Semantic validation of standard based electronic health record documents with W3C XML Schema

++43 1 40400 6693 (Phone) [email protected] 2 Summary Objectives. The goal of this article is to examine whether W3C XML Schema provides a practicable solution for the semantic validation of standard based electronic health record (EHR) documents. With semantic validation we mean that the EHR documents are checked for conformance with the underlying archetypes and reference model. Methods. We describe an approach that allows XML Schemas to be derived from archetypes based on a specific naming convention. The archetype constraints are augmented with additional components of the reference model within the XML Schema representation. A copy of the EHR document that is transformed according to the before-mentioned naming convention is used for the actual validation against the XML Schema. Results. We tested our approach by semantically validating EHR documents conformant to three different ISO/EN 13606 archetypes respective to three sections of the CDA implementation guide "Continuity of Care Document (CCD)" and an implementation guide for diabetes therapy data. We further developed a tool to automate the different steps of our semantic validation approach. Conclusions. For two particular kinds of archetype prescriptions, individual transformations are required for the corresponding EHR documents. Otherwise, a fully generic validation is possible. In general, we consider W3C XML Schema as a practicable solution for the semantic validation of standard based EHR documents

The EHR-ARCHE project: Satisfying clinical information needs in a Shared Electronic Health Record System based on IHE XDS and Archetypes

AbstractPurposeWhile contributing to an improved continuity of care, Shared Electronic Health Record (EHR) systems may also lead to information overload of healthcare providers. Document-oriented architectures, such as the commonly employed IHE XDS profile, which only support information retrieval at the level of documents, are particularly susceptible for this problem. The objective of the EHR-ARCHE project was to develop a methodology and a prototype to efficiently satisfy healthcare providers’ information needs when accessing a patient's Shared EHR during a treatment situation. We especially aimed to investigate whether this objective can be reached by integrating EHR Archetypes into an IHE XDS environment.MethodsUsing methodical triangulation, we first analysed the information needs of healthcare providers, focusing on the treatment of diabetes patients as an exemplary application domain. We then designed ISO/EN 13606 Archetypes covering the identified information needs. To support a content-based search for fine-grained information items within EHR documents, we extended the IHE XDS environment with two additional actors. Finally, we conducted a formative and summative evaluation of our approach within a controlled study.ResultsWe identified 446 frequently needed diabetes-specific information items, representing typical information needs of healthcare providers. We then created 128 Archetypes and 120 EHR documents for two fictive patients. All seven diabetes experts, who evaluated our approach, preferred the content-based search to a conventional XDS search. Success rates of finding relevant information was higher for the content-based search (100% versus 80%) and the latter was also more time-efficient (8–14min versus 20min or more).ConclusionsOur results show that for an efficient satisfaction of health care providers’ information needs, a content-based search that rests upon the integration of Archetypes into an IHE XDS-based Shared EHR system is superior to a conventional metadata-based XDS search

ICC-CLASS: isotopically-coded cleavable crosslinking analysis software suite

<p>Abstract</p> <p>Background</p> <p>Successful application of crosslinking combined with mass spectrometry for studying proteins and protein complexes requires specifically-designed crosslinking reagents, experimental techniques, and data analysis software. Using isotopically-coded ("heavy and light") versions of the crosslinker and cleavable crosslinking reagents is analytically advantageous for mass spectrometric applications and provides a "handle" that can be used to distinguish crosslinked peptides of different types, and to increase the confidence of the identification of the crosslinks.</p> <p>Results</p> <p>Here, we describe a program suite designed for the analysis of mass spectrometric data obtained with isotopically-coded <it>cleavable </it>crosslinkers. The suite contains three programs called: DX, DXDX, and DXMSMS. DX searches the mass spectra for the presence of ion signal doublets resulting from the light and heavy isotopic forms of the isotopically-coded crosslinking reagent used. DXDX searches for possible mass matches between cleaved and uncleaved isotopically-coded crosslinks based on the established chemistry of the cleavage reaction for a given crosslinking reagent. DXMSMS assigns the crosslinks to the known protein sequences, based on the isotopically-coded and un-coded MS/MS fragmentation data of uncleaved and cleaved peptide crosslinks.</p> <p>Conclusion</p> <p>The combination of these three programs, which are tailored to the analytical features of the specific isotopically-coded cleavable crosslinking reagents used, represents a powerful software tool for automated high-accuracy peptide crosslink identification. See: <url>http://www.creativemolecules.com/CM_Software.htm</url></p

A reinforcement learning model for AI-based decision support in skin cancer

: We investigated whether human preferences hold the potential to improve diagnostic artificial intelligence (AI)-based decision support using skin cancer diagnosis as a use case. We utilized nonuniform rewards and penalties based on expert-generated tables, balancing the benefits and harms of various diagnostic errors, which were applied using reinforcement learning. Compared with supervised learning, the reinforcement learning model improved the sensitivity for melanoma from 61.4% to 79.5% (95% confidence interval (CI): 73.5-85.6%) and for basal cell carcinoma from 79.4% to 87.1% (95% CI: 80.3-93.9%). AI overconfidence was also reduced while simultaneously maintaining accuracy. Reinforcement learning increased the rate of correct diagnoses made by dermatologists by 12.0% (95% CI: 8.8-15.1%) and improved the rate of optimal management decisions from 57.4% to 65.3% (95% CI: 61.7-68.9%). We further demonstrated that the reward-adjusted reinforcement learning model and a threshold-based model outperformed naïve supervised learning in various clinical scenarios. Our findings suggest the potential for incorporating human preferences into image-based diagnostic algorithms

How sick is Austria? – A decision support framework for different evaluations of the burden of disease within the Austrian population based on different data sources

ABSTRACT Objectives In healthcare it is crucial to have a fundamental knowledge of the burden of diseases within the population. Therefore we aimed to develop an Atlas of Epidemiology to gain better insight on the epidemiological situation. Based on primary and secondary health care data, we aimed to present results in interactive charts and maps, comprehensible to experts and the general public. The atlas builds a framework for rapid deployment of new data and results in a reproducible and efficient way. As a first use case three methods based on two different databases for the estimation of diabetes prevalence in Austria are compared. Approach Datasources: (i) reimbursement data 2006/2007 (GAP-DRG); (ii) national routine health survey (ATHIS) for 2006/2007. Methods for diabetes prevalence estimation: 1) ATC-ICD statistically relates pseudonymized data on medications to data on diagnoses from hospitalizations and sick leaves. 2) With the method Experts, medical experts assign specific medications to diabetes diagnoses. Patients with these medications are identified together with hospitalized diabetes diagnosed patients in GAP-DRG. 3) In ATHIS a sample of 15.000 persons was questioned if they a) ever had diabetes and b) were treated against diabetes in the last 12 months. Results are projected onto the Austrian population. Patients are divided by 10-year age-classes, gender and state. For the publicly online framework, implemented in html and javascript, pre-processed data in different granularity is required and used. Results Maps of Austria represent the prevalence of diabetes for each method and granularity level. The difference of the methods can be seen by clicking on the next map. For different age-classes (resp. different gender) the three methods can be compared directly within a bar chart. The technology for a rapid deployment of new data is now developed. For the use case first results have already been presented to decision makers, and feedback has been incorporated. Conclusion Besides depicting disease prevalence, the atlas of epidemiology also allows to visualize health care service data and results of simulation models in a fast and efficient way, which is important for decision makers. Soon the results of the ATC-ICD project on the prevalence of different diseases based on ICD9 diagnoses and medication data will be published in an aggregated form. This project is part of the K-Project dexhelpp in COMET – Competence Centers for Excellent Technologies that is funded by BMVIT, BMWGJ and transacted by FFG

Content-based search in standardized electronic health records by means of Archetypes

Abweichender Titel laut Übersetzung der Verfasserin/des VerfassersZsfassung in engl. SpracheElektronische Gesundheitsakten (engl. "Electronic Health Records", abgekürzt EHRs) sollen helfen das immer größere Datenaufkommen in der Medizin zu bewältigen. Um die integrierte Versorgung und den Austausch von EHR-Daten zwischen Gesundheitsdiensteanbietern (GDAs) auf regionaler und nationaler Ebene zu ermöglichen, gewinnen sogenannte Shared-EHR-Systeme immer mehr an Bedeutung. Es müssen jedoch effiziente Methoden entwickelt werden, um die für eine konkrete Behandlungssituation relevanten Informationen in umfangreichen EHRs von Patienten zu finden und eine Informationsüberflutung der GDAs zu verhindern. Aktuelle Standards zur Spezifikation von strukturierten EHRs (i.e. ISO/EN 13606, HL7 CDA, openEHR) bauen auf dem sogenannten Zwei-Modell-Ansatz auf. Die Inhalte von EHRs werden dabei mittels sogenannter Archetypen spezifiziert (erste Modell-Ebene), die aus vordefinierten Komponenten eines Referenzmodells (zweite Modell-Ebene) zusammengesetzt werden. Ziel der Arbeit ist es anhand von fünf Anwendungsszenarien das Potenzial von Archetypen in Shared-EHR-Systemen aufzuzeigen. Der Hauptfokus der vorliegenden Arbeit liegt auf der Entwicklung einer inhaltsbasierten Suche in einer auf der weit verbreiteten Spezifikation Integrating the Healthcare Enterprise (IHE) Cross Enterprise Document Sharing (IHE XDS) basierenden Shared-EHR-Systemarchitektur. Dies stellt eine wesentliche Erweiterung der bisherigen Standard-Suchfunktionalität von IHE XDS dar, welche auf einige wenige Dokument-Metadaten limitiert ist und als Resultat der Suche ausschließlich gesamte EHR-Dokumente zurückliefert. Die inhaltsbasierte Suche ermöglicht es im Gegensatz dazu, nach elementaren Inhalten in EHR-Dokumenten zu suchen und liefert als Ergebnis diese, aus den EHR-Dokumenten extrahierten Inhalte zurück. Im vorgestellten Ansatz werden sämtliche EHR-Dokumente mittels Archetypen beschrieben und dem GDA darauf basierende Suchbegriffe zur Verfügung gestellt. Die Suchanfrage wird mithilfe der Archetypen in eine standardkonforme metadatenbasierte Suchanfrage, sowie eine an den EHR-Standard angepasste XQuery konvertiert. Durch die metadatenbasierte Suchanfrage werden alle für die Suchanfrage potentiell relevanten EHR-Dokumente geladen, die XQuery extrahiert die Suchergebnisse, die dann für den GDA aufbereitet und visualisiert werden. Ergänzend zur inhaltsbasierten Suche wird ein iterativer Archetypen-Entwicklungszyklus vorgestellt, um GDAs besser in die Entwicklung von Archetypen integrieren zu können, eine Methode zur Plug-and-Play-Formulargenerierung, um archetypkonforme EHR-Dokumente erzeugen zu können, sowie eine auf XML-Schema basierende Methode zur semantischen Validierung von EHR-Dokumenten. Mit dem iterativen Archetypen-Entwicklungszyklus wurden 133 ISO/EN 13606 Archetypen erzeugt. Die Plug-and-Play-Formulargenerierung wurde in einer Webapplikation umgesetzt, mittels derer 82 archetypbasierte ISO/EN 13606 EHR-Extrakte erzeugt wurden. Im Zuge der Evaluierung wurde die inhaltsbasierte Suche von sieben GDAs als schnell und ausreichend stabil empfunden, sie sei intuitiver und schneller als die herkömmliche metadatenbasierte Suche in IHE XDS. Archetypen erwiesen sich für die in dieser Arbeit präsentierten Anwendungen als geeignet. Der vorgestellte Ansatz zur inhaltsbasierten Suche stellt eine punktuelle Erweiterung von IHE XDS dar und kann somit in existierende IHE XDS Umgebungen eingebunden werden, ohne bisherige Funktionalitäten zu beeinträchtigen. Die vorliegende Arbeit entstand im Kontext des FWF-Projekts EHR-Arche.Electronic Health Records (EHRs) should help to handle the increasing amount of data in the health care domain. Shared EHR systems are getting more important to enable an integrated care and the exchange of EHRs between health care providers on a regional and national level. In order to prevent information overload, efficient methods to find relevant information in large EHRs of a patient have to be developed. Current standards in the domain of structured EHRs (i.e. ISO/EN 13606, HL7 CDA, openEHR) apply the so called dual model approach. The EHR content is described by so called Archetypes (first model), which specify how the generic classes from the Reference Model (second layer) have to be assembled. The goal of this work is to show the potential of Archetypes in Shared EHR systems by means of five areas of application. The main focus of the present work is the implementation of a contentbased search in the widely-used Integrating the Health care Enterprise (IHE) Cross Enterprise Document Sharing Profile (IHE XDS) Shared EHR system architecture. This is an extension to the basic search function from IHE XDS, which only allows retrieval of complete documents by querying a restricted set of document metadata. In contrast the content-based search allows to search medical content within EHR documents and only the relevant parts of EHRs are returned. In the presented approach all EHR documents are described by Archetypes. Based on these Archetypes possible search terms are offered to the health care professional. The resulting search query is transformed into a standardized meta-data-based search query and a content-based XQuery using the knowledge about the structure of the EHR documents in the Archetypes. The meta-data-based search query is used to retrieve a set of potentially relevant EHR documents from the shared EHR system, the XQuery is used to extract the relevant parts from the EHR documents, which are then visualised. Additionally to the content-based search, an iterative archetype development cycle to support the participation of the health care professionals in the development of archetypes, a method for a plug-and-play case form generation to create archetype-based EHR documents as well as a method to semantically validate EHR documents using XML Schema are presented. Using the iterative archetype development cycle 133 ISO/EN 13606 Archetypes were developed. The plug-and-play case form generation was implemented in a web application and 82 archetype-based ISO/EN 13606 EHR extracts were created. They were the basis for an evaluation of the implemented content-based search in an IHE XDS environment. The evaluation of the implemented content-based search with seven health care professionals has been found fast and stable, it was found more intuitive and faster than conventional meta-data based search in IHE XDS. Archetypes have proven themselves adequate to solve the presented areas of application. The presented content-based search is an add-on to IHE XDS and can be incorporated into existing IHE XDS environments without affecting existing functionality. This work was written in the context of the FWF project EHR-Arche.18

Towards plug-and-play integration of archetypes into legacy electronic health record systems: the ArchiMed experience

Abstract Background The dual model approach represents a promising solution for achieving semantically interoperable standardized electronic health record (EHR) exchange. Its acceptance, however, will depend on the effort required for integrating archetypes into legacy EHR systems. Methods We propose a corresponding approach that: (a) automatically generates entry forms in legacy EHR systems from archetypes; and (b) allows the immediate export of EHR documents that are recorded via the generated forms and stored in the EHR systems’ internal format as standardized and archetype-compliant EHR extracts. As a prerequisite for applying our approach, we define a set of basic requirements for the EHR systems. Results We tested our approach with an EHR system called ArchiMed and were able to successfully integrate 15 archetypes from a test set of 27. For 12 archetypes, the form generation failed owing to a particular type of complex structure (multiple repeating subnodes), which was prescribed by the archetypes but not supported by ArchiMed’s data model. Conclusions Our experiences show that archetypes should be customized based on the planned application scenario before their integration. This would allow problematic structures to be dissolved and irrelevant optional archetype nodes to be removed. For customization of archetypes, openEHR templates or specialized archetypes may be employed. Gaps in the data types or terminological features supported by an EHR system will often not preclude integration of the relevant archetypes. More work needs to be done on the usability of the generated forms.</p

Analysis of collective human intelligence for diagnosis of pigmented skin lesions harnessed by gamification via a web-based training platform: simulation reader study

Background: The diagnosis of pigmented skin lesion is error prone and requires domain-specific expertise, which is not readily available in many parts of the world. Collective intelligence could potentially decrease the error rates of nonexperts.Objective: The aim of this study was to evaluate the feasibility and impact of collective intelligence for the detection of skin cancer.Methods: We created a gamified study platform on a stack of established Web technologies and presented 4216 dermatoscopic images of the most common benign and malignant pigmented skin lesions to 1245 human raters with different levels of experience. Raters were recruited via scientific meetings, mailing lists, and social media posts. Education was self-declared, and domain-specific experience was tested by screening tests. In the target test, the readers had to assign 30 dermatoscopic images to 1 of the 7 disease categories. The readers could repeat the test with different lesions at their own discretion. Collective human intelligence was achieved by sampling answers from multiple readers. The disease category with most votes was regarded as the collective vote per image.Results: We collected 111,019 single ratings, with a mean of 25.2 (SD 18.5) ratings per image. As single raters, nonexperts achieved a lower mean accuracy (58.6%) than experts (68.4%; mean difference=-9.4%; 95% CI -10.74% to -8.1%;