CLINICAL AND TRANSLATIONAL SCIENCE INFORMATICS

Translating Information to Transform Health Care

Arkalgud Ramaprasad, Annette L. Valenta

University of Illinois at Chicago, Chicago, IL 60607, U.S.A.

Ian Brooks

National Center for Supercomputing Applications,Urbana, IL 61801, U.S.A.

Keywords: Clinical and translational science, Informatics, Ontology, Ontological analysis.

Abstract: Clinical and Translational Science (CTS) is a new and emerging academic discipline which seeks to reduce

(a) the time-to-application of research to, and (b) the time-to-research of, health care problems. Translating

information within and between the research and practice silos is central to CTS. The role of CTS

Informatics (CTSI) can be stated as ‘translating information to transform health care’. We present an

ontological analysis of the transformation of health care by CTSI. The five dimensions of the ontology are

derived by parsing the above definition of CTSI. They are: (a) information, (b) semiotic translation, (c)

spatial translation, (d) temporal translation, and (e) health care. Each dimension is defined by a taxonomy.

Each sentence, formed by concatenating categories across the five dimensions using appropriate prefixes

and conjunctive words and phrases, is a natural language descriptor of CTSI. The set of all such sentences is

a closed description of CTSI.

1 INTRODUCTION

Clinical and Translational Science, at its core, is

information intensive. The guiding mission of CTS

Informatics (CTSI) is to develop, support, and

continuously improve the research workflow to

reduce the time-to-application of basic research and

the time-to-research of clinical and community

health problems. CTS’ success will depend upon the

“bi-directional information flow between basic and

translational scientists….” (Zerhouni 2005, p. 1621)

It is expected that the “CTSA [CTS Awards]

institutions will work as part of a national effort to

expand and improve clinical research informatics to

share data across disciplines and across

institutions….New and expanded IT will help to

solve issues related to workflow, usability, and

interoperability with collaborating organizations,

along with the need to ensure privacy and

confidentiality of human subjects.” (Zerhouni 2007,

p. 127) The informatics infrastructure will be

essential “[t]o develop the preemptive, predictive

medicine of the 21

century.” (Zerhouni 2005, p.

1355) Overall “the NIH aims to develop a national

system of interconnected clinical research networks

capable of more quickly and efficiently mounting

large-scale clinical studies. As currently conceived,

this system of networks will integrate and expand

extant research networks using common or

interoperable infrastructure, including harmonized

informatics, governance, terminology, and training.”

(Zerhouni 2005, p. 1356)

Thus, we can succinctly encapsulate the above

visions and objectives of CTSI in the statement

‘translating information to transform health care.’

Using this statement we will deconstruct the

complexity of CTSI.

The following ontological analysis (Ladkin

2005) of the transformation is a step to capture

CTSI’s complexity with natural language

descriptions using a structured terminology. We

address the complexity using an ontology derived by

parsing the above stated definition of CTSI. We

synthesize the extant literature on CTSI using the

ontology. The analysis is used to develop a strategy

for CTSI. At a time when CTS is still developing a

CTSI strategy will be critical to its wellbeing.

This method of logical analysis, synthesis of the

literature, interpretation, and application is

systematic, repeatable, and extensible. It is also

novel. In the following we will first present the

ontology derived by parsing our definition of CTSI.

Then, we will discuss the topics corresponding to the

135

Ramaprasad A., L. Valenta A. and Brooks I. (2009).

CLINICAL AND TRANSLATIONAL SCIENCE INFORMATICS - Translating Information to Transform Health Care.

In Proceedings of the International Conference on Health Informatics, pages 135-141

DOI: 10.5220/0001431701350141

 SciTePress

ontology in the following sequence: (a) ontological

analysis, (b) CTSI ontology, (c) translating

information between research and practice; (d)

translating information semiotically, spatially, and

temporally; and (e) translating information to

transform health care.

2 ONTOLOGICAL ANALYSIS

Ontologies are used in informatics systems design to

standardize terminologies, map requirements,

organize them systematically, facilitate integration

of systems, promote information exchange between

systems, etc. (Gruber 1995; Gruber 2008)

Ontologies are related to but different from

taxonomies, typologies, concept hierarchies,

thesauri, and dictionaries. (Gilchrist 2003) They are

tools for systematizing the description of complex

systems (Cimino 2006); a way of deconstructing the

architecture of complexity (Simon 1962). Such

systematization, in turn, facilitates analysis and

design of these systems.

There is no standard definition of ontology of an

informatics system. We will define it as a logically

constructed n-dimensional natural language

description of the system. The dimensions are

derived from the problem statement, system’s

definition, or objectives. Each dimension is

independent of the other and is a taxonomy of

discrete categories. Each taxonomy may be flat or

hierarchical. Further, the order of categories in a

particular dimension at a particular level of the

taxonomy may be nominal (no particular order) or

ordinal (based on some parameter). The stages of

progression along the dimension, the sequence of

evolution, the progressive part-whole relationships,

the scale, etc. are some bases for ordering the

categories. Last, a dimension may have sub-

dimensions with their own taxonomies. That is, a

dimension itself may be hierarchical.

A combination of categories across all the

dimensions, with appropriate conjunctive words, is a

natural language descriptor of the system in the form

of a sentence – albeit a little awkward grammatically

sometimes. The set of all combinations across all

categories – that is all possible sentences – is a

closed description of the system. The full set can

have a very large number of descriptors (individual

combinations). However, many of the combinations

may be uninterpretable or empirically unviable –

they may be discarded from further analysis. At the

same time some combinations may be novel and

creative, providing valuable insights into the

properties and possibilities of the system.

3 CTSI ONTOLOGY

The ontology we use for the analysis of CTSI has

five dimensions, namely: (a) information, (b)

semiotic translation, (c) spatial translation, (d)

temporal transportation, and (e) health care. The

logic of the derivation of the dimensions from the

definition of ‘translating information to transform

health care’ should be intuitively clear. We have

deconstructed translation into three dimensions: (a)

semiotic translation, (b) spatial translation, and (c)

temporal transportation. Information and health care

have been retained as such.

The five dimensions and their corresponding

taxonomies are shown in Figure 1 and discussed

below. The conjunctive prefixes, words, and phrases

to concatenate the columns are shown before and

between the columns. They make the concatenations

across dimensions natural and understandable. Five

illustrative combinations are shown at the bottom of

the figure. The ontology as presented can be

expanded into 7*6*4*6*7 = 7,056 combinations.

The above representation is a concise way of

representing them and analyzing them

systematically. A listing of all the combinations

would likely take about 100 pages.

4 TRANSLATING

INFORMATION BETWEEN

RESEARCH AND PRACTICE

This section focuses on the first and the last

dimensions of the ontology. They mirror each other

with the order reversed – the first dimension is

focused on information and the last on health care.

In a sense they can be seen as the input and the

output of CTSI, cross-linking research to practice

and practice to research, with the translation process

in between.

The dichotomy of research and practice in health

care (and other disciplines) is historical and

persistent. While there is some overlap between the

two, they are often seen as polar opposites. CTS

seeks to transform the relationship between the two

into a seamless continuum. Ideally perhaps they

should be like the Yin and the Yang, separate but

simultaneously containing the other – research

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136

Information Semiotic Spatial Temporal Health Care

Research

[

]

Observation

[

]

Physical Minutes Practice

Basic Data Internal Hours Clinical

Animal Analysis External Days Public health

Clinical Interpretation Virtual Weeks Community health

Public health Conclusion Internal Months Research

Practice Recommendation External Years Basic

Clinical Animal

Public health Clinical

Community health Public health

Illustrative combinations

Translating basic research conclusion in external virtual space in months to transform clinical practice.

Translating clinical practice observation in external physical space in months to transform basic research.

Translating public health research data in external virtual space in days to transform public health

practice.

Translating clinical practice recommendation in external virtual space in months to transform community

health research.

[to transform]

Translation

[Translating]

[space in]

Figure 1: CTSI Ontology.

embedding practice and practice embedding

research.

We will use the research-practice dichotomy for

the taxonomy of information and of health care in

the ontology. There are many other taxonomies; any

one of them could be used in the ontology. A

different taxonomy would naturally yield a different

perspective on the issue. Our choice would be in

keeping with the actuality (Cicmil, Williams et al.

2006) of CTSI. Research information and health

care research are further categorized as basic,

animal, clinical, or public health; practice

information and health care practice as clinical,

public health, or community health. The four

categories of research have been construed to

connote two stages – from basic research to clinical

trials, and from clinical trials to clinical practice

(Sung, Crowley et al. 2003). The two-level

taxonomy shown in Figure 1 is adequate for the

present discussion. It can be extended or refined

subsequently if necessary.

At the core of CTSI is the translation of research

information into health care practice, and health care

practice information into research (Sung, Crowley et

al. 2003). The absence of these two types of

translation results in the “knowledge to action gap”

(Graham, Logan et al. 2006) or the “Evidence-to-

Practice Gap” (Lang, Wyer et al. 2007, p. 355) and

the “action to knowledge gap” – the last has not

received as much attention as the first two in health

care literature (Westfall, Mold et al. 2007). While

there is a lot of concern about the accumulation of

basic research that does not get translated into

clinical trials and then to clinical practice (Sung,

Crowley et al. 2003), there is some but not the same

amount of concern about the accumulation of

practice knowledge that does not get fed back to

inform the basic research and be validated by it. For

example, the “Institute of Medicine Strategies for

Knowledge Translation Related to Health Care

Quality” cited by Hedges (Hedges 2007) has no

feedback loop from practice/outcomes to

research/literature. Translation has to be “a 2-way

connection between the interstates of academic

scientific discoveries and the patients receiving care

in the ambulatory practice.” (Westfall, Mold et al.

2007, p. 404) Interestingly, the CTSA programs are

supposed to create “two-way synergies with local

and regional communities by reaching out to

underserved populations, community-based groups,

and healthcare providers.” (Zerhouni 2007, p. 127)

However their purpose is to help “deliver improved

medical care to the entire population, helping to

disseminate new technologies and new advances

into clinical practice.” (Zerhouni 2007, p. 127) This

could also take place by translating practice

knowledge into research and back to practice – thus

completing the feedback loop (Ramaprasad 1983).

There can be many barriers to and facilitators of

translation of research information to practice and

vice-versa (Sung, Crowley et al. 2003; Ghosh and

Ghosh 2005; Gaughan 2006; Anderson, Lee et al.

2007; Westfall, Mold et al. 2007). We categorize

them along the three dimensions of the ontology as

semiotic, spatial, or temporal. In the next section, we

discuss these three dimensions of translation and

how they can inhibit or engender translation, starting

with a lexical and linguistic discussion of translation

itself.

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5 TRANSLATING

INFORMATION

SEMIOTICALLY, SPATIALLY,

AND TEMPORALLY

Translating information – we use information to

generically connote data, information, and

knowledge – is the key to CTS and hence to

bringing about the transformation it seeks. We

address the complexity of translation by breaking it

down into three dimensions: (a) semiotic, (b) spatial,

and (c) temporal translation. We discuss each of

these three and their taxonomies below.

5.1 Semiotic Translation

The process of translating research information to

practice and practice information to research to

improve health care is semiotic. It is an ongoing

series of iterative cycles of generation and

dissipation cutting across the semiotic layers of

morphology, syntax, semantics, and pragmatics

(Ramaprasad and Rai 1996; Ramaprasad and

Ambrose 1999; Ambrose, Ramaprasad et al. 2003;

Payne, Mendonca et al. 2007). In lay terms, it is the

process of moving from observation to data to

analysis to interpretation to conclusion to

recommendation, then feeding back into

observation. These steps form the taxonomy of the

semiotic dimension in the ontology (Figure 1).

The CTSI should support the semiotic

translation of information (a) by researchers and

practitioners, and (b) between and among

researchers and practitioners. The traditional

research process is one of semiotic translation by

researchers; the semiotic translation by practitioners

when it occurs is akin to the grounded research

(Glaser and Strauss 1964) process. The exchange of

information among and between researchers and

practitioners may occur at a semiotic level or cut

across many of them. Thus, for example, two

researchers may simply exchange data or

conclusions. Or, one researcher may send his data to

another researcher who may analyze it and send her

results back to the first researcher. The network of

researchers and practitioners involved in CTS is

likely to be far more complex than the above dyadic

examples; correspondingly the semiotic translation

entailed and hence CTSI has to support will be

complex too.

The informatics tools and techniques required to

move across the semiotic levels vary. For example,

in some types of laboratory research all the steps up

to and including analysis can be automated; on the

other hand in some qualitative field research none of

the steps can be automated. Similarly, a simple

interpretation may be communicated succinctly

without loss of fidelity, while communicating a

complex interpretation may require a

correspondingly complex explanation.

The study of the semiotics of translation is not

new. “Medical semiotics in the 18th century was

more than a premodern form of diagnosis. Its

structure allowed for the combination of empirically

proven rules of instruction with the theoretical

knowledge of the new sciences, employing the

relation between the sign and the signified.” (Hess

1998, p. 203) The semiotic dimension of translation,

however, has received little explicit attention

recently (seeGraham and Tetroe 2007, for example).

As Scott et al. discuss “the challenge of translating

evidence from SRs [systematic reviews], while

maintaining a sufficient level of validity and

relevance to satisfy both clinicians and researchers,

is rarely discussed.” (Scott, Moga et al. 2007, p.

681) The complexity of the semiotics is indicated by

their conclusion: “The key elements for creating

clinically relevant knowledge from SRs are: a

flexible, consistent and transparent methodology;

credible research; involvement of renowned content

experts to translate the evidence into clinically

meaningful guidance; and an open, trusting

relationship among all contributors.” (Scott, Moga et

al. 2007, p. 681) The lack of attention to semiotics is

particularly glaring at a time when the so called

semantic web (perhaps better called the semiotic

web (Ramaprasad and Kashyap 2008)) is under

development and is likely to have a major impact on

CTSI.

5.2 Spatial Translation

Researchers and practitioners who have to be

networked for CTS are likely to be distributed

internally within an organization, locally, regionally,

nationally, and even globally. Among them, the silos

of research and practice, and of the different

categories of research and practice, may not just be

artifacts of the mind fostered by specialized

disciplines but also manifest in their physical

location, their offices, and labs. The challenge for

CTSI is to spatially translate the information (a)

from research to practitioners, (b) from practice to

researchers, (c) from research to researchers, and (d)

from practice to practitioners, across the virtual and

physical silos.

The broad presumption of spatial translation is to

eliminate the physical location dependence of CTS

(Ambrose, Ramaprasad et al. 2003). The NECTAR

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Network (Zerhouni and Alving 2006), The Family

Practice Inquiries Network and other practice-based

research-networks (Westfall, Mold et al. 2007), the

International Clinical Epidemiology Network,

(Tugwell, Robinson et al. 2006), and the Oklahoma

Physicians Resource/Research Network (OKPRN)

(Nagykaldi and Mold 2007) are examples of systems

set up for this purpose. In the same vein, the CTSA

mandates that all the centers should be networked.

Not only should any researcher or practitioner be

able to access the information ‘anywhere’, but

should be able to process it ‘anywhere’. Today, with

the internet, there is a rising expectation that

information be available ‘anytime, anywhere’.

Today’s information and communication

technologies – the internet is one of them – have not

only altered the constraints of physical space but

have also created an entirely new virtual space. The

dynamics of the physical and virtual spaces affect

and are affected by each other. The capabilities of

the virtual space can be used to overcome the

constraints of the physical space and vice-versa.

Thus, CTSI can be used to create a virtual space that

complements the capabilities and constraints of the

physical space in which its users operate. It must be

noted that the exchange of information in the virtual

space has not obviated the need for exchange in the

physical space – despite e-mails and webinars face-

to-face conversations and meetings continue to be

important. The barriers to and facilitators of spatial

translation for CTS have to be understood in the

context of the convergence of the physical and

virtual space.

There is almost always a distinction between the

‘internal’ and the ‘external’ in discussing physical

space and virtual space. The boundary between the

two can be an important barrier to or filter of the

information translated. The rules governing the

translation internally – like security, privacy, and

confidentiality rules – are different from those for

translation externally. The boundary separating the

two may itself be arbitrary or adaptive to the

context. Thus for some information everybody in the

organization may be internal, but for others only the

members of the research group may be internal.

Despite the fuzziness and variability of the boundary

the internal-external distinction is an important

consideration in the spatial translation in CTSI.

Thus, there are four categories of spatial

translation in the ontology: (a) internal-physical, (b)

external-physical, (c) internal-virtual, and (d)

external-virtual. Each of these can play a different

role in the translation of research to practice and

practice to research. In a given context a mix of

them may be used. The CTSI should facilitate and

remove barrier to the use of all four.

5.3 Temporal Translation

The temporal dimension is intrinsic in the objectives

of CTS to minimize the time-to-practice and the

time-to-research. It is also implicit in the concept of

preemptive and predictive medicine (Zerhouni

2005). The scale of these times varies by context.

Bringing the current best research evidence to bear

upon the diagnosis of a patient in the emergency

room may have to be done in minutes (Holroyd,

Bullard et al. 2007); research on and response to an

epidemic such as SARS may spread over weeks and

months; response to Avian Flu (Eysenbach 2003)

can be planned months or years ahead and activated

in hours or days; and taking a drug from discovery

to clinical deployment may take over ten years. For

example, “[i]n the first documented instance of bird-

to human infection with the H5N1 flu virus in 1997,

Hong Kong reacted by destroying its entire poultry

population of 1.5 million birds within three days.”

(Webster and Hulse 2005, 415) For another

example, in the case of SARS the first “[u]nusual

atypical pneumonia was documented in Foshan,

Guangdong Province, China” in November 2002;

the virus was identified in March 21-27, 2003; and

the full genome was mapped by April 12, 2003.

(Peiris, Yuen et al. 2003, p. 2432)The total time was

less than six months. The role of a CTIS in a SARS-

like epidemic is highlighted by the recommendation

for an “efficient information technology systems that

provide a means to link clinical, epidemiological,

and laboratory data on SARS cases and to

disseminate this information locally, nationally, and

globally, and systems that allow rapid identification,

tracking, evaluation, and monitoring of contacts of

SARS cases.” (Parashar and Anderson 2004, p. 632)

Thus, in the temporal dimension of the ontology

we categorize time by the common units of minutes,

hours, days, weeks, months, and years. The

categories are ordinal and the progression is not

linear. To continuously improve the time-to-practice

and time-to-research it will be necessary to map and

measure the corresponding workflows. The

workflows are likely to be complex, fragmented, and

widely distributed in physical and virtual space. To

date the whole process of translation has not been

conceptualized as a system; it has been an

agglomeration of a number of ad hoc systems. The

CTSAs are compelling the (re)conceptualization of

the entire system. In that context, the CTSI should

reflect the requirements of these workflows and

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reengineer them to make them more efficient and

effective.

6 TRANSLATING

INFORMATION TO

TRANSFORM HEALTH CARE

Consider the four illustrative combinations at the

bottom of Figure 1. These four sentences are natural

language descriptions of the functions of CTSI.

They encapsulate the translation requirements in the

context of the emergency medicine, SARS, and

Avian Flu discussed earlier. Although a little

awkward grammatically, they make sense and can

be related to specific requirements of CTSI. There

are 7,052 similar sentences that can be constructed

from the ontology. Each of these sentences can

connote a number of requirements. No one system is

likely to fulfil all the requirements connoted by all

the sentences. On the other hand, a selection of

sentences can be a high level description of the

requirements of a CTSI.

REFERENCES

Ambrose, P., A. Ramaprasad, et al. (2003). "Managing

thin and thinly distributed knowledge in medical

genetics using the Internet." Logistics Information

Management 16(3/4): 207-214.

Anderson, N. R., E. S. Lee, et al. (2007). "Issues in

Biomedical Research Data Management and Analysis:

Needs and Barriers." Journal of the American Medical

Informatics Association 14(4): 478-488.

Cicmil, S., T. Williams, et al. (2006). "Rethinking Project

Management: Researching the actuality of projects."

International Journal of Project Management 24(8):

675-686.

Cimino, J. J. (2006). "In defense of the Desiderata."

Journal of Biomedical Informatics 39(3): 299-306.

Eysenbach, G. (2003). "SARS and Population Health

Technology." Journal of Medical Internet Research

5(2).

Gaughan, A. (2006). "Bridging the divide: the need for

translational informatics." Pharmacogenomics 7(1):

117.

Ghosh, A. K. and K. Ghosh (2005). "Translating evidence-

based information into effective risk communication:

Current challenges and opportunities." Journal of

Laboratory and Clinical Medicine 145(4): 171-180.

Gilchrist, A. (2003). "Thesauri, taxonomies and ontologies

-- an etymological note." Journal of Documentation

59(1): 7-18.

Glaser, B. G. and A. Strauss (1964). The discovery of

grounded theory: Strategies for grounded research,

New York: Aldine.

Graham, I. D., J. Logan, et al. (2006). "Lost in knowledge

translation: time for a map." J Contin Educ Health

Prof 26(1): 13-24.

Graham, I. D. and J. Tetroe (2007). "Some Theoretical

Underpinnings of Knowledge Translation." Academic

Emergency Medicine 14(11): 936-941.

Gruber, T. R. (1995). "Toward Principles for the Design of

Ontologies Used for Knowledge Sharing."

International Journal Human-Computer Studies 43(5-

6): 907-928.

Gruber, T. R. (2008). Ontology. Encyclopedia of Database

Systems. L. Liu and M. T. Ozsu, Springer-Verlag.

Hedges, J. R. (2007). "The Knowledge Translation

Paradigm: Historical, Philosophical, and Practice

Perspectives." Academic Emergency Medicine 14(11):

924-927.

Hess, V. (1998). "Medical semiotics in the 18th century: A

theory of practice?" Theoretical Medicine and

Bioethics 19(3): 203-213.

Holroyd, B. R., M. J. Bullard, et al. (2007). "Decision

Support Technology in Knowledge Translation."

Academic Emergency Medicine 14(11): 942-948.

Ladkin, P. B. (2005). "Ontological Analysis." Safety

Systems 14(3): 1-7.

Lang, E. S., P. C. Wyer, et al. (2007). "Knowledge

Translation: Closing the Evidence-to-Practice Gap."

Annals of Emergency Medicine 49(3): 355-363.

Nagykaldi, Z. and J. W. Mold (2007). "The Role of Health

Information Technology in the Translation of

Research into Practice: An Oklahoma Physicians

Resource/Research Network (OKPRN) Study." J Am

Board Fam Med 20(2): 188-195.

Parashar, U. D. and L. J. Anderson (2004). "Severe acute

respiratory syndrome: review and lessons of the 2003

outbreak." Int. J. Epidemiol. 33(4): 628-634.

Payne, P. R. O., E. A. Mendonca, et al. (2007).

"Conceptual knowledge acquisition in biomedicine: A

methodological review." Journal of Biomedical

Informatics 40: 582-602.

Peiris, J. S. M., K. Y. Yuen, et al. (2003). "The Severe

Acute Respiratory Syndrome." N Engl J Med 349(25):

2431-2441.

Ramaprasad, A. (1983). "On the Definition of Feedback."

Behavioral Science 28(1): 4-13.

Ramaprasad, A. and P. J. Ambrose (1999). The Semiotics

of Knowledge Management. Ninth Workshop on

Information Technology and Systems, Charlotte,

North Carolina.

Ramaprasad, A. and V. Kashyap (2008). Semiotic Web for

Translational Medicine (Poster). AMIA 2008 Annual

Symposium. Washington D.C.

Ramaprasad, A. and A. Rai (1996). "Envisioning

management of information." Omega-International

Journal of Management Science 24(2): 179-193.

Scott, N. A., C. Moga, et al. (2007). "Creating clinically

relevant knowledge from systematic reviews: the

challenges of knowledge translation." Journal of

Evaluation in Clinical Practice 13(4): 681-688.

Simon, H. A. (1962). "The architecture of complexity."

Proceedings of the American Philosophical Society

106(6): 467-482.

HEALTHINF 2009 - International Conference on Health Informatics

140

Sung, N. S., W. F. Crowley, Jr., et al. (2003). "Central

Challenges Facing the National Clinical Research

Enterprise." JAMA 289(10): 1278-1287.

Tugwell, P., V. Robinson, et al. (2006). "Systematic

reviews and knowledge translation." Bulletin of the

World Health Organization 84(8): 643-651.

Webster, R. and D. Hulse (2005). "Controlling avian flu at

the source." Nature 435(7041): 415-416.

Westfall, J. M., J. Mold, et al. (2007). "Practice-Based

Research--" Blue Highways" on the NIH Roadmap."

JAMA: The Journal of the American Medical

Association 297(4): 403.

Zerhouni, E. A. (2005). "Translational and clinical

science—Time for a new vision." N Engl J Med

353(15): 1621-1623.

Zerhouni, E. A. (2005). US Biomedical Research Basic,

Translational, and Clinical Sciences, Am Med Assoc.

294: 1352-1358.

Zerhouni, E. A. (2007). "Translational Research: Moving

Discovery to Practice." CLINICAL

PHARMACOLOGY & THERAPEUTICS 81(1): 126-

128.

Zerhouni, E. A. and B. Alving (2006). "Clinical and

Translational Science Awards: a framework for a

national research agenda." Translational Research

148(1): 4-5.

CLINICAL AND TRANSLATIONAL SCIENCE INFORMATICS - Translating Information to Transform Health Care

141