Facilitating the design of HL7 domain models through a model-driven solution.

BACKGROUND AND GOAL: Health information systems are increasingly sophisticated and developing them is a challenge for software developers. Software engineers usually make use of UML as a standard model language that allows defining health information system entities and their relations. However, working with health system requires learning HL7 standards, that defines and manages standards related to health information systems. HL7 standards are varied, however this work focusses on v2 and v3 since these are the most used one on the area that this work is being conducted. This works aims to allow modeling HL7 standard by using UML. METHODS: Several techniques based on the MDE (Model-Driven Engineering) paradigm have been used to cope with it. RESULTS: A useful reference framework, reducing final users learning curve and allowing modeling maintainable and easy-going health information systems. CONCLUSIONS: By using this approach, a software engineer without any previous knowledge about HL7 would be able to solve the problem of modeling HL7-based health information systems. Reducing the learning curve when working in projects that need HL7 standards.

Working with the HL7 metamodel in a Model Driven Engineering context.

HL7 (Health Level 7) International is an organization that defines health information standards. Most HL7 domain information models have been designed according to a proprietary graphic language whose domain models are based on the HL7 metamodel. Many researchers have considered using HL7 in the MDE (Model-Driven Engineering) context. A limitation has been identified: all MDE tools support UML (Unified Modeling Language), which is a standard model language, but most do not support the HL7 proprietary model language. We want to support software engineers without HL7 experience, thus real-world problems would be modeled by them by defining system requirements in UML that are compliant with HL7 domain models transparently. The objective of the present research is to connect HL7 with software analysis using a generic model-based approach. This paper introduces a first approach to an HL7 MDE solution that considers the MIF (Model Interchange Format) metamodel proposed by HL7 by making use of a plug-in developed in the EA (Enterprise Architect) tool.

An Innovative Platform to Analyze Heart Failure Biomarkers in Saliva.

This paper explains a methodology to improve patient safety through early detecting of HF complications and analyzing HF biomarkers in saliva obtained using a biosensor developed in HEARTEN project. A pilot study was performed in two hospitals in Spain and Italy respectively. A direct correlation was identified between TNF-alpha levels in saliva and weight. The weight gain in HF patients could predict a HF decompensation, consequently TNF-alpha could be a new biomarker of these decompensations.

EIP on AHA Ontology for adherence: Knowledge representation advanced tools.

Nowadays diseases tend to chronicle, mainly due to the increase in life expectancy and this leads to a state of polypharmacy. More than 1.5% of Spain's GDP is spent on pharmaceuticals and healthcare products. Complex chronic patients (pluripathological and polymedicated) account for most of the expenditure. The "Action Group A1" of the European Innovation Partnership develops in the "Active and Healthy Ageing" programme actions to improve the quality of life and health outcomes of these patients. On the other hand, the PITeS TIiSS project develops decision support tools to improve this scenario. An ontology has been developed as a tool on adherence. The domain of this ontology is mainly focused on medication adherence and measurement methods. This ontology gathers the necessary knowledge about the domain allowing the use of the ontology as part for is possible.

An Ontology-Based Personalized Decision Support System for Use in the Complex Chronically Ill Patient.

Management of the complex chronically ill patient is highly problematic. This is due to the need to complement recommendations in clinical guidelines with recommendations based on treatments performed on a representative set of patients. It is necessary to identify conflicts between the recommendations of different guidelines designed for handling specific chronic diseases. The PITeS-TIiSS project (code PI15/01213) aims to overcome this problem by designing and deploying an ontology-based personalized clinical decision support tool. This helps to improve personalized decisions and reduces variability in clinical practice in an integrated care environment. This paper describes the methodology followed in developing the ontology used to infer clinical knowledge and to develop and implement the support tool. The tool will access the information provided by the Electronic Health Record of Andalusian Health Public Service, the main healthcare provider in a region in Spain with more than eight million inhabitants.