Chair:

Patricia (Pat) L. Thibodeau, MLS, MBA

Associate Dean for Library Services and Archives (Retired April 2017)

Duke Medical Center Library & Archives

Members:

Amy Bernard, PhD

Product Architect

Allen Institute for Brain Science

Laura Brown, MA, MFA

Executive Vice President

Ithaka

JSTOR Managing Director

Sayeed Choudhury, MS

Associate Dean, Research Data Management

Director, Digital Research and Curation Center

Sheridan Libraries 

Johns Hopkins University

Yolanda Cooper, BGS, MLS

University Librarian

Robert W. Woodruff Library

Emory Libraries and Information Technology
30

Wayne Crocker, MLS

Director of Library Services

Petersburg Public Library

(Petersburg, VA)

Noémie Elhadad, PhD

Associate Professor

Biomedical Informatics, Computer Science

Columbia University

E. Thomas Ewing, PhD

Professor of History

Virginia Tech

Lorraine J. Haricombe, PhD

Vice Provost and Director of Libraries

University of Texas at Austin

Michele Klein-Fedyshin, MSLS, BSN, RN, AHIP 

Research & Clinical Instruction Librarian

Health Sciences Library System

University of Pittsburgh

John A. Kunze

Identifier Systems Architect

University of California Curation Center

California Digital Library

Kenneth Mandl, MD, MPH

Director

Computational Health Informatics Program

Boston Children’s Hospital

Donald A.B. Lindberg Professor of Pediatrics and 

Professor of Biomedical Informatics

Harvard Medical School

Sandra Martin, MSLS

Director, Shiffman Medical Library

Wayne State University

Neil Rambo, MLibr

Director 

Health Sciences Library

New York University

Nigam H. Shah, MBBS, PhD

Associate Professor of Medicine

Stanford University

Jonathan Teich, MD, PhD

Assistant Professor of Medicine and Emergency 

Medicine

Harvard / Brigham and Women’s Hospital

Role of NLM in Advancing Data Science, Open Science, 

and Biomedical Informatics 

Chair:

Russ Altman, MD, PhD

Director, Stanford Biomedical Informatics Training Program 

Professor, Bioengineering, Genetics, Medicine, Biomedical Data Science 

(and Computer Science, by courtesy)

Stanford University

Members:

Suzanne Allard, PhD

Associate Dean for Research, College of 

Communication & Information

Professor, School of Information Sciences

University of Tennessee, Knoxville

Francine Berman, PhD 

Edward P. Hamilton Distinguished Professor in 

Computer Science 

Rensselaer Polytechnic Institute

Christine L. Borgman, PhD, MLS

Distinguished Professor and Presidential Chair in 

Information Studies 

University of California, Los Angeles (UCLA)

Wendy Chapman, PhD

Chair, Department of Biomedical Informatics

University of Utah 
31

Greg Farber, PhD

Director, Office of Technology Development and 

Coordination

National Institute of Mental Health, NIH 

Lisa Federer, MLIS, MA

Research Data Informationist

NIH Library

Eric Horvitz, MD, PhD

Director and Technical Fellow

Microsoft Research

Debra R. Lappin, JD

Principal

Faegre Baker Daniels Consulting

Erez Lieberman Aiden, PhD

Assistant Professor

Baylor College of Medicine

Clifford Lynch, PhD 

Executive Director 

Coalition for Networked Information (CNI) 

Washington, DC

Elizabeth Marincola, MBA

Senior Advisor for Science Communications and 

Advocacy

The African Academy of Sciences

Patricia Matthews-Juarez, PhD

Professor, Department of Family and Community 

Medicine/Vice President for Faculty Affairs and 

Development 

Meharry Medical College, and

Chair of the NLM Environmental Health 

Information Partnership Advisory Board 

Lucila Ohno-Machado, MD, PhD, MBA 

Professor of Medicine

Chair, Department of Biomedical Informatics 

Associate Dean for Informatics and Technology

University of California San Diego 

Brian Nosek, PhD

Executive Director and Co-Founder

Center for Open Science (COS)

James L. Olds, PhD 

Assistant Director

Directorate for Biological Sciences

National Science Foundation

Kevin B. Read, MLIS, MAS

Assistant Curator

NYU Health Sciences Library

NYU School of Medicine

Chris Shaffer, MS, AHIP

University Librarian and Associate Professor

Oregon Health & Science University 

R. Joe Stanley, PhD, MS

Associate Professor, Electrical and Computer

Engineering

Associate Chairman, Computer Engineering

Missouri University of Science and Technology

(Rollo, MO)

Xuemao Wang, MBA, MLIS, MLS

Dean and University Librarian

Special Advisor to Provost on China Initiative

University of Cincinnati Libraries

Susan J. Winter, PhD

Associate Dean for Research

Co-Director, Center for Advanced Study of 

Communities and Information (CASCI)

College of Information Studies

University of Maryland
32

Working Group of NLM Internal Experts

Chair:

Michael F. Huerta, PhD

NLM Coordinator of Data & Open Science Initiatives

Associate Director for Program Development

Members:

Stacey Arnesen, MS

Head, Office of Disaster Information Management 

Research Center (DIMRC)

Division of Specialized Information Services 

Joyce Backus, MS

Associate Director for Library Operations

Ivor D’Souza, MS

Director, Information Systems

Office of Computer and Communications 

Systems

Todd Danielson, MS

Executive Officer and Associate Director for 

Administrative Management

Dina Demner-Fushman, MD, PhD

Staff Scientist, Computer Engineering Branch

Lister Hill National Center for Biomedical 

Communications 

Valerie Florance, PhD

Associate Director for Extramural Programs 

Kathryn Funk, MLIS

Program Specialist, PubMed Central 

National Center for Biotechnology Information

Pertti (Bert) Hakkinen, PhD

Acting Head, Office of Clinical Toxicology

Division of Specialized Information Services 

Stefan Jaeger, PhD

Visiting Scientist, Computer Engineering Branch

Lister Hill National Center for Biomedical 

Communications

David Landsman, PhD

Chief, Computational Biology Branch

Bioinformatics of Chromatin Structure Group

National Center for Biotechnology Information 

Wei Ma, MS

Chief, Applications Branch

Office of Computer and Communications 

Systems 

Jennifer Marill, MS, MA

Chief, Technical Services Division 

Division of Library Operations 

James G. Mork, MS

Computer Scientist, Computer Engineering 

Branch & Lead Developer and Technical Project 

Lead, Medical Text Indexer (MTI) project

Lister Hill National Center for Biomedical 

Communications

Duc Nguyen, MS

Chief, Medical Language Branch

Office of Computer and Communications 

Systems

Ilene Mizrachi, PhD

Staff Scientist, GenBank

National Center for Biotechnology Information 

Jeffrey S. Reznick, PhD

Chief

History of Medicine Division

Division of Library Operations

Valerie Schneider, PhD

Staff Scientist, RefSeq

National Center for Biotechnology Information 

Jerry Sheehan, MS

Assistant Director for Policy Development

Office of the Director

Hua-Chuan Sim, MD

Chief Program Officer, Clinical and Public Health 

Informatics Research, Applied informatics and 

Career Awards

Extramural Programs Division
33

NLM Board of Regents Subcommittee

Co-Chairs:

Daniel R. Masys, MD

Affiliate Professor

Biomedical and Health Informatics

University of Washington School of Medicine

Jill Taylor, PhD

Director, Wadsworth Center

Faculty, Wadsworth School of Laboratory Sciences

New York State Department of Health

Members:

Robert Greenes, MD, PhD 

Professor and Ira A. Fulton Chair in Biomedical 

Informatics 

Arizona State University

Eric Horvitz, MD, PhD

Director and Technical Fellow

Microsoft Research 

Sandra Martin, MSLS

Director, Shiffman Medical Library

Wayne State University

James L. Olds, PhD 

Assistant Director

Directorate for Biological Sciences

National Science Foundation

NLM Planning Team

Patricia Flatley Brennan, RN, PhD

Director

National Library of Medicine, NIH

Betsy L. Humphreys, MLS

Deputy Director (retired June 2017)

National Library of Medicine, NIH

Jerry Sheehan, MS

Deputy Director (effective July 2017)

National Library of Medicine, NIH

Michael F. Huerta, PhD

NLM Coordinator of Data & Open Science 

Initiatives

Associate Director for Program Development

Barbara A. Rapp, PhD

Chief, Office of Planning and Analysis

Office of Health Information Programs 

Development

Rebecca M. Goodwin, JD

Data Science Specialist

Office of Health Information Programs 

Development

Elizabeth Robboy Kittrie, MMHS

Strategic Advisor for Data Science and Open 

Science

Office of Health Information Programs 

Development
Cover image by Donny Bliss, Lister Hill National Center for Biomedical Communications, 

National Library of Medicine, National Institutes of Health 2017

U.S. Department of Health and Human Services

Public Health Service

National Institutes of Health

National Library of Medicine