Michael J. Lincoln

VistA--U.S. Department of Veterans Affairs national-scale HIS.

The Veterans Health Administration of the U.S. Department of Veterans Affairs has a long, successful, and interesting history of using information technology to meet its mission. Each medical center is computerized to a degree that surprises the uninitiated. For example, medical documentation and ordering are computerized at every facility. A sophisticated national infrastructure has been developed to replicate, support, and evolve single-center successes. With advances in inter-facility networking, data sharing, and specialized central support and technical tools, VistA is becoming a single, highly scalable national health information system (HIS) solution. In this paper, we present an historical overview of VistAs development, describe its current functionality, and discuss its emergence as a national-scale hospital information system.

Reliability and Validity of Eight Dental Age Estimation Methods for Adults

This paper evaluates the reliability and validity of eight published dental age estimation methods for adults that may aid in victim identification. Age was calculated on 20 Caucasian teeth of known age according to the methods of Kvaal (for in situ and extracted teeth), Solheim (for in situ and sectioned teeth), Lamendin (for extracted teeth), Johanson (for sectioned teeth) and Bang (for extracted and sectioned teeth) by one independent observer. For each method, mean age error and standard error were assessed as the measures of accuracy and precision. In addition, method simplicity, requirements for tooth preparation and the equipment necessary were assessed and recommendations given for forensic use in various situations. Methods for sectioned teeth gave more reliable results when compared to methods for intact teeth.

VA national drug file reference terminology: A cross-institutional content coverage study

BACKGROUND Content coverage studies provide valuable information to potential users of terminologies. We detail the VA National Drug File Reference Terminologys (NDF-RT) ability to represent dictated medication list phrases from the Mayo Clinic. NDF-RT is a description logic-based resource created to support clinical operations at one of the largest healthcare providers in the US. METHODS Medication list phrases were extracted from dictated patient notes from the Mayo Clinic. Algorithmic mappings to NDF-RT using the SmartAccess Vocabulary Server (SAVS) were presented to two non-VA physicians. The physicians used a terminology browser to determine the accuracy of the algorithmic mapping and the content coverage of NDF-RT. RESULTS The 509 extracted documents on 300 patients contained 847 medication concepts in medication lists. NDF-RT covered 97.8% of concepts. Of the 18 phrases that NDF-RT did not represent, 10 were for OTCs and food supplements, 5 were for prescription medications, and 3 were missing synonyms. The SAVS engine properly mapped 773 of 810 phrases with an overall sensitivity (precision) was 95.4% and positive predictive value (recall) of 99.9%. CONCLUSIONS This study demonstrates that NDF-RT has more general utility than its initial design parameters dictated

Data representation in healthcare: A design for a freely-available openly-developed international reference terminology for healthcare

The importance of data representation in healthcare has been of concern for centuries. In the last fifty years there has been an increasing awareness of the need for formal representations of terminological systems. We propose that terminological system development should be consensus driven and the product of iterative open refinement in order to practically serve the needs of the healthcare community. The system of development and maintenance of such a system must involve recruitment of the best and the brightest in the medical community to take responsibility for insuring the accuracy and completeness of such an effort. We suggest a method for algorithmically building a starting point for such a reference terminology. Further we suggest a distributed authoring environment that would allow domain experts to contribute regardless of their location or language. Open intellectual contribution is the necessary ingredient for a consensus based international health reference terminology.