Chronic cardiovascular disease (CVD) is increasingly becoming a burden for global healthcare systems. This burden can be attributed in part to traditional methods of managing CVD in an aging population that involves periodic meetings between the patient and their healthcare provider. There is growing interest in developing continuous monitoring systems to assist in the management of CVD. Monitoring systems can utilize advances in wearable devices and health records, which provides minimally invasive methods to monitor a patient’s health. Despite these advances, the algorithms deployed to automatically analyze the wearable sensor and health data is considered too computationally expensive to run on the mobile device. Instead, current mobile devices continuously transmit the collected data to a server for analysis at great computational and data transmission expense. In this thesis a novel mobile system designed for monitoring CVD is presented. Unlike existing systems, the proposed system allows for the continuous monitoring of physiological sensors, data from a patient’s health record and analysis of the data directly on the mobile device using machine learning algorithms (MLA) to predict an individual’s CVD severity level. The system successfully demonstrated that a mobile device can act as a complete monitoring system without requiring constant communication with a server. A comparative analysis between the support vector machine (SVM) and multilayer perceptron (MLP) to explore the effectiveness of each algorithm for monitoring CVD is also discussed. Both models were able to classify CVD risk with the SVM achieving the highest accuracy (63%) and specificity (76%). Finally, unlike current systems the resource requirements for each component in the system was evaluated. The MLP was found to be more efficient when running on the mobile device compared to the SVM. The results of thesis also show that the MLAs complexity was not a barrier to deployment on a mobile device. / Thesis / Master of Applied Science (MASc) / In this thesis, a novel mobile system for monitoring cardiovascular (CVD) disease is presented. The system allows for the continuous monitoring of both physiological sensors, data from a patient’s health record and analysis of the data directly on the mobile device using machine learning algorithms (MLA) to predict an individual’s CVD severity level. The system successfully demonstrated that a mobile device can act as a complete monitoring system without requiring constant communication with a remote server. A comparative analysis between the support vector machine (SVM) and multilayer perceptron (MLP) to explore the effectiveness of each MLA for monitoring CVD is also discussed. Both models were able to classify CVD severity with the SVM achieving the highest accuracy (63%) and specificity (76%). Finally, the resource requirements for each component in the system were evaluated. The results show that the MLAs complexity was not a barrier to deployment on a mobile device.

Clinical research based on data from patient or study data management systems plays an important role in transferring basic findings into the daily practices of physicians. To support study recruitment, diagnostic processes, and risk factor evaluation, search queries for such management systems can be used. Typically, the query syntax as well as the underlying data structure vary greatly between different data management systems. This makes it difficult for domain experts (e.g., clinicians) to build and execute search queries. In this work, the Core Ontology of Phenotypes is used as a general model for phenotypic knowledge. This knowledge is required to create search queries that determine and classify individuals (e.g., patients or study participants) whose morphology, function, behaviour, or biochemical and physiological properties meet specific phenotype classes. A specific model describing a set of particular phenotype classes is called a Phenotype Specification Ontology. Such an ontology can be automatically converted to search queries on data management systems. The methods described have already been used successfully in several projects. Using ontologies to model phenotypic knowledge on patient or study data management systems is a viable approach. It allows clinicians to model from a domain perspective without knowing the actual data structure or query language.

Deep learning models are increasingly used to analyze health records to model disease progression. Two characteristics of health records present challenges to developers of deep learning-based medical systems. First, the veracity of the estimation of missing health data must be evaluated to optimize the performance of deep learning models. Second, the currently most successful deep learning diagnostic models, called transformers, lack a mechanism to analyze the temporal characteristics of health records. In this thesis, these two challenges are investigated using a real-world medical dataset of longitudinal health records from 340,143 patients over ten years called MIIDD: McMaster Imaging Information and Diagnostic Dataset. To address missing data, the performance of imputation models (mean, regression, and deep learning) were evaluated on a real-world medical dataset. Next, techniques from adversarial machine learning were used to demonstrate how imputation can have a cascading negative impact on a deep learning model. Then, the strengths and limitations of evaluation metrics from the statistical literature (qualitative, predictive accuracy, and statistical distance) to evaluate deep learning-based imputation models were investigated. This research can serve as a reference to researchers evaluating the impact of imputation on their deep learning models. To analyze the temporal characteristics of health records, a new model was developed and evaluated called DTTHRE: Decoder Transformer for Temporally-Embedded Health Records Encoding. DTTHRE predicts patients' primary diagnoses by analyzing their medical histories, including the elapsed time between visits. The proposed model successfully predicted patients' primary diagnosis in their final visit with improved predictive performance (78.54 +/- 0.22%) compared to existing models in the literature. DTTHRE also increased the training examples available from limited medical datasets by predicting the primary diagnosis for each visit (79.53 +/- 0.25%) with no additional training time. This research contributes towards the goal of disease predictive modeling for clinical decision support. / Dissertation / Doctor of Philosophy (PhD) / In this thesis, two challenges using deep learning models to analyze health records are investigated using a real-world medical dataset. First, an important step in analyzing health records is to estimate missing data. We investigated how imputation can have a cascading negative impact on a deep learning model's performance. A comparative analysis was then conducted to investigate the strengths and limitations of evaluation metrics from the statistical literature to assess deep learning-based imputation models. Second, the most successful deep learning diagnostic models to date, called transformers, lack a mechanism to analyze the temporal characteristics of health records. To address this gap, we developed a new temporally-embedded transformer to analyze patients' medical histories, including the elapsed time between visits, to predict their primary diagnoses. The proposed model successfully predicted patients' primary diagnosis in their final visit with improved predictive performance (78.54 +/- 0.22%) compared to existing models in the literature.

<p>With the increasing prevalence of chronic disease throughout the world, electronic Personal Health Records (ePHRs) have been suggested as a way to improve chronic disease self-management. However, ePHRs are not yet widely used by consumers. Protection Motivation Theory (PMT) has been successfully used to explain health related behaviours among chronic disease patients. In addition, Information Systems (IS) theories such as Task Technology Fit (TTF) have been successfully used to explain information technology adoption. This study combines PMT with Perceived Task Technology Fit (PTTF) and the health self-management readiness concept of the Patient Activation Measure (PAM) to propose a research model which will aid in the understanding of ePHR adoption by chronic disease patients. The role of educational interventions on various elements of the proposed model is also examined. A survey-based study of 230 participants is used to empirically validate the proposed model via structural equation modeling techniques. Results reveal that the PMT constructs, as well as PTTF and PAM all have significant direct or indirect effects on the intention to adopt an ePHR. In addition, the educational intervention analysis indicates that the provision of advanced ePHR education positively influences various constructs in the model, while the use of fear appeals through Diabetes complication education does not have an effect.</p> / Doctor of Philosophy (PhD)

The objectives of this study were to establish the effectiveness of chlorpyrifos in suppressing H. armigera population in oranges and to determine the effect chlorpyrifos had on the health of farm pesticide operators. Experiments showed that by applying chlorpyrifos on orange trees, H. armigera larvae population was suppressed significantly. High fruit yields were realized from trees that were sprayed with chlorpyrifos. Visual observation of personnel involved in pesticide related duties, revealed that judicious use of pesticides was not practiced by farm workers in all three farms. Data analysis from questionnaires, health records and interviews proved that farm workers suffered from illnesses that were pesticide related. It was mainly those farm workers in the age group of 31 to 35 years who suffered the most from pesticide related illnesses. / Agriculture and Life Sciences / M. Sc. (Agriculture)

Several factors are driving the transition from paper-based health records to electronic health record systems. In the United States, the adoption rate of electronic health record systems significantly increased after "Meaningful Use" incentive program was started in 2009. While increased use of electronic health record systems could improve the efficiency and quality of healthcare services, it can also lead to a number of security and privacy issues, such as identity theft and healthcare fraud. Such incidents could have negative impact on trustworthiness of electronic health record technology itself and thereby could limit its benefits. In this dissertation, we tackle three challenges that we believe are important to improve the security and privacy in electronic health record systems. Our approach is based on an analysis of real-world incidents, namely theft and misuse of patient identity, unauthorized usage and update of electronic health records, and threats from insiders in healthcare organizations. Our contributions include design and development of a user-centric monitoring agent system that works on behalf of a patient (i.e., an end user) and securely monitors usage of the patient's identity credentials as well as access to her electronic health records. Such a monitoring agent can enhance patient's awareness and control and improve accountability for health records even in a distributed, multi-domain environment, which is typical in an e-healthcare setting. This will reduce the risk and loss caused by misuse of stolen data. In addition to the solution from a patient's perspective, we also propose a secure system architecture that can be used in healthcare organizations to enable robust auditing and management over client devices. This helps us further enhance patients' confidence in secure use of their health data.

The need to increase the hostile attack resilience of distributed and internet-worked computer systems is critical and pressing. This thesis contributes to concrete improvements in distributed systems trustworthiness through an enhanced understanding of a technical approach known as trusted computing hardware. Because of its physical and logical protection features, trusted computing hardware can reliably enforce a security policy in a threat model where the authorised user is untrusted or when the device is placed in a hostile environment. We present a critical analysis of vulnerabilities in current systems, and argue that current industry-driven trusted computing initiatives will fail in efforts to retrofit security into inherently flawed operating system designs, since there is no substitute for a sound protection architecture grounded in hardware-enforced domain isolation. In doing so we identify the limitations of hardware-based approaches. We argue that the current emphasis of these programs does not give sufficient weight to the role that operating system security plays in overall system security. New processor features that provide hardware support for virtualisation will contribute more to practical security improvement because they will allow multiple operating systems to concurrently share the same processor. New operating systems that implement a sound protection architecture will thus be able to be introduced to support applications with stringent security requirements. These can coexist alongside inherently less secure mainstream operating systems, allowing a gradual migration to less vulnerable alternatives. We examine the effectiveness of the ITSEC and Common Criteria evaluation and certification schemes as a basis for establishing assurance in trusted computing hardware. Based on a survey of smart card certifications, we contend that the practice of artificially limiting the scope of an evaluation in order to gain a higher assurance rating is quite common. Due to a general lack of understanding in the marketplace as to how the schemes work, high evaluation assurance levels are confused with a general notion of 'high security strength'. Vendors invest little effort in correcting the misconception since they benefit from it and this has arguably undermined the value of the whole certification process. We contribute practical techniques for securing personal trusted hardware devices against a type of attack known as a relay attack. Our method is based on a novel application of a phenomenon known as side channel leakage, heretofore considered exclusively as a security vulnerability. We exploit the low latency of side channel information transfer to deliver a communication channel with timing resolution that is fine enough to detect sophisticated relay attacks. We avoid the cost and complexity associated with alternative communication techniques suggested in previous proposals. We also propose the first terrorist attack resistant distance bounding protocol that is efficient enough to be implemented on resource constrained devices. We propose a design for a privacy sensitive electronic cash scheme that leverages the confidentiality and integrity protection features of trusted computing hardware. We specify the command set and message structures and implement these in a prototype that uses Dallas Semiconductor iButtons. We consider the access control requirements for a national scale electronic health records system of the type that Australia is currently developing. We argue that an access control model capable of supporting explicit denial of privileges is required to ensure that consumers maintain their right to grant or withhold consent to disclosure of their sensitive health information in an electronic system. Finding this feature absent in standard role-based access control models, we propose a modification to role-based access control that supports policy constructs of this type. Explicit denial is difficult to enforce in a large scale system without an active central authority but centralisation impacts negatively on system scalability. We show how the unique properties of trusted computing hardware can address this problem. We outline a conceptual architecture for an electronic health records access control system that leverages hardware level CPU virtualisation, trusted platform modules, personal cryptographic tokens and secure coprocessors to implement role based cryptographic access control. We argue that the design delivers important scalability benefits because it enables access control decisions to be made and enforced locally on a user's computing platform in a reliable way.

Na prática clínica, o enfermeiro precisa sistematizar o cuidado baseado em aspectos que visam garantir a segurança e a qualidade do cuidado aos pacientes. Entre esses aspectos salienta-se que os registros de enfermagem sejam realizados de maneira plena e, principalmente, que sejam compreendidos e valorizados. É nessa perspectiva que a utilização de uma terminologia e sistemas eletrônicos aliados ao processo de enfermagem ganham espaço, favorecendo a qualidade dos registros. A avaliação da qualidade desses registros pode se dar por meio de um instrumento denominado Quality of Diagnoses, Interventions and Outcomes (Q-DIO), publicado em língua inglesa e validado apenas na Suíça. O Q-DIO tem como principal objetivo avaliar a qualidade dos registros de enfermagem. Somado a isso, tem sido utilizado como um indicador para comparar a qualidade dos registros com e sem linguagem padronizada, definir metas, avaliar o impacto da implementação de programas educacionais e ainda, em sistemas de auditoria. Existe uma lacuna no Brasil, assim como nos Estados Unidos da América (EUA), de instrumentos que avaliem questões relativas à qualidade dos registros de enfermagem. Foi nessa perspectiva que esse estudo metodológico foi desenvolvido para validar o Q-DIO no Brasil e nos EUA. O Q-DIO é composto por 29 itens, dividido em quatro domínios (diagnósticos de enfermagem como processo, diagnósticos de enfermagem como produto, intervenções de enfermagem, resultados de enfermagem), composto por escala Likert de três pontos. Para validação do instrumento foram elegíveis registros de pacientes em pós-operatório de cirurgia cardíaca que tiveram registrados em prontuário o histórico, as evoluções e as prescrições de enfermagem entre um período mínimo de quatro dias. A amostra foi de 180 registros, distribuídos igualmente entre os três centros do estudo, dois no Brasil (centros 1 e 2) e um nos EUA (centro 3). Dentre as propriedades psicométricas, foram avaliadas fidedignidade (consistência interna e estabilidade) e a validade de constructo divergente. Os valores do alfa de Cronbach para as 29 questões foram superiores a 0,70 para todos os centros. Com relação à estabilidade, o coeficiente de correlação intraclasse variou entre 0,64 e 0,85 para intraobservador e 0,68 a 0,82 para interobservador, o que indica níveis satisfatórios e excelentes de concordância. Na validade de constructo divergente observou-se diferença estatística significativa entre as médias da soma dos 29 itens do instrumento entre os três centros. O centro 1 (registros eletrônicos com linguagem padronizada) apresentou média de 36,8(±4,5) [IC95%: 35,63-37,94]; o centro 2 (registros manuais sem linguagem padronizada) obteve média de 11,533(±6,2) [IC95%:9,93-13,14]; o centro 3 (registros eletrônicos sem linguagem padronizada) teve média de 31,2(±5,3) [IC95%: 29,87-32,63]. Esses resultados indicam que o Q-DIO é fidedigno e válido para avaliar a qualidade de registros de enfermagem eletrônicos ou não, e que utilizem ou não linguagem padronizada no Brasil; também nos EUA esse instrumento se mostrou fidedigno e válido para dados eletrônicos sem uso de terminologia padrão. / In clinical practice, nurses must systematize their practice based in certain aspects intended to ensure the safety and quality of the patient care. Among such aspects should be noted that the nursing records must be fully completed, understood, and valued. Taking this into consideration, the use of terminology and electronic systems along with the nursing processes favor the quality of nursing records. The assessment of the quality of such records may be obtained through an instrument called Quality of Diagnoses, Interventions and Outcomes (Q-DIO), published in English and validated in Switzerland. The Q-DIO's main objective is to assess the quality of the nursing records, although it has also been used as an indicator to compare the quality of records with and without standardized language, to set goals, to evaluate the impact of implementing educational programs, and to give some help in audit systems. There is a lack of instruments capable of assessing issues related to the quality of nursing records in Brazil and in the United States (U.S.). It was because of it that this methodological study was developed: to validate the Q-DIO instrument in Brazil and in the U.S. The Q-DIO is composed of 29 items, divided into four domains (nursing diagnoses as process, nursing diagnoses as product, nursing interventions, and nursing outcomes), composed of a three-point Likert scale. To validate the instrument, records from patients in the period after a cardiac surgery, and who had in their historical records trends and nursing prescriptions between a minimum of four days, were selected. The sample has a total of 180 records, divided equally between the three study centers, being two located in Brazil (center 1 and 2) and one in the U.S. (center 3). Among the psychometric properties, reliability (internal consistency and stability) and divergent construct validity were those evaluated. The values of Cronbach's Alpha for the 29 questions were superior to 0.70 for all centers. Regarding stability, the intraclass correlation coefficient ranged between 0.64 to 0.85 for intraobserver, and 0.68 to 0.82 for inter-observer, which indicates excellent and satisfactory levels of agreement. In divergent construct validity, statistically significant differences were observed in the average of the sum of the 29 items of the instrument among the three centers. Center 1 (electronic records with standardized language) had an average of 36.8 (± 9.5) [95%CI: 35.63-37.94]; center 2 (manual records without standardized language) had an average of 11.53 (± 6,2) [95%CI: 9.93-13.14]; and center 3 (electronic records without standardized language) presented an average of 31.2 (± 5.3) [95%CI: 29.87-32.63]. These results indicate that Q-DIO is valid and reliable for assessing the quality of nursing records, being them electronic or not, using standardized language or not, at least in Brazil. In the U.S., this instrument has also proved to be reliable and valid for electronic nursing records without use of standardized language. / En la práctica clínica el enfermero precisa sistematizar el cuidado a partir de aspectos que objetivan garantizar seguridad y calidad del cuidado a los pacientes. Entre esos aspectos destacamos que los registros de enfermería sean realizados de manera plena y principalmente que sean comprendidos, valorados. Es en esta perspectiva, que la utilización de una terminología y de sistemas electrónicos coligados al proceso de enfermería obtienen espacio favoreciendo la calidad de los registros. La evaluación de la calidad de dichos registros puede ser a través de un instrumento nombrado Quality of Diagnoses, Interventions and Outcomes (Q-DIO) publicado en idioma inglés y validado solamente en Suiza. Q-DIO posee por objetivo principal evaluar la calidad de los registros en enfermería. A eso se suma su utilización como un indicador para comparar la calidad de registros con y sin lenguaje patrón, establecer fines, evaluar impacto de la implementación de programas educativos y aún, en sistemas de auditoría. Existe una falla en Brasil, así como en Estados Unidos de América (EUA) sobre instrumentos que evalúen cuestiones relacionadas a la calidad de los registros de enfermería. Fue en esa perspectiva que este estudio metodológico ha sido desarrollado para validar el Q-DIO en Brasil y EUA. Q-DIO está compuesto de 29 puntos, dividido en cuatro aspectos (diagnósticos de enfermería como proceso, diagnósticos de enfermería como producto, intervenciones de enfermería, resultados de enfermería), compuesto por escala Likert de tres puntos. Para validación del instrumento han sido elegidos registros de pacientes en pos operatorio de cirugía cardiaca, que tuvieron registrados en prontuario o histórico y las evoluciones y prescripciones de enfermería entre un periodo mínimo de cuatro días. La muestra fue de 180 registros, distribuidos igualmente entre los tres centros del estudio; dos en Brasil (centro 1 y 2) y uno en EUA (centro 3). Entre las propiedades psicométricas fueron evaluadas la fidedignidad (consistencia interna y estabilidad) y la validez del constructo divergente. Los valores de Alfa de Cronbach para las 29 cuestiones fueron superiores a 0,70 para todos los centros. En lo que se refiere a la estabilidad, el coeficiente de correlación intra-clase tuvo variación entre 0,64 y 0,85 para intra-observador y 0,68 a 0,82 para inter-observador, lo que indica niveles satisfactorios y excelentes de concordancia. En la validad de constructo divergente se pudo observar una diferencia estadística significativa entre las medias de la suma de los 29 puntos del instrumento entre los tres centros. El centro 1 (registros electrónicos con lenguaje patrón) presentó media de 36,8 (+_ 4,5) [IC95%: 35,63 – 37,94], centro 2 (registros manuales sin lenguaje patrón) obtuvo media de 11,53 (+_ 6,2) [IC 95%: 9,93-13,14] y el centro 3 (registros electrónicos sin lenguaje patrón) con media de 31,2 (+_5,3) [IC95%: 29,87-32,63]. Tales resultados indican que Q-DIO es fidedigno y válido para evaluar la calidad de los registros de enfermería, sean ellos electrónicos o no, y que utilicen lenguaje patrón o no en Brasil, así como, en EUA dicho instrumento también se ha mostrado fidedigno y válido para datos electrónicos sin uso de la terminología patrón.

The objectives of this study were to establish the effectiveness of chlorpyrifos in suppressing H. armigera population in oranges and to determine the effect chlorpyrifos had on the health of farm pesticide operators. Experiments showed that by applying chlorpyrifos on orange trees, H. armigera larvae population was suppressed significantly. High fruit yields were realized from trees that were sprayed with chlorpyrifos. Visual observation of personnel involved in pesticide related duties, revealed that judicious use of pesticides was not practiced by farm workers in all three farms. Data analysis from questionnaires, health records and interviews proved that farm workers suffered from illnesses that were pesticide related. It was mainly those farm workers in the age group of 31 to 35 years who suffered the most from pesticide related illnesses. / Agriculture and Life Sciences / M. Sc. (Agriculture)

Na prática clínica, o enfermeiro precisa sistematizar o cuidado baseado em aspectos que visam garantir a segurança e a qualidade do cuidado aos pacientes. Entre esses aspectos salienta-se que os registros de enfermagem sejam realizados de maneira plena e, principalmente, que sejam compreendidos e valorizados. É nessa perspectiva que a utilização de uma terminologia e sistemas eletrônicos aliados ao processo de enfermagem ganham espaço, favorecendo a qualidade dos registros. A avaliação da qualidade desses registros pode se dar por meio de um instrumento denominado Quality of Diagnoses, Interventions and Outcomes (Q-DIO), publicado em língua inglesa e validado apenas na Suíça. O Q-DIO tem como principal objetivo avaliar a qualidade dos registros de enfermagem. Somado a isso, tem sido utilizado como um indicador para comparar a qualidade dos registros com e sem linguagem padronizada, definir metas, avaliar o impacto da implementação de programas educacionais e ainda, em sistemas de auditoria. Existe uma lacuna no Brasil, assim como nos Estados Unidos da América (EUA), de instrumentos que avaliem questões relativas à qualidade dos registros de enfermagem. Foi nessa perspectiva que esse estudo metodológico foi desenvolvido para validar o Q-DIO no Brasil e nos EUA. O Q-DIO é composto por 29 itens, dividido em quatro domínios (diagnósticos de enfermagem como processo, diagnósticos de enfermagem como produto, intervenções de enfermagem, resultados de enfermagem), composto por escala Likert de três pontos. Para validação do instrumento foram elegíveis registros de pacientes em pós-operatório de cirurgia cardíaca que tiveram registrados em prontuário o histórico, as evoluções e as prescrições de enfermagem entre um período mínimo de quatro dias. A amostra foi de 180 registros, distribuídos igualmente entre os três centros do estudo, dois no Brasil (centros 1 e 2) e um nos EUA (centro 3). Dentre as propriedades psicométricas, foram avaliadas fidedignidade (consistência interna e estabilidade) e a validade de constructo divergente. Os valores do alfa de Cronbach para as 29 questões foram superiores a 0,70 para todos os centros. Com relação à estabilidade, o coeficiente de correlação intraclasse variou entre 0,64 e 0,85 para intraobservador e 0,68 a 0,82 para interobservador, o que indica níveis satisfatórios e excelentes de concordância. Na validade de constructo divergente observou-se diferença estatística significativa entre as médias da soma dos 29 itens do instrumento entre os três centros. O centro 1 (registros eletrônicos com linguagem padronizada) apresentou média de 36,8(±4,5) [IC95%: 35,63-37,94]; o centro 2 (registros manuais sem linguagem padronizada) obteve média de 11,533(±6,2) [IC95%:9,93-13,14]; o centro 3 (registros eletrônicos sem linguagem padronizada) teve média de 31,2(±5,3) [IC95%: 29,87-32,63]. Esses resultados indicam que o Q-DIO é fidedigno e válido para avaliar a qualidade de registros de enfermagem eletrônicos ou não, e que utilizem ou não linguagem padronizada no Brasil; também nos EUA esse instrumento se mostrou fidedigno e válido para dados eletrônicos sem uso de terminologia padrão. / In clinical practice, nurses must systematize their practice based in certain aspects intended to ensure the safety and quality of the patient care. Among such aspects should be noted that the nursing records must be fully completed, understood, and valued. Taking this into consideration, the use of terminology and electronic systems along with the nursing processes favor the quality of nursing records. The assessment of the quality of such records may be obtained through an instrument called Quality of Diagnoses, Interventions and Outcomes (Q-DIO), published in English and validated in Switzerland. The Q-DIO's main objective is to assess the quality of the nursing records, although it has also been used as an indicator to compare the quality of records with and without standardized language, to set goals, to evaluate the impact of implementing educational programs, and to give some help in audit systems. There is a lack of instruments capable of assessing issues related to the quality of nursing records in Brazil and in the United States (U.S.). It was because of it that this methodological study was developed: to validate the Q-DIO instrument in Brazil and in the U.S. The Q-DIO is composed of 29 items, divided into four domains (nursing diagnoses as process, nursing diagnoses as product, nursing interventions, and nursing outcomes), composed of a three-point Likert scale. To validate the instrument, records from patients in the period after a cardiac surgery, and who had in their historical records trends and nursing prescriptions between a minimum of four days, were selected. The sample has a total of 180 records, divided equally between the three study centers, being two located in Brazil (center 1 and 2) and one in the U.S. (center 3). Among the psychometric properties, reliability (internal consistency and stability) and divergent construct validity were those evaluated. The values of Cronbach's Alpha for the 29 questions were superior to 0.70 for all centers. Regarding stability, the intraclass correlation coefficient ranged between 0.64 to 0.85 for intraobserver, and 0.68 to 0.82 for inter-observer, which indicates excellent and satisfactory levels of agreement. In divergent construct validity, statistically significant differences were observed in the average of the sum of the 29 items of the instrument among the three centers. Center 1 (electronic records with standardized language) had an average of 36.8 (± 9.5) [95%CI: 35.63-37.94]; center 2 (manual records without standardized language) had an average of 11.53 (± 6,2) [95%CI: 9.93-13.14]; and center 3 (electronic records without standardized language) presented an average of 31.2 (± 5.3) [95%CI: 29.87-32.63]. These results indicate that Q-DIO is valid and reliable for assessing the quality of nursing records, being them electronic or not, using standardized language or not, at least in Brazil. In the U.S., this instrument has also proved to be reliable and valid for electronic nursing records without use of standardized language. / En la práctica clínica el enfermero precisa sistematizar el cuidado a partir de aspectos que objetivan garantizar seguridad y calidad del cuidado a los pacientes. Entre esos aspectos destacamos que los registros de enfermería sean realizados de manera plena y principalmente que sean comprendidos, valorados. Es en esta perspectiva, que la utilización de una terminología y de sistemas electrónicos coligados al proceso de enfermería obtienen espacio favoreciendo la calidad de los registros. La evaluación de la calidad de dichos registros puede ser a través de un instrumento nombrado Quality of Diagnoses, Interventions and Outcomes (Q-DIO) publicado en idioma inglés y validado solamente en Suiza. Q-DIO posee por objetivo principal evaluar la calidad de los registros en enfermería. A eso se suma su utilización como un indicador para comparar la calidad de registros con y sin lenguaje patrón, establecer fines, evaluar impacto de la implementación de programas educativos y aún, en sistemas de auditoría. Existe una falla en Brasil, así como en Estados Unidos de América (EUA) sobre instrumentos que evalúen cuestiones relacionadas a la calidad de los registros de enfermería. Fue en esa perspectiva que este estudio metodológico ha sido desarrollado para validar el Q-DIO en Brasil y EUA. Q-DIO está compuesto de 29 puntos, dividido en cuatro aspectos (diagnósticos de enfermería como proceso, diagnósticos de enfermería como producto, intervenciones de enfermería, resultados de enfermería), compuesto por escala Likert de tres puntos. Para validación del instrumento han sido elegidos registros de pacientes en pos operatorio de cirugía cardiaca, que tuvieron registrados en prontuario o histórico y las evoluciones y prescripciones de enfermería entre un periodo mínimo de cuatro días. La muestra fue de 180 registros, distribuidos igualmente entre los tres centros del estudio; dos en Brasil (centro 1 y 2) y uno en EUA (centro 3). Entre las propiedades psicométricas fueron evaluadas la fidedignidad (consistencia interna y estabilidad) y la validez del constructo divergente. Los valores de Alfa de Cronbach para las 29 cuestiones fueron superiores a 0,70 para todos los centros. En lo que se refiere a la estabilidad, el coeficiente de correlación intra-clase tuvo variación entre 0,64 y 0,85 para intra-observador y 0,68 a 0,82 para inter-observador, lo que indica niveles satisfactorios y excelentes de concordancia. En la validad de constructo divergente se pudo observar una diferencia estadística significativa entre las medias de la suma de los 29 puntos del instrumento entre los tres centros. El centro 1 (registros electrónicos con lenguaje patrón) presentó media de 36,8 (+_ 4,5) [IC95%: 35,63 – 37,94], centro 2 (registros manuales sin lenguaje patrón) obtuvo media de 11,53 (+_ 6,2) [IC 95%: 9,93-13,14] y el centro 3 (registros electrónicos sin lenguaje patrón) con media de 31,2 (+_5,3) [IC95%: 29,87-32,63]. Tales resultados indican que Q-DIO es fidedigno y válido para evaluar la calidad de los registros de enfermería, sean ellos electrónicos o no, y que utilicen lenguaje patrón o no en Brasil, así como, en EUA dicho instrumento también se ha mostrado fidedigno y válido para datos electrónicos sin uso de la terminología patrón.