**Panel 3. NLM Support for Clinical and Public Health Systems of the
21st Century**

Contents

[Introduction 1](#introduction)

[3.1. Objective 1: Electronic health record systems for clinical and
public health applications
3](#objective-1-electronic-health-record-systems-for-clinical-and-public-health-applications)

[Issues and Trends 3](#issues-and-trends)

[Findings 5](#findings)

[Recommendations 6](#recommendations)

[3.2. Objective 2: Clinical knowledge content and decision support tools
7](#objective-2-clinical-knowledge-content-and-decision-support-tools)

[Issues and Trends 7](#issues-and-trends-1)

[Findings 8](#findings-1)

[Recommendations 9](#recommendations-1)

[3.3. Objective 3: Public information, public health, and disaster
preparedness
11](#objective-3-public-information-public-health-and-disaster-preparedness)

[Issues and Trends 11](#issues-and-trends-2)

[Findings 12](#findings-2)

[Recommendations 12](#recommendations-2)

[3.4. Objective 4: Education, training, and dissemination
14](#objective-4-education-training-and-dissemination)

[Issues and Trends 14](#issues-and-trends-3)

[Findings 14](#findings-3)

[Recommendations 15](#recommendations-3)

[Appendix 1: Potential CDS-related NLM projects
17](#appendix-1-potential-cds-related-nlm-projects)

[Panel Roster 18](#panel-roster)

3.  Panel 3: NLM Support for Clinical and Public Health Systems\
    of the 21st Century

# Introduction 

The focus of this panel is to recommend strategic directions for the
National Library of Medicine's mission over the next decade in support
of clinical medicine and public health services. For more than 20 years
NLM has been the primary source of support for informatics research,
training, and dissemination in clinical, public health, and basic
science domains, in addition to its long-standing role as the foremost
steward and provider of definitive information resources for these
areas. The existing NLM programs in areas such as health services
research, telemedicine, imaging, toxicology and environmental health,
disease surveillance and disaster management, and clinical trials
information resources will continue to be important. These programs are
key to enabling biomedical research, supporting health care and public
health, and promoting healthy behavior. They will continue to evolve
over the coming decades by incorporating many on-going scientific and
technological advances. Central among these will be:

-   More and more detailed knowledge of life processes from genomic
    research.

-   New generations of clinical trials to determine the interactions
    between genetic make-up and other factors related to health and
    clinical outcomes.

-   More effective and universal implementation of interoperating
    patient health records and knowledge bases

-   Increased computerization of biomedical knowledge so it can be
    brought to bear as best practice for health-related practices
    throughout the world.

-   Improved ways to achieve life-long professional and patient learning
    that draws upon personalized models of learning and just-in-time
    access to knowledge

-   Better and more ubiquitous ways to use computing and communications
    to promote community and global health

-   Evolving models for information authoring, access, and delivery.

It is difficult at best to predict how progress will be made along these
many dimensions. The NLM's programs must be prepared to promote research
in areas involving promising opportunities and a range of risks and to
develop strategies and an overall program structure for synthesizing
results for practical dissemination as they become established. The
members of this panel identified two overarching objectives for NLM's
efforts to support clinical and public health systems of the next
decade:

1)  Develop tools, information structures, vocabularies, and processes
    to enlarge and improve the availability, delivery, and consumption
    of biologic and healthcare data and knowledge among patients,
    providers, public heath, and researchers for long-term improvement
    in healthcare quality and costs for all.

2)  Re-engineer the clinical research enterprise, improve quality,
    enhance trust, develop a research catalog of variables, and
    emphasize structured data and use of standards in sponsored research
    arena.

In pursuit of these overarching objectives goals, the panel identified
four main topic areas for its recommendations:

1)  Electronic health record systems for clinical and public health
    applications

2)  Clinical knowledge content and decision support tools

3)  Public information, public health, and disaster preparedness

4)  Education, training, and dissemination

We discuss each of these topics areas in succeeding sections, where we
present descriptions of the Issues and Trends, key Findings, and
specific Recommendations for future NLM actions under each the topic
area.

Before undertaking these detailed descriptions, however, we note that
there are many common themes and cross-cutting issues underlying the
findings and recommendations among these topic areas, and among the
other study panels on resources and infrastructure, outreach efforts,
and genomic science. Some of these include advanced ideas in computer
science, support for large-scale prospective cohort studies, achieving
critical mass in biomedical informatics research and training, and
knowledge support for clinical care, research, public information, and
disaster management. These cross-cutting issues must be integrated and
coordinated as NLM develops its long-term implementation plan.

In our discussion of NLM's programs in clinical care and public health,
one indispensable concept is that of an integrated, ubiquitous, and
interoperating Electronic Health Record system (EHR)[^1]. Current EHRs
typically comprise \"electronic\" patient charts. To a variable extent,
today\'s EHRs provide some or all of the record-keeping functionality of
paper-based systems (e.g., recording history and physical examination
notes, progress notes, consultation notes, \"written\" orders,
laboratory and imaging study results, and so on). In addition, EHRs
provide digital image storage and retrieval, clinical decision support,
and analysis capabilities derived from their computer-based environment.
In the next decade, EHRs will merge with Personal Health Record systems
(PHRs) to form integrated lifelong health records that will be
maintained collaboratively by healthcare providers and individuals (who
may be patients or healthy persons). As noted in a recent white
paper,[^2]

\"While EHR systems function to serve the information needs of health
care professionals, PHR systems capture health data entered by
individuals and provide information related to the care of those
individuals. Personal health records include tools to help individuals
take a more active role in their own health. In part, PHRs represent a
repository for patient data, but PHR systems can also include
decision-support capabilities that can assist patients in managing
chronic conditions. Most consumers and patients receive care from many
health care providers, and consequently their health data are dispersed
over many facilities\' paper- and EHR-based record systems.\"

# Objective 1: Electronic health record systems for clinical and public health applications {#objective-1-electronic-health-record-systems-for-clinical-and-public-health-applications .Objective}

## Issues and Trends

NIH and NLM have a long-standing interest in and expertise about the
capture, transformation, management and delivery of health information.
NLM developed the Unified Medical Language System (UMLS) to standardize
vocabularies and their interrelationships. The Lister Hill Center has
developed tools for displaying radiology images obtained from National
Health Nutrition Examination Surveys (NHANE) study subjects. The
National Center for Biotechnology Information (NCBI) is preparing to
provide access to de-identified clinical data collected over decades
from the Framingham study. NIH requires funded researchers to share
clinical data collected in research studies, and some investigators
include the raw clinical data as attachments to electronically published
papers. NIH's road map describes plans to move clinical research out
into the community and to disseminate the results of that research back
to the physicians in the community through efficient electronic
mechanisms. These goals all highlight the need for ubiquitous,
standardized, electronic clinical data that can be used efficiently and
securely among functions such as patient care, research, public health,
and education.

As our population enjoys increased life spans, intelligent information
and communication technologies may help the elderly, disabled,
chronically ill, and the underserved to have voice, text/data, and even
visual contact with health care providers without leaving their
environments. This might lead to improved preventive care and decision
making to avoid or postpone care in nursing homes or urgent care
settings, resulting in better quality of care and lower costs.

At present, many health care providers store patient data in electronic
health records (EHRs). At least three kinds of EHRs are in use today:

-   Institutional records --computer-based content of what has
    conventionally been kept in the paper medical record for many years.
    There is great variability between record systems in terms of what
    data are stored and how they are stored.

-   Summary patient records or population patient records -- these
    contain aggregated data from multiple sites of care. The summary
    record is similar to the outpatient record and is used to exchange
    data for new sites delivering care. Patients can control the flow of
    data between sites.

-   Personal health record -- includes data in the summary record and
    additional data created by and for the patient. As home monitoring
    increases, the personal health record will contain such data.

There are few identical clinical information systems, even if they were
purchased from the same vendor. Most commercial products are not
integrated and are maintained as "stand-alone" products. Customization,
incomplete implementations, and rapidly changing products all add to the
incompatibility.

Large parts of the information in existing EHRs are still stored as free
text. Even names of drugs, laboratory tests, diagnoses or therapies are
often not stored in coded form. Patient data should be useable for many
purposes, not just patient care. However, when such data are not well
structured, it is difficult to exchange them and use them in other
areas. The ultimate quality of the EHR depends on the quality of the
data acquired in clinician-patient encounters. There is a "golden
moment" for capturing accurate, structured information -- when the
clinician enters the data in the EHR. This moment never comes back and
systems must make it as easy as possible for clinicians to record high
quality information.

Many of the problems with implementing standardized and interoperating
information systems in support of clinical care and population health
seem to have more to do with organizational and individual resistance
than with technical aspects of the system design. No single agency can
overcome these issues, but by working together and providing
technological solutions along with examples of successful
implementations and financial and other incentives, the benefits of
electronic health record systems can be realized. An approach to
standards development that has been effective in evolving the global
Internet for more than 30 years, including the World Wide Web and
electronic commerce, may offer a sound model for biomedical informatics.
Information representation and interchange protocols based on small,
modular, and connectable standards make possible the development, to the
maximum extent possible, of higher level services as composites of the
standards already defined and deployed. This protects investments in
previous developments and minimizes the retooling of working systems.

Concerns about personal data privacy, identity theft, and other
violations based on exploiting personal records make people hesitant to
participate in systems that record and share information about them. In
parallel with organizational, standards, and technical developments to
make EHRs possible, well-founded public understanding, confidence, and
trust must be developed at the same time so that the broadest
cross-section of our society chooses to participate in EHR-based health
care.

## Findings

1.  There exist highly successful EHRs, such as the Veterans
    Administration National Electronic Medical Record and the National
    Surgical Quality Improvement Program, that have been able to
    document and improve outcomes in patient care.

```{=html}
<!-- -->
```
1.  In order to develop ubiquitous cooperating EHR systems for care,
    research, public health, and administration, many stakeholders must
    be involved. These include healthcare providers; researchers;
    payers; government agencies; standards development organizations;
    informaticians; computer scientists; vendors; auditors; reviewers;
    and patients. Such stakeholders must represent international
    communities.

2.  Many presume the clinician enters high quality data, but systematic
    studies indicate this is not the case. In the top-down development
    of large-scale EHR systems, the quality of data acquired from the
    clinician-patient encounter must be a key consideration. This
    requires bottom-up clinician-directed, problem-focused projects that
    complement the top-down development.

3.  The EHR should contain all present and future patient data such as
    history, laboratory, physiologic, imaging, and therapeutic data.

4.  Patient data in EHRs that are structured, follow definitions, and
    make use of clinical evidence and experience embedded in ontologies
    are more usable for many purposes:

    -   Testing for semantic flaws, bad codes, and wrong content.

    -   Improving readability and interpretability.

    -   Generating reminders, advice, and decision support.

    -   Coupling with knowledge, to supplement the physician\'s
        knowledge.

    -   Constructing longitudinal records, e.g., for monitoring of
        chronically ill patients.

    -   Use in web-enabled versions of EHRs for telemedicine and patient
        data entry.

    -   Creating databases for research and assessment of health care,
        including population studies, clinical trials, and
        post-marketing drug surveillance.

5.  Industry has a corresponding key role in structuring EHR data:

    -   Developing user-friendly interfaces so that structured patient
        data can be entered by the clinician, without too much time and
        effort.

    -   Evolve the ability of a structured EHR system to "intelligently"
        follow the user input and, on the basis of embedded knowledge,
        generate the most appropriate questions to be asked.

## Recommendations

1.  NLM should enable the development of structured EHRs that include
    history, laboratory, physiologic, imaging, therapeutic data, and
    genomic/genetic data for use in patient care and clinical research.

NLM\'s role in promoting the structuring of EHR data comprises:

2.  Developing and making available knowledge representations for
    structured EHRs, building on NLM's strength in coding and
    documentation, in constructing knowledge bases, and in natural
    language research.

3.  Developing vocabularies (e.g., RxNorm), and mapping vocabularies
    such as UMLS, ICD, LOINC, SNOMED, HL7 Tables, and repositories for
    drugs, laboratory data and images.

4.  Developing new advanced methods for clinical data acquisition from
    care providers, patients, and instruments at the source with the
    goal of increasing structuring when feasible.

5.  Supporting the development of tools, strategies and standards for
    producing a comprehensive cross institutional patient record that
    can extend to the home -- for care, research and public health
    purposes.

6.  Supporting tools and standards for the integration of
    genomic/proteomic data into an appropriately protected part of the
    EHR for use in care and research

7.  Coordinating the construction of intelligent databases for drugs,
    laboratory test results, and normal values, and providing order
    sets, alerts, and decision support.

8.  Investigating the extent to which clinical content should be
    standardized and the limits beyond which information must be
    expressed as text instead of formal codes, but amenable to computer
    understanding for use in decision support.

# Objective 2: Clinical knowledge content and decision support tools {#objective-2-clinical-knowledge-content-and-decision-support-tools .Objective}

> The field of knowledge is the common property of all mankind.\
> -- Thomas Jefferson, 1807 (in letter to Henry Dearborn)

## Issues and Trends

As they make decisions regarding patient care, research, and policies,
health professionals, patients, caregivers, and organizations experience
information needs. Clinical decision support (CDS) systems use
information technology to address systematically questions (and other
information needs) that arise during patient care and clinical research.

Developers and users of CDS systems seek to improve clinical and public
health outcomes. CDS can inform and alter healthcare decisions, and
standardize decisions and procedures for clinical research. To be
effective, CDS systems must provide concise, accurate, unambiguous, and
relevant factual information and/or advice at the point and time of
decision-making. Factual information consists of commonly accepted,
scientific knowledge, or in some instances expert opinion obtained by
consensus. Advice involves synthesis of patient observations, factual
information, and procedural/algorithmic (protocol) knowledge. Such
knowledge will soon include not only traditional clinical
healthcare-related information but emerging biomolecular and genetic
information as well. Advice should be evidence-based when possible, and
should include a level-of-evidence designation. Advice should contain
information, evidence, and recommendations that the decision-maker
considers when making judgments or decisions.

Unless it is adequately explicit, advice alone cannot standardize
decisions. Adequately explicit CDS protocols can standardize therapeutic
decisions and standardize research interventions for single- and
multi-institutional clinical trials. Adequately explicit therapeutic
protocols have already been implemented in intensive care units for
mechanical ventilation, intravenous fluid and hemodynamic support, and
blood glucose control with intravenous insulin.

Computerized CDS systems require representations of general medical
knowledge (content or domain knowledge), application-specific decision
logic (clinical algorithms), and accurate, standardized representations
of current patient data and clinical states. CDS knowledge bases should
be based on the peer-reviewed, scientific literature; in the absence of
relevant literature, carefully derived expert consensus may suffice.
Users' acceptance of CDS systems depends on how systems are installed
and supported, how systems impact users' workflows, how systems provide
advice, and human-computer interfaces.

Appropriate CDS requires standardized characterization of clinical
states and detailed, accurate, current patient data. The possible
clinical states and contexts for CDS are extensive. CDS decision logic
should include: a formal representation of the objectives and expected
results of system-generated advice or instructions; a consideration of
the resources available; and, when appropriate, patient/practitioner
assessments of risk/benefit preferences.

The history of formal clinical decision support systems extends over a
half-century. Throughout this period, the NLM has participated in
CDS-related work. Through intramural projects, NLM has developed and
distributed highly successful, focused decision support systems.
Examples include MEDLINE, TOXLINE, PDQ (developed by NCI), the UMLS
project, RxNORM, NDF-RT, clinicaltrials.gov, and the Genetics Home
Reference. The NLM has also played a key enabling role for CDS
nationally and internationally, through decades of extramural research
support to academic and other institutions.

## Findings

1.  No centralized high-quality repository of ready-to-use biomedical
    knowledge content exists for CDS systems. As a result, CDS
    developers often recapitulate previous content development work,
    with unnecessary variation in both quality and format.

    -   For example, many drug information databases now provided by
        commercial vendors have non-standard formats, and have varying
        depth and quality, especially in pediatrics and neonatal care.

    -   Unnecessary duplication in CDS work is common and squanders
        scarce resources. Centralized, more authoritative, adequately
        maintained, standard-format CDS knowledge sources would
        stimulate more efficient development of high-quality CDS
        systems. Feedback to a central source would enable more
        effective and timely CDS content maintenance.

    -   Any centralized repository for CDS content and research-related
        CDS protocols would require standards for CDS-related data
        representations and system integration.

```{=html}
<!-- -->
```
6.  No centralized repository exists for adequately explicit CDS therapy
    protocols. Research projects do not archive adequately explicit CDS
    protocols even when they produce substantial improvements in
    important clinical outcomes.

    -   A centralized source of validated, adequately explicit protocols
        from a single source, in a standardized format, with supporting
        research trial data documenting effectiveness, would enhance
        their utilization for research and clinical care.

7.  The NLM is uniquely qualified as a respected and unbiased, highly
    capable distributor of biomedical information.

8.  The NLM is well-positioned to play a national-level enabling role
    for CDS development and distribution, though it lacks the budget,
    personnel, and in-house expertise to serve as the primary developer.

    -   The NLM could become the integrated repository or bank of choice
        for high-quality CDS content and for adequately explicit
        research-related CDS protocols (algorithms).

    -   A centralized distribution mechanism for CDS content and
        research-related CDS protocols would enable individuals,
        institutions, and vendors to implement more easily standards for
        patient safety, improve the quality of patient care, and to
        implement replicable methodologies for conducting clinical
        trials (e.g., adequately explicit protocols).

    -   Such CDS activities are not new to the NLM. For example:

        The NLM is the crucial convener and purveyor of MEDLINE as the
        literature gateway, even thought it does not write or
        peer-review the indexed biomedical literature.

        The NLM organizes, interrelates, standardizes, and distributes
        UMLS content developed elsewhere by specialty organizations.

        The NLM provides the NCBI repository with its useful algorithms
        that profoundly changed the daily work of biological
        researchers, though the NLM does not directly conduct that work.

9.  Current data mining techniques, while improving rapidly, are still
    quite primitive. Central to the validity of data mining is the level
    of validity or credibility of the clinical data that are ultimately
    stored and queried.

10. Availability of good decision support systems does not guarantee
    successful installations and use by all. Many institutions
    installing clinical software systems could benefit from a central
    repository documenting others' experiences regarding pitfalls and
    best practices.

## Recommendations

9.  The NLM should undertake an explicit formal CDS mission intended to
    replicate the NLM's successful development and support for the
    National Center for Biotechnology Information (NCBI). The NLM should
    integrate new CDS content and tools with related NCBI content and
    tools.

10. The NLM should champion CDS-related knowledge creation,
    representation, and effective use.

    -   Within 10 years, practitioners, vendors, and citizens should
        routinely access and use for healthcare decision-making the
        national CDS knowledge base coordinated by the NLM.

    -   The NLM should not do this alone -- it should serve as the
        convener, integrator, and distributor. The national decision
        support content repository (knowledge base) should include
        clinical information and NCBI-based molecular information.

    -   The decision support knowledge base will provide the basis for
        academic institutions, specialty societies, healthcare
        providers, public health agencies, commercial vendors, and
        others to develop and distribute derivative clinical education
        programs and clinical decision support applications.

    -   Please refer to Appendix 1 for examples of potential CDS
        projects.

11. The NLM should support clinical data mining strategies that derive
    new knowledge from local, regional, and national clinical trial data
    sources, using text understanding and structured content extraction
    techniques.

    -   The NLM should play a leading role in encouraging the clinical
        and data mining communities to establish criteria for quality in
        clinical databases.

12. The NLM should become the repository of choice for safe and
    effective strategies for installing and maintaining CDS and other
    clinical software systems.

    -   This should be done in cooperation with commercial and academic
        clinical system developers and evaluators.

    -   The NLM should serve as a repository for documenting both best
        practices and suboptimal or failed approaches to clinical system
        installation and maintenance.

13. The NLM should become the repository of choice for adequately
    explicit and validated CDS research protocols. (See Appendix 1).

14. The NLM should continue to provide, and expand, its support for
    extramural research related to CDS.

    -   This should include directed basic computer science research in
        areas such as knowledge representation, visual reasoning,
        natural language processing, integration standards, and system
        integrity management.

    -   Support for CDS research should include, but not be limited to,
        knowledge representation and content development methods,
        clinical content development and maintenance, research-related
        CDS protocols, underlying informatics paradigms, and evaluation
        methodologies.

    -   The NLM should support research on decision support at both
        focused and general clinical levels.

# Objective 3: Public information, public health, and disaster preparedness {#objective-3-public-information-public-health-and-disaster-preparedness .Objective}

## Issues and Trends

Prevailing economic, political, and health trends suggest that in the
years to come, consumers will be increasingly responsible for making
complex decisions about their health. There will be an increasing
diversity of consumers who need information and tools to assist with
their decisions. They are diverse across the dimensions of race,
ethnicity, culture, language, age, health status, health literacy,
learning style, and others. They will be exposed to, seek, and receive
information from an increasingly broad array of media, such as the
Internet, blogs, podcasts, and video games. At the same time, they will
participate more directly with providers in care management by use of
EHRs and extensions to encompass telemedicine and home care. Discerning
high quality information from inaccurate or misleading information will
be increasingly difficult. The challenge for NLM will be to make sure
that people know how to get the information they need, when they need
it, and tailored in a way that helps them make decisions and take
action.

The term "Public Health" has both a specific meaning and a general
meaning. In its specific sense, "Public Health" refers to the
governmental functions vested in local, regional, state and federal
public health agencies. These functions include:

-   Monitor health status to identify community health problems

-   Diagnose and investigate health problems and health hazards in the
    community

-   Inform, educate, and empower people about health issues

-   Mobilize community partnerships to identify and solve health
    problems

-   Develop policies and plans that support individual and community
    health efforts

-   Enforce laws and regulations that protect health and ensure safety

-   Link people to needed personal health services and assure the
    provision of health care when otherwise unavailable

-   Assure a competent public health and personal health care workforce

-   Evaluate effectiveness, accessibility, and quality of personal and
    population-based health services

-   Research for new insights and innovative solutions to health
    problems

In its general sense, "Public Health" refers more broadly to
responsibilities for the health of individual members of the public.
This sense of Public Health straddles a unique place in many locales, in
that it partners with community-based organizations, such as federally
qualified health centers, to deliver a range of non-reimbursable,
preventive services. In many rural locales, Public Health is primary
care, preventive service delivery, environmental services, and
population-based health services.

The nation's public health system will be increasingly challenged to
fend off both emerging threats (like the spread of drug-resistant
tuberculosis or a possible H5N1 bird flu epidemic) and the growing
prevalence of chronic diseases. In addition, the tragic experiences of
disasters like hurricanes Katrina and Rita expose the nation's
vulnerability to large-scale localized disasters, let alone one of
national scope. It will be essential for NLM to take account of public
health in both senses when developing education, communications, and
information support strategies and programs for the next 20 years. The
key trend is that Public Health must be viewed as a useful conduit for
integrating information that supports population-based health services
and individual health care.

## Findings

1.  High quality reliable information is essential for public
    information purposes, public health, and disaster preparedness.

```{=html}
<!-- -->
```
1.  Cooperative relationships in producing and disseminating
    health-related information, e.g., the NIHSeniorHealth.gov website
    done in association with the NIH Aging Institute, can be a powerful
    means of tailoring materials, educating, and informing an
    increasingly older US population that will reach retirement during
    the coming decade.

2.  Public health lies at the cross-roads where medical, social, and
    economic policies converge and thus its practitioners often need to
    interpret health and medical information in the context of housing,
    economic, environmental and other data that relate to the multiple
    determinants of health.

3.  The integration of public health data and tool requirements into
    clinical informatics, such that, for example, clinical data can
    inform public health decisions and population level data can inform
    clinical decisions.

4.  NLM's role can take several forms:

    -   Maintaining and organizing information

    -   Convening experts and stakeholders

    -   Supporting basic and applied research.

## Recommendations

Public Information

1.  Continue to develop and provide the highest quality information for
    an increasingly diverse public that will assist in educating and
    informing them when and where necessary in support of decisions
    about health maintenance and care. NLM should be proactive in this
    endeavor:

    -   Develop new partnerships with groups that can assist in creating
        and disseminating content targeted at various communities with
        special needs based on factors such as ethnicity, culture,
        language, age, health status, health literacy, and educational
        background.

    -   Develop new ways of packaging, translating, organizing,
        integrating, and delivering information that take advantage of
        evolving technologies in areas such as wired and wireless
        connectivity, portable personal electronics, EHR/PHR systems,
        telemedicine, and "push" and "pull" models of access.

    -   Work with other agencies as appropriate with the goal of
        improving communications and computing access to information,
        educational tools, and health care support for communities that
        are underserved or untrained in using information services.

    -   Continue to enhance NLM's "brand name" to be synonymous with the
        highest quality, most diverse, and most accessible source of
        health-related information tools and services.

Public Health

2.  Support integration of public health data and tool requirements into
    clinical informatics, such that, for example, clinical data can
    inform public health decisions and population level data can inform
    clinical decisions.

3.  Develop the nascent field of public health informatics by supporting
    training and research. Integrate public health informatics with
    clinical informatics.

4.  Become a more active library, i.e., partner with public health to
    support them with information as they anticipate insults to the
    public's health. Information needs to be current, localized and
    ready to distribute (canned search strategies, etc.)

5.  Enrich links to information such as housing, economic, environmental
    and other data that relate to the multiple determinants of health
    --- non-medical barriers to health.

Disaster Preparedness and Response

6.  In coordination with other national response planning programs,
    provide tools and information resources that will assist in both
    preparedness and response -- convene national forum to achieve this
    goal.

7.  Develop plans for how to engage both medical librarians and public
    librarians as early responders to provide valuable information to
    the public and others in times of disaster. Librarians (public,
    hospital, etc.) can be positioned to support disaster information
    and to be included in disaster planning -- i.e., NLM needs to figure
    out what information local librarians need to be effective in this
    role (using ALA, PLA) and also use NNLM in doing this.

8.  Explore a partnership role (with ONC, ASTHO, NACCHO, CDC, etc.) to
    define standards to facilitate storage and access to person-specific
    basic, lifesaving medical information about individuals (e.g.,
    Katrinahealth.org).

# Objective 4: Education, training, and dissemination {#objective-4-education-training-and-dissemination .Objective}

## Issues and Trends

After thirty or more years of development, Informatics relating to
health and biomedicine is at the end of its adolescence. Billions of
dollars are being invested by nations across the world in the discipline
and indeed within each of its major sub-disciplines. As such, the field
now needs to take the appropriate steps to assure acknowledged stature
as a full academic discipline parallel to other mature disciplines in
order to meet the critical and growing educational requirements for
informaticians in the United States and worldwide. This process will
assure the creation of appropriate curricula and program review
mechanisms as well as the creations and maintenance of appropriate
educational materials.

At least three major sub-specialties in Informatics are needed in order
to assure sufficient structure, visibility, vitality, and critically, a
sensible alignment with the other health disciplines and health
professions:

-   Translational Bioinformatics

-   Applied Clinical Informatics

-   Population or Public Health Informatics.

Whether or not Telemedicine should be a distinct fourth area needs
active discussion and resolution. Others could argue that Imaging and
Simulation should also be areas of special recognition rather than being
folded into the three listed above. In some respects, Bioinformatics,
Imaging, Simulation and perhaps others comprise the basic sciences of
Informatics. Doctoral preparation is seen as the essential level of
education to achieve professional competence. The clinical disciplines
of the field are Applied Clinical Informatics, Public Health, and
Telemedicine. Both masters and doctoral levels of preparation have merit
for clinicians, depending on one's plans for professional practice. For
example, if one combines clinical practice with informatics in nursing
or medicine, a master's level education, or its equivalent, may be
sufficient for practice requirements.

## Findings

1.  If the field is to address the many demands for an educated
    informatics workforce posed by practice, teaching, and research, the
    times call for movement on Applied Clinical and Public Health
    Informatics. Current NLM training programs are likely to deal with
    translational bioinformatics. Nursing took this step some years ago
    in the clinical domains and this experience can be most helpful to
    its other clinical colleagues.

```{=html}
<!-- -->
```
1.  A proper workforce will include a mix of professional informaticians
    while assuring that non-informaticians and indeed, citizens, get
    sufficient training to practice at a competent level in IT-rich
    environments.

2.  High priority educational areas that incorporate knowledge, skills,
    and relevant research include:

    -   Simulation

    -   Ongoing work on global standards for educational materials,
        terminology, and classification with open access through the NLM

    -   Implementation of EHR systems with relevant decision support

    -   Knowledge management within and external to libraries.

## Recommendations

1.  Develop a validated patient record database for education and
    training. The NLM should collect a large (1-10K) database of
    de-identified patients records structured to be importable into EHRs
    for learning & testing purposes and be accessible in multiple
    languages. A matrix approach should cover:

    -   A variety of patient care sites

    -   Individual and population care

    -   Different severities of illness and wellness

    -   A variety of users (professionals, patients, public health
        worker, librarian, etc.)

    -   Data sources might include VA, Academic Health Centers, DoD,
        other

2.  Stimulate simulation. The NLM should inventory the field in order to
    enable basic and applied research and education relating to
    simulation 'packages' needed for:

    -   Basic level education for a variety of users including patients
        (routine and 'games')

    -   Psychomotor skills

    -   Complex applications utilizing "Visible Human", etc.

        These materials should be web-enabled for users and be
        accessible in multiple languages.

3.  Learning modules and objects.[^3] The NLM should collate and make
    searchable learning 'modules and objects' developed by health
    professional groups in the US and elsewhere in the world, including
    Nursing groups, AAMC, MLA, APHA, etc. for their constituents.

4.  Assure that information technology (IT) is central to professional
    competence. The NLM should inventory current experience in order to
    enhance educational training programs for all health professionals
    who use IT resources, including librarians, so as to integrate IT
    knowledge and skills at the center of their professional competence.

5.  Continuing competency research program. An active research program
    should complement this effort so that education continues to keep
    pace with changing technology and knowledge bases.

6.  The NLM should support efforts to develop systems and educate
    professionals, students, residents, and other providers in
    real-world EHR-based care, and especially ambulatory care.

7.  Evaluate NLM programs to determine if additional efforts are needed
    to help people use IT to manage their own health.

# Appendix 1: Potential CDS-related NLM projects 

1.  Collaborative construction and distribution of an evidence-based
    diagnostic knowledge base that contains the findings reported to
    occur in various diseases, with related predictive values,
    sensitivities, and literature-based evidence summaries. Such a
    knowledge base could help to standardize research regarding
    diagnosis, prognosis, and therapy by providing standardized
    descriptions of the findings in each clinical disorder. The
    availability of standardized finding descriptors could enhance
    detection of new disorders when reported to the CDC and other public
    health agencies.

```{=html}
<!-- -->
```
3.  Collaborative construction and distribution of a drug information
    database that includes information on drug dosing (especially in
    pediatrics, neonates, geriatrics, and patients with renal impairment
    or severe hepatic impairment); representations of drug side effects
    in the same clinical vocabulary used to describe clinical diseases
    (project 1 above); and, detailed information on drug-drug
    interactions. The NLM has already worked with other federal agencies
    such as the FDA, VA, NCI, and DOD, to create RxNORM and NDF-RT. The
    drug information database would represent a logical extension of
    such work. Vendors would then have a common, high-quality
    information base upon which they could build advice-giving products,
    or complementary knowledge resources

4.  Collaborative archiving and distributing adequately explicit
    research protocols. For example, one institution might develop an
    adequately explicit ICU protocol that standardizes clinician
    decision-making for blood glucose control. This protocol might be
    validated and then archived in an NLM repository. Another
    investigative group then might improve the protocol by modifying its
    logic content. They might then conduct a multi-center trial and
    validate the modified protocol. The new and clinically preferable
    protocol would be stored and distributed by NLM. The newer one would
    replace the older one and the older protocol might be archived with
    annotations indicating its replacement and the evidence for which it
    was retired.

# Panel Roster

Chair:

Dr. Reed Gardner\
Professor of Medical Informatics (retired)\
University of Utah\
Salt Lake City, UT

Members:

Ms. Kristine Alpi\
Associate Library Director\
Samuel J. Wood Library\
Weill Medical College\
Cornell University\
New York, NY

Dr. F. Charles Brunicardi\
DeBakey/Bard Professor and Chairman of the Michael E. DeBakey Department
of Surgery\
Baylor College of Medicine\
Houston, TX

Dr. Don E. Detmer\
President and CEO\
American Medical Informatics Association\
Bethesda, MD

Dr. Stephen Downs\
Robert Wood Johnson Foundation\
Princeton, NJ

Ms. Jacque Doyle\
Fletcher Library\
Arizona State University at the West Campus\
Phoenix, AZ

Ms. Judy Faulkner\
Chief Executive Officer\
Epic Systems Corporation\
Madison, WI

Dr. Henry Foster\
Professor of Obstetrics and Gynecology\
Meharry Medical College\
Nashville, TN

Dr. Henry Fuchs\
Frederico Gil Professor\
University of North Carolina at Chapel Hill, Department of Computer
Science\
Chapel Hill, NC

Dr. W. Edward Hammond\
Professor, Division of Medical Informatics\
Professor, Biomedical Engineering\
Durham, NC

Mr. Carl E. Hendricks\
Chief Information Officer\
Military Health System\
Department of Defense

Prof. Casimir Kulikowski\
Board of Governors Professor of Computer Science\
Rutgers, The State University of New Jersey\
Piscataway, NJ

Dr. John Lumpkin\
Senior Vice President for Health Care\
Robert Wood Johnson Foundation\
Princeton, NJ

Dr. Clement McDonald\
Regenstrief Chair in Health Services\
Indiana University School of Medicine\
Indianapolis, IN

Dr. J. Michael McGinnis\
Senior Scholar\
Institute of Medicine, The National Academies\
Washington, DC

Dr. Randolph A. Miller, M.D.\
Donald A.B. and Mary M. Lindberg University Professor of Biomedical
Informatics, Medicine & Nursing\
Vanderbilt University\
Nashville, TN

Dr. Alan H. Morris\
Director of Research\
Pulmonary Division, LDS Hospital\
Professor of Medicine\
Adjunct Professor of Medical Informatics\
University of Utah\
Salt Lake City, UT

Dr. Pearl O\'Rourke\
Director of Human Research Affairs\
Partners Healthcare Systems\
Boston, MA

Dr. Herbert Pardes\
President and Chief Executive Officer\
New York Presbyterian Hospital\
New York, NY

Dr.  William O. Robertson\
Medical Director\
Washington Poison Center\
Seattle, WA

Dr. David Ross\
Director, Public Health Informatics Institute\
Decatur, GA

Dr. Jay Sanders\
President Emeritus\
American Telemedicine Association\
McLean, VA

Dr. Richard Satava\
Professor, Department of Surgery\
University of Washington\
Seattle, WA

Ms. Eileen Stanley\
Director, Library Services\
Allina Health System\
Minneapolis, MN

Dr. George Stetten\
Department of Bioengineering\
University of Pittsburgh\
The Robotics Institute\
Carnegie Mellon University\
Pittsburgh, PA

Dr. Cheryl Taylor\
Interim Chair, Graduate Nursing Programs\
REACH for Health Online\
Southern University\
New Orleans, LA

Dr. Jan van Bemmel\
Professor of Medical Informatics\
Erasmus MC, Department of Medical Informatics\
The Netherlands

Dr. Norman W. Weissman\
Professor and LR Jordan Endowed Chair in Health Services Administration\
University of Alabama at Birmingham\
Birmingham, AL

Writing assistance:

Mr. Thomas C. Rindfleisch\
Director Emeritus\
Lane Medical Library\
Stanford University\
Palo Alto, CA

[^1]: Over the years Electronic Health Record systems have variably been
    called Computerized Patient Record systems (CPRs) and Electronic
    Medical Record systems (EMRs). This panel adopted the term EHR as
    representative of modern usage and of its focus on a broadly used
    and accessible information resource for health care.

[^2]: Paul C. Tang, MD, MS, Joan S. Ash, PhD, David W. Bates, MD, J.
    Marc Overhage, MD, PhD and Daniel Z. Sands, MD, MPH, "Personal
    Health Records: Definitions, Benefits, and Strategies for Overcoming
    Barriers to Adoption," J Am Med Inform Assoc. 2006;13:121-126.

[^3]: Modules and Objects: Basic chunks of knowledge or skills that
    together compose pieces of educational programs or courses.