A Context-Oriented Extension of F#

Context-Oriented programming languages provide us with primitive constructs to adapt program behaviour depending on the evolution of their operational environment, namely the context. In previous work we proposed ML_CoDa, a context-oriented language with two-components: a declarative constituent for programming the context and a functional one for computing. This paper describes the implementation of ML_CoDa as an extension of F#.Comment: In Proceedings FOCLASA 2015, arXiv:1512.0694

An Interoperable Clinical Cardiology Electronic Health Record System - a standards based approach for Clinical Practice and Research with Data Reuse

Currently in hospitals, several information systems manage, very often autonomously, the patient’s personal, clinical and diagnostic data. This originates a clinical information management system consisting of a myriad of independent subsystems which, although efficient in their specific purpose, make the integration of the whole system very difficult and limit the use of clinical data, especially as regards the reuse of these data for research purposes. Mainly for these reasons, the management of the Genoese ASL3 decided to commission the University of Genoa to set up a medical record system that could be easily integrated with the rest of the information system already present, but which offered solid interoperability features, and which could support the research skills of hospital health workers. My PhD work aimed to develop an electronic health record system for a cardiology ward, obtaining a prototype which is functional and usable in a hospital ward. The choice of cardiology was due to the wide availability of the staff of the cardiology department to support me in the development and in the test phase. The resulting medical record system has been designed “ab initio” to be fully integrated into the hospital information system and to exchange data with the regional health information infrastructure. In order to achieve interoperability the system is based on the Health Level Seven standards for exchanging information between medical information systems. These standards are widely deployed and allow for the exchange of information in several functional domains. Specific decision support sections for particular aspects of the clinical life were also included. The data collected by this system were the basis for examples of secondary use for the development of two models based on machine learning algorithms. The first model allows to predict mortality in patients with heart failure within 6 months from their admission, and the second is focused on the discrimination between heart failure versus chronic ischemic heart disease in the elderly population, which is the widest population section served by the cardiological ward

Chaste: a test-driven approach to software development for biological modelling

Chaste (‘Cancer, heart and soft-tissue environment’) is a software library and a set of test suites for computational simulations in the domain of biology. Current functionality has arisen from modelling in the fields of cancer, cardiac physiology and soft-tissue mechanics. It is released under the LGPL 2.1 licence.\ud \ud Chaste has been developed using agile programming methods. The project began in 2005 when it was reasoned that the modelling of a variety of physiological phenomena required both a generic mathematical modelling framework, and a generic computational/simulation framework. The Chaste project evolved from the Integrative Biology (IB) e-Science Project, an inter-institutional project aimed at developing a suitable IT infrastructure to support physiome-level computational modelling, with a primary focus on cardiac and cancer modelling

Electronic Health Record Optimization for Cardiac Care

Electronic health record (EHR) systems have been studied for over 30 years, and despite the benefits of information technology in other knowledge domains, progress has been slow in healthcare. A growing body of evidence suggests that dissatisfaction with EHR systems was not simply due to resistance to adoption of new technology but also due to real concerns about the adverse impact of EHRs on the delivery of patient care. Solutions for EHR improvement require an approach that combines an understanding of technology adoption with the complexity of the social and technical elements of the US healthcare system. Several studies are presented to clarify and propose a new framework to study EHR-provider interaction. Four focus areas were defined - workflow, communication, medical decision-making and patient care. Using Human Computer Interaction best practices, an EHR usability framework was designed to include a realistic clinical scenario, a cognitive walkthrough, a standardized simulated patient actor, and a portable usability lab. Cardiologists, fellows and nurse practitioners were invited to participate in a simulation to use their institution’s EHR system for a routine cardiac visit. Using a mixed methods approach, differences in satisfaction and effectiveness were identified. Cardiologists were dissatisfied with EHR functionality, and were critical of the potential impact of the communication of incorrect information, while displaying the highest level of success in completing the tasks. Fellows were slightly less dissatisfied with their EHR interaction, and demonstrated a preference for tools to improve workflow and support decision-making, and showed less success in completing the tasks in the scenario. Nurse practitioners were also dissatisfied with their EHR interaction, and cited poor organization of data, yet demonstrated more success than fellows in successful completion of tasks. Study results indicate that requirements for EHR functionality differ by type of provider. Cardiologists, cardiology fellows, and nurse practitioners required different levels of granularity of patient data for use in medical decision-making, defined different targets for communication, sought different solutions to workflow which included distribution of data input, and requested technical solutions to ensure valid and relevant patient data. These findings provide a foundation for future work to optimize EHR functionality