A domain analysis model for eIRB systems: addressing the weak link in clinical research informatics.

Institutional Review Boards (IRBs) are a critical component of clinical research and can become a significant bottleneck due to the dramatic increase, in both volume and complexity of clinical research. Despite the interest in developing clinical research informatics (CRI) systems and supporting data standards to increase clinical research efficiency and interoperability, informatics research in the IRB domain has not attracted much attention in the scientific community. The lack of standardized and structured application forms across different IRBs causes inefficient and inconsistent proposal reviews and cumbersome workflows. These issues are even more prominent in multi-institutional clinical research that is rapidly becoming the norm. This paper proposes and evaluates a domain analysis model for electronic IRB (eIRB) systems, paving the way for streamlined clinical research workflow via integration with other CRI systems and improved IRB application throughput via computer-assisted decision support.

Standardized mapping of nursing assessments across 59 U.S. military treatment facilities.

OBJECTIVE: Create an interoperable set of nursing flowsheet assessment measures within military treatment facility electronic health records using the 3M Healthcare Data Dictionary (HDD). DESIGN: The project comprised three phases: 1) discovery included an in-depth analysis of the Essentris data to be mapped in the HDD; 2) mapping encompassed the creation of standard operating procedures, mapping heuristics, and the development of mapping tools; and 3) quality assurance incorporated validation of mappings using inter-rater agreement. RESULTS: Of 569,073 flowsheet concepts, 92% were mapped to the HDD. Of these, 31.5% represented LOINC concepts, 15% represented SNOMED CT and 1% represented both. 52.5% were mapped to HDD concepts with no standardized terminology representations. CONCLUSIONS: Nursing flowsheet data can be mapped to standard terminologies but there is not the breadth of coverage necessary to represent nursing assessments. Future work is necessary to develop a standard information model for the nursing process.

Collaborative authoring of biomedical terminologies using a semantic Wiki.

To be broadly useful, biomedical terminologies need to capture the knowledge and expertise of multiple experts, research groups and end users. Consequently, the construction of such a terminology requires collaboration among multiple participants. This paper summarizes several terminology projects which employ a collaborative authoring mode in development, along with the various tools used by these projects to support collaboration, such as mailing list, issue tracking system, client-side GUI software and Wiki system. We then discuss the essential functional requirements for a collaborative authoring tool in the context of terminology development, and analyze the inherent features of Semantic Wiki that enable it to be a competent tool used in this type of effort. To demonstrate, we describe a prototype system of collaborative authoring for health care terminologies built upon the Semantic Wiki technology. Finally, we discuss the potential issues that might be associated with this open Semantic Wiki platform.

Practical issues in using SNOMED CT as a reference terminology.

SNOMED CT was created by the merger of SNOMED RT (Reference Terminology) and Read Codes Version 3 (also known as Clinical Terms Version 3). SNOMED CT is considered to be among the most extensive and comprehensive biomedical vocabularies available today. It is considered for use as the Reference Terminology of various institutions. We review the adequacy of SNOMED CT as a Reference Terminology and discuss the issues in its use as such. We discuss issues with content coverage of various clinical domains, data integrity and validity, and the update frequency of SNOMED CT, and why SNOMED CT alone is not adequate to serve as the Reference Terminology of a healthcare organization.

Using knowledge rules for pharmacy mapping.

The 3M Health Information Systems (HIS) Healthcare Data Dictionary (HDD) is used to encode and structure patient medication data for the Electronic Health Record (EHR) of the Department of Defense's (DoD's) Armed Forces Health Longitudinal Technology Application (AHLTA). HDD Subject Matter Experts (SMEs) are responsible for initial and maintenance mapping of disparate, standalone medication master files from all 100 DoD host sites worldwide to a single concept-based vocabulary, to accomplish semantic interoperability. To achieve higher levels of automation, SMEs began defining a growing set of knowledge rules. These knowledge rules were implemented in a pharmacy mapping tool, which enhanced consistency through automation and increased mapping rate by 29%.

Mapping Department of Defense laboratory results to Logical Observation Identifiers Names and Codes (LOINC).

The Department of Defense (DoD) has used a common application, Composite Health Care System (CHCS), throughout all DoD facilities. However, the master files used to encode patient data in CHCS are not identical across DoD facilities. The encoded data is thus not interoperable from one DoD facility to another. To enable data interoperability in the next-generation system, CHCS II, and for the DoD to exchange laboratory results with external organizations such as the Veterans Administration (VA), the disparate master file codes for laboratory results are mapped to Logical Observation Identifier Names and Codes (LOINC) wherever possible. This paper presents some findings from our experience mapping DoD laboratory results to LOINC.

Applying hybrid algorithms for text matching to automated biomedical vocabulary mapping.

Several biomedical vocabularies are often used by clinical applications due to their different domain(s) of coverage, intended use, etc. Mapping them to a reference terminology is essential for inter-systems interoperability. Manual vocabulary mapping is labor-intensive and allows room for inconsistencies. It requires manual searching for synonyms, abbreviation expansions, variations, etc., placing additional burden on the mappers. Furthermore, local vocabularies may use non-standard words and abbreviations, posing additional problems. However, much of this process can be automated to provide decision-support, allowing mappers to focus on steps that absolutely need their expertise. We developed hybrid algorithms comprising of rules, permutations, sequence alignment and cost algorithms that utilize the UMLS SPECIALIST Lexicon, a custom knowledgebase and a search engine to automatically find probable matches, allowing mappers to select the best match from this list. We discuss the techniques, results from assisting to map a local codeset, and scope for generalizability.

Managing vocabulary mapping services.

The efficient management and maintenance of large-scale and high-quality vocabulary mapping is an operational challenge. The 3M Health Information Systems (HIS) Healthcare Data Dictionary (HDD) group developed an information management system to provide controlled mapping services, resulting in improved efficiency and quality maintenance.

The evolution of tools and processes for data mapping.

The 3M Healthcare Data Dictionary (HDD) group provides vocabulary mapping services for the Department of Defense (DoD). The process to manage changes in these mappings, the so-called "delta process," is complex and requires the exchange and management of large amounts of data. To aid in this process, the 3M HDD group has created and refined a vocabulary Mapping Environment (ME).