Designing an automated clinical decision support system to match clinical practice guidelines for opioid therapy for chronic pain

Abstract Background Opioid prescribing for chronic pain is common and controversial, but recommended clinical practices are followed inconsistently in many clinical settings. Strategies for increasing adherence to clinical practice guideline recommendations are needed to increase effectiveness and reduce negative consequences of opioid prescribing in chronic pain patients. Methods Here we describe the process and outcomes of a project to operationalize the 2003 VA/DOD Clinical Practice Guideline for Opioid Therapy for Chronic Non-Cancer Pain into a computerized decision support system (DSS) to encourage good opioid prescribing practices during primary care visits. We based the DSS on the existing ATHENA-DSS. We used an iterative process of design, testing, and revision of the DSS by a diverse team including guideline authors, medical informatics experts, clinical content experts, and end-users to convert the written clinical practice guideline into a computable algorithm to generate patient-specific recommendations for care based upon existing information in the electronic medical record (EMR), and a set of clinical tools. Results The iterative revision process identified numerous and varied problems with the initially designed system despite diverse expert participation in the design process. The process of operationalizing the guideline identified areas in which the guideline was vague, left decisions to clinical judgment, or required clarification of detail to insure safe clinical implementation. The revisions led to workable solutions to problems, defined the limits of the DSS and its utility in clinical practice, improved integration into clinical workflow, and improved the clarity and accuracy of system recommendations and tools. Conclusions Use of this iterative process led to development of a multifunctional DSS that met the approval of the clinical practice guideline authors, content experts, and clinicians involved in testing. The process and experiences described provide a model for development of other DSSs that translate written guidelines into actionable, real-time clinical recommendations.http://deepblue.lib.umich.edu/bitstream/2027.42/78267/1/1748-5908-5-26.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/2/1748-5908-5-26.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/3/1748-5908-5-26-S3.TIFFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/4/1748-5908-5-26-S2.TIFFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/5/1748-5908-5-26-S1.TIFFPeer Reviewe

Multi-attribute decision making with weighted description logics

We introduce a decision-theoretic framework based on Description Logics (DLs), which can be used to encode and solve single stage multi-attribute decision problems. In particular, we consider the background knowledge as a DL knowledge base where each attribute is represented by a concept, weighted by a utility value which is asserted by the user. This yields a compact representation of preferences over attributes. Moreover, we represent choices as knowledge base individuals, and induce a ranking via the aggregation of attributes that they satisfy. We discuss the benefits of the approach from a decision theory point of view. Furthermore, we introduce an implementation of the framework as a Protégé plugin called uDecide. The plugin takes as input an ontology as background knowledge, and returns the choices consistent with the user’s (the knowledge base) preferences. We describe a use case with data from DBpedia. We also provide empirical results for its performance in the size of the ontology using the reasoner Konclude

Ontology Application for the Al-Quran

Knowledge is related to data and information that already modified. Knowledge management can be shared and interlinked. For broader scope, it can be used to structure the ontology. The use of ontology on knowledge helps users to understand the structure easier. The important matter in ontology is represented by showing the relation among classes in domain. When the relation is already identified, then the structure of ontology for the Al-Quran can be modeled. Protégé is used to model the structure of ontology. Then, the pray visualization form will be produced. This structure of ontology will assists in sharing knowledge

Automated clinical decision model construction from knowledge-based GLIF guideline models

Master'sMASTER OF ENGINEERIN

Guideline-based decision support in medicine : modeling guidelines for the development and application of clinical decision support systems

Guideline-based Decision Support in Medicine Modeling Guidelines for the Development and Application of Clinical Decision Support Systems The number and use of decision support systems that incorporate guidelines with the goal of improving care is rapidly increasing. Although developing systems that are both effective in supporting clinicians and accepted by them has proven to be a difficult task, of the systems that were evaluated by a controlled trial, the majority showed impact. The work, described in this thesis, aims at developing a methodology and framework that facilitates all stages in the guideline development process, ranging from the definition of models that represent guidelines to the implementation of run-time systems that provide decision support, based on the guidelines that were developed during the previous stages. The framework consists of 1) a guideline representation formalism that uses the concepts of primitives, Problem-Solving Methods (PSMs) and ontologies to represent guidelines of various complexity and granularity and different application domains, 2) a guideline authoring environment that enables guideline authors to define guidelines, based on the newly developed guideline representation formalism, and 3) a guideline execution environment that translates defined guidelines into a more efficient symbol-level representation, which can be read in and processed by an execution-time engine. The described methodology and framework were used to develop and validate a number of guidelines and decision support systems in various clinical domains such as Intensive Care, Family Practice, Psychiatry and the areas of Diabetes and Hypertension control

An extended HD Fluent Analysis of Temporal knowledge in OWL-based clinical Guideline System

The Web Ontology Language (OWL) based clinical guideline system is a kind of clinical decision support system which is often used to assist health professionals to find clinical recommendations from the guidelines and check clinical compliance issues in terms of the guideline recommendations. However, due to some limitations of the current OWL language constructs, temporal knowledge contained in various knowledge domains cannot be directly represented in OWL. As a result, the representation, query and reasoning of temporal knowledge are largely ignored in many OWL-based clinical guideline ontology systems. The aim of this research is to investigate a temporal knowledge modelling method namely “4D fluent” and extend it to represent the temporal constraints contained in clinical guideline recommendations within OWL language constructs. The extended 4D fluent method can model temporal constraints including valid calendar time, interval, duration, repetitive or cyclical temporal constraints and temporal relations such that it can enable reasoning over these temporal constraints in the OWL-based clinical guideline ontology system and overcome the shortcoming of the traditional OWL-based clinical guideline system to an extent

Combining ontologies and rules with clinical archetypes

Al igual que otros campos que dependen en gran medida de las funcionalidades ofrecidas por las tecnologías de la información y las comunicaciones (IT), la biomedicina y la salud necesitan cada vez más la implantación de normas y mecanismos ampliamente aceptados para el intercambio de datos, información y conocimiento. Dicha necesidad de compatibilidad e interoperabilidad va más allá de las cuestiones sintácticas y estructurales, pues la interoperabilidad semántica es también requerida. La interoperabilidad a nivel semántico es esencial para el soporte computarizado de alertas, flujos de trabajo y de la medicina basada en evidencia cuando contamos con la presencia de sistemas heterogéneos de Historia Clínica Electrónica (EHR). El modelo de arquetipos clínicos respaldado por el estándar CEN/ISO EN13606 y la fundación openEHR ofrece un mecanismo para expresar las estructuras de datos clínicos de manera compartida e interoperable. El modelo ha ido ganando aceptación en los últimos años por su capacidad para definir conceptos clínicos basados en un Modelo de Referencia común. Dicha separación a dos capas permite conservar la heterogeneidad de las implementaciones de almacenamiento a bajo nivel, presentes en los diferentes sistemas de EHR. Sin embargo, los lenguajes de arquetipos no soportan la representación de reglas clínicas ni el mapeo a ontologías formales, ambos elementos fundamentales para alcanzar la interoperabilidad semántica completa pues permiten llevar a cabo el razonamiento y la inferencia a partir del conocimiento clínico existente. Paralelamente, es reconocido el hecho de que la World Wide Web presenta requisitos análogos a los descritos anteriormente, lo cual ha fomentado el desarrollo de la Web Semántica. El progreso alcanzado en este terreno, con respecto a la representación del conocimiento y al razonamiento sobre el mismo, es combinado en esta tesis con los modelos de EHR con el objetivo de mejorar el enfoque de los arquetipos clínicos y ofrecer funcionalidades que se corresponden con nivel más alto de interoperabilidad semántica. Concretamente, la investigación que se describe a continuación presenta y evalúa un enfoque para traducir automáticamente las definiciones expresadas en el lenguaje de definición de arquetipos de openEHR (ADL) a una representación formal basada en lenguajes de ontologías. El método se implementa en la plataforma ArchOnt, que también es descrita. A continuación se estudia la integración de dichas representaciones formales con reglas clínicas, ofreciéndose un enfoque para reutilizar el razonamiento con instancias concretas de datos clínicos. Es importante ver como el acto de compartir el conocimiento clínico expresado a través de reglas es coherente con la filosofía de intercambio abierto fomentada por los arquetipos, a la vez que se extiende la reutilización a proposiciones de conocimiento declarativo como las utilizadas en las guías de práctica clínica. De esta manera, la tesis describe una técnica de mapeo de arquetipos a ontologías, para luego asociar reglas clínicas a la representación resultante. La traducción automática también permite la conexión formal de los elementos especificados en los arquetipos con conceptos clínicos equivalentes provenientes de otras fuentes como son las terminologías clínicas. Dichos enlaces fomentan la reutilización del conocimiento clínico ya representado, así como el razonamiento y la navegación a través de distintas ontologías clínicas. Otra contribución significativa de la tesis es la aplicación del enfoque mencionado en dos proyectos de investigación y desarrollo clínico, llevados a cabo en combinación con hospitales universitarios de Madrid. En la explicación se incluyen ejemplos de las aplicaciones más representativas del enfoque como es el caso del desarrollo de sistemas de alertas orientados a mejorar la seguridad del paciente. No obstante, la traducción automática de arquetipos clínicos a lenguajes de ontologías constituye una base común para la implementación de una amplia gama de actividades semánticas, razonamiento y validación, evitándose así la necesidad de aplicar distintos enfoques ad-hoc directamente sobre los arquetipos para poder satisfacer las condiciones de cada contexto

An ontological clinical decision support system based on clinical guidelines for diabetes patients in Sri Lanka

Health professionals should follow the clinical guidelines to decrease healthcare costs to avoid unnecessary testing and to minimize the variations among healthcare providers. In addition, this will minimize the mistakes in diagnosis and treatment processes. To this end, it is possible to use Clinical Decision Support Systems that implement the clinical guidelines. Clinical guidelines published by international associations are not suitable for developing countries such as Sri Lanka, due to the economic background, lack of resources, and unavailability of some laboratory tests. Hence, a set of clinical guidelines has been formulated based on the various published international professional organizations from a Sri Lankan context. Furthermore, these guidelines are usually presented in non-computer-interpretable narrative text or non-executable flow chart formats. In order to fill this gap, this research study finds a suitable approach to represent/organize the clinical guidelines in a Sri Lankan context that is suitable to be used in a clinical decision support system. To this end, we introduced a novel approach which is an ontological model based on the clinical guidelines. As it is revealed that there are 4 million diabetes patients in Sri Lanka, which is approximately twenty percent of the total population, we used diabetes-related guidelines in this research. Firstly, conceptual models were designed to map the acquired diabetes-related clinical guidelines using Business Process Model and Notation 2.0. Two models were designed in mapping the diagnosis process of Type 1 and Type 2 Diabetes, and Gestational diabetes. Furthermore, several conceptual models were designed to map the treatment plans in guidelines by using flowcharting. These designs were validated by domain experts by using questionnaires. Grüninger and Fox’s method was used to design and evaluate the ontology based on the designed conceptual models. Domain experts’ feedback and several real-life diabetic scenarios were used to validate and evaluate the developed ontology. The evaluation results show that all suggested answers based on the proposed ontological model are accurate and well addressed with respect to the real-world scenarios. A clinical decision support system was implemented based on the ontological knowledge base using the Jena Framework, and this system can be used to access the diabetic information and knowledge in the Sri Lankan context. However, this contribution is not limited to diabetes or a local context, and can be applied to any disease or any context

OntoPlot: A Novel Visualisation for Non-hierarchical Associations in Large Ontologies

Ontologies are formal representations of concepts and complex relationships among them. They have been widely used to capture comprehensive domain knowledge in areas such as biology and medicine, where large and complex ontologies can contain hundreds of thousands of concepts. Especially due to the large size of ontologies, visualisation is useful for authoring, exploring and understanding their underlying data. Existing ontology visualisation tools generally focus on the hierarchical structure, giving much less emphasis to non-hierarchical associations. In this paper we present OntoPlot, a novel visualisation specifically designed to facilitate the exploration of all concept associations whilst still showing an ontology's large hierarchical structure. This hybrid visualisation combines icicle plots, visual compression techniques and interactivity, improving space-efficiency and reducing visual structural complexity. We conducted a user study with domain experts to evaluate the usability of OntoPlot, comparing it with the de facto ontology editor Prot{\'e}g{\'e}. The results confirm that OntoPlot attains our design goals for association-related tasks and is strongly favoured by domain experts.Comment: Accepted at IEEE InfoVis 201