Clinical Decision Support Systems (CDSS) for preventive management of COPD patients

Background The use of information and communication technologies to manage chronic diseases allows the application of integrated care pathways, and the optimization and standardization of care processes. Decision support tools can assist in the adherence to best-practice medicine in critical decision points during the execution of a care pathway. Objectives The objectives are to design, develop, and assess a clinical decision support system (CDSS) offering a suite of services for the early detection and assessment of chronic obstructive pulmonary disease (COPD), which can be easily integrated into a healthcare providers' work-flow. Methods The software architecture model for the CDSS, interoperable clinical-knowledge representation, and inference engine were designed and implemented to form a base CDSS framework. The CDSS functionalities were iteratively developed through requirement-adjustment/development/validation cycles using enterprise-grade software-engineering methodologies and technologies. Within each cycle, clinical-knowledge acquisition was performed by a health-informatics engineer and a clinical-expert team. Results A suite of decision-support web services for (i) COPD early detection and diagnosis, (ii) spirometry quality-control support, (iii) patient stratification, was deployed in a secured environment on-line. The CDSS diagnostic performance was assessed using a validation set of 323 cases with 90% specificity, and 96% sensitivity. Web services were integrated in existing health information system platforms. Conclusions Specialized decision support can be offered as a complementary service to existing policies of integrated care for chronic-disease management. The CDSS was able to issue recommendations that have a high degree of accuracy to support COPD case-finding. Integration into healthcare providers' work-flow can be achieved seamlessly through the use of a modular design and service-oriented architecture that connect to existing health information systems

Context-aware solutions for asthma condition management: a survey

The evolution of information technology has allowed the development of ubiquitous, user-centred, and context-aware solutions. This article considers existing context-aware systems supporting asthma management with the aim of describing their main benefits and opportunities for improvement. To achieve this, the main concepts related to asthma and context awareness are explained before describing and analysing the existing context-aware systems aiding asthma. The survey shows that the concept of personalisation is the key when developing context-aware solutions supporting asthma management because of the high level of heterogeneity of this condition. Hence, the benefits and challenges of context-aware systems supporting asthma management are strongly linked to contextual Just-In-Time information of internal and external factors related to a person and the heterogeneity it represents

ADAPT: Approach to Develop context-Aware solutions for Personalised asthma managemenT

The creation of sensors allowing the collection of a high amount of data has been possible thanks to the evolution of information and communication technology. These data must be properly interpreted to deliver meaningful information and services. Context-aware reasoning plays an important role in this task, and it is considered as a hot topic to study in the development of solutions that can be categorised under the scope of Intelligent Environments. This research work studies the use of context-aware reasoning as a tool to provide support in the asthma management process. The contribution of this study is presented as the Approach to Develop context-Aware solutions for Personalised asthma managemenT (ADAPT), which can be used as a guideline to create solutions supporting asthma management in a personalised way. ADAPT proposes context-aware reasoning as an appropriate tool to achieve the personalisation that is required to address the heterogeneity of asthma. This heterogeneity makes people with asthma have different triggers provoking their exacerbations and to experience different symptoms when their exacerbations occur, which is considered as the most challenging characteristic of the condition when it comes to implementing asthma treatments. ADAPT context dimensions are the main contribution of the research work as they directly address the heterogeneity of asthma management by allowing the development of preventive and reactive features that can be customised depending on the characteristics of a person with asthma. The approach also provides support to people not knowing their triggers properly through case-based reasoning, and includes virtual assistant as a complementing technology supporting asthma management. The comprehensive nature of ADAPT motivates the study of the interaction between context-aware reasoning and case-based reasoning in Intelligent Environments, which is also reported as a key contribution of the research work. The inclusion of people with asthma, carers and experts in respiratory conditions in the experiments of the research project was possible to achieve thanks to the collaboration formed with Asthma UK

Mobile Health Technologies

Mobile Health Technologies, also known as mHealth technologies, have emerged, amongst healthcare providers, as the ultimate Technologies-of-Choice for the 21st century in delivering not only transformative change in healthcare delivery, but also critical health information to different communities of practice in integrated healthcare information systems. mHealth technologies nurture seamless platforms and pragmatic tools for managing pertinent health information across the continuum of different healthcare providers. mHealth technologies commonly utilize mobile medical devices, monitoring and wireless devices, and/or telemedicine in healthcare delivery and health research. Today, mHealth technologies provide opportunities to record and monitor conditions of patients with chronic diseases such as asthma, Chronic Obstructive Pulmonary Diseases (COPD) and diabetes mellitus. The intent of this book is to enlighten readers about the theories and applications of mHealth technologies in the healthcare domain

ADAPT: Approach to Develop context-Aware solutions for Personalised asthma managemenT

People with asthma have heterogeneous triggers and symptoms, which they need to be aware of in order to implement the strategies to manage their condition. Context-aware reasoning has the potential to provide the personalisation that is required to address the heterogeneity of asthma by helping people to define the information that is relevant considering the characteristics of their condition and delivering services based on this information. This research work proposes the Approach to Develop context-Aware solutions for Personalised asthma managemenT (ADAPT), whose aim is to facilitate the creation of solutions allowing the required customisation to address the heterogeneity of asthma. ADAPT is the result of the constant interaction with people affected by asthma throughout the research project, which was possible to achieve thanks to the collaboration formed with the Centre for Applied Research of Asthma UK. ADAPT context dimensions facilitate the development of preventive and reactive features that can be configured depending on the characteristics of the person with asthma. The approach also provides support to people not knowing their triggers through case-based reasoning and includes virtual assistant as a complementing technology supporting asthma management. ADAPT is validated by people with asthma, carers and experts in respiratory conditions, who evaluated a mobile application that was built based on the approach

Utilizing Consumer Health Posts for Pharmacovigilance: Identifying Underlying Factors Associated with Patients’ Attitudes Towards Antidepressants

Non-adherence to antidepressants is a major obstacle to antidepressants therapeutic benefits, resulting in increased risk of relapse, emergency visits, and significant burden on individuals and the healthcare system. Several studies showed that non-adherence is weakly associated with personal and clinical variables, but strongly associated with patients’ beliefs and attitudes towards medications. The traditional methods for identifying the key dimensions of patients’ attitudes towards antidepressants are associated with some methodological limitations, such as concern about confidentiality of personal information. In this study, attempts have been made to address the limitations by utilizing patients’ self report experiences in online healthcare forums to identify underlying factors affecting patients attitudes towards antidepressants. The data source of the study was a healthcare forum called “askapatients.com”. 892 patients’ reviews were randomly collected from the forum for the four most commonly prescribed antidepressants including Sertraline (Zoloft) and Escitalopram (Lexapro) from SSRI class, and Venlafaxine (Effexor) and duloxetine (Cymbalta) from SNRI class. Methodology of this study is composed of two main phases: I) generating structured data from unstructured patients’ drug reviews and testing hypotheses concerning attitude, II) identification and normalization of Adverse Drug Reactions (ADRs), Withdrawal Symptoms (WDs) and Drug Indications (DIs) from the posts, and mapping them to both The UMLS and SNOMED CT concepts. Phase II also includes testing the association between ADRs and attitude. The result of the first phase of this study showed that “experience of adverse drug reactions”, “perceived distress received from ADRs”, “lack of knowledge about medication’s mechanism”, “withdrawal experience”, “duration of usage”, and “drug effectiveness” are strongly associated with patients attitudes. However, demographic variables including “age” and “gender” are not associated with attitude. Analysis of the data in second phase of the study showed that from 6,534 identified entities, 73% are ADRs, 12% are WDs, and 15 % are drug indications. In addition, psychological and cognitive expressions have higher variability than physiological expressions. All three types of entities were mapped to 811 UMLS and SNOMED CT concepts. Testing the association between ADRs and attitude showed that from twenty-one physiological ADRs specified in the ASEC questionnaire, “dry mouth”, “increased appetite”, “disorientation”, “yawning”, “weight gain”, and “problem with sexual dysfunction” are associated with attitude. A set of psychological and cognitive ADRs, such as “emotional indifference” and “memory problem were also tested that showed significance association between these types of ADRs and attitude. The findings of this study have important implications for designing clinical interventions aiming to improve patients\u27 adherence towards antidepressants. In addition, the dataset generated in this study has significant implications for improving performance of text-mining algorithms aiming to identify health related information from consumer health posts. Moreover, the dataset can be used for generating and testing hypotheses related to ADRs associated with psychiatric mediations, and identifying factors associated with discontinuation of antidepressants. The dataset and guidelines of this study are available at https://sites.google.com/view/pharmacovigilanceinpsychiatry/hom