9 research outputs found
An adaptive physiology-aware communication framework for distributed medical cyber physical systems
For emergency medical cyber-physical systems, enhancing the safety and effectiveness of patient care, especially in remote rural areas, is essential. While the doctor to patient ratio in the United States is 30 to 10,000 in large metropolitan areas, it is only 5 to 10,000 in most rural areas; and the highest death rates are often found in the most rural counties.
Use of telecommunication technologies can enhance effectiveness and safety of emergency ambulance transport of patients from rural areas to a regional center hospital. It enables remote monitoring of patients by the physician experts at the tertiary center. There are critical times during transport when physician experts can provide vital assistance to the ambulance Emergency Medical Technicians (EMT) to associate best treatments. However, the communication along the roads in rural areas can range irregularly from 4G to low speed 2G links, including some parts of routes with cellular network communication breakage. This unreliable and limited communication bandwidth together with the produced mass of clinical data and the many information exchanges pose a major challenge in real-time supervision of patients.
In this study, we define the notion of distributed emergency care, and propose a novel adaptive physiology-aware communication framework which is aware of the patient condition, the underlying network bandwidth, and the criticality of clinical data in the context of the specific diseases. Using the concept of distributed medical CPS models, we study the semantics relation of communication Quality of Service (QoS) with clinical messages, criticality of clinical data, and an ambulance's undertaken route all in a disease-aware manner. Our proposed communication framework is aimed to enhance remote monitoring of acute patients during ambulance transport from a rural hospital to a regional center hospital. We evaluate the components of our framework through various experimentation phases including simulation, instrumentation, real-world profiling, and validation
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
Development and implementation of clinical guidelines : an artificial intelligence perspective
Clinical practice guidelines in paper format are still the preferred form of delivery of medical knowledge and recommendations to healthcare professionals. Their current support and development process have well identified limitations to which the healthcare community has been continuously searching solutions. Artificial intelligence may create the conditions and provide the tools to address many, if not all, of these limitations.. This paper presents a comprehensive and up to date review of computer-interpretable guideline approaches, namely Arden Syntax, GLIF, PROforma, Asbru, GLARE and SAGE. It also provides an assessment of how well these approaches respond to the challenges posed by paper-based guidelines and addresses topics of Artificial intelligence that could provide a solution to the shortcomings of clinical guidelines. Among the topics addressed by this paper are expert systems, case-based reasoning, medical ontologies and reasoning under uncertainty, with a special focus on methodologies for assessing quality of information when managing incomplete information. Finally, an analysis is made of the fundamental requirements of a guideline model and the importance that standard terminologies and models for clinical data have in the semantic and syntactic interoperability between a guideline execution engine and the software tools used in clinical settings. It is also proposed a line of research that includes the development of an ontology for clinical practice guidelines and a decision model for a guideline-based expert system that manages non-compliance with clinical guidelines and uncertainty.This work is funded by national funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project PEst-OE/EEI/UI0752/2011"
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
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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
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