NISTIR 7263

Healthcare Strategic Focus Area: 

Clinical Informatics

Conrad Bock

Lisa Carnahan

Steven Fenves

Michael Gruninger

Vipul Kashyap

Bettijoyce Lide

James Nell

Ravi Raman

Ram D. Sriram

 
NISTIR 7263

Healthcare Strategic Focus Area: 

Clinical Informatics

Conrad Bock 

Lisa Carnahan 

Steven Fenves 

Michael Gruninger 

Vipul Kashyap 

Bettijoyce Lide 

James Nell 

Ravi Raman 

Ram D. Sriram 

Manufacturing Systems Integration Division 

Manufacturing Engineering Laboratory 

National Institute of Standards and Technology 

Gaithersburg, MD 20899-8260 

September 2005

U.S. DEPARTMENT OF COMMERCE 

Carlos M. Gutierrez, Secretary 

TECHNOLOGY ADMINISTRATION 

Michelle O’Neill, Acting Under Secretary of Commerce for Technology 

NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY 

William Jeffrey, Director 

 
CONTENTS 

ABSTRACT........................................................................................................................ 2

1. INTRODUCTION...................................................................................................... 3

2. POSITION OF NIST IN ADDRESSING CLINICAL INFORMATICS ............. 5

3. INFORMATION FLOW IN HEALTHCARE SERVICES................................... 6

4. ELECTRONIC MEDICAL RECORDS.................................................................. 9

5. VOCABULARIES ................................................................................................... 13

6. EVIDENCE-BASED MEDICINE......................................................................... 15

7. METRICS................................................................................................................. 18

8. POTENTIAL COMPONENTS OF CLINICAL INFORMATICS..................... 18

9. ROLES FOR NIST .................................................................................................. 21

10. SUMMARY AND CONCLUSIONS .................................................................... 24

DISCLAIMER................................................................................................................. 25

APPENDIX: SUMMARY OF CLINICAL INFORMATICS STANDARDS ........... 26
Healthcare focus area: clinical informatics 

Abstract 

NIST has conducted several workshops and other information gathering activities, that 

have identified several key strategic opportunities in healthcare information technology 

(HIT) for NIST: clinical informatics; bioinformatics; medical devices; pharmaceuticals; 

biosurveillance; and enterprise modeling. This report addresses the first area, clinical 

informatics, as being of prime interest to NIST and focuses on roles that are suitable for 

NIST. The report presents three aspects of clinical informatics of most immediate interest 

to NIST’s Information Technology (ITL) and Manufacturing Engineering Laboratories 

(MEL): 

• electronic medical records, approaches and standards for the integration and 

interchange of health information; 

• vocabularies, controlled collections of concepts that cover medical knowledge for 

particular purposes; and 

• evidence-based medicine (EBM), applying up-to-date healthcare knowledge by 

filtering and disseminating it in a way that integrates into clinical experience and 

patient values. 

The report then presents an initial set of components needed to realize an interoperable 

framework for clinical informatics: systems engineering for clarifying the meaning of 

information; semantic languages for expressing information with increased precision and 

expressiveness; model-driven architecture for translating semantically developed system 

specifications into multiple technologies; and interoperability and conformance testing to

help ensure that standards-based solutions provide the necessary infrastructure and 

seamless information integration across applications within and between homogenous 

healthcare organizations. A number of potential roles that NIST’s ITL and MEL could 

play in the area of clinical informatics are also identified. 

Keywords: clinical informatics; bioinformatics; electronic medical records; systems 

engineering; semantic languages; interoperability; standards

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Healthcare focus area: clinical informatics 

1. Introduction 

The healthcare industry is facing major challenges: increasing costs, unacceptable error 

rates, and dissatisfied patients and providers. Healthcare costs in the United States were 

about 14.9 % of the GDP - $1.6 trillion - in 20021, estimated to be 1.9 trillion in 20052

and projected to rise to 3.6 trillion by 20143

. These costs are also a major concern for 

U.S. industry, as escalating healthcare costs are impeding our ability to globally compete. 

According to a February 11, 2005 issue4 of the Washington Post, General Motors spent 

$5.2 billion on healthcare in 2004 for its employees, retirees and their families. These 

healthcare expenses added $1,500 to the price of each GM car. Other U.S. automobile 

manufacturers face similar costs. 

In addition, medical errors are of great concern. In a much debated Institute of Medicine 

(IOM) report entitled “To Err is Human: Building a Safer Health System,” it is claimed 

that “at least 44,000, and perhaps as many as 98,000, Americans die in hospitals each 

year as a result of medical errors.” The numbers may not be exact, but the key point is 

that there is considerable loss of life due to medical errors that can be avoided if 

appropriate safety mechanisms are put in place.5

Steps toward addressing the various challenges facing the healthcare system were 

articulated in a report entitled “Information for Health: A Strategy for Building the 

National Health Information Infrastructure” by the National Committee on Vital and 

Health Statistics, a public advisory body to the Secretary of Health and Human Services.6 

The report emphasized the need for effective sharing and communication of data, 

information, and knowledge among various stakeholders in the healthcare network. This 

can be achieved through the effective use of information technology (IT). The need for 

knowledge mining, molding, and sharing is also underscored by the Institute of 

Medicine's report entitled “Crossing the Quality Chasm: A New Health System for the 

21st Century.”7

 The Center for Information Technology Leadership recently published a 

report stating that “standardized health care information exchange among health care IT 

systems would deliver national savings of $77.8 billion dollars every year ...”8

 

1 National Center for Health Statistics. Health, United States, 2004. With Chartbook on Trends in the 

Health of Americans. Hyattsville, Maryland: 2004. Table 116. Page 326. Available at: 

http://www.cdc.gov/nchs/data/hus/hus04trend.pdf#116 . 2

 National Healthcare Expenditures Projections: 2004-2014. Office of the Actuary. Centers for Medicare 

and Medicaid Services. 

Available at: http://www.cms.hhs.gov/statistics/nhe/projections-2004/proj2004.pdf. 3

 ibid. 

4

 Ceci Connolly. U.S. Firms losing Healthcare Battle, GM Chairman Says. Washington Post. February 11, 

2005. Available at: http://www.washingtonpost.com/wp-dyn/articles/A15828-2005Feb10.html 5

 The report “To Err is Human” is published by the National Academy Press and is available at 

http://www.nap.edu/books/0309068371/html/. Several articles in the JAMA (Journal of the American 

Medical Association), July, 2000, issue debate the various findings of the report. 

6

 See http://aspe.hhs.gov/sp/nhii/Documents/NHIIReport2001/default.htm 7

 See http://www.nap.edu/books/0309072808/html/ 8

 See http://www.citl.org

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Healthcare focus area: clinical informatics 

The Office of the National Coordinator for Health Information Technology (ONCHIT) 

published a report outlining a strategic plan to guide the nationwide implementation of 

health information technology (HIT). 9

 The report identifies 4 major goals: 

• Inform clinical practice by incentivizing the adoption of Electronic Health 

Records (EHRs), reducing the risk of EHR investment, and promoting its use in 

rural and underserved areas. 

• Interconnect clinicians by fostering regional collaboration, development of a 

national health information network, and the coordination of federal health 

information systems. 

• Personalize care by encouraging use of Personal Health Records (PHRs), 

enhancing informed consumer choice, and promoting use of telehealth systems.10

• Improve population health by unifying public health surveillance architectures, 

streamlining quality and health status monitoring, and accelerating research and 

dissemination of evidence. 

Of particular interest to NIST are the report’s recommendations pertaining to the need for 

health information technology standards for clinical trial data, personal health records, as 

and the need for defining minimal product standards for EHR functionality, 

interoperability and security which will help reduce the risk of EHR investment and 

encourage adoption by healthcare providers. 

There is growing adoption of Internet-based personal health records by patients, 

frustrated by lack of access to fragmented medical records maintained by each of their 

healthcare providers. These Web-based systems also enable patients to update their own 

records and provide health logs. A recent article in the Wall Street Journal describes this 

trend and how patient data entry improves the reliability and efficiency of health 

reporting during clinical encounters. 11 However, lack of standards in PHRs and lack of 

interoperability with EHRs remains an issue. 

We believe that IT, in general, and the Internet, in particular, provide major opportunities 

for improved healthcare delivery. IT holds the promise of providing information and 

knowledge representation and sharing, establishing a seamless continuum of healthcare to 

all segments of the population. This will result in better quality healthcare provided in a 

timely manner, reduced care variance, reduced medical errors, and decreased costs. For 

example, the use of computerized patient order entry systems at Brigham and Women's 

Hospital in Boston, MA, has resulted in a 55 % reduction in medical errors.12

 

9

 The Decade of Health Information Technology: Delivering Consumer-centric and Information-rich Health 

Care. Framework for Strategic Action. July 21, 2004. Tommy G. Thompson, Secretary of Health and 

Human Services, and David J. Brailer, M.D., Ph.D. National Coordinator for Health Information 

Technology. Available at: http://www.hhs.gov/healthit/documents/hitframework.pdf 10 While the EHR is maintained by the healthcare service organization (e.g., physician, hospital, HMO, 

etc.), a PHR is maintained by an individual. 

11 Sarah Rubenstein. Next Step Toward Digitized Health Records. Wall Street Journal. May 9, 2005. pp. 

B1, B5. 

12 As reported by Dr. Blackford Middleton in the June 2003 eHealth workshop held at NIST. 

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Healthcare focus area: clinical informatics 

A 1993 report entitled “Health Security: The President's Report to the American People” 

underscored the fact that the medical field lagged behind other industries in the utilization 

of computers.13 Ten years later, this is still true. Other sectors of society have had better 

success in implementing IT. For example, we have witnessed considerable improvement 

in productivity, reduction in costs, and timely delivery of products in the manufacturing 

industry in the last decade due to the effective use of IT. Hence, based on NIST’s 

experience with the application of IT in other sectors such as manufacturing and 

electronic commerce, we believe that NIST can play an active role in addressing the 

information management problems of the healthcare industry. 

A major area of the healthcare complex, clinical informatics, addresses the efficient and 

accurate use of medical knowledge and information in patient care settings. It addresses 

how data, information, and knowledge are captured, represented, used, stored, and 

transmitted for clinical applications. Subfields include: electronic health records (EHR); 

clinical decision support systems (including expert systems); medical imaging and image 

processing; and standards for vocabularies for representing and disseminating medical 

knowledge. Based on the above reports and the workshops held at NIST (one in August 

2002 and the other in June 2003)14 and the information gathering activities we have 

conducted, we believe that clinical informatics should be the area of prime interest to 

NIST. In this report we focus on this area and the role within it that is suitable for NIST. 

Organization of report 

The report is organized as follows. We first describe the position of NIST in addressing 

the issue of clinical informatics. Next, in order to set the stage, we sketch the information 

flow among the various participants in the healthcare industry. Then we present three 

aspects of clinical informatics of most immediate interest to NIST in terms of the current 

state of the art and the gaps and problems we identified within each aspect. Next, we 

present metrics for the evaluation of the efficacy of IT intervention and an initial set of 

potential solutions applicable to the aspects presented. We conclude the coverage of 

clinical informatics by describing potential roles for NIST. 

2. Position of NIST in addressing clinical informatics 

The mission of NIST is to develop and promote measurements, standards, and technology 

to enhance productivity, facilitate trade, and improve the quality of life. This mission 

includes research addressed to the improvement of enterprise efficiency in all sectors of 

the economy, including healthcare. NIST’s Information Technology Laboratory (ITL), 

provides measurement expertise and technology to the IT sector. NIST’s Manufacturing 

Engineering Laboratory (MEL) contributes concepts and expertise from the 

manufacturing sector, including advanced work on the interoperability of manufacturing 

information systems. 

 

13 Health Security: The President’s Report to the American People, DIANE Publishing Company, ISBN: 0-

7881-1106-X, 1993. 

14 A CD-ROM of the June 2003 eHealth workshop can be obtained by sending e-mail to 

bettijoyce.lide@nist.gov. 

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Healthcare focus area: clinical informatics 

NIST's mission complements that of the National Institutes of Health (NIH) and major 

federal agencies dealing with healthcare delivery, including the full Department of Health 

and Human Services, the Department of Defense, the Veterans Administration, the 

Centers for Medicare and Medicaid Services, the Indian Health Service and others. 

The institutes and centers that comprise NIH focus on research, education and 

dissemination of information geared towards developing new treatments and drugs for 

healthcare. NIH contains an institute doing research on bioinformatics that addresses 

computational issues in biology and a department for Clinical Research Information 

Systems that provides computational and data warehousing support for the storage and 

analysis of clinical research and protocol data. However, there is no unit within NIH, or 

in the other federal agencies, that addresses the critical need to efficiently and effectively 

administer and manage healthcare delivery and the particular role that may be played by 

IT in optimizing the healthcare delivery processes and reducing costs. 

By contrast, NIST has developed expertise in the application of IT to manufacturing and 

the integration of IT standards and emerging technologies into various industry domains. 

This positions NIST well among the various federal research agencies to investigate and 

provide roadmaps and solutions for efficient healthcare delivery. Interoperability of 

information systems is one of the central problems in the healthcare sector. Current 

NIST projects addressing this problem include conformance and interoperability test 

development for the exchange of clinical information, as well as efforts at integrating 

multiple standards for healthcare and IT to provide use-case based solutions. 

3. Information flow in healthcare services 

The healthcare services industry generates and processes large amounts of complex 

information relating to the diagnosis, testing, monitoring, treatment and health 

management of patients, billing for healthcare services and asset-management of 

healthcare resources. Healthcare delivery is a collaborative process with many 

physicians, healthcare specialists, nursing staff and healthcare technicians from multiple 

healthcare organizations participating in the treatment of patients. In addition, a number 

of external organizations utilize healthcare information including government, insurance 

companies and employers who pay for healthcare services, medical researchers, life 

insurance companies, pharmacists, and even lawyers in malpractice suits. The major 

participants in the information flow process are shown in Figure 1. 

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Healthcare focus area: clinical informatics 

Figure 1 Healthcare Information Flows15

The classes of information created and used by the healthcare industry include: 

• detailed medical records of each patient for every episode of illness or type of 

healthcare delivered; 

• workflow for referral of patients to specialists, physician orders for diagnostic 

tests or procedures, and admission/discharge from hospitals; 

 

15 Source: Figure 3.1 in For the Record: Protecting Electronic Health Information, Computer Science and 

Telecommunications Board, National Research Council, National Academy Press, 1997, page 73. 

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Healthcare focus area: clinical informatics 

• detailed administrative records for managing healthcare resources (ranging from 

scheduling patient appointments, tracking hospital bed utilization to inventory 

management of pharmaceutical supplies); 

• billing for healthcare services, healthcare cost control procedures and 

coordination of benefits; and 

• research reports, clinical observations and results of clinical trials of new 

pharmaceuticals and new guidelines. 

The major information flows within the healthcare services industry fall into the 

following categories: 

a) Information flows within a healthcare facility: 

 An administrator obtains patient medical history, employment and social data 

and health insurance information and enters these into the patient chart; 

 A nurse records the patient’s vital signs, medications and chief complaints for 

a particular visit; and 

 A physician conducts an examination and writes or dictates an “encounter 

note” for subsequent transcription and signoff that would be included in the 

chart. The physician also submits billing information identifying services 

rendered (CPT code) and the diagnosis code (DRG/ICD) for use with 

insurance claims. 

Larger healthcare institutions may have internal advisory groups that recommend 

treatment guidelines (evidence based medicine) to be followed by the healthcare staff to 

improve healthcare quality. 

b) Information flows between healthcare facilities: 

 The physician may prescribe an order for laboratory or diagnostic imaging test 

or procedure, or she/he may refer the patient to a specialist or have the patient 

admitted to a hospital; in some of these cases the physician would include the 

patient’s relevant clinical history; 

• The results of a laboratory test or the report and images of a diagnostic 

imaging would subsequently be sent to the physician; and 

• Upon discharge from the hospital, the patient’s discharge summary would be 

sent to the admitting physician. 

c) Information flows between a healthcare facility and other agencies: 

 The clinic Office Administrator submits claims to the insurance agency using 

the physician-supplied codes for claims processing, and sometimes may seek 

benefits authorization for specialized treatment for the patient; 

 Health insurance agencies may seek additional justification for treatment 

provided to the patient or for recommended course of tests or treatment; 

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Healthcare focus area: clinical informatics 

 Health insurance agencies may provide a list of preferred medications 

(formularies) and other cost containment measures to healthcare facilities; 

 Managed care organizations and other payor agencies may also provide 

treatment guidelines to be used in the treatment of patients; 

 Life insurance companies may seek a patient’s medical record to evaluate the 

risk of a policy applicant, or determine fraud due to a known, but undisclosed 

pre-existing medical condition; 

 Clinic nursing staff may, on occasion, have to report incidents of certain 

diseases to public health agencies and record pediatric immunizations with the 

appropriate state’s vital statistics bureau; 

 Medical researchers may seek medical records of patients with certain profiles 

for investigations; the clinic may provide the information (with patient 

consent) after removing patient-identifiable data; 

 Malpractice lawsuits may require a healthcare facility to submit medical 

records of patients (with profile similar to the litigant’s) to determine 

adherence to standards of practice; and 

 Accrediting organizations may review patient records to review operational 

and quality standards. 

4. Electronic medical records 

 4.1 State of the art 

Penetration of information technology and automation into healthcare settings varies 

widely. David Kibbe, Director of Health Information Technology of the American 

Academy of Family Physicians stated at the NIST 2003 eHealth Workshop that only 5 % 

of the family physicians in the U.S. use an Electronic Medical Record (EMR) system in 

their daily practice.16 Predictably, large healthcare institutions such as hospitals have 

significant investments in computer systems, but in a majority of healthcare settings 

(especially small clinics) paper-based records and fax-based communications is still the 

norm, with computers used primarily for billing and administrative functions. 

In a recent report, the Institute of Medicine identifies six major healthcare networks 

(including the Veterans Health Administration and the New England Healthcare 

Electronic Data Interchange Network) as noteworthy examples of implementations of 

electronic health records deployed on secure platforms.17 Yet the same report notes that 

these are exceptional cases, with the norm being that at most hospitals, much patient 

 

16 David Kibbe, “From the Provider’s Perspective,” Designing for Quality: eHealth Development 

Workshop, NIST, Gaithersburg, MD, June 24-25, 2003. 

17 Key Capabilities of an Electronic Health Record System, Letter Report, Institute of Medicine, The 

National Academies Press, Washington, DC, July 31, 2003. Available at: 

http://www.nap.edu/html/ehr/NI000427.pdf

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Healthcare focus area: clinical informatics 

information was written on paper and many hospitals even lacked the facilities for 

automated delivery of laboratory results. 

In some of the leading healthcare institutions, there is a high degree of use of IT systems 

and of integration of information resources leading to improved flow of patient 

information. For example, the scheduling of the order for diagnostic imaging would have 

been performed online along with the submission of relevant patient clinical history. 

A variety of messaging and information exchange standards (e. g., HL7, DICOM, E1460, 

E1467, E1381, E1394, IEEE 1073 – see Appendix) permits an enterprise to integrate the 

various health information systems and archive the data as an electronic medical record. 

Specifically, HL7 messaging standards allow disparate healthcare information systems to 

communicate with each other. Independent healthcare institutions can submit orders and 

referrals via HL7 for healthcare services for their patients. HL7 has recently released a 

first version of an EHR functionality specification and ASTM has recently released a 

specification for the exchange of patient information for continuity of care purposes such 

as transfer and referrals. DICOM standards enable the interchange of information 

between imaging systems and facilitate remote access for physicians at their clinic. With 

standards-based integration of information systems and authenticated remote access to 

reports and images, physicians can have access to the radiologist’s report as well as the 

diagnostic images for review and patient counseling. 

Mapping and correlation of patient identifiers and standards-based information 

interchange facilities can enable a large healthcare enterprise to provide a virtual patient 

record that overlies the fragmented records of the patient in the different healthcare 

facilities. 

Figure 2 illustrates the classes of clinical information and some of the standards that link 

these classes to a full electronic medical record. 

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Healthcare focus area: clinical informatics 

Figure 2. Electronic Medical Record18

 4.2 Gaps and problems 

As Table 1 in the Appendix shows, there are both significant gaps and overlaps in the 

standards covering electronic medical records. Both the gaps and the overlaps contribute 

to the major problem pertaining to clinical informatics: the lack of interoperability among 

the standards. 

There are several standards activities working on improving the effectiveness of 

healthcare informatics exchange. However, for the most part there has been no systems 

view tying the various parts of the healthcare informatics domain together. Many of the 

 

18 Source: U.S. Congress, Office of Technology Assessment, “Bringing Healthcare Online: The Role of 

Information Technologies,” OTA-ITC-624, Washington, DC: U.S. Government Printing Office, September 

1995. 

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Healthcare focus area: clinical informatics 

groups that create standards have recognized the problem of achieving a fully consistent 

and unambiguous terminology among the standards. Potentially, there may be several 

standards-writing activities or organizations working in the same, or in related areas. The 

domain of healthcare informatics is large enough so that several activities are warranted, 

but these groups should work, from the start, with a strategy to connect the end results. 

Often, however, funds and time are limited, and competitive forces are great, so that the 

terminology-defining activity covers the scope of a single domain and there is no effort 

directed at any harmonization strategy. 

Another challenge for healthcare informatics standards is that healthcare information has 

existed for a long time and providers have used several vehicles to enter, store, and relay 

information about patients. Practice tends to rely on the fixed methods of information 

gathering used in the past. Who has time to learn a new system, and how forgiving will 

those affected be when some of their data gets lost as the new system is proceeding down 

the learning curve? Electronic exchange of healthcare information in general, and EHR 

in particular, needs to recognize the diversity of techniques in use and be aware that 

unless the proposed new system is user-friendly, mnemonic, inexpensive, and easy to use, 

the new system will likely go unused. Switching over from a paper-based patient chart to 

an EHR is a difficult task. Healthcare facilities have large archives of longitudinal 

patient records. These must be accessible and incorporated into any new system. 

Importing this information is expensive and time-consuming, but essential for medical 

and legal reasons and for providing quality care to patients. Otherwise healthcare 

providers will have to learn a new system and incorporate new records within the EHR, 

but keep having to refer to the paper charts for historical patient data. 

Proper timing of the standardization process is important also. Ideally, a standard should 

be ready for use when the technology to permit its use is available at a price that makes 

widespread implementation productive. If the standard is too early it becomes an 

academic exercise. A further drain on standards-development costs occurs when the 

domain being standardized requires technology development at the same time as 

standards development. On the other hand, developing a standard too late may be a loser 

if the demand for the technology covered by the standard is high, because ad hoc 

implementations will occur making backwards standard application unfeasible. The 

solution will likely be an open environment that would allow extensions to the 

information system. 

There are several EHR system vendors; however their products are too expensive for 

many healthcare facilities (a large majority of which are small clinics). Despite the 

growing acceptance of the HL7 standard, interoperability and interchange of healthcare 

information remains a barrier due to different degrees of adoption of the revisions of the 

HL7 standard. While vendors may have developed products consistent with HL7 version 

2.5, most EHR implementations are still at versions 2.1 to 2.3. The high cost of 

upgrading and interfacing each of the hospital's information systems often results in 

legacy systems that prevent adoption of newer standards. The high cost of integration of 

health information systems is a barrier that prevents hospitals from switching to vendors 

with lower costs, newer products. 

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Healthcare focus area: clinical informatics 

The adoption of standards for information interchange will facilitate integration of 

disparate healthcare systems. However, implementations of healthcare data integration 

should not be simply geared to support human readability of medical reports, but should 

incorporate the formalism and details necessary for proper computer interpretability of 

healthcare information. Such measures would prevent the loss of information during data 

interchange that may otherwise occur due to differences in terms and codes and their 

semantics in the various healthcare vocabularies. Healthcare institutions would then be 

able to deal transparently with information obtained from external agencies as well as 

that generated by in-house healthcare information systems. Their applications could 

perform data mining of patient medical records for healthcare quality metrics, identify 

patients across populations for timely medical interventions, and check for compliance 

with preventive-service protocols.19

5. Vocabularies 

Vocabularies are controlled collections of concepts that seek to capture medical 

knowledge from a particular perspective or point of view. A concept may be defined as a 

cluster of terms or strings that have the same and unique meaning. These vocabularies 

are developed in the context of various assumptions and targeted towards multiple uses: 

• vocabularies such as MeSH have been developed for annotation and indexing of biomedical research articles, as in the MEDLINE database; concepts from MeSH can 

then be used for retrieving research articles related to those concepts; and 

• vocabularies such as Diagnosis Related Groups (DRGs) have been used for 

cataloging and categorizing various patient populations according to criteria such as 

severity of illness; this finds application in epidemiological health studies and 

analyses. 

An important role of a medical vocabulary is knowledge representation, i.e., the 

representation of medical knowledge in a standardized manner, for example in an 

electronic patient record. This enables data integration and interoperability across 

multiple healthcare information systems. 

 5.1 Current state of the art 

There has been a plethora of vocabularies in the field of healthcare and medical 

informatics targeted towards different applications (e.g., billing, statistics, and 

epidemiology) and sub-fields (e.g., mental health, nursing, diseases). Primarily, they 

have been used to abstract and structure the information in clinical patient records. New 

emerging fields such as genomics and bio-informatics have developed their own 

vocabularies (e. g., gene ontology) targeted to their specific needs. 

 

19 Balas, E. A., Weingarten, S., Garb, C. T., Blumenthal, D., Boren, S. A,, Brown, G. D., Improving 

preventive care by prompting physicians, Arch Intern Med, 2000, Feb 14;160(3):301-8. 

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Healthcare focus area: clinical informatics 

A list of the principal vocabularies and their target applications is as follows (see 

Appendix for details): 

• International Classification of Diseases (ICD) Family, one of the oldest and 

archetypical coding systems for patient record abstraction. The basic ICD is 

meant to be used for coding diagnostic terms, but ICD-9 as well as ICD-10 also 

contains a set of expansions for other families of medical terms. ICD-9 has 

generally been perceived as inadequate for the level of detail desired for statistical 

reporting in the United States 

• Diagnosis Related Groups (DRGs), developed in the US for use in prospective 

payments in the Medicare program. It is an abstraction of an abstraction and 

applies to lists of ICD9-CM codes that are themselves derived from medical 

records. The purpose of DRG coding is to provide a relatively small number of 

codes for classifying patient hospitalizations while also providing some separation 

of cases based on the severity of illness. 

• Current Procedural Terminology (CPT), a pre-coordinated20 coding scheme for 

diagnostic and therapeutic procedures that has been adopted in the US for billing 

and reimbursement. CPT codes specify information that differentiates the codes 

based on cost. 

• Systematized Nomenclature of Medicine (SNOMED), multi-axial coding system 

with 11 axes, each of which serves as a taxonomy for a specific set of concepts (e. 

g., organisms, diseases, procedures). Coding of patient information is 

accomplished through the post-coordination21 of terms from multiple axes to 

represent complex terms that might be desired but do not exist in SNOMED. Its 

goal is to provide the codes needed for electronic medical records. 

• Read Clinical Codes (RCC), a set of codes for electronic medical records 

developed to support medical record summarization and patient care applications. 

Both pre-coordination and post-coordination of terms are used. 

• Logical Observations, Identifiers, Names and Codes (LOINC), a naming system 

for laboratory tests and observations, being extended to include vital signs, 

electrocardiograph, and so on. 

• Medical Subject Headings (MeSH), a vocabulary, mainteained by the National 

Library of Medicine (NLM), by which the world medical literature is indexed 

and searched. It is not generally used as a direct coding scheme for patient 

information. 

 5.2 Gaps and problems 

The vocabularies introduced above each model pieces of information at different levels of 

granularity and cover different domains of healthcare information. For every vocabulary, 

 

20 Pre-coordinated coding implies that all possible concepts in a vocabulary are exhaustively enumerated 

21 Post-coordinated coding implies that there exist a set of “atomic” or primitive concepts that can be 

combined to construct new concepts as required. 

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Healthcare focus area: clinical informatics 

there exists a set of terms that are likely to overlap with, but are inconsistent with, terms 

in another vocabulary. This will obviously lead to conflict if information needs to be 

integrated or exchanged across systems using different coding schemes and vocabularies. 

Thus, to enable the interoperation of clinical information, it is crucial to develop tools and 

techniques to translate concepts across multiple vocabularies. 

Sometimes codes for multiple information systems and vocabularies are mixed together 

in a way that might not make sense, for example, “transportation to remove abscess.” 

Semantics need to be modeled more clearly to disallow such combinations. The 

relationship of the various medical vocabularies to the HL 7 Reference Information 

Model (RIM) needs to be investigated (see Appendix). 

Sometimes combinations of codes might be ambiguous, for example, combining the 

codes for (larynx, tube) and for a removal procedure does not specify whether it is the 

tube or the larynx that needs to be removed. A more precise semantic specification can 

help resolve such ambiguities. 

In the case of vocabularies supporting post-coordination, there might be multiple 

expressions referring to the same concept. Inference mechanisms are required to 

determine the equivalence of these concepts. 

6. Evidence-based medicine 

Medical knowledge is increasing, but exceeds our ability to disseminate it for use by 

healthcare workers. Starting in the second half of the 20th century, we have been 

witnessing a rapid growth of medical knowledge. According to Janet Zipser, MEDLARS 

Management Section, NLM, “approximately 10 million records are available in 

MEDLINE back to 1966; 120 million searches are conducted just on the NLM computer, 

and 400 000 new citations are added to MEDLINE each year. To keep up with the 

400 000 articles, a physician could read 2 articles each day, every day of the year and by 

the end of the year fall 550 years behind.”22 There is a major gap in translating such 

advances in medical knowledge into useful clinical guidelines and knowledge. 

 6.1 Current state of the art 

An important approach to applying the most up-to-date healthcare knowledge is 

Evidence-based medicine (EBM). EBM is the continual dissemination of verified 

medical knowledge in a way that integrates into clinical experience and patient values, 

and is applied to individual cases: 

“EBM's ultimate application is at the level of the individual clinician's decisions 

about managing patients. It is an explicit approach to problem solving and 

 

22 See http://www.nlm.nih.gov/bsd/historypresentation.html. 

15 
Healthcare focus area: clinical informatics 

continual professional learning which requires the use of current best evidence in 

making medical decisions about individual patients.”23

EBM addresses the large amount of medical information available and the decreasing 

amount of time that physicians have to track it. In EBM, the literature is filtered for 

information that has passed rigorously-controlled testing and is applicable to individual 

patient situations, not just general knowledge of disease. Organizations such as the EBM 

Resource Center and the Institute for Clinical Systems Improvement (ICSI)24 distill 

medical knowledge into a form usable by physicians. 

ICSI, in particular, defines guidelines for handling cases with procedures that physicians 

can easily follow. ICSI’s clinical practice guidelines are systematically developed 

statements intended to assist practitioner and patient decisions about appropriate 

healthcare for specific clinical circumstances. Guidelines synthesize medical knowledge 

and offer formal recommendations for patient care. Many experts believe that 

widespread adherence to clinical practice guidelines could significantly improve patient 

safety, quality of care, improve outcomes, and reduce healthcare costs. 

To bring the guideline aspect of EBM to fruition, NIST’s Advanced Technology Program 

(ATP) funded a project called the Standards-based Sharable Active Guideline 

Environment (SAGE).25 Its aim is to support the authoring of clinical guidelines in a 

computable environment accessible by clinical information systems (CIS). It will present 

guidelines to physicians in a way that is applicable to individual patients, and will be 

accessible through the local CIS. 

 6.2 Gaps and problems 

One of the key hurdles for EBM is to integrate distilled medical knowledge into clinical 

practice.26 These problems begin when the knowledge is first written down. It is usually 

expressed in text-based documents that must be found and read by physicians. This is 

less onerous that sifting through the literature, but still very time-consuming in 

comparison to the average physician’s schedule. In the case of clinical guidelines, 

studies have shown that physician compliance improves dramatically when patientappropriate guidelines are presented when care is being delivered. Practically, this is 

only possible when guidelines are embedded electronically in a CIS. Few institutions 

have developed in-house technologies to deploy guidelines through CISs. These efforts 

usually require significant resources for the implementation of even a few guidelines. 

Further, implemented guidelines cannot generally be shared with other institutions or 

systems. 

One approach to addressing the integration problem is to record distilled medical 

information in a computable format that can be integrated into the CISs that practitioners 

 

23 Evidence-based Medicine Resource Center, http://www.ebmny.org. 24 Institute for Clinical Systems Improvement, http://www.icsi.org. 25 The SAGE Project, http://www.sageproject.net. 26 See http://www.sageproject.net/guidelines/guidelines.htm. 

16 
Healthcare focus area: clinical informatics 

already use. This folds the latest medical information into the clinical workflow. The 

flow of information in an ideal EBM scenario would be: 

Research 

and 

Clinical 

Trials 

Distilled 

Knowledge

-base 

Local 

Clinical 

Information 

System 

Physician 

and 

Patient 

Issues arise at each of the transition points in the above process: 

• Medical information might be expressed in a way that is suitable for physicians 

but not necessarily for computation or even implementers of information systems. 

It is necessary to bridge the gap between subject matter experts and information 

systems; 

• Local CISs have their own ways of recording information, including workflow 

information. These are usually not compatible with each other or with any 

centralized medical knowledge base; and 

• Physicians and patients have their own experience and opinions. In the case of 

guidelines, existing procedures and expectations from patients must be integrated 

with established guidelines. 

The above problems revolve around the differing styles in which information is expressed 

at each stage. These styles may have the same conceptual content, but differ in the 

particulars of language and implementation. This causes disconnects in communication 

that are not necessarily due to differences of opinion, but to differences in terminology 

and computational implementation. It is a significant barrier to the adoption of EBM, and 

will impact the success of projects such as SAGE that attempt to create more uniform 

dissemination and integration of evidence-based medical information. 

In the particular area of guidelines, medical information content describes processes and 

workflows. There are a number of computational languages for processes and 

workflows, such as BPEL, WSCI, and BPML27, backed by major corporations. The 

healthcare standards community constructed process languages separately from the 

mainstream, such as the one used in SAGE28 and the HL 7 Act model.29 It is unlikely 

that process languages developed in the medical community will become widespread 

given that the implementations will most likely be provided by major corporate vendors. 

 

27 See http://www-106.ibm.com/developerworks/library/ws-bpel, http://www.w3.org/TR/wsci, and 

http://www.bpmi.org/specifications.esp. 28 Campbell, J., et. al., “The SAGE Guideline Model,” Available at: 

http://www.sageproject.net/links/Sage%20Guideline%20Model%20Paper.pdf. 29 See http://www.hl7.org/library/data-model/RIM/modelpage_mem.htm. 

17 
Healthcare focus area: clinical informatics 

In general, the insulation of healthcare information systems from the mainstream will 

lead to chronic incompatibility with other systems and increased cost due to 

unnecessarily specialized implementations. 

7. Metrics 

Metrics are important to determine the efficacy of information technology for increasing 

healthcare quality while reducing costs. The Agency for Healthcare Research and 

Quality (AHRQ) has published indicators for inpatient quality and patient safety.30 At 

present, these metrics are not well characterized for the application of IT in the healthcare 

domain. Hence, we need to conduct further research to provide metrics for evaluating the 

ability of information systems to improve healthcare quality. 

8. Potential components of clinical informatics 

Data becomes information when it is interpreted in its proper context. Metadata provides 

a descriptive framework for the data and is the overhead for effective information 

interchange. Metadata consists of: 1) machine interpretable representational notation, 2) 

data descriptors based on terms in a domain vocabulary, and 3) contextual semantics. 

Communication facilities enable information to be manipulated, transmitted, shared and 

integrated. 

In well-understood (homogeneous, highly integrated) application scenarios, data can be 

transmitted in the raw without the overhead of an explicit metadata framework. For 

example, a medical specialist can return results of a test without sending descriptions of 

all the equipment that produced the test results. More often, information exchange can 

cause interoperability problems in healthcare (just as in other domains), because raw data 

without the metadata framework can be misinterpreted or overlooked. For example, the 

format for describing test results used by a laboratory (see Figure 1) might be so different 

from the format used by a clinic where the patient is being examined that results may be 

confused or misinterpreted. 

Semantics and information models help improve information interchange. These models 

are developed by subject matter experts to define the terms and relationships of concepts 

and behaviors in a particular domain. Incorporation of these terms and semantics in the 

metadata of the information transferred between systems enables automation to interpret 

the data and translate it into site and system-specific data representations. 

Effective clinical information interchange requires the following four components which 

are described in the following sections: 

1. Systems engineering techniques to develop semantic models 

2. Languages for expressing semantic information 

 

30 See http://www.qualityindicators.ahrq.gov. 

18 
Healthcare focus area: clinical informatics 

3. Model-centered architecture for platform-neutral system design 

4. Testbeds for assessing interoperability and conformance 

NIST's expertise in these areas can be applied to improve the approaches for 

interoperability of healthcare information systems. 

8.1 Systems engineering 

Semantic and information models should be developed using a systems engineering31

process that ensures that the models reflect the requirements and perspectives, 

vocabularies and transactions of all relevant players in that domain. 

Systems engineering ties each element in a system to stakeholder requirements and 

enables the evaluation of alternatives on how to address those requirements. It begins 

with the goals and requirements of a desired system independently of which parts of it are 

assigned to human, hardware, or information components, and then incrementally 

translates these goals into system designs that satisfy the goals. This is important for 

healthcare applications, which use many kinds of elements to achieve their purpose. 

For example, a model for a patient referral process may include terminologies and 

relationships reflecting the requirements and viewpoints of: 

• healthcare providers (who initiate the referral request), 

• benefits eligibility coordinators or payers (who review the request and authorize the 

payment for services), 

• clinical information system vendors (whose system will export relevant data from the 

patient’s electronic medical record associated with the referral), and 

• internal, industry and regulatory privacy guidelines (which define the scope of patient 

information relevant for processing referral requests). 

Given the complexity and range of medical vocabularies, a systems engineering approach 

would help reduce the size and scope of information and semantic models for specific 

healthcare transactions and healthcare information interchanges. 

8.2 Semantic languages 

Semantic approaches to expressing information raise the bar on precision and 

expressiveness, and in particular support more accurate reflections of clinical reality in 

vocabularies and guidelines. Advances in modeling, ontology, and knowledge 

representation can generally be applied to clinical systems. Leading industry standard 

development organizations have been developing tools and languages that help define 

information models and semantic concepts. For example, OMG defines the Unified 

 

31 Journal of The International Council on Systems Engineering, Wiley, www.incose.org. 

19 
Healthcare focus area: clinical informatics 

Modeling Language (UML), with improved support for semantic expressiveness.32 W3C 

is developing the Ontology Web Language,33 for recording concepts independent of 

implementations, such as XML Schema. ISO is defining an ontology that focuses on 

unambiguous description of processes, the Process Specification Language.34 All of 

these can be extended and applied to the healthcare domain. 

8.3 Model-centered architecture 

Disciplines concerned with software quality are recognizing the benefits of semanticbased development in addressing implementations that span platforms and organizations. 

Model-driven architecture,35 (MDA) is a platform-neutral approach for software 

development that emphasizes the development of models that capture application and 

domain concepts, relationships and behaviors and isolates functional specification from 

platform-specific implementation considerations. Also known as repository-centered 

development,36 this form of development can be used as the basis for systems that 

operate on multiple technologies and that are accessible through multiple user interfaces 

styles. Most importantly for clinical informatics, these techniques provide a common way 

to describe vocabularies and guidelines, from authoring to deployment in clinical 

information systems. A generalized form of model-centered architecture is computationindependence, which covers those aspects of a total system that will be handled manually 

or by hardware, as well as software, and elements in the environment of the system. 

The use of standard models and service interfaces will make medical knowledge 

accessible to a wider set of tools and user bases, than is possible in today’s proprietary 

systems. Functional specifications could be automatically translated to various platforms 

facilitating integration with existing clinical information systems. Hospitals could 

customize information for their local systems while maintaining compatibility across 

organizations.37 Moreover, authors of evidence-based medical information would find 

their products have wider use. 

 8.4 Interoperability and Conformance Testing 

The creation of a viable national healthcare information infrastructure in the U.S. depends 

on all parties involved (consumers, healthcare professionals, researchers, and insurers) 

having systems, tools, and information that are complete, correct, secure and 

 

32 Object Management Group, “UML 2.0 Superstructure Specification,” http://www.omg.org/cgibin/doc?ptc/03-08-02, August 2003. 33 W3C, “Web Ontology Language (OWL) Guide Version 1.0,” http://www.w3.org/TR/owl-guide, August 

2003. 

34 Gruninger, M., “PSL 2.0 Ontology -- Current Theories and Extensions,” 

http://www.mel.nist.gov/psl/pslontology, 2003. 35 Object Management Group, “Model-driven Architecture”, http://www.omg.org/mda. 36 Bock, C., “UML without Pictures,” IEEE Software Special Issue on Model-driven Development, 

http://csdl.computer.org/comp/mags/so/2003/05/s5toc.htm, September/October 2003. 37 Denno, P., et. al., “Model-driven Integration Using Existing Models,” IEEE Software Special Issue on 

Model-driven Development, op. cit. 

20 
Healthcare focus area: clinical informatics 

interoperable. Much depends on the availability of development, adoption and 

interoperability of healthcare information standards that are complete and implementable. 

NIST can assist the healthcare industry by developing tools and techniques to ensure that 

clinical informatics standards are complete and testable and by providing conformance 

interoperability tests by which vendors may validate their systems. Testbeds that utilize 

semantic information models would be well positioned to validate interoperability and 

conformance to standards of vendor products regardless of their design, platform or 

implementation. 

9. Roles for NIST 

The potential roles that NIST could play in the area of clinical informatics are described 

below, grouped into four major categories: 

Clinical informatics research activities: 

• Investigate semantic consistency issues pertaining to HL7’s Functional Electronic 

Health Record Model (EHR);38

• Develop reference architecture to confirm semantic consistency for healthcare 

information interchange; 

• Investigate information models and standards such as the Reference Information 

Model (RIM) and their relationship to various medical vocabularies; 

• Investigate approaches to enable semantic translations of concepts across multiple 

vocabularies; design and develop semantic distance metrics in the context of 

healthcare information and use them to identify semantically closest translations 

of a concept into different vocabularies; 

• Apply and extend semantic concepts and languages to representative examples of 

guidelines, in particular for evidence-based medicine, aiming to increase the precision

and expressiveness, especially as used in the SAGE project; 

• Use results of above to compare and link representative examples of existing 

healthcare guidelines;

• Investigate standards for representing clinical trial data, including the 

incorporation of image data; 

• Develop extensions to the current DICOM family of imaging standards to support 

semantics-based interoperability linking all images associated with a single 

patient into an integrated master patient record across specialty domains (e.g., 

radiology, pathology, ophthalmology, dermatology, gastroenterology, etc.);

 

38 DHHS had asked the Institute of Medicine (IOM) to provide guidance for developing an EHR. IOM 

drew up the Core Functional Specifications in 2003. HL7 collaborated in this endeavor and developed an 

EHR System Functional Model in 2004 that is being reviewed for eventual submission as a standard. 

21 
Healthcare focus area: clinical informatics 

• Develop conformance tests for relevant messaging and information exchange 

standards; and

• Participate in interoperability testing activities that seek to provide system-wide 

standards-based solutions.

Standards-related activities: 

• Increase participation in healthcare standards development organizations; 

• Provide aid in the harmonization of different healthcare standards; 

• Engage stakeholders in the healthcare standards development process; 

• Develop strategies for enabling adoption of healthcare standards; 

• Help define standards that are correct and testable; 

• Develop tests and operations for running healthcare standard conformance tests; 

and 

• Establish a leadership role in developing accrediting mechanisms for healthcare 

standard interfaces. 

Healthcare information infrastructure development activities: 

• Create model of interoperability of laboratories and other healthcare facilities for 

testing healthcare standard interfaces; 

• Develop a prototype healthcare informatics knowledge library, including rules, 

alerts, clinical guidelines, terminology servers, and use it to populate medical 

information systems; and 

• Develop a reference architecture for describing clinical informatics model 

implementations in a variety of settings (e. g., small physicians’ offices, large 

healthcare delivery organizations, insurance providers), with expandability to 

current standards. 

Social setting related activities: 

• Help develop technology insertion environments that allow small practices to 

adopt/adapt technologies in common use by large organizations;

• Help develop best practice studies (studies that describe measure of information 

systems effectiveness and efficiency) as a means to establish migration models to 

go from current state to next state; 

• Set up a community process and portal for developing a healthcare information 

infrastructure: 

• Create and maintain a portal to act as a forum for participants seeking 

guidelines on how to pursue tasks related to the Healthcare Information 

22 
Healthcare focus area: clinical informatics 

Infrastructure, such as standardizing EHR concepts, vocabularies and 

formats, integration approaches and methodologies; 

• Publish standardized data sets for benchmarking and research purposes; 

and 

• Establish a clearinghouse to promote and standardize emerging and 

promising approaches for creating an interoperable healthcare 

infrastructure and to help harness the enthusiasm and creative energy 

available that needs to be directed towards accelerating the growth of this 

promising area. 

• Create a community process for developing and deploying software: 

o Identify a set of concrete applications that provide a compelling value 

proposition for emerging technologies; 

o Develop detailed specifications of these applications, along with a 

reference architecture and a component/service model; 

o Consider initiating an open organization of international software 

developers whose charter is to develop and revise technology 

specifications, reference implementations and technology compatibility 

kits, in the manner of the Java Community Process; and 

o Consider initial support for this process, which over time will evolve to a 

formalized process overseen by representative from many healthcare 

provider organizations and software vendors. 

Organizations that NIST may potentially work with include: 

• Healthcare providers such as Cleveland Clinic, Kaiser Permanente, Mayo Clinic,

and Union Hospital, Elkton, MD; 

• Healthcare Organizations such as Partners Health Care; 

• Government agencies such as the National Institutes of Health (NIH), Department of 

Veterans Affairs (VA), Department of Defense (e.g., Telemedicine and Advanced 

Technology Research Center), Food and Drug Administration (FDA), Department of 

Homeland Security (DHS), and Agency for Healthcare Research and Quality 

(AHRQ); 

• Standards organizations such as American National Standards Institution (ANSI), 

International Organization for Standards (ISO), Object Management Group (OMG), 

Health Level Seven (HL7), and Clinical Data Interchange Standards Consortium 

(CDISC); 

• Professional Organizations such as the Radiological Society of North America; and 

• Various vendors. 

23 
Healthcare focus area: clinical informatics 

10. Summary and Conclusions 

The following areas of healthcare provide major opportunities for NIST:39

1. Clinical Informatics, the efficient and accurate use of medical knowledge and 

information in patient care settings; 

2. Bioinformatics, the computational tools and approaches for expanding the use of 

biological (e.g., proteomics, systems biology, microarray analysis, nanobiosensing, 

etc.) and non-clinical medical data; 

3. Medical Devices, an industry that encompasses a range of manufacturing engineering 

issues; 

4. Pharmaceuticals, an industry with major issues of product design, manufacturing 

process and supply chain management improvement; 

5. Biosurveillance, technologies for public health surveillance information in response 

to disease outbreaks and bioterrorism attacks; and 

6. Enterprise modeling, the application of simulation technologies developed in 

NIST/MEL/ITL to healthcare systems. 

The report addresses the first area, clinical informatics, as being of prime interest to NIST 

and we focus on the roles within it that are suitable for NIST. Three aspects are of most 

immediate interest to NIST in terms of the current state of the art and the gaps and 

problems identified: 

1. Electronic medical records, approaches and standards for the authorized integration 

and interchange of health information; 

2. Vocabularies, controlled collections of concepts that cover medical knowledge for 

particular purposes; and 

3. Evidence-based medicine (EBM), applying up-to-date healthcare knowledge by 

filtering and disseminating it in a way that integrates into clinical experience and 

patient values. 

An initial set of potential solutions applicable to the above aspects of clinical informatics 

includes: 

1. Systems engineering as a tool for clarifying the meaning of information; 

2. Semantic languages for expressing information with increased precision and 

expressiveness; 

3. Model-driven architecture, a technique for translating system specifications into 

multiple technologies; and 

 

39 Since the primary authors of this report are members of ITL and MEL, the focus reflects the expertise in 

these laboratories. 

24 
Healthcare focus area: clinical informatics 

4. Interoperability and conformance testing to ensure that standards-based solutions 

provide the necessary infrastructure and seamless information integration across 

applications. 

The potential roles that NIST could play in the area of clinical informatics are identified 

in categories pertaining to: 

1. Clinical informatics research; 

2. Standards development, harmonization and conformance testing;

3. Healthcare information infrastructure development; and

4. Activities affecting the social setting. 

Disclaimer 

Any mention of organizations, agencies, vendors or commercial products in this 

document is for illustration only. It does not imply sponsorship, contract, 

recommendation or endorsement by NIST. 

25 
Healthcare focus area: clinical informatics 

Appendix: Summary of Clinical Informatics Standards 

Table 1 is the result of a review of clinical informatics standards. The list is meant to be 

representative of current efforts in clinical informatics standards. It is by no means a 

comprehensive list. The Consolidated Health Informatics (CHI) Initiative of the 

Department of Health and Human Services aims to maintain a comprehensive standards 

list needed for smooth interoperation of the healthcare enterprise. The URL for CHI is: 

http://www.hhs.gov/healthit/chiinitiative.html. 

The columns in Table 1 refer to the various aspects of information transfer standardization. 

The rows refer to the major subdisciplines, activities or processes in the healthcare domain. 

 Note that not all cells contain entries, because: either no standard exists or could be identified; 

we have misinterpreted the scope of some standards; or there is no need to standardize that aspect 

of the healthcare activity. 

Following the table, the standards referenced are briefly described and characterized. 

26 
Healthcare focus area: clinical informatics 

Standards 

domain 

\ 

Healthcare 

domain 

Coded Data 

Elements: 

Thesaurus, 

Terminology 

Reference 

Concept 

Model 

Information 

Transfer, 

Message 

Structure 

Information 

Formats, 

Languages 

Architecture Meta 

Schema 

Laboratory 

Information 

LOINC 

RELMA 

E1712 

ASTM 

E1238 

HL7 

XML 

XML 

E1238 

CANON 

Group 

Clinical 

Documents 

MEDLINE CDA 

(HL7) 

CANON 

Group 

Patient Medical 

Record 

Information 

(PMRI) 

HL7 

E1633 

SNOMED 

GALEN 

HL7 

(RIM) 

E1384 

GALENCore 

HL7 E1633 

GRAIL 

GALEN IR 

E1384 E1384 

MESH 

Prescriptions SCRIPT SCRIPT 

Pharmaceuticals RxNORM 

(NIH) 

NDF-RT(VA) 

Reference and 

Knowledge 

OpenGALEN 

UMLS 

GALENCORE E1460 

GRAIL 

GALEN IR 

Diseases ICDO 

ICD9CM 

ICD10 

General MSDS (ANSI 

MEDLINE 

SNOMED 

SDM 

SNOMED

? 

SDM 

IEEE P1157 

IEEE 1073 

SDM 

E1467 

Digital Imaging DICOM DICOM DICOM 

E1713 

DICOM DICO

M 

DICOM 

Equipment 

Manufacturing 

STEP STEP 

X12 

XML 

IGES 

STEP 

IGES 

InterInstrument 

Information 

Transfers 

E1381 

E1238 

E1394 

Payer-Provider 

Communication 

X12 

Table 1: Standards Relevant to Clinical Informatics 

27 
Healthcare focus area: clinical informatics 

Brief descriptions of standards 

ANSI X12 (American National Standards Institute) Electronic Data Interchange (EDI).

Provides for the application-to-application exchange of business information in a 

structured, standard format without human intervention. There are several EDIs required 

by HIPAA, The Health Insurance and Accountability Act: 

270--Eligibility Inquiry 

271--Eligibility Response 

276--Claim Status Inquiry 

277--Claim Status Response 

278--Certification Request and Response 

820--Premium Payment 

834--Enrollment 

835--Claim Payment 

837--Claim Encounter 

ASTM Standards (American Society of Testing and Materials). These standards are 

developed by ASTM Committee E31 – Health Care Informatics. Only the standards 

from that committee that are relevant to this work are listed: 

E1238--Standard Specification for Transferring Clinical Information between 

Independent Systems. Defines message structure for all clinical data such as 

history, consultant notes, and obstetrical ultrasound. Used as a basis for HL7. 

E1381--Specification for the Low-Level Protocol to Transfer Messages between 

Instruments and computer Systems. Companion to E1394, this standard defines 

the low-level protocol to establish plug-together compatibility between 

instruments and computers. 

E1384--Standard Guide for Description for Content and Structure on an Automated 

Patient Health record. Identifies common patient information elements at all care 

sites; such as, demographics, diagnostic-test results, clinical orders, and 

medications. Defines a structure of these information segments. [3] 

E1394--Standard Specification for Transferring Information between Clinical 

Instruments and Computer Systems. Companion to E1381, this standard deals 

with the electrical and mechanical connections between computers and the 

clinical instruments and the methodology for establishing communication. 

E1460--Standard Specification for defining and Sharing Modular Health Knowledge 

Bases. A standard syntax for description and sharing of the healthcare knowledge 

bases. The Arden Syntax for Medical Logic Modules is now owned by the HL7 

activity. 

E1633--Specification for the Coded Values Used in the Automated Primary Record of 

Care. Catalogs the value sets for those data elements in E1384. 

28 
Healthcare focus area: clinical informatics 

E1712--Specification for Representing Clinical Laboratory Test and Analyte Names. 

Universal definition of the terms used for clinical laboratory tests and analyses. 

E1713--Specification for Transferring Digital Waveform Data between Independent 

Computer Systems. Takes digitally recorded electrophysiological waveform data 

and defines the message to be transmitted among between laboratories and clinics, 

instrumentation and computer systems. 

E1769--Guide for Properties of Electronic Health Records and Record Systems. Defines 

the requirements, properties, and attributes of a computer-based patient record; in 

computer-sensible form. 

CANON Group, a group of researchers addressing technical issues about various medical 

vocabularies seeking coherent conceptual representation across applications and subject 

domains. Five aspects are important to adequately represent concepts across the medical 

domain: controlled vocabulary (ontology); typology (organizing terms into semantic 

domains); concept model (combine simpler concepts into more complex ones); notation 

(the way the models are represented, such as conceptual graphs); and granularity (dealing 

with level of definitions and coarseness of semantic classes). 

DICOM (Digital Imaging and Communication), developed by the American College of 

Radiology - National Electronics Manufacturers Association committee, ACR-NEMA, to 

exchange imaging information for PMRI and within a medical center. Expanded in scope 

drastically, see SDM below. Consists of a hardware interface, data dictionary, objectoriented data model, and a set of commands about the description of how the image was 

made including what settings were used. 

GALEN or Open GALEN (General Architecture for Languages Encyclopaedias and 

Nomenclatures in Medicine), an open source continuation of the original European 

Community project. It consists of nineteen extensible, application-independent concepts 

that form a framework for building and integrating terminology from different systems. 

Contains a concept reference model (CORE) and an intermediate representation, and an 

integration-language kernel. [http://www.opengalen.org] 

GALEN CT (Clinical Terminology), a language-independent concept representation 

developed as a foundation for the next-generation, multilingual coding systems. Includes 

a Master Notation for medical terminology and Coding Reference (CORE) model. 

GALEN IR (Intermediate Representation), acts as a high-level language for GRAIL, and 

it is an abstraction between clinical end users and underlying technology. 

GRAIL (GALEN Representation and Integration Language kernel), the description logic 

used in Open GALEN clinical terminology. Techniques include particularization for 

composing more specific concepts, and sanctioning, or type constraints, to indicate 

whether a particularization is sensible and to point out if it is a redundant concept. 

HL7 (Health Level 7), ANSI accredited organization preparing standards that focus on 

messages that are to be communicated between heterogeneous systems within a medical 

center relating to patient-medical-record information. [http://www.hl7.org]. The "7" 

29 
Healthcare focus area: clinical informatics 

represents the seventh layer in the ISO OSI (International Organization for 

Standardization, Open System Interconnection Reference Model) ISO 7498 architecture. 

HL7 is the most widely implemented healthcare data-messaging standard. 

HL CDA (Clinical Document Architecture), document markup standard that specifies 

structure and semantics of clinical documents. The DCA can include text, images, 

sounds, and other media content. The clinical document can be sent inside an HL7 

message. Information comes from the HL7 RIM and is implemented in the Extensible 

Markup Language, XML. 

HL7 encompasses the following Methodology standards: 

• HL7 RIM (Reference Information Model, the source of the data content of HL7 

messages. Each coded attribute in the RIM requires a vocabulary-domain 

specification. The RIM consists of items such as: subject areas, scenarios, 

classes, attributes, use cases, actors, and trigger events. 

• HL7 Vocabulary Domain Specification, identifies, organizes, and maintains terms 

used in coded fields of HL7 messages. It defines a vocabulary domain for each 

coded-entry message field. A domain specification is a formal ontology for a 

concept, and a set of allowed values for a coded field. 

ICD (International Classification of Diseases), a coding standard to promote consistent 

worldwide reporting of causes of death and incidence of disease. ICD-O covers 

oncology; ICD-9-CM, clinical modifications, including diagnosis codes; ICD-10-PCS is 

intended to replace revision 9 by replacing the diagnosis part with a procedure-coding 

system, the PCS. ICD-10-PCS was developed by 3M Health Information Systems 

through a project funded by HCFA, the Health Care Financing Administration. 

IEEE MEDIX (Institute of Electrical and Electronic Engineers, Medical Data 

Interchange Standard), a committee formed to draft a standard set of hospital-system 

interface transactions based on ISO standards for all seven layers of the ISO OSI 

Reference model (International Organization for Standardization, Open System 

Interconnection Reference Model, and ISO 7498). IEEE P1157 was prepared to exchange 

data between hospital computer systems. IEEE 1073, Standard for Medical Device 

Communications, has produced a family of documents covering the seven-layer 

communications requirements for the Medical Information Bus, a communications 

service for bedside devices in major hospital areas, such as patient rooms, emergency 

rooms, intensive care units, and operating rooms. 

LOINC (Laboratory (originally Logical) Observations, Identifiers, Names, and Codes), a 

public domain set of codes and names intended for reporting laboratory test results and 

clinical observations. See RELMA. The Regenstrief Institute in Indianapolis IN 

maintains the LOINC database [http://www.regenstrief.com]. 

MEDLINE, NLM’s bibliographic database of citations and abstracts from nearly 4500 

biomedical journals published in the United States and worldwide. Coverage extends 

30 
Healthcare focus area: clinical informatics 

back to the mid-1960s. All citations in MEDLINE are assigned Medical Subject 

Headings (MeSH), from NLM's controlled vocabulary to assist users in their searches 

[http://www.nlm.nih.gov/pubs/]. 

MeSH (Medical Subject Headings), part of the UMLS activity. Developed to reflect the 

concepts appearing in medical literature. See MEDLINE. 

Read Clinical Codes, a set of codes designed to encode information on electronic 

medical records. The first version was developed by James Read in the 1980s and 

adopted by the British National Health Service (NHS). Version 2 was developed to meet 

hospital needs to cross-map their data to ICD-9. The NHS is expanding the content of 

the Read Codes to ensure that terms needed by practitioners are represented. In 1999 

SNOMED and Read Codes agreed to merge their efforts to have a single terminology 

suitable for clinical-patient records. 

RELMA (Regenstrief LOINC Mapping Assistant), assists a user to map the local 

laboratory code to the LOINC code. RELMA requires human assistance in choosing 

among candidate matches for terms. [http://www.regenstrief.com/loinc]. 

SCRIPT (Standard for the Exchange of Prescription Information), provides 

communication between providers and pharmacies, and for patient-medical-record 

information. SCRIPT was developed by the National Council for Prescription Drug 

Programs. DICOM and SCRIPT were recommended by the National Committee on Vital 

and Health Statistics (NCVHS) to the US Department of Health and Human Services to 

support the HL7 standard. With HL7 as a core, SCRIPT and DICOM exchange marketspecific Patient Medical Record Information (PMRI) 

SNOMED International (Systemized Nomenclature of Human and Veterinary Medicine), 

a worldwide effort to create a machine-readable, standardized medical nomenclature of 

basic concepts that would encompass the domain of human and veterinary medicine. 

SNOMED lacks semantic constraints and a framework for discourse, therefore bridges 

such as SDM, below, are constructive. Concepts are grouped according to similar 

characteristics rather than alphabetically. These are called axes or dimensions and there 

are eleven of them. Concepts are related hierarchically on the same axis and nonhierarchically on different axes. There are relationships, linkages, between concepts, 

such as between a disease and its cause. The result is a comprehensive information 

model called the SNOMED Data Model. SNOMED was developed by several American 

medical associations and translated into several languages--hence the word International

in the title. See Read Clinical Codes. 

SDM (SNOMED DICOM Micro-glossary), a combination of the DICOM message 

standard and the SNOMED computerized lexicon, making it part protocol and part 

database. DICOM’s scope expanded from radiology to the general medical domain. 

UMLS (Unified Medical Language System), developed by the National Library of 

Medicine. Large repository and meta-thesaurus of biomedical concepts (~300 000) to 

support retrieval and integration of information from disparate sources. 

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