XML as standard for communicating in a document-based electronic patient record: a 3 years experiment

During the past few years, the eXtensible Markup Language (XML) has progressively become a gold standard for accessing, representing and exchanging information, especially in the health care environment. This paper presents an implementation of the use of XML for the electronic patient record (EPR) and discusses more specifically its growing use in two areas of the EPR: first, as a format for the exchange of structured messages, and second, as a comprehensible way of representing patient documents. These statements rely on a 3 years experiment conducted at the Geneva University Hospital as part of its document-centered EPR.     

as standard for communicating in a document-based electronic patient record: a 3 years experiment

During the past few years, the eXtensible Markup Language ( ) has progressively become a gold standard for accessing, representing and exchanging information, especially in the health care environment. This paper presents an implementation of the use of for the electronic patient record (EPR) and discusses more specifically its growing use in two areas of the EPR: first, as a format for the exchange of structured messages, and second, as a comprehensible way of representing patient documents. These statements rely on a 3 years experiment conducted at the Geneva University Hospital as part of its document-centered EPR.     

Improving Radiology Report Quality by Rapidly Notifying Radiologist of Report Errors

Radiology report errors occur for many reasons including the use of pre-filled report templates, wrong-word substitution, nonsensical phrases, and missing words. Reports may also contain clinical errors that are not specific to the speech recognition including wrong laterality and gender-specific discrepancies. Our goal was to create a custom algorithm to detect potential gender and laterality mismatch errors and to notify the interpreting radiologists for rapid correction. A JavaScript algorithm was devised to flag gender and laterality mismatch errors by searching the text of the report for keywords and comparing them to parameters within the study’s HL7 metadata (i.e., procedure type, patient sex). The error detection algorithm was retrospectively applied to 82,353 reports 4 months prior to its development and then prospectively to 309,304 reports 15 months after implementation. Flagged reports were reviewed individually by two radiologists for a true gender or laterality error and to determine if the errors were ultimately corrected. There was significant improvement in the number of flagged reports (pre, 198/82,353 [0.24 %]; post, 628/309,304 [0.20 %]; P = 0.04) and reports containing confirmed gender or laterality errors (pre, 116/82,353 [0.014 %]; post, 285/309,304 [0.09 %]; P < 0.0001) after implementing our error notification system. The number of flagged reports containing an error that were ultimately corrected improved dramatically after implementing the notification system (pre, 17/116 [15 %]; post, 239/285 [84 %]; P < 0.0001). We developed a successful automated tool for detecting and notifying radiologists of potential gender and laterality errors, allowing for rapid report correction and reducing the overall rate of report errors.     

Translating the IHE Teaching File and Clinical Trial Export (TCE) Profile Document Templates into Functional DICOM Structured Report Objects

The Integrating the Healthcare Enterprise (IHE) Teaching File and Clinical Trial Export (TCE) integration profile describes a standard workflow for exporting key images from an image manager/archive to a teaching file, clinical trial, or electronic publication application. Two specific digital imaging and communication in medicine (DICOM) structured reports (SR) reference the key images and contain associated case information. This paper presents step-by-step instructions for translating the TCE document templates into functional and complete DICOM SR objects. Others will benefit from these instructions in developing TCE compliant applications.     

An Ontology for PACS Integration

An ontology describes a set of classes and the relationships among them. We explored the use of an ontology to integrate picture archiving and communication systems (PACS) with other information systems in the clinical enterprise. We created an ontological model of thoracic radiology that contained knowledge of anatomy, imaging procedures, and performed procedure steps. We explored the use of the model in two use cases: (1) to determine examination completeness and (2) to identify reference (comparison) images obtained in the same imaging projection. The model incorporated a total of 138 classes, including radiology orderables, procedures, procedure steps, imaging modalities, patient positions, and imaging planes. Radiological knowledge was encoded as relationships among these classes. The ontology successfully met the information requirements of the two use-case scenarios. Ontologies can represent radiological and clinical knowledge to integrate PACS with the clinical enterprise and to support the radiology interpretation process.