Which of the following describe information system functionality Select all that apply

Standardized terminologies facilitate electronic data collection at the point of care; retrieval of relevant data, information, and knowledge (i.e., evidence); and data reuse for multiple purposes, such as automated surveillance, clinical decision support, and quality and cost monitoring. To promote patient safety and enable quality management, standardized terminologies that represent the focus (e.g., medical diagnosis, nursing diagnosis, patient problem) and interventions of the variety of clinicians involved in health care as well as data about the patient (e.g., age, gender, ethnicity, severity of illness, preferences, functional status) are necessary. Significant efforts during the last quarter-century have resulted in the development of standardized terminologies for the core phenomena of clinical practice: (1) diagnoses, symptoms, and observations (e.g., medical diagnoses, nursing diagnoses, problem list); (2) interventions, procedures, and treatments, including those focused on prevention and health promotion; and (3) health outcomes (e.g., disability, functional status, symptom status, quality of life) (Wang et al., 2002a). Although standardized measures for health outcomes have been developed, the incorporation of such measures into standardized terminologies has lagged behind that of measures for problems and interventions. Additionally, standardized terms for patient goals (i.e., expected outcomes) have been addressed only minimally and almost exclusively by the nursing community (Johnson et al., 2000). While no single current terminology has the breadth and depth needed for health care data, the National Library of Medicine (NLM) houses the world's largest database of standardized terminologies from a broad array of digital knowledge sources—the Unified Medical Language System (UMLS) (see Chapter 3). The terminology resources available through the UMLS are critical to initiatives to establish the NHII and to corresponding use of the EHR and patient safety systems.

Technical Criteria and Representation of Clinical Domains

Standardized terminologies vary along many dimensions; most important is the primary purpose of the terminology, as well as the extent to which it is concept oriented and possesses the semantic structures that enable computer (algorithmic) processing (Ingenerf, 1995; Rossi et al., 1998). To achieve the integrated approach to patient safety envisioned by the committee, the terminology must serve the purposes of decision support tools, the EHR, and knowledge resources (Chute et al., 1998). Terminology efforts for the EHR have focused on how to represent the history, findings, diagnoses, management, and outcomes of patients in a way that can preserve clinical detail and identify characteristics that enable improved risk adjustment, the development of common guidelines, aggregate outcome analyses, and shared decision support rules.

While a number of diverse terminologies are required for clinical care, patient safety, and other aspects of biomedicine, a central group of terminologies can serve as the backbone of clinical information systems. A number of technical criteria must be met for terminologies to function in a way that serves these purposes. The most basic criteria for a controlled medical vocabulary are identified by Cimino (1998); they include domain completeness, nonredundancy, synonymy, nonambiguity, multiple classification, consistency of views, and explicit relationships. In 1998, the ANSI Health Informatics Standards Board went a step further and created a detailed framework of informatics criteria for the development and evolution of terminologies with high functionality (Chute et al., 1998). The National Committee on Vital and Health Statistics (NCVHS) used these informatics criteria to evaluate and select a core set of well-integrated, nonredundant clinical terminologies that will serve as the national standard for medical terminology for the EHR (Sujansky, 2003) (Table 4-2).

TABLE 4-2

Technical Criteria Used by the National Committee on Vital and Health Statistics for Evaluating and Selecting Terminologies.

Minimization of overlap in domain representation was another important criterion for selection of the NCVHS core terminology group. The CHI initiative is also evaluating the terminologies in this regard, as well as assessing their ability to meet the extensive data representation requirements for the common clinical domains that cut across the three dimensions of the NHII (i.e., provider health, personal health, and public health), points of overlap, and gaps in coverage. Issues related to data collection, sharing, and reuse are being addressed during the evaluations, as well as identification of the overlap and gaps in clinical representation. Table 4-3 provides an overview of the cross-cutting domains identified by CHI to date. The terminologies determined by CHI to best represent requirements of the clinical domain areas, after consultation with NCVHS, will be accepted for federal government–wide implementation. Additional areas within the clinical domains, including those relevant to patient safety, will be added as the process proceeds.

TABLE 4-3

Clinical Domain Areas of the Consolidated Health Informatics Initiative.

CHI is working rapidly and expects to make recommendations on terminologies to represent many, if not all, of the domain areas identified in Table 4-3 by late 2003. The first round of terminology evaluations includes laboratory results content, medications, demographics, immunizations, and interventions and procedures. Initially, CHI identified many of the domain areas that support the corresponding domains needed for patient safety reporting systems; however, the list is not comprehensive, and there will likely be a need to expand or extend the domains. For example, in the domain area for medications, CHI identifies clinical drugs, warnings, allergic reactions, and adverse drug events (ADEs) as primary areas for clinical representation. For patient safety, representation is also needed for subcategories, such as nutritional supplements and alternative medicines.

Expansion of the domain areas for comprehensive clinical and patient safety data is a subject for additional work. Appendix F provides a comprehensive listing of the additional clinical domain areas needed to represent patient safety.

Selection of the Core Terminology Group

The NCVHS core terminology group comprises a core set of medical terminologies that together are sufficiently comprehensive, technically sound, mutually consistent, and readily available to deliver most of the envisioned functionality of a national standard medical terminology for the EHR (Sujansky, 2003). Having a common clinical reference terminology is expected to reduce the cost, increase the efficiency, and improve the quality of data exchange, clinical research, patient safety, sharing of computer guidelines, and public health monitoring. Terminologies to be included in the core group must have sufficient clinical granularity and serve multiple functions, including decision support, interoperability, aggregation and reporting, EHR data entry, order entry, indexing for data retrieval, and domain ontology. Supplemental terminologies should be mapped to the core terminologies to provide the functionalities associated with the use of data standards and information systems. Box 4-1 provides a brief overview of these terminologies.

BOX 4-1

Overview of Core and Supplemental Terminologies. CORE TERMINOLOGIES Systemized Nomenclature of Human and Veterinary Medicine, Clinical Terms (SNOMED CT)—developed by the College of American Pathologists, SNOMED CT is an inventory of medical terms (more...)

On November 13, 2003, NCVHS officially recommended that the Department of Health and Human Services (DHHS) adopt five medical terminologies for use by federal health care services programs: SNOMED, Clinical Terms CT; Laboratory LOINC; RxNORM; the National Drug File Clinical Drug Reference Terminology (NDF RT); and the Food and Drug Administration's terminology sets for drug ingredient name, dosage form, and package form for drugs (National Committee on Vital and Health Statistics, 2003b). NCVHS continues to study additional terminologies that it may recommend for adoption at a later date. Also of note, NCVHS recently voted to recommend that HHS adopt the International Classification of Disease, 10th revision, or ICD-10, as the new coding system under the HIPAA rule, replacing the current ICD-9 system (National Committee on Vital and Health Statistics, 2003b).

SNOMED CT SNOMED CT is the most well-developed concept-oriented terminology to date. A concept-oriented reference terminology can be defined as one that has such characteristics as a grammar that defines the rules for automated generation and classification of new concepts, as well as the combining of atomic concepts to form molecular expressions (Spackman et al., 1997). SNOMED CT is based on a formal terminology model that provides nonambiguous definitions of health care concepts and contains the most granular concepts for representing clinical and patient safety information. For example, the atomic concepts of “diabetes mellitus,” “self-management,” and “education” could be combined to form “diabetes self-management and education,” one of the priority areas identified by the IOM, as a precoordinated concept within a terminology or postcoordinated for a particular quality indicator report addressing errors of omission. SNOMED CT is designed to be the primary support for knowledge-based systems, the expression of clinical guidelines and datasets for the IOM priority conditions, and a key source for the development of new concepts for clinical and patient safety data. SNOMED CT's model was recently submitted to ANSI for approval as a standard. As part of the UMLS, it will serve as the core clinical reference terminology for the NHII (Department of Health and Human Services, 2003).

Laboratory LOINC Even with its comprehensiveness, SNOMED CT requires the support of additional terminologies to capture certain clinical data not currently available in the terminology with sufficient granularity or scope, namely laboratory, medication, and medical device data. LOINC has already been designated by CHI (in May 2003) as the terminology for representing laboratory test results and is a part of the NCVHS core terminology group (Consolidated Health Informatics Initiative, 2003). LOINC is the available terminology that most fully represents laboratory data in terms of naming for tests (e.g., chemistry, hematology) and clinical observations (e.g., blood pressure, respiratory rate). The LOINC terms are composed of up to eight dimensions derived from component (e.g., analyte), type of property (e.g., mass concentration), timing (e.g., 24-hour specimen), specimen (e.g., urine), and method (American National Standards Institute, 1997). LOINC also contains information for clinical observations that is not included in the core terminology group at this time, since it may be possible to represent many of the observations with SNOMED CT, and one of the criteria for selection is to minimize overlap in terminology representation. However, Clinical LOINC currently is and will continue to be used by a number of organizations.

For LOINC clinical measures, the code usually includes identification of the organ system. In addition, with Clinical LOINC, many measurements are distinguished for estimated, reported, and measured values (e.g., patient's report of his/her body weight versus a measured result or a physician's estimate) (American National Standards Institute, 1997). Varying degrees of precoordination for an observation are also provided for (e.g., cardiac output based on the Fick method versus based on the 2D method) (American National Standards Institute, 1997). Both Laboratory LOINC and those portions of Clinical LOINC that do not overlap with SNOMED CT are important terminologies for patient safety, as well as for the EHR.

Drug terminologies Drug terminologies are an important part of the core group. NCVHS has been evaluating which drug (and device) terminologies best represent these areas. The process for determining drug terminologies is more complex than that for identifying a comprehensive reference terminology and laboratory terminology. Representation of drug information involves both definitional and knowledge-based information (National Committee on Vital and Health Statistics, 2003a). Definitional information serves the purpose of interoperability by providing standardized terms to represent clinical drugs in clinical information systems. Knowledge-based information provides terminology for such phenomena as drug interactions, allergies, and contraindications, thus supporting greater functionality of clinical systems (National Committee on Vital and Health Statistics, 2003a). For purposes of standardizing data elements for patient medical records information, the core terminology group will be focused on definitional terms.

NLM has developed a normalized (i.e., standard) form for clinical drugs and their components—the RxNORM terminology. RxNORM assigns a standardized name for the active ingredient (i.e., generic), strength and physical form as given to the patient (e.g., 120 milligrams), and standard dosage form (e.g., tablet) (Brown et al., 2003). The semantic form provides the ability to link drug concepts from disparate vocabularies with naming variations developed by different pharmacy knowledge base vendors and drug manufacturers to match more closely the actual form a physician would order for a patient (Nelson et al., 2002). RxNORM was developed to be fully compatible with the FDA's system that provides identifiers/codes for active and inactive ingredients—the Unique Ingredient Identifier (UNII) project (Brown et al., 2003). Preliminary research on incorporating RxNORM into actual systems indicates that some refinements are needed (e.g., a few drugs need to be added) for greater precision and comprehensiveness; however, it will be possible to begin implementing it for use with clinical systems in the near term (National Committee on Vital and Health Statistics, 2003b).

In addition to RxNORM, other drug-related terminologies under consideration for inclusion in the core terminology group are the UNIIs and National Drug Codes (NDCs), both managed by the FDA, and the NDF RT being developed by the Veterans Health Administration. Following CHI's evaluation of the terminologies for representing the medication domain and presentation of findings in October 2003, NCVHS included these drug terminologies in its recommendation to DHHS.

Medical device terminologies A medical device terminology is also a must for the core terminology group. The two medical device terminologies being considered by NCVHS are the Global Medical Device Nomenclature (GMDN), developed by an international consortium to harmonize terms for regulatory purposes, and the Universal Medical Device Nomenclature System (UMDNS), developed and maintained by the Emergency Care Research Institute (ECRI). The terminology selected should be comprehensive in scope to cover the range of devices and their functions; capable of representing adverse events and malfunctions related to the devices; inclusive of emerging technologies used in investigative settings; sufficiently granular to capture essential data without losing critical information; and capable of being continuously maintained at a high level of technical quality, being mapped to other terms in use, and supporting high-quality translation to other languages for international use (Coates, 2003a).

Although the GMDN consortium initiated its activities using international standards and collaborated with six primary device terminology developers, the FDA found that the final resulting terminology did not meet the above criteria. The terminology that most closely meets these criteria is UMDNS. UMDNS provides a formal hierarchical system for representing complex medical device concepts, with content expressed in preferred terms and codes, entry terms, parent–child–sibling relationships, attributes, definitions, mappings, and linkages (Coates, 2003b). The process for maintaining the terminology is well developed at ECRI. In addition, ECRI functions as a collaborating center for the World Health Organization (WHO), an Agency for Healthcare Research and Quality (AHRQ) Evidence-Based Practice Center, a National Guideline Clearinghouse, and a National Quality Measures Clearinghouse, and it maintains an extensive patient safety reporting system (Coates, 2003b). These functions are important to the development of an integrated information infrastructure and the NHII, and the committee supports the inclusion of UMDNS in the NCVHS core terminology group. For international regulatory purposes, subsequent modifications and enhancements of the GMDN by the FDA may render it mappable to the terminologies in the core terminology group.

Mapping terminologies Mapping terminologies is a challenging task. The detailed terminologies of the core group and less granular classifications can be thought of as existing along a continuum of detail; for example, patient information can be expressed in a detailed nomenclature, such as SNOMED CT, funneling into a classification rubric, such as an ICD-9, Clinical Modification (CM) code (Chute, 2003). This is a limited one-way process in that once patient data have been expressed solely in the form of classifications, the original detail is lost and generally cannot be recovered. In many cases, this funneling process can be accomplished satisfactorily through a simple mapping or table that indicates which classification code subsumes a detailed description. However, such code-to-code mappings often fail since some terminologies incorporate complex criteria that can be reliably achieved only with rules for aggregating several patient details (Chute, 2003). Thus, such “aggregation logics” afford the automated and accurate mapping of detailed patient data into broader classifications, even for complex cases.

To satisfy the needs for the NHII and the EHR, computer-executable aggregation logic would stem from SNOMED CT and the other terminologies in the core group to the supplemental terminologies. It is also critical that the integration and mapping of the terminologies be based on the same information model as that of the data interchange standards—the HL7 RIM—to ensure optimum system functionality and interoperability (National Committee on Vital and Health Statistics, 2003a).

The committee believes that several supplemental terminologies are necessary to support the requirements for an integrated information infrastructure that supports multiple methods of collecting, analyzing, disseminating, and incorporating patient safety data with consideration for the differences among health care settings. As noted earlier, the terminologies must support system functionality and knowledge-based activities such as automated chart reviews and surveillance, voluntary reporting, natural language processing of narrative text, decision support tools (e.g., alerts and reminders), and the use of computer-readable evidence-based clinical guidelines. The supplemental terminologies outlined in Box 4-1 would be mapped through aggregation logic to the NCVHS core terminology group. These terminologies include HIPAA-designated code sets (i.e., ICD-9 CM, Current Procedural Terminology [CPT]-4, the Health Care Financing Administration Common Procedure Coding System [HCPCS], NDCs, Current Dental Terminology [CDT]), primary pharmacy knowledge bases (i.e., FirstDatabank National Drug Data File [NDDF]; plus MediSpan, Multum Lexicon), the Medical Dictionary for Drug Regulatory Affairs (MedDRA), UNII, International Society for Blood Transfusion [ISBT] 128, the Diagnostic and Statistical Manual for Mental Disorders [DSM-IV], and those nursing terminologies not already incorporated into SNOMED CT.

Terminologies for further investigation and research The NCVHS core terminology group and the supplemental terminologies support the basic functionalities of the conceptual model for integrated systems presented in Chapter 2. However, the committee has determined that two additional terminologies are also needed and warrant further investigation and research—the International Classification of Functioning, Disability and Health (ICF) to represent outcomes data, and the International Classification of Primary Care (ICPC) to represent the data needs of the office practice clinician.

Promising sources for standardized representation of functional status and outcome reporting include the WHO International Classification of Functioning and Disability (WHO ICF) and nursing terminologies such as the Nursing Outcomes Classification (Johnson et al., 2000). Functional status can be regarded as the demonstrated or anticipated capacity of an individual to perform or undertake actions or activities deemed essential for independent living and physiological sustenance (Ruggieri et al., forthcoming). Computer formats for clinical data describing the functional status of patients will be in increasing demand for measuring the impact of health care interventions and gauging quality of life (Ruggieri et al., forthcoming). These outcome measures can be used not only to capture the effect of an intervention on health status but also to control symptoms of a chronic condition, supplement specific clinical findings, or understand the patient's perception of care (Nerenz and Neil, 2001). Information on functioning as a supplement to diagnosis provides a broader, more meaningful picture of individual or population health over time that can be used for clinical decision making (World Health Organization, 2001), reporting and surveillance, and research and analysis.

The Mayo Clinic is undertaking a study to determine how well ICF can represent functional status data as they emerge traditionally within the health care setting (Ruggieri et al., forthcoming). Preliminary findings suggest that in their current state, ICF terms lack unambiguous clarity, fidelity, and hence usability across the ranges of clinical data and granularity required for the varied and extensive use cases that rely on the representation of functional status data (Ruggieri et al., forthcoming). However, ICF provides an important foundation from which clinical modifications and extensions can be developed to support robust functional status descriptions and representations in a broad spectrum of clinical domains and use cases (Ruggieri et al., forthcoming). Further study and development of outcome terminologies for patient safety applications, including nursing terminologies, are recommended.

ICPC was developed by the World Organization of National Colleges and Academic Associations of General Practice/Family Doctors (WONCA) to provide a system for classifying the broad range of symptoms, unease and difficulties, and conditions that make up those problems related to primary care that cannot be documented with the ICD codes (WONCA, 1998). More specifically, ICPC provides for simultaneous classification of the three elements of an encounter: the process of care, the reason for the encounter, and the health problem diagnosed (WONCA, 1998). Although ICPC is not widely used in the United States, it is the primary classification system used by much of the international community for electronic documentation of clinical practice in primary care or for reporting to national governments (Marshall, 2003). ICPC is used (in conjunction with ICD-9) extensively in the European Union and former U.K. countries, which have the most robust EHRs in the world. A study in Finland found repeatedly that ICPC permits coding of 95 percent or more of primary care visits (episodes of care), compared with 50 percent for ICD-9 (diagnosis) (Jamoulle, 2001). The ability to monitor episodes of care would support concurrent surveillance efforts by permitting a longitudinal look at patient symptoms, encounters (including diagnoses and treatments), and outcomes.

Because ICPC captures episodes of care, it has also been used to produce probability tables for presenting symptoms and diagnoses. This function could support the development of triggers in data monitoring or data mining systems and could be the basis for a much more robust decision support function. The ability of U.S. primary care practitioners to evaluate their practice and compare it with those of other physicians around the world relates directly to their ability to use the ICPC terminology in association with the NCVHS core terminology group.

With regard to patient safety, the University of Colorado Department of Family Medicine and numerous other organizations are involved in a collaborative project entitled Applied Strategies for Improving Patient Safety. This project, sponsored by AHRQ to analyze the causes and effects of adverse events in primary care and reduce the incidence of errors, is using ICPC as its classification system (Pace, 2003). Preliminary results are not available at this time.

A conceptual diagram of the core terminology group and associated mappings to supplemental terminologies is presented in Figure 4-4, which shows the possible relationships among the terminologies and the use of aggregation logic for mapping through various levels of granularity. This figure was developed as a modification of a presentation in August 2002 to NCVHS on clinical semantic interoperability by Dr. James R. Campbell of the University of Nebraska Medical Center (Campbell, 2002).

FIGURE 4-4

Conceptual diagram of the core terminology group and mappings to supplemental terminologies. SOURCE: Adapted from Campbell, 2002.

Which items are included in the IOM six aims for improvement select all that apply?

Which of the following describes healthcare informatics?

Is are the most important part of an information system?

Which information source is most reliable for presenting evidence based information?