Modelado de procesos y desarrollo de sistemas software: integración entre UML y EPC.

Los objetivos de UML (Unified Modeling Language – Lenguaje Unificado de Modelado) y de la EPC (Event-driven Process Chain - Cadena de Procesos guiada por Eventos), están bien diferenciados: mientras que UML se enfoca al diseño de sistemas de información (SI), las EPCs se emplean para el modelado de procesos de negocios (BPM) dentro de la metodología ARIS. No obstante, es evidente la relación entre ambas técnicas: por una parte, un correcto diseño de SI debe basarse en los requisitos definidos por el modelo de procesos de negocio. Por otra parte, las mejoras de los procesos existentes a menudo deben llevarse a cabo mediante el desarrollo o modificación de los SI que soportan dichos procesos. En esta comunicación se analizan las posibilidades de integración de ambas técnicas de modelad

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 realworld 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.Ministerio de Ciencia e Innovación TIN2013-46928-C3-3-RInstituto de Salud Carlos III PI12/01571Instituto de Salud Carlos III PT13/0006/003

Experiencias en la aplicación de Modelado de Procesos de Negocio (BPM) en el sector sanitario

VIII Congreso de Ingeniería de Organización Leganés, 9 y 10 de septiembre de 2004Esta comunicación se deriva de las experiencias obtenidas mediante la participación de los autores en un proyecto de rediseño y reingeniería de procesos de un conjunto de servicios de salud implantados en distintos hospitales nacionales. Una de las primeras fases del proyecto consistió en el modelado del conjunto de procesos actuales (modelos as-is) para su posterior análisis mediante simulación y la obtención de modelos que representen los procesos objetivo (modelos to-be). En esta comunicación se describe la metodología empleada en el proyecto y se realiza una discusión de las ventajas y limitaciones de la aplicación del modelado de procesos en el sector de la salud. Palabras clave: modelado de procesos de negocio, simulación, sanidad, telemedicinaMinisterio de Sanidad y Consumo G03/11

Continuous convex relaxation methodology applied to retroperitoneal tumors

In this paper, two algorithms for the segmentation of tumors in soft tissues are presented and compared. These algorithms are applied to the segmentatiion of retroperitoneal tumors. Method: The algorithms are based on a continuous convex relaxation methodology with the introduction of an accumulated gradient distance (AGD). Algorithm 1 is based on two-label convex relaxation and Algorithm 2 applies multilabel convex relaxation. Results: Algorithms 1 and 2 are tested on a database of 6 CT volumes and their results are compared with the manual segmentation. The multilabel version performs better, achieving a 91% of sensitivity, 100% of specificity, 88% of PPV and 89% of Dice index. Conclusions: To the best of our knowledge, this is the first time that the segmentation of retroperitoneal tumors has been addressed. Two segmentation algorithms have been compared and the multilabel version obtains very good resultsJunta de Andalucía P11-TIC-7727Junta de Andalucía PT13/0006/003

Low-cost measurement for a secondary Mode S radar transmitter

A low-cost, multiple-purpose, and high-precision timing test setup for the measurements of secondary Mode S radar transmission signal was proposed. The goal was to fully guarantee compliance of the proposed transmitter under test with the really hard International Civil Aircraft Organization requirements using traditional measurement equipment, which was difficult or even impossible to ensure up to now. The low-cost structure proposed in this paper allows the user to perform measurements independently of the measurements performed by the pieces of test equipment shelled by the manufacturer of radar, which is a very important aspect since the independence of the verifications is a mandatory requirement established by the safety standards of civil aviation. The proposed setup has been used to verify several transmitters with some defects that are not detected by monopulse secondary surveillance radar specific pieces of test equipment that are focused on more high-level functionalities. It also is valid and it has been used, as a general-purpose setup, for testing other radio navigation aids

Using the Social-Local-Mobile App for Smoking Cessation in the SmokeFreeBrain Project: Protocol for a Randomized Controlled Trial

Background: Smoking is considered the main cause of preventable illness and early deaths worldwide. The treatment usually prescribed to people who wish to quit smoking is a multidisciplinary intervention, combining both psychological advice and pharmacological therapy, since the application of both strategies significantly increases the chance of success in a quit attempt. Objective: We present a study protocol of a 12-month randomized open-label parallel-group trial whose primary objective is to analyze the efficacy and efficiency of usual psychopharmacological therapy plus the Social-Local-Mobile app (intervention group) applied to the smoking cessation process compared with usual psychopharmacological therapy alone (control group). Methods: The target population consists of adult smokers (both male and female) attending the Smoking Cessation Unit at Virgen del Rocío University Hospital, Seville, Spain. Social-Local-Mobile is an innovative intervention based on mobile technologies and their capacity to trigger behavioral changes. The app is a complement to pharmacological therapies to quit smoking by providing personalized motivational messages, physical activity monitoring, lifestyle advice, and distractions (minigames) to help overcome cravings. Usual pharmacological therapy consists of bupropion (Zyntabac 150 mg) or varenicline (Champix 0.5 mg or 1 mg). The main outcomes will be (1) the smoking abstinence rate at 1 year measured by means of exhaled carbon monoxide and urinary cotinine tests, and (2) the result of the cost-effectiveness analysis, which will be expressed in terms of an incremental cost-effectiveness ratio. Secondary outcome measures will be (1) analysis of the safety of pharmacological therapy, (2) analysis of the health-related quality of life of patients, and (3) monitoring of healthy lifestyle and physical exercise habits. Results: Of 548 patients identified using the hospital’s electronic records system, we excluded 308 patients: 188 declined to participate and 120 did not meet the inclusion criteria. A total of 240 patients were enrolled: the control group (n=120) will receive usual psychopharmacological therapy, while the intervention group (n=120) will receive usual psychopharmacological therapy plus the So-Lo-Mo app. The project was approved for funding in June 2015. Enrollment started in October 2016 and was completed in October 2017. Data gathering was completed in November 2018, and data analysis is under way. The first results are expected to be submitted for publication in early 2019. Conclusions: Social networks and mobile technologies influence our daily lives and, therefore, may influence our smoking habits as well. As part of the SmokeFreeBrain H2020 European Commission project, this study aims at elucidating the potential role of these technologies when used as an extra aid to quit smoking

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

The need for patient adherence standard measures for Big Data

Despite half a century of dedicated studies, medication adherence remains far from perfect, with many patients not taking their medications as prescribed. The magnitude of this problem is rising, jeopardizing the effectiveness of evidence-based therapies. An important reason for this is the unprecedented demographic change at the beginning of 21st century. Ageing leads to multimorbidity and complex therapeutic regimens that create fertile ground for non-adherence. As this scenario is a global problem, it needs a worldwide answer. Might this answer be provided, given the new opportunities created by the digitization of healthcare? Day by day health-related information is collected in electronic health records, pharmacy dispensing databases, health insurance systems and national health system records. These Big Data repositories offer a unique chance to study adherence both retrospectively and prospectively, at population level, as well as its related factors. In order to make the full use of this opportunity, there is a need to develop standardised measures of adherence, which can be applied globally to Big Data and will inform scientific research, clinical practice and public health. These standardized measures may also enable a better understanding of the relationship between adherence and clinical outcomes, and allow for fair benchmarking of effectiveness and cost-effectiveness of adherence-targeting interventions. Unfortunately, despite this obvious need, such standards are still lacking. Therefore, the aim of this paper is to call for producing a consensus on global standards for measuring adherence with Big Data. More specifically, sound standards of formatting, and analysing Big Data are needed in order to assess, uniformly present and compare patterns of medication adherence across studies. Wide use of these standards may improve adherence, and make healthcare systems more effective and sustainable

A Mobile Health Solution Complementing Psychopharmacology-Supported Smoking Cessation: Randomized Controlled Trial

Background: Smoking cessation is a persistent leading public health challenge. Mobile health (mHealth) solutions are emerging to improve smoking cessation treatments. Previous approaches have proposed supporting cessation with tailored motivational messages. Some managed to provide short-term improvements in smoking cessation. Yet, these approaches were either static in terms of personalization or human-based nonscalable solutions. Additionally, long-term effects were neither presented nor assessed in combination with existing psychopharmacological therapies. Objective: This study aimed to analyze the long-term efficacy of a mobile app supporting psychopharmacological therapy for smoking cessation and complementarily assess the involved innovative technology. Methods: A 12-month, randomized, open-label, parallel-group trial comparing smoking cessation rates was performed at Virgen del Rocío University Hospital in Seville (Spain). Smokers were randomly allocated to a control group (CG) receiving usual care (psychopharmacological treatment, n=120) or an intervention group (IG) receiving psychopharmacological treatment and using a mobile app providing artificial intelligence–generated and tailored smoking cessation support messages (n=120). The secondary objectives were to analyze health-related quality of life and monitor healthy lifestyle and physical exercise habits. Safety was assessed according to the presence of adverse events related to the pharmacological therapy. Per-protocol and intention-to-treat analyses were performed. Incomplete data and multinomial regression analyses were performed to assess the variables influencing participant cessation probability. The technical solution was assessed according to the precision of the tailored motivational smoking cessation messages and user engagement. Cessation and no cessation subgroups were compared using t tests. A voluntary satisfaction questionnaire was administered at the end of the intervention to all participants who completed the trial. Results: In the IG, abstinence was 2.75 times higher (adjusted OR 3.45, P=.01) in the per-protocol analysis and 2.15 times higher (adjusted OR 3.13, P=.002) in the intention-to-treat analysis. Lost data analysis and multinomial logistic models showed different patterns in participants who dropped out. Regarding safety, 14 of 120 (11.7%) IG participants and 13 of 120 (10.8%) CG participants had 19 and 23 adverse events, respectively (P=.84). None of the clinical secondary objective measures showed relevant differences between the groups. The system was able to learn and tailor messages for improved effectiveness in supporting smoking cessation but was unable to reduce the time between a message being sent and opened. In either case, there was no relevant difference between the cessation and no cessation subgroups. However, a significant difference was found in system engagement at 6 months (P=.04) but not in all subsequent months. High system appreciation was reported at the end of the study. Conclusions: The proposed mHealth solution complementing psychopharmacological therapy showed greater efficacy for achieving 1-year tobacco abstinence as compared with psychopharmacological therapy alone. It provides a basis for artificial intelligence–based future approaches. Trial Registration: ClinicalTrials.gov NCT03553173; https://clinicaltrials.gov/ct2/show/NCT03553173 International Registered Report Identifier (IRRID): RR2-10.2196/12464H2020 European Commission research and innovation program grant agreement 68112

A Clinical Decision Support System (KNOWBED) to Integrate Scientific Knowledge at the Bedside: Development and Evaluation Study

[Background] The evidence-based medicine (EBM) paradigm requires the development of health care professionals’ skills in the efficient search of evidence in the literature, and in the application of formal rules to evaluate this evidence. Incorporating this methodology into the decision-making routine of clinical practice will improve the patients’ health care, increase patient safety, and optimize resources use.[Objective] The aim of this study is to develop and evaluate a new tool (KNOWBED system) as a clinical decision support system to support scientific knowledge, enabling health care professionals to quickly carry out decision-making processes based on EBM during their routine clinical practice.[Methods] Two components integrate the KNOWBED system: a web-based knowledge station and a mobile app. A use case (bronchiolitis pathology) was selected to validate the KNOWBED system in the context of the Paediatrics Unit of the Virgen Macarena University Hospital (Seville, Spain). The validation was covered in a 3-month pilot using 2 indicators: usability and efficacy.[Results] The KNOWBED system has been designed, developed, and validated to support clinical decision making in mobility based on standards that have been incorporated into the routine clinical practice of health care professionals. Using this tool, health care professionals can consult existing scientific knowledge at the bedside, and access recommendations of clinical protocols established based on EBM. During the pilot project, 15 health care professionals participated and accessed the system for a total of 59 times.[Conclusions] The KNOWBED system is a useful and innovative tool for health care professionals. The usability surveys filled in by the system users highlight that it is easy to access the knowledge base. This paper also sets out some improvements to be made in the future.This project has received funding from the Andalusian Ministry of Health from Spain (reference PIN-0213-2016), and FEDER funds.Peer reviewe