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OBJECTIVE: This article describes a study protocol for the implementation of quality and traceability control in the hazardous  medication circuit through an analysis of risks and the development and  introduction of a Big Data-based software application aimed at performing  a continuous and dynamic audit of the whole system. Method: A standardized graphical modeling tool called Business Process Model Notation will be used to generate a detailed description of each of the stages in the hazardous medication circuit with a view to  ensuring full traceability of the system. The information on each stage will  be collected in a flowchart, which will be used -together with each event's likelihood of occurrence and severity- as a basis to calculate the  criticality index of the different control points established and to determine  any control measures that may be required. The flowcharts will  also be used to develop the technological support needed to capture  all such data as may be relevant to the model. Proper quality control of the process will be ensured by client software agents intended to allow  automatic applica tion of efficient data processing tools at the different  phases. In addition, Big Data methodologies, in particular machine  learning, will be used to develop algorithms based on the repository of  generated data to come up with patterns capable of improving the  protocols to be applied. Lastly, proper operation of the process will be  ensured by means of clinicalpharmaceutical verification and a full  technical-documentary review of control and registration systems. CONCLUSIONS: The development of a risk management system based on  mobile technology will allow integration of hazardous drugs into a standardized system, ensuring the safety, quality, and traceability of the hazardous medication handling process.

Objetivo: Describir el protocolo del estudio para la instauración del control del proceso de los medicamentos peligrosos que asegure la calidad y su trazabilidad, mediante el análisis de riesgos, desarrollando e  Implantando una herramienta informatizada que, gracias a la utilización de técnicas de big data, permita conocer y auditar el conjunto del sistema de  forma continua y dinámica.Método: Mediante los procesos de notación gráfica normalizada Business Process Model Notation se desarrollarán los flujogramas  Específicos que permitan conocer las etapas del proceso de los  Medicamentos peligrosos que determinen la trazabilidad total del sistema.  Cada una de las etapas será recogida en los cuadros de gestión, donde a  través de la probabilidad del suceso y su gravedad se calculará el índice de criticidad de cada punto de control que se determine, y se establecerán las medidas de control. A partir de los cuadros de gestión se desarrollará el  soporte tecnológico para la captura de todos los datos que sean  pertinentes al modelo. Para asegurar el control de la calidad del proceso se optará por agentes software cliente, que permitan en fases posteriores  aplicar herramientas eficientes en el procesamiento de datos de modo  automático. A partir de aproximaciones metodológicas del big data, y en  particular del ámbito de machine learning, se desarrollarán algoritmos  sobre el repositorio de datos generado para poder obtener patrones que  permitan mejorar los protocolos de aplicación. Por último, para asegurar el funcionamiento del proceso se realizará la verificación clínico-farmacéutica  y la revisión completa, técnico-documental, de los sistemas de control y  registro.Conclusiones: La generación del sistema de gestión de riesgos mediante  tecnología móvil permitirá integrar los medicamentos peligrosos en un sistema normalizado, con el fin de mejorar la seguridad, calidad y  trazabilidad del proceso de manipulación de los medicamentos peligrosos.

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Objetivo: Describir el protocolo del estudio para la instauración del control del proceso de los medicamentos peligrosos que asegure la calidady su trazabilidad, mediante el análisis de riesgos, desarrollando e implantando una herramienta informatizada que, gracias a la utilización de técnicas de big data, permita conocer y auditar el conjunto del sistema de formacontinua y dinámica.Método: Mediante los procesos de notación gráfica normalizada Business Process Model Notation se desarrollarán los flujogramas específicosque permitan conocer las etapas del proceso de los medicamentos peligrosos que determinen la trazabilidad total del sistema. Cada una de lasetapas será recogida en los cuadros de gestión, donde a través de laprobabilidad del suceso y su gravedad se calculará el índice de criticidadde cada punto de control que se determine, y se establecerán las medidasde control. A partir de los cuadros de gestión se desarrollará el soportetecnológico para la captura de todos los datos que sean pertinentes al modelo. Para asegurar el control de la calidad del proceso se optará poragentes software cliente, que permitan en fases posteriores aplicar herramientas eficientes en el procesamiento de datos de modo automático. Apartir de aproximaciones metodológicas del big data, y en particular delámbito de machine learning, se desarrollarán algoritmos sobre el repositorio de datos generado para poder obtener patrones que permitan mejorarlos protocolos de aplicación. Por último, para asegurar el funcionamientodel proceso se realizará la verificación clínico-farmacéutica y la revisióncompleta, técnico-documental, de los sistemas de control y registro. (AU)

Objective: This article describes a study protocol for the implementation of quality and traceability control in the hazardous medication circuitthrough an analysis of risks and the development and introduction of a BigData-based software application aimed at performing a continuous anddynamic audit of the whole system.Method: A standardized graphical modeling tool called Business Process Model Notation will be used to generate a detailed description ofeach of the stages in the hazardous medication circuit with a view to ensuring full traceability of the system. The information on each stage will becollected in a flowchart, which will be used —together with each event’slikelihood of occurrence and severity— as a basis to calculate the criticality index of the different control points established and to determine anycontrol measures that may be required. The flowcharts will also be usedto develop the technological support needed to capture all such data asmay be relevant to the model. Proper quality control of the process will be ensured by client software agents intended to allow automatic application of efficient data processing tools at the different phases. In addition,Big Data methodologies, in particular machine learning, will be used todevelop algorithms based on the repository of generated data to comeup with patterns capable of improving the protocols to be applied. Lastly,proper operation of the process will be ensured by means of clinicalpharmaceutical verification and a full technical-documentary review ofcontrol and registration systems. (AU)

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Flowcharts used for hospital protocols have a series of ambiguities and limitations in order to express some types of information. In this article, a notation proposal for flowcharts that partially avoids these problems is presented. This new notation is an adaptation of BPMNE2, an extension of the Business Process Model and Notation (BPMN), which allows direct modelling of procedures that follow the Hazard Analysis and Critical Control Points (HACCP) model. The new notation has been validated in the hospital context, specifically in the field of hazardous drugs (HDs). To measure usability from the perspective of the health staff and auditors, the System Usability Scale (SUS) was used. A total of 47 experts took part in the assessment, resulting in a SUS score of 71, that corresponds to an acceptable level of usability. The feedback provided by these participants allows us to discover benefits and drawbacks of the proposal. Also, it is noteworthy that 76.6% of professionals prefer to migrate to the new notation from the ISO 5807:1985 notation, the most commonly used model. In addition to the direct benefits of this notation from the human point of view, its machine-understandable nature provides the required support for its integration into software tools for intelligent monitoring and auditing.

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OBJECTIVE: To review the scientific literature related to the safe handling of hazardous drugs (HDs). METHOD: Critical analysis of works retrieved from MEDLINE, the Cochrane Library, Scopus, CINHAL, Web of Science and LILACS using the terms "Hazardous Substances", "Antineoplastic Agents" and "Cytostatic Agents", applying "Humans" and "Guidelines" as filters. Date of search: January 2017. RESULTS: In total, 1100 references were retrieved, and from those, 61 documents were selected based on the inclusion and exclusion criteria: 24 (39.3%) documents related to recommendations about HDs; 27 (44.3%) about antineoplastic agents, and 10 (33.3%) about other types of substances (monoclonal antibodies, gene medicine and other chemical and biological agents). In 14 (23.3%) guides, all the stages in the manipulation process involving a risk due to exposure were considered. Only one guide addressed all stages of the handling process of HDs (including stages with and without the risk of exposure). The most described stages were drug preparation (41 guides, 67.2%), staff training and/or patient education (38 guides, 62.3%), and administration (37 guides, 60.7%). No standardized informatics system was found that ensured quality management, traceability and minimization of the risks associated with these drugs. CONCLUSIONS: Most of the analysed guidelines limit their recommendations to the manipulation of antineoplastics. The most frequently described activities were preparation, training, and administration. It would be convenient to apply ICTs (Information and Communications Technologies) to manage processes involving HDs in a more complete and simpler fashion.

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BACKGROUND: The parenteral nutrient (PN) mixtures may pose great risks of physical, microbiological, and chemical contamination during their preparation, storage, distribution, and administration. These potential hazards must be controlled under high levels of excellence to prevent any serious complications for the patients. As a result, management control and traceability of any of these medications is of utmost relevance for the patient care, along with ensuring treatment continuity and adherence. OBJECTIVE: The aim of this study is to develop a mobile-based platform to support the control procedures and traceability services in the domain of parenteral nutrient (PN) mixtures in an efficient and nonintrusive manner. METHODS: A comprehensive approach combining techniques of software engineering and knowledge engineering was used for the characterization of the framework. Local try-outs for evaluation were performed in a number of application areas, carrying out a test/retest monitoring to detect possible errors or conflicts in different contexts and control processes throughout the entire cycle of PN. From these data, the absolute and relative frequencies (percentages) were calculated. RESULTS: A mobile application for the Android operating system was developed. This application allows reading different types of tags and interacts with the local server according to a proposed model. Also, through an internal caching mechanism, the availability of the system is preserved even in the event of problems with the network connection. A set of 1040 test traces were generated for the assessment of the system under various environments tested. Among those, 102 traces (9.81%) involved conflictive situations that were properly taken care of in this paper by suggesting solutions to overcome them. CONCLUSIONS: A mobile oriented system was generated and tested in order to allow enhanced control and quality management of PN mixtures that is easy to integrate into the daily praxis of health care processes.

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QR codes opens up the possibility to develop simple-to-use, cost-effective-cost, and functional systems based on the optical recognition of inexpensive tags attached to physical objects. These systems, combined with Web platforms, can provide us with advanced services that are already currently broadly used on many contexts of the common life. Due to its philosophy, based on the automatic recognition of messages embedded on simple graphics by means of common devices such as mobile phones, QR codes are very convenient for the average user. Regretfully, its potential has not yet been fully exploited in the domains of food science and nutrition. This paper points out some applications to make the most of this technology for these domains in a straightforward manner. For its characteristics, we are addressing systems with low barriers to entry and high scalability for its deployment. Therefore, its launching among professional and final users is quite simple. The paper also provides high-level indications for the evaluation of the technological frame required to implement the identified possibilities of use.

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