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

Information Systems and Healthcare XXXIV: Clinical Knowledge Management Systems—Literature Review and Research Issues for Information Systems

Knowledge Management (KM) has emerged as a possible solution to many of the challenges facing U.S. and international healthcare systems. These challenges include concerns regarding the safety and quality of patient care, critical inefficiency, disparate technologies and information standards, rapidly rising costs and clinical information overload. In this paper, we focus on clinical knowledge management systems (CKMS) research. The objectives of the paper are to evaluate the current state of knowledge management systems diffusion in the clinical setting, assess the present status and focus of CKMS research efforts, and identify research gaps and opportunities for future work across the medical informatics and information systems disciplines. The study analyzes the literature along two dimensions: (1) the knowledge management processes of creation, capture, transfer, and application, and (2) the clinical processes of diagnosis, treatment, monitoring and prognosis. The study reveals that the vast majority of CKMS research has been conducted by the medical and health informatics communities. Information systems (IS) researchers have played a limited role in past CKMS research. Overall, the results indicate that there is considerable potential for IS researchers to contribute their expertise to the improvement of clinical process through technology-based KM approaches

Ocorrencia de hipotermia não planejada em sala de recuperação pós-anestésica

Trabalho de Conclusão de Curso - Universidade Federal de Santa Catarina, Centro de Ciências da Saúde, Departamento de Enfermage

MuCIGREF: multiple computer-interpretable guideline representation and execution framework for managing multimobidity care

Clinical Practice Guidelines (CPGs) supply evidence-based recommendations to healthcare professionals (HCPs) for the care of patients. Their use in clinical practice has many benefits for patients, HCPs and treating medical centres, such as enhancing the quality of care, and reducing unwanted care variations. However, there are many challenges limiting their implementations. Initially, CPGs predominantly consider a specific disease, and only few of them refer to multimorbidity (i.e. the presence of two or more health conditions in an individual) and they are not able to adapt to dynamic changes in patient health conditions. The manual management of guideline recommendations are also challenging since recommendations may adversely interact with each other due to their competing targets and/or they can be duplicated when multiple of them are concurrently applied to a multimorbid patient. These may result in undesired outcomes such as severe disability, increased hospitalisation costs and many others. Formalisation of CPGs into a Computer Interpretable Guideline (CIG) format, allows the guidelines to be interpreted and processed by computer applications, such as Clinical Decision Support Systems (CDSS). This enables provision of automated support to manage the limitations of guidelines. This thesis introduces a new approach for the problem of combining multiple concurrently implemented CIGs and their interrelations to manage multimorbidity care. MuCIGREF (Multiple Computer-Interpretable Guideline Representation and Execution Framework), is proposed whose specific objectives are to present (1) a novel multiple CIG representation language, MuCRL, where a generic ontology is developed to represent knowledge elements of CPGs and their interrelations, and to create the multimorbidity related associations between them. A systematic literature review is conducted to discover CPG representation requirements and gaps in multimorbidity care management. The ontology is built based on the synthesis of well-known ontology building lifecycle methodologies. Afterwards, the ontology is transformed to a metamodel to support the CIG execution phase; and (2) a novel real-time multiple CIG execution engine, MuCEE, where CIG models are dynamically combined to generate consistent and personalised care plans for multimorbid patients. MuCEE involves three modules as (i) CIG acquisition module, transfers CIGs to the personal care plan based on the patient’s health conditions and to supply CIG version control; (ii) parallel CIG execution module, combines concurrently implemented multiple CIGs by performing concurrency management, time-based synchronisation (e.g., multi-activity merging), modification, and timebased optimisation of clinical activities; and (iii) CIG verification module, checks missing information, and inconsistencies to support CIG execution phases. Rulebased execution algorithms are presented for each module. Afterwards, a set of verification and validation analyses are performed involving real-world multimorbidity cases studies and comparative analyses with existing works. The results show that the proposed framework can combine multiple CIGs and dynamically merge, optimise and modify multiple clinical activities of them involving patient data. This framework can be used to support HCPs in a CDSS setting to generate unified and personalised care recommendations for multimorbid patients while merging multiple guideline actions and eliminating care duplications to maintain their safety and supplying optimised health resource management, which may improve operational and cost efficiency in real world-cases, as well

Применение методов клинической информатики в комплексных исследованиях и лечении больных : учебное пособие

Одним из прикладных разделов медицинской информатики, ориентированным в первую очередь на клиническое использование, является клиническая информатика. По определению Э. Шортлиффа, клиническая информатика– наука, являющаяся комбинацией информатики, вычислительной техники и клинических дисциплин, призванная содействовать получению и обработке медицинских данных, информациии знанийс целью повышения эффективности медицинской помощи. Задачи клинической информатики сфокусированы на компьютерных приложениях для обработки, анализа и представления медицинских данных. Разделы клинической информатики охватывают следующие виды деятельности: • электронные истории болезни ; • медицинские информационные системы; • системы поддержки принятия решений и экспертные системы; • технологии Medical Data Mining. Большая часть настоящего учебного пособия посвящена описанию методов и инструментов именно клинической информатики. Отдельный раздел посвящен бурно развивающимся в настоящее время телемедицинским технологиям. Предлагаемый учебный курс, равно как и данное учебное пособие, предназначен прежде всего для специалистов в области медицинской информатики, технических специалистов медицинских учреждений, исследователей в области медицины. Тем не менее он может быть полезен и для практикующих врачей, а также для студентов старших курсов высших медицинских учебных заведений. В текст не вошли разделы, относящиеся к так называемой «базовой» информатике, так как читатель, на наш взгляд, имеет определенные представления в этой области

Use of Decision Tables to Model Assistance Knowledge to Train Medical Residents

En aquesta tesi es presenta un model de coneixement clínic basat en taules de decisió que permet representar les fases de diagnòstic, tractament i pronòstic de diferents malalties. Les taules de decisió que s'obtenen per a cada fase del model han estat utilitzades per representar malalties reals a partir de guies de pràctica clínica. En el cas del diagnòstic s'han representat les vuit causes secundàries més comuns de la hipertensió arterial. En el cas del tractament i pronòstic s'han representat set diferents xocs en emergències. Les taules de decisió que hem obtingut per a cadascuna de les malalties s'han utilitzat com a base per crear dues eines d'entrenament mèdic, dirigides a residents. Totes dues eines s'han provat a l'Hospital Clínic de Barcelona amb diferents grups de residents. Després de les proves s'ha conclòs que les taules de decisió són adequades per a la representació del coneixement mèdic en totes tres fases. A més, les eines d'aprenentatge han estat efectives a l'hora d'ensenyar els procediments mèdics, especialment als residents amb menys experiència prèvia.En esta tesis se presenta un modelo de conocimiento clínico basado en tablas de decisión que permite representar las fases de diagnostico, tratamiento y pronostico de distintas enfermedades. Las tablas de decisión que se obtienen para cada fase del modelo han sido utilizadas para representar enfermedades reales a partir de guías de práctica clínica. En el caso del diagnóstico se han representado las ocho causas secundarias más comunes de la hipertensión arterial. En el caso del tratamiento y pronóstico se han representado siete diferentes shocks en emergencias. Las tablas de decisión que hemos obtenido para cada una de las enfermedades se han usado como base para crear dos herramientas de entrenamiento médico, dirigido a residentes. Ambas herramientas se han probado en el Hospital Clínic de Barcelona con distintos grupos de residentes. Tras las pruebas se ha concluido que las tablas de decisión son adecuadas para la representación del conocimiento medico en las tres fases. Además, las herramientas de aprendizaje han sido efectivas a la hora de enseñar los procedimientos médicos, en especial a los residentes con menos experiencia previa.In this thesis a clinical knowledge model based on decision tables is presented. This model allows us to represent the stages of diagnosis, treatment, and prognosis of different diseases. The decision tables obtained for each phase of the model have been used to represent real diseases from clinical practice guidelines. In the case of diagnosis, we represented eight of the most common secondary causes of hypertension. For the treatment and prognosis we represented seven different emergency shocks. The decision tables obtained for each disease have been used as the basis for two medical training tools aimed to residents. Both tools have been tested in the Hospital Clínic de Barcelona with different groups of residents. After testing, it was concluded that decision tables are suitable for the representation of medical knowledge in all three phases. In addition, the learning tools have been effective in teaching medical procedures, especially for untrained residents

Agent-based management of clinical guidelines

Les guies de pràctica clínica (GPC) contenen un conjunt d'accions i dades que ajuden a un metge a prendre decisions sobre el diagnòstic, tractament o qualsevol altre procediment a un pacient i sobre una determinada malaltia. És conegut que l'adopció d'aquestes guies en la vida diària pot millorar l'assistència mèdica als pacients, pel fet que s'estandarditzen les pràctiques. Sistemes computeritzats que utilitzen GPC poden constituir part de sistemes d'ajut a la presa de decisions més complexos amb la finalitat de proporcionar el coneixement adequat a la persona adequada, en un format correcte i en el moment precís. L'automatització de l'execució de les GPC és el primer pas per la seva implantació en els centres mèdics.Per aconseguir aquesta implantació final, hi ha diferents passos que cal solucionar com per exemple, l'adquisició i representació de les GPC, la seva verificació formal, i finalment la seva execució. Aquesta Tesi està dirigida en l'execució de GPC i proposa la implementació d'un sistema multi-agent. En aquest sistema els diferents actors dels centres mèdics coordinen les seves activitats seguint un pla global determinat per una GPC. Un dels principals problemes de qualsevol sistema que treballa en l'àmbit mèdic és el tractament del coneixement. En aquest cas s'han hagut de tractar termes mèdics i organitzatius, que s'ha resolt amb la implementació de diferents ontologies. La separació de la representació del coneixement del seu ús és intencionada i permet que el sistema d'execució de GPC sigui fàcilment adaptable a les circumstàncies concretes dels centres, on varien el personal i els recursos disponibles.En paral·lel a l'execució de GPC, el sistema proposat manega preferències del pacient per tal d'implementar serveis adaptats al pacient. En aquesta àrea concretament, a) s'han definit un conjunt de criteris, b) aquesta informació forma part del perfil de l'usuari i serveix per ordenar les propostes que el sistema li proposa, i c) un algoritme no supervisat d'aprenentatge permet adaptar les preferències del pacient segons triï.Finalment, algunes idees d'aquesta Tesi actualment s'estan aplicant en dos projectes de recerca. Per una banda, l'execució distribuïda de GPC, i per altra banda, la representació del coneixement mèdic i organitzatiu utilitzant ontologies.Clinical guidelines (CGs) contain a set of directions or principles to assist the health care practitioner with patient care decisions about appropriate diagnostic, therapeutic, or other clinical procedures for specific clinical circumstances. It is widely accepted that the adoption of guideline-execution engines in daily practice would improve the patient care, by standardising the care procedures. Guideline-based systems can constitute part of a knowledge-based decision support system in order to deliver the right knowledge to the right people in the right form at the right time. The automation of the guideline execution process is a basic step towards its widespread use in medical centres.To achieve this general goal, different topics should be tackled, such as the acquisition of clinical guidelines, its formal verification, and finally its execution. This dissertation focuses on the execution of CGs and proposes the implementation of an agent-based platform in which the actors involved in health care coordinate their activities to perform the complex task of guideline enactment. The management of medical and organizational knowledge, and the formal representation of the CGs, are two knowledge-related topics addressed in this dissertation and tackled through the design of several application ontologies. The separation of the knowledge from its use is fully intentioned, and allows the CG execution engine to be easily customisable to different medical centres with varying personnel and resources.In parallel with the execution of CGs, the system handles citizen's preferences and uses them to implement patient-centred services. With respect this issue, the following tasks have been developed: a) definition of the user's criteria, b) use of the patient's profile to rank the alternatives presented to him, c) implementation of an unsupervised learning method to adapt dynamically and automatically the user's profile.Finally, several ideas of this dissertation are being directly applied in two ongoing funded research projects, including the agent-based execution of CGs and the ontological management of medical and organizational knowledge