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Health care in the United States has become an information-intensive industry, yet electronic health records represent patient data inconsistently for lack of clinical data standards. Classifications that have achieved common acceptance, such as the ICD-9-CM or ICD, aggregate heterogeneous patients into broad categories, which preclude their practical use in decision support, development of refined guidelines, or detailed comparison of patient outcomes or benchmarks. This document proposes a framework for the integration and maturation of clinical terminologies that would have practical applications in patient care, process management, outcome analysis, and decision support. Arising from the two working groups within the standards community--the ANSI (American National Standards Institute) Healthcare Informatics Standards Board Working Group and the Computer-based Patient Records Institute Working Group on Codes and Structures--it outlines policies regarding 1) functional characteristics of practical terminologies, 2) terminology models that can broaden their applications and contribute to their sustainability, 3) maintenance attributes that will enable terminologies to keep pace with rapidly changing health care knowledge and process, and 4) administrative issues that would facilitate their accessibility, adoption, and application to improve the quality and efficiency of American health care. [ABSTRACT FROM AUTHOR]

Background and Objective: Patient conditions and events are the core of patient record content. Computer-based records will require standard vocabularies to represent these data consistently, thereby facilitating clinical decision support, research, and efficient care delivery. To address whether existing major coding systems can serve this function, the authors evaluated major clinical classifications for their content coverage.Methods: Clinical text from four medical centers was sampled from inpatient and outpatient settings. The resultant corpus of 14,247 words was parsed into 3,061 distinct concepts. These concepts were grouped into Diagnoses, Modifiers, Findings, Treatments and Procedures, and Other. Each concept was coded into ICD-9-CM, ICD-10, CPT, SNOMED III, Read V2, UMLS 1.3, and NANDA; a secondary reviewer ensured consistency. While coding, the information was scored: 0 = no match, 1 = fair match, 2 = complete match.Results: ICD-9-CM had an overall mean score of 0.77 out of 2; its highest subscore was 1.61 for Diagnoses. ICD-10 scored 1.60 for Diagnoses, and 0.62 overall. The overall score of ICD-9-CM augmented by CPT was not materially improved at 0.82. The SNOMED International system demonstrated the highest score in every category, including Diagnoses (1.90), and had an overall score of 1.74.Conclusion: No classification captured all concepts, although SNOMED did notably the most complete job. The systems in major use in the United States, ICD-9-CM and CPT, fail to capture substantial clinical content. ICD-10 does not perform better than ICD-9-CM. The major clinical classifications in use today incompletely cover the clinical content of patient records; thus analytic conclusions that depend on these systems may be suspect. [ABSTRACT FROM AUTHOR]

We fully agree with Carol Diamond and Clay Shirky that deployment of health information technology (IT) is necessary but not sufficient for transforming U.S. health care. However, the recent work to advance health IT is far from an exercise in 'magical thinking.' It has been strategic thinking. To illustrate this, we highlight recent initiatives and progress under four focus areas: adoption, governance, privacy and security, and interoperability. In addition, solutions exist for health IT to advance rapidly without adversely affecting future policy choices. A broad national consensus is emerging in support of advancing health IT to enable the transformation of health and care. [ABSTRACT FROM AUTHOR]

As the size and complexity of medical terminologies increase, terminology modelers are increasingly hampered by lack of tools and methods to manage the development process. This paper presents our use and ongoing evaluation of a description-logic classifier to support cognitive scalability of the underlying terminology and our enhancements to that classifier to support concurrent development utilizing semantics-based concurrency control methods. Our enhancements, collectively referred to as the Gálapagos, consist of several applications that take locally-developed terminology enhancements from multiple sites, identify conflicting design decisions, support the modelers' reconciliation of the conflicting designs, and efficiently disseminate updates tailored for locally enhanced terminologies. We have tested our ideas through concurrent evolutionary enhancement of SNOMED International at three Kaiser Permanente regions and the Mayo Clinic. We have found that the underlying environment has met our design objectives, and supports semantic-based concurrency control, and identification and resolution of conflicting design decisions.

Current controlled medical terminologies fall short of the needs of informatics application developers. To overcome the limitations of current medical terminologies, many groups are independently enhancing existing terminologies to meet their local needs. With proper computer-based support, local enhancements can be used as evolutionary stepping stones toward a convergent medical terminology. Gálapagos is a collection of applications that can take local enhancements from multiple sites, identify conflicting design decisions, allow developers to reconcile the conflicting designs, and efficiently disseminate updates tailored specifically for compatibility with locally enhanced terminologies. This paper describes an initial proof-of-concept of the Gálapagos programs using data generated during concurrent SNOMED enhancement by Kaiser Permanente and the Mayo Clinic.