Solution for the management of several clinical practice guidelines in a domain-independent decision support system

Malnutrition is a very frequent and serious problem in humans, even more in the elderly. Advanced age brings with it a series of physiological (e.g., swallowing or chewing problems) and psychological changes that can be considered risk factors for malnutrition. It is triggered by loss, dependency, loneliness, and chronic illness, and potentially impacts on higher morbidity, mortality and the worsen of the quality of life. Without intervention, it presents as a downward trajectory leading to poor health and decreased quality of life. That is why it is essential to assess whether a risk situation exists and to evaluate to what extent it can be evitable. Therefore, the main objective of this work is to provide nutritional recommendations through a decision support system considering not only the different nutritional needs, also the whole environment of an elderly patient, such as socio-demographic and economic factors (sex, marital status, education...), psychosocial factors (social relationships, family, physical exercise...), and morbidity factors (diseases). Having this in mind, the aim of this work is to provide the most personalized nutritional recommendations. For this purpose, Clinical Practice Guidelines have been formalized along with experienced nutritionists on the domain within the NUTRIGEP project. The project consists of a product that, in addition to predicting the risk of malnutrition, can prevent it in the geriatric environment, contributing to the good nutritional management of the elderly in order to improve their health condition. Physically it consists of a back-end and a frontend for clinicians and nutritionists, which conceives an integrated solution to support the healthcare professional on the one hand, and to guide the nutritionist in developing personalized diets and preventing malnutrition on the other hand. This work has been focused on the back-end part of this application for the creation, evaluation and management of different rules, using the Drools rule engine and leaning on decision flows, which helped us generating personalized recommendations for patients affected by either one or more pathologies at the same time

Towards a Learning Health System: a SOA based platform for data re-use in chronic infectious diseases

Abstract Information and Communication Technology (ICT) tools can efficiently support clinical research by providing means to collect automatically huge amount of data useful for the management of clinical trials conduction. Clinical trials are indispensable tools for Evidence-Based Medicine and represent the most prevalent clinical research activity. Clinical trials cover only a restricted part of the population that respond to particular and strictly controlled requirements, offering a partial view of the overall patients\u2019 status. For instance, it is not feasible to consider patients with comorbidities employing only one kind of clinical trial. Instead, a system that have a comprehensive access to all the clinical data of a patient would have a global view of all the variables involved, reflecting real-world patients\u2019 experience. The Learning Health System is a system with a broader vision, in which data from various sources are assembled, analyzed by various means and then interpreted. The Institute of Medicine (IOM) provides this definition: \u201cIn a Learning Health System, progress in science, informatics, and care culture align to generate new knowledge as an ongoing, natural by-product of the care experience, and seamlessly refine and deliver best practices for continuous improvement in health and health care\u201d. The final goal of my project is the realization of a platform inspired by the idea of Learning Health System, which will be able to re-use data of different nature coming from widespread health facilities, providing systematic means to learn from clinicians\u2019 experience to improve both the efficiency and the quality of healthcare delivery. The first approach is the development of a SOA-based architecture to enable data collection from sparse facilities into a single repository, to allow medical institutions to share information without an increase in costs and without the direct involvement of users. Through this architecture, every single institution would potentially be able to participate and contribute to the realization of a Learning Health System, that can be seen as a closed cycle constituted by a sequential process of transforming patient-care data into knowledge and then applying this knowledge to clinical practice. Knowledge, that can be inferred by re-using the collected data to perform multi-site, practice-based clinical trials, could be concretely applied to clinical practice through Clinical Decision Support Systems (CDSS), which are instruments that aim to help physicians in making more informed decisions. With 4 this objective, the platform developed not only supports clinical trials execution, but also enables data sharing with external research databases to participate in wider clinical trials also at a national level without effort. The results of these studies, integrated with existing guidelines, can be seen as the knowledge base of a decision support system. Once designed and developed, the adoption of this system for chronical infective diseases management at a regional level helped in unifying data all over the Ligurian territory and actively monitor the situation of specific diseases (like HIV, HCV and HBV) for which the concept of retention in care assumes great importance. The use of dedicated standards is essential to grant the necessary level of interoperability among the structures involved and to allow future extensions to other fields. A sample scenario was created to support antiretroviral drugs prescription in the Ligurian HIV Network setting. It was thoroughly tested by physicians and its positive impact on clinical care was measured in terms of improvements in patients\u2019 quality of life, prescription appropriateness and therapy adherence. The benefits expected from the employment of the system developed were verified. Student\u2019s T test was used to establish if significant differences were registered between data collected before and after the introduction of the system developed. The results were really acceptable with the minimum p value in the order of 10 125 and the maximum in the order of 10 123. It is reasonable to assess that the improvements registered in the three analysis considered are ascribable to this system introduction and not to other factors, because no significant differences were found in the period before its release. Speed is a focal point in a system that provides decision support and it is highly recognized the importance of velocity optimization. Therefore, timings were monitored to evaluate the responsiveness of the system developed. Extremely acceptable results were obtained, with the waiting times of the order of 10 121 seconds. The importance of the network developed has been widely recognized by the medical staff involved, as it is also assessed by a questionnaire they compiled to evaluate their level of satisfaction

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

Clinical decision support: Knowledge representation and uncertainty management

Programa Doutoral em Engenharia BiomédicaDecision-making in clinical practice is faced with many challenges due to the inherent risks of being a health care professional. From medical error to undesired variations in clinical practice, the mitigation of these issues seems to be tightly connected to the adherence to Clinical Practice Guidelines as evidence-based recommendations The deployment of Clinical Practice Guidelines in computational systems for clinical decision support has the potential to positively impact health care. However, current approaches to Computer-Interpretable Guidelines evidence a set of issues that leave them wanting. These issues are related with the lack of expressiveness of their underlying models, the complexity of knowledge acquisition with their tools, the absence of support to the clinical decision making process, and the style of communication of Clinical Decision Support Systems implementing Computer-Interpretable Guidelines. Such issues pose as obstacles that prevent these systems from showing properties like modularity, flexibility, adaptability, and interactivity. All these properties reflect the concept of living guidelines. The purpose of this doctoral thesis is, thus, to provide a framework that enables the expression of these properties. The modularity property is conferred by the ontological definition of Computer-Interpretable Guidelines and the assistance in guideline acquisition provided by an editing tool, allowing for the management of multiple knowledge patterns that can be reused. Flexibility is provided by the representation primitives defined in the ontology, meaning that the model is adjustable to guidelines from different categories and specialities. On to adaptability, this property is conferred by mechanisms of Speculative Computation, which allow the Decision Support System to not only reason with incomplete information but to adapt to changes of state, such as suddenly knowing the missing information. The solution proposed for interactivity consists in embedding Computer-Interpretable Guideline advice directly into the daily life of health care professionals and provide a set of reminders and notifications that help them to keep track of their tasks and responsibilities. All these solutions make the CompGuide framework for the expression of Clinical Decision Support Systems based on Computer-Interpretable Guidelines.A tomada de decisão na prática clínica enfrenta inúmeros desafios devido aos riscos inerentes a ser um profissional de saúde. Desde o erro medico até às variações indesejadas da prática clínica, a atenuação destes problemas parece estar intimamente ligada à adesão a Protocolos Clínicos, uma vez que estes são recomendações baseadas na evidencia. A operacionalização de Protocolos Clínicos em sistemas computacionais para apoio à decisão clínica apresenta o potencial de ter um impacto positivo nos cuidados de saúde. Contudo, as abordagens atuais a Protocolos Clínicos Interpretáveis por Maquinas evidenciam um conjunto de problemas que as deixa a desejar. Estes problemas estão relacionados com a falta de expressividade dos modelos que lhes estão subjacentes, a complexidade da aquisição de conhecimento utilizando as suas ferramentas, a ausência de suporte ao processo de decisão clínica e o estilo de comunicação dos Sistemas de Apoio à Decisão Clínica que implementam Protocolos Clínicos Interpretáveis por Maquinas. Tais problemas constituem obstáculos que impedem estes sistemas de apresentarem propriedades como modularidade, flexibilidade, adaptabilidade e interatividade. Todas estas propriedades refletem o conceito de living guidelines. O propósito desta tese de doutoramento é, portanto, o de fornecer uma estrutura que possibilite a expressão destas propriedades. A modularidade é conferida pela definição ontológica dos Protocolos Clínicos Interpretáveis por Maquinas e pela assistência na aquisição de protocolos fornecida por uma ferramenta de edição, permitindo assim a gestão de múltiplos padrões de conhecimento que podem ser reutilizados. A flexibilidade é atribuída pelas primitivas de representação definidas na ontologia, o que significa que o modelo é ajustável a protocolos de diferentes categorias e especialidades. Quanto à adaptabilidade, esta é conferida por mecanismos de Computação Especulativa que permitem ao Sistema de Apoio à Decisão não só raciocinar com informação incompleta, mas também adaptar-se a mudanças de estado, como subitamente tomar conhecimento da informação em falta. A solução proposta para a interatividade consiste em incorporar as recomendações dos Protocolos Clínicos Interpretáveis por Maquinas diretamente no dia a dia dos profissionais de saúde e fornecer um conjunto de lembretes e notificações que os auxiliam a rastrear as suas tarefas e responsabilidades. Todas estas soluções constituem a estrutura CompGuide para a expressão de Sistemas de Apoio à Decisão Clínica baseados em Protocolos Clínicos Interpretáveis por Máquinas.The work of the PhD candidate Tiago José Martins Oliveira is supported by a grant from FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) with the reference SFRH/BD/85291/ 2012

A Generic Approach to Supporting the Management of Computerised Clinical Guidelines and Protocols

Clinical guidelines or protocols (CGPs) are statements that are systematically developed for the purpose of guiding the clinician and the patient in making decisions about appropriate healthcare for specific clinical problems. Using CGPs is one of the most effective and proven ways to attaining improved quality, optimised resource utilisation, cost containment and reduced variation in healthcare practice. CGPs exist mainly as paper-based natural language statements, but are increasingly being computerised. Supporting computerised CGPs in a healthcare environment so that they are incorporated into the routine used daily by clinicians is complex and presents major information management challenges. This thesis contends that the management of computerised CGPs should incorporate their manipulation (operations and queries), in addition to their specification and execution, as part of a single unified management framework. The thesis applies modern advanced database technology to the task of managing computerised CGPs. The event-condition-action (ECA) rule paradigm is recognised to have a huge potential in supporting computerised CGPs. In this thesis, a unified generic framework, called SpEM and an approach, called MonCooS, were developed for enabling computerised CGPs, to be specified by using a specification language, called PLAN, which follows the ECA rule paradigm; executed by using a software mechanism based on the ECA mechanism within a modern database system, and manipulated by using a manipulation language, called TOPSQL. The MonCooS approach focuses on providing clinicians with assistance in monitoring and coordinating clinical interventions while leaving the reasoning task to domain experts. A proof-of-concepts system, TOPS, was developed to show that CGP management can be easily attained, within the SpEM framework, by using the MonCooS approach. TOPS is used to evaluate the framework and approach in a case study to manage a microalbuminuria protocol for diabetic patients. SpEM and MonCooS were found to be promising in supporting the full-scale management of information and knowledge for the computerised clinical protocol. Active capability within modern DBMS is still experiencing significant limitations in supporting some requirements of this application domain. These limitations lead to pointers for further improvements in database management system (DBMS) functionality for ECA rule support. The main contributions of this thesis are: a generic and unified framework for the management of CGPs; a general platform and an advanced software mechanism for the manipulation of information and knowledge in computerised CGPs; a requirement for further development of the active functionality within modern DBMS; and a case study for the computer-based management of microalbuminuria in diabetes patients

A pilot ontology for a large, diverse set of national health service healthcare quality indicators

Objectives: This project seeks to reduce duplication of effort in finding data for NHS healthcare quality indicators, to resolve issues identified in previous efforts to develop quality-monitoring ontologies and to identify areas for future computer-interpretable quality indicator development for the United Kingdom’s Department of Health and National Health Service (NHS). Outcomes will include specification of inclusion and exclusion criteria for a set of healthcare quality indicators, along with categorisation beyond screening and prevention and identification of levels of indicator relationships Methodology: Following an exploration of potential methods for ontology development, Methontology was the method chosen to develop the ontology. This involved a conceptual analysis to inform the development of an ontology for a 2009 set of healthcare quality indicators made available on the NHS Information Centre website. Indicators were categorised by NHS Dimension, NHS-specified clinical pathway and by United States Institute of Medicine purpose. Relationships between indicators were identified, as well as an initial set of inclusion and exclusion criteria. Protégé 3.4.1 was the platform used to develop a pilot ontology. Results: NHS quality indicators that share some of the same criteria were made searchable, along with broader and narrower related criteria. Up to six layers of inclusion and exclusion criteria were specified and incorporated into the ontology. Search capabilities were created for indicators originating from the same source and from more than one source, along with indicators assigned to specific care pathways. It was shown that indicators have purposes other than prevention and screening, rendering Arden Syntax, intended for computer-interpretable guidelines and previously tested on a specialised set of healthcare quality indicators, unsuitable for a large, diverse set of quality indicators. A large number, 222, of quality indicators with different purposes justified the development of a separate ontology. Conclusions: This ontology could reduce duplication of effort in finding data for NHS healthcare quality indicators. There is potential to link to components of queries currently in use in the NHS, as an interim step away from the need to develop separate queries for each indicator. Areas for future computer- interpretable quality indicator development include resolving Electronic Health Record compatibility issues and improved indicator metadata quality. The ontology could be useful to NHS indicator developers, NHS data xtractors and vendors of electronic health records who supply to the NHS