RESUMO
Adverse drug events (ADE) in a neonatal unit can be of great importance due to the underlying nature and the special characteristics of the patients. This paper presents our work on the development of a knowledge base (KB) for supporting the identification and prevention of ADEs. First, a literature review was conducted to identify ADEs observed through the use of the most commonly-used drugs in a specific neonatal unit. Then, the acquired knowledge was encoded according to an ontological data model developed for the representation of the specific facts for the neonatal unit. Finally, a rule-based prototype consisting of 164 rules was implemented in order to represent and simulate the inference procedure about preventing ADEs.
Assuntos
Algoritmos , Sistemas de Gerenciamento de Base de Dados , Bases de Dados de Produtos Farmacêuticos , Sistemas de Apoio a Decisões Clínicas , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/prevenção & controle , Terapia Intensiva Neonatal/métodos , Interface Usuário-Computador , Humanos , Recém-NascidoRESUMO
The primary aim of this work was the development of a uniform, contextualized and sustainable knowledge-based framework to support adverse drug event (ADE) prevention via Clinical Decision Support Systems (CDSSs). In this regard, the employed methodology involved first the systematic analysis and formalization of the knowledge sources elaborated in the scope of this work, through which an application-specific knowledge model has been defined. The entire framework architecture has been then specified and implemented by adopting Computer Interpretable Guidelines (CIGs) as the knowledge engineering formalism for its construction. The framework integrates diverse and dynamic knowledge sources in the form of rule-based ADE signals, all under a uniform Knowledge Base (KB) structure, according to the defined knowledge model. Equally important, it employs the means to contextualize the encapsulated knowledge, in order to provide appropriate support considering the specific local environment (hospital, medical department, language, etc.), as well as the mechanisms for knowledge querying, inference, sharing, and management. In this paper, we present thoroughly the establishment of the proposed knowledge framework by presenting the employed methodology and the results obtained as regards implementation, performance and validation aspects that highlight its applicability and virtue in medication safety.
Assuntos
Sistemas de Apoio a Decisões Clínicas , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/prevenção & controle , Bases de Conhecimento , HumanosRESUMO
Knowledge representation is an important part of knowledge engineering activities that is crucial for enabling knowledge sharing and reuse. In this regard, standardised formalisms and technologies play a significant role. Especially for the medical domain, where knowledge may be tacit, not articulated and highly diverse, the development and adoption of standardised knowledge representations is highly challenging and of outmost importance to achieve knowledge interoperability. To this end, this paper presents a research effort towards the standardised representation of a Knowledge Base (KB) encapsulating rule-based signals and procedures for Adverse Drug Event (ADE) prevention. The KB constitutes an integral part of Clinical Decision Support Systems (CDSSs) to be used at the point of care. The paper highlights the requirements at the domain of discourse with respect to knowledge representation, according to which GELLO (an HL7 and ANSI standard) has been adopted. Results of our prototype implementation are presented along with the advantages and the limitations introduced by the employed approach.
Assuntos
Inteligência Artificial , Sistemas de Apoio a Decisões Clínicas/organização & administração , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/prevenção & controle , Humanos , Bases de Conhecimento , Guias de Prática Clínica como Assunto , Vocabulário ControladoRESUMO
BACKGROUND: Obesity is a major public health problem globally and in Europe. The prevalence of childhood obesity is also soaring. Several parameters of the living environment are contributing to this increase, such as the density of fast food retailers, and thus, preventive health policies against childhood obesity must focus on the environment to which children are exposed. Currently, there are no systems in place to objectively measure the effect of living environment parameters on obesogenic behaviors and obesity. The H2020 project "BigO: Big Data Against Childhood Obesity" aims to tackle childhood obesity by creating new sources of evidence based on big data. OBJECTIVE: This paper introduces the Obesity Prevention dashboard (OPdashboard), implemented in the context of BigO, which offers an interactive data platform for the exploration of objective obesity-related behaviors and local environments based on the data recorded using the BigO mHealth (mobile health) app. METHODS: The OPdashboard, which can be accessed on the web, allows for (1) the real-time monitoring of children's obesogenic behaviors in a city area, (2) the extraction of associations between these behaviors and the local environment, and (3) the evaluation of interventions over time. More than 3700 children from 33 schools and 2 clinics in 5 European cities have been monitored using a custom-made mobile app created to extract behavioral patterns by capturing accelerometer and geolocation data. Online databases were assessed in order to obtain a description of the environment. The dashboard's functionality was evaluated during a focus group discussion with public health experts. RESULTS: The preliminary association outcomes in 2 European cities, namely Thessaloniki, Greece, and Stockholm, Sweden, indicated a correlation between children's eating and physical activity behaviors and the availability of food-related places or sports facilities close to schools. In addition, the OPdashboard was used to assess changes to children's physical activity levels as a result of the health policies implemented to decelerate the COVID-19 outbreak. The preliminary outcomes of the analysis revealed that in urban areas the decrease in physical activity was statistically significant, while a slight increase was observed in the suburbs. These findings indicate the importance of the availability of open spaces for behavioral change in children. Discussions with public health experts outlined the dashboard's potential to aid in a better understanding of the interplay between children's obesogenic behaviors and the environment, and improvements were suggested. CONCLUSIONS: Our analyses serve as an initial investigation using the OPdashboard. Additional factors must be incorporated in order to optimize its use and obtain a clearer understanding of the results. The unique big data that are available through the OPdashboard can lead to the implementation of models that are able to predict population behavior. The OPdashboard can be considered as a tool that will increase our understanding of the underlying factors in childhood obesity and inform the design of regional interventions both for prevention and treatment.
Assuntos
COVID-19 , Criança , Europa (Continente) , Grécia , Humanos , SARS-CoV-2 , SuéciaRESUMO
Adverse Drug Events (ADEs) are currently considered as a major public health issue, endangering patients' safety and causing significant healthcare costs. Several research efforts are currently concentrating on the reduction of preventable ADEs by employing Information Technology (IT) solutions, which aim to provide healthcare professionals and patients with relevant knowledge and decision support tools. In this context, we present a knowledge engineering approach towards the construction of a Knowledge-based System (KBS) regarded as the core part of a CDSS (Clinical Decision Support System) for ADE prevention, all developed in the context of the EU-funded research project PSIP (Patient Safety through Intelligent Procedures in Medication). In the current paper, we present the knowledge sources considered in PSIP and the implications they pose to knowledge engineering, the methodological approach followed, as well as the components defining the knowledge engineering framework based on relevant state-of-the-art technologies and representation formalisms.
Assuntos
Sistemas de Apoio a Decisões Clínicas , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/prevenção & controle , Bases de Conhecimento , Gestão da Segurança , HumanosRESUMO
Adverse Drug Events (ADE) represent a key problem in Public Health. The detection and prevention of ADE is a real challenge for hospitals and healthcare professionals. Healthcare Information and Communication Technologies can contribute to reduce the incidence of preventable ADE. During this workshop, we will discuss the various aspects of detection of ADE through methods like data and semantic mining in medical databases; the possibility of preventing ADE by using clinical decision support systems; the importance of Human Factors Engineering and the contextualization of knowledge. Examples and demonstrations will come from the European Project PSIP, devoted to the detection and prevention of ADE in Hospitals.
Assuntos
Erros de Medicação/prevenção & controle , Gestão da Segurança/métodos , Sistemas de Apoio a Decisões Clínicas , Educação , Humanos , Saúde Pública , Qualidade da Assistência à SaúdeRESUMO
BACKGROUND: Mobile health (mHealth) technology has the potential to play a key role in improving the health of patients with chronic non-communicable diseases. OBJECTIVES: We present a review of systematic reviews of mHealth in chronic disease management, by showing the features and outcomes of mHealth interventions, along with associated challenges in this rapidly growing field. METHODS: We searched the bibliographic databases of PubMed, Scopus, and Cochrane to identify systematic reviews of mHealth interventions with advanced technical capabilities (e.g., Internet-linked apps, interoperation with sensors, communication with clinical platforms, etc.) utilized in randomized clinical trials. The original studies included the reviews were synthesized according to their intervention features, the targeted diseases, the primary outcome, the number of participants and their average age, as well as the total follow-up duration. RESULTS: We identified 5 reviews respecting our inclusion and exclusion criteria, which examined 30 mHealth interventions. The highest percentage of the interventions targeted patients with diabetes (n = 19, 63%), followed by patients with psychotic disorders (n = 7, 23%), lung diseases (n = 3, 10%), and cardiovascular disease (n = 1, 3%). 14 studies showed effective results: 9 in diabetes management, 2 in lung function, and 3 in mental health. Significantly positive outcomes were reported in 8 interventions (n = 8, 47%) from 17 studies assessing glucose concentration, one intervention assessing physical activity, 2 interventions (n = 2, 67%) from 3 studies assessing lung function parameters, and 3 mental health interventions assessing N-back performance, medication adherence, and number of hospitalizations. Divergent features were adopted in 14 interventions with significantly positive outcomes, such as personalized goal setting (n = 10, 71%), motivational feedback (n = 5, 36%), and alerts for health professionals (n = 3, 21%). The most significant found challenges in the development and evaluation of mHealth interventions include the design of studies with high quality, the construction of robust interventions in combination with health professional inputs, and the identification of tools and methods to improve patient adherence. CONCLUSIONS: This review found mixed evidence regarding the health benefits of mHealth interventions for patients living with chronic diseases. Further rigorous studies are needed to assess the outcomes of personalized mHealth interventions toward the optimal management of chronic diseases.
Assuntos
Doença Crônica/terapia , Comunicação em Saúde , Cooperação do Paciente/estatística & dados numéricos , Telemedicina/estatística & dados numéricos , Gerenciamento Clínico , Humanos , Revisões Sistemáticas como Assunto , Telemedicina/métodos , Resultado do TratamentoRESUMO
Current healthcare systems are struggling with rising costs and unbalanced quality of care. Integrated care (IC) is a worldwide trend in healthcare reforms designed to tackle these problems. ACT@Scale is a partnership of leading European regions, industry and academia which aims to identify, transfer and scale-up existing integrated healthcare practices. In this context, participating programs are applying iterative process improvement cycles using collaborative methodologies. The vision of learning health systems (LHS) is similar to IC, but it focuses on IT means as a change enabler for rapid and continuous knowledge integration into better outcomes. In this paper, we present the ACT@Scale program as an example of an LHS that monitors integrated care performance and effects of process improvement cycles.
Assuntos
Atenção à Saúde , Reforma dos Serviços de Saúde , Humanos , AprendizagemRESUMO
BACKGROUND: Exercise-based rehabilitation plays a key role in improving the health and quality of life of patients with Cardiovascular Disease (CVD). Home-based computer-assisted rehabilitation programs have the potential to facilitate and support physical activity interventions and improve health outcomes. OBJECTIVES: We present the development and evaluation of a computerized Decision Support System (DSS) for unsupervised exercise rehabilitation at home, aiming to show the feasibility and potential of such systems toward maximizing the benefits of rehabilitation programs. METHODS: The development of the DSS was based on rules encapsulating the logic according to which an exercise program can be executed beneficially according to international guidelines and expert knowledge. The DSS considered data from a prescribed exercise program, heart rate from a wristband device, and motion accuracy from a depth camera, and subsequently generated personalized, performance-driven adaptations to the exercise program. Communication interfaces in the form of RESTful web service operations were developed enabling interoperation with other computer systems. RESULTS: The DSS was deployed in a computer-assisted platform for exercise-based cardiac rehabilitation at home, and it was evaluated in simulation and real-world studies with CVD patients. The simulation study based on data provided from 10 CVD patients performing 45 exercise sessions in total, showed that patients can be trained within or above their beneficial HR zones for 67.1⯱â¯22.1% of the exercise duration in the main phase, when they are guided with the DSS. The real-world study with 3 CVD patients performing 43 exercise sessions through the computer-assisted platform, showed that patients can be trained within or above their beneficial heart rate zones for 87.9⯱â¯8.0% of the exercise duration in the main phase, with DSS guidance. CONCLUSIONS: Computerized decision support systems can guide patients to the beneficial execution of their exercise-based rehabilitation program, and they are feasible.
Assuntos
Doenças Cardiovasculares/terapia , Sistemas de Apoio a Decisões Clínicas , Terapia por Exercício/métodos , Reabilitação/métodos , Idoso , Comunicação , Simulação por Computador , Feminino , Humanos , Internet , Masculino , Pessoa de Meia-Idade , Processamento de Sinais Assistido por Computador , Software , Resultado do TratamentoRESUMO
Rehabilitation is important for patients with cardiovascular diseases (CVD) to improve health outcomes and quality of life. However, adherence to current exercise programmes in cardiac rehabilitation is limited. We present the design and development of a Decision Support System (DSS) for telerehabilitation, aiming to enhance exercise programmes for CVD patients through ensuring their safety, personalising the programme according to their needs and performance, and motivating them toward meeting their physical activity goals. The DSS processes data originated from a Microsoft Kinect camera, a blood pressure monitor, a heart rate sensor and questionnaires, in order to generate a highly individualised exercise programme and improve patient adherence. Initial results within the EU-funded PATHway project show the potential of our approach.
Assuntos
Reabilitação Cardíaca/métodos , Sistemas de Apoio a Decisões Clínicas , Terapia por Exercício , Telemedicina/métodos , Frequência Cardíaca , Humanos , Motivação , Cooperação do Paciente , Medicina de PrecisãoRESUMO
The increasingly aging population in Europe and worldwide brings up the need for the restructuring of healthcare. Technological advancements in electronic health can be a driving force for new health management models, especially in chronic care. In a patient-centered e-health management model, communication and coordination between patient, healthcare professionals in primary care and hospitals can be facilitated, and medical decisions can be made timely and easily communicated. Bringing the right information to the right person at the right time is what connected health aims at, and this may set the basis for the investigation and deployment of the integrated care models. In this framework, an overview of the main technological axes and challenges around connected health technologies in chronic disease management are presented and discussed. A central concept is personal health system for the patient/citizen and three main application areas are identified. The connected health ecosystem is making progress, already shows benefits in (a) new biosensors, (b) data management, (c) data analytics, integration and feedback. Examples are illustrated in each case, while open issues and challenges for further research and development are pinpointed.
Assuntos
Doença Crônica/terapia , Atenção à Saúde , Gerenciamento Clínico , Comunicação , Europa (Continente) , Humanos , Atenção Primária à SaúdeRESUMO
The Citizen Health System (CHS) is a European Commission (CEC) funded project in the field of IST for Health. Its main goal is to develop a generic contact center which in its pilot stage can be used in the monitoring, treatment and management of chronically ill patients at home in Greece, Spain, and Germany. Such contact centers, using any type of communication technology, and providing timely and preventive prompting to the patients are envisaged in the future to evolve into well-being contact centers providing services to all citizens. In this paper, we present the structure of such a generic contact center and present its major achievements, and their impact to the quality of health delivery.