Identifying and mitigating biases in EHR laboratory tests.

Electronic health record (EHR) data show promise for deriving new ways of modeling human disease states. Although EHR researchers often use numerical values of laboratory tests as features in disease models, a great deal of information is contained in the context within which a laboratory test is taken. For example, the same numerical value of a creatinine test has different interpretation for a chronic kidney disease patient and a patient with acute kidney injury. We study whether EHR research studies are subject to biased results and interpretations if laboratory measurements taken in different contexts are not explicitly separated. We show that the context of a laboratory test measurement can often be captured by the way the test is measured through time. We perform three tasks to study the properties of these temporal measurement patterns. In the first task, we confirm that laboratory test measurement patterns provide additional information to the stand-alone numerical value. The second task identifies three measurement pattern motifs across a set of 70 laboratory tests performed for over 14,000 patients. Of these, one motif exhibits properties that can lead to biased research results. In the third task, we demonstrate the potential for biased results on a specific example. We conduct an association study of lipase test values to acute pancreatitis. We observe a diluted signal when using only a lipase value threshold, whereas the full association is recovered when properly accounting for lipase measurements in different contexts (leveraging the lipase measurement patterns to separate the contexts). Aggregating EHR data without separating distinct laboratory test measurement patterns can intermix patients with different diseases, leading to the confounding of signals in large-scale EHR analyses. This paper presents a methodology for leveraging measurement frequency to identify and reduce laboratory test biases.

[Can a laboratory investigation be called anything? "The NPU system" sorts out the concepts and gives systematic stringency]. / Får en laboratorieundersökning kallas vad som helst? "NPU-systemet" reder upp i begreppsröran och ger systematisk stringens.

When communicating results from laboratory investigations from the laboratory to the requesters and further between different information systems, it is important that the value as well as the unique identity and name of the laboratory investigation are correctly cited. A committee under the International Federation for Clinical Chemistry and Laboratory Medicine (IFCC) and International Union for Pure and Applied Chemistry (IUPAC) has developed a systematic nomenclature for the correct classification of laboratory investigations. Each generic laboratory investigation is provided with a unique NPU code. The system is in use among approximately 30 different clinical laboratories in Sweden, and has capacity to be the common denominator of all laboratory investigations, and to be used as the identifier in various information systems. The NPU system for the Swedish laboratories is currently administered by EQUALIS and partly financed by the participating laboratories. Other ways of funding, of benefit for the whole health care sector, will be investigated.

Advances In Infection Surveillance and Clinical Decision Support With Fuzzy Sets and Fuzzy Logic.

By the use of extended intelligent information technology tools for fully automated healthcare-associated infection (HAI) surveillance, clinicians can be informed and alerted about the emergence of infection-related conditions in their patients. Moni--a system for monitoring nosocomial infections in intensive care units for adult and neonatal patients--employs knowledge bases that were written with extensive use of fuzzy sets and fuzzy logic, allowing the inherent un-sharpness of clinical terms and the inherent uncertainty of clinical conclusions to be a part of Moni's output. Thus, linguistic as well as propositional uncertainty became a part of Moni, which can now report retrospectively on HAIs according to traditional crisp HAI surveillance definitions, as well as support clinical bedside work by more complex crisp and fuzzy alerts and reminders. This improved approach can bridge the gap between classical retrospective surveillance of HAIs and ongoing prospective clinical-decision-oriented HAI support.

Specific list for categorization of laboratory test systems, assays and examinations by complexity--PHS. Notice with comment period.

The Clinical Laboratory Improvement Amendments of 1988, Public Law 100-578, requires that the Secretary provide for the categorization of specific laboratory test systems, assays and examinations by level of complexity. 42 CFR 493.17, published elsewhere in this issue of the Federal Register, establishes criteria for such categorization. It is the Department's intention to complete the categorization of all currently available clinical laboratory test systems, assays and examinations prior to the effective date of the amendments to 42 CFR Part 493 (September 1, 1992). This Notice announces the first of a series of lists containing specific clinical laboratory test systems, assays and examinations, categorized by complexity. Additional lists of test systems, assays and examinations will be published periodically. On or before September 1, 1992, a complete list of all laboratory test systems, assays and examinations, categorized by complexity, will be published in the form of a compilation of these Notices. Any clinical laboratory test system, assay or examination that is not on that final list will be considered high complexity, until categorized otherwise, as provided under 42 CFR 493.17. After publication of the compilation, applications will be taken to categorize (or recategorize) other laboratory test systems, assays and examinations following the procedures delineated in 42 CFR 493.17(d). Notices will be published periodically in the Federal Register to announce any additional test system, assay or examination that has been categorized (or re-categorize) during the preceding interval.

[Coding for clinical laboratory information].

The field of clinical laboratory tests is facing an increase in the number of test items as well as a corresponding diversification due to the demands of medical institutions as well as improvements in analytical techniques. To respond to this situation, medical institutions have been promoting systematization of their testing procedures; information exchange among the institutions has likewise expanded with the use of media such as on-line systems and internet. Standardization of interfaces has been proposed to secure a common framework compatible with different types of information. Some embodiments in this country includes; (1) Interface Standards on Clinical Laboratory Information For information exchange, the format and reporting comments used in the media systems were standardized under the sponsorship of The Medical Information System Development Center, with a publication issued on 1993. (2) Standardization of Laboratory Test Code Standardization of codes for information exchange has been established under the sponsorship of The Japan Society of Clinical Pathology (Laboratory Test Coding Committee), through the systematization of laboratory test code used in media systems. A publication entitled "Classification & Coding for Clinical Laboratory Tests (8th edition in 1992, 9th edition in 1994 and supplement in 1996)" has been issued. The system for "Classification & Coding for Clinical Laboratory Tests" is divided into 5 components; (1) analyte code, (2) identification code, (3) specimen code, (4) methodology code, and (5) data classification code. The Laboratory test codes are precisely classified by "(1) analyte code", and then are identified by combination of additional codes such as specimen and methodology codes. In this year, we are making a new easily-used-codes composed of 5 Arabic figures.

Visualization of CDA laboratory reports and long term trends as a possible EHR application for patients and physicians.

To increase the patient's acceptance of electronic health records and understanding for their laboratory findings a web application was developed which presents all parameters and possible deviations of standard values in a clear way and visualizes the time based trend of all recorded parameters graphically. Documents corresponding to the Clinical document architecture (CDA) R2 laboratory reports standard and a rapid prototyping framework called Groovy on Grails were used. This work shows, that it is possible to create a useful, standards based tool for patients and physicians with comparatively few resources - an application that could be in similar form a part of an electronic Health Record (EHR) system like the Austrian electronic Health Record (ELGA).

Preguntas y recomendaciones sobre integración de los archivos diagnósticos de los servicios de Anatomía Patológica en biobancos / Questions and recommendations on the integration of diagnostic files of the Anatomical-Pathology departments into biobanks

No disponible

Sistemas de información en anatomía patológica: análisis de las soluciones existentes / Information System in anatomical pathology: Analysis of the existing solutions

Antecedentes: En los últimos dos años se está procediendo a la renovación de los sistemas informáticos en anatomía patológica de numerosos hospitales. Por ello consideramos interesante revisar los sistemas informáticos más habituales y conocer los principales aspectos que debemos exigir en estos programas. Material y métodos: Cuatro sistemas informáticos (Master-Pat NovoPath, PatWin e InfoPAT) han sido evaluados por los usuarios de siete hospitales distribuyendo entre los usuarios y los fabricantes una encuesta con 105 variables. Resultados: Los sistemas más modernos ofrecen un número muy alto de funcionalidades, destacando los más modernos por su integración en el resto del sistema de información hospitalario, si bien los sistemas de ayuda, la codificación y la gestión de imágenes son algunos aspectos que la mayoría de programas necesitan mejorar. Conclusión: Los nuevos programas informáticos son de gran calidad y las prestaciones muy parecidas, por lo que es aconsejable evaluar las necesidades de cada servicio para poder escogerla solución óptima (AU)