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Clinical trials are studies designed to assess whether a new intervention is better than the current alternatives. However, most of them fail to recruit participants on schedule. It is hard to use Electronic Health Record (EHR) data to find eligible patients, therefore studies rely on manual assessment, which is time consuming, inefficient and requires specialized training. In this work we describe the design and development of an information retrieval system with the objective of finding eligible patients for cancer trials. The Recruit system has been in use at A. C. Camargo Cancer Center since August/2014 and contains data from more than 500,000 patients and 9 databases. It uses ontologies to integrate data from several sources and represent medical knowledge, which helps enhance results. One can search both in structured data and inside free text reports. The preliminary quality assessments shows excellent recall rates. Recruit proved to be an useful tool for researchers and its modular design could be applied to other clinical conditions and hospitals.

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In this work we present a system to identify and extract patient's smoking status from clinical narrative text in Spanish. The clinical narrative text was processed using natural language processing techniques, and annotated by four people with a biomedical background. The dataset used for classification had 2,465 documents, each one annotated with one of the four smoking status categories. We used two feature representations: single word token and bigrams. The classification problem was divided in two levels. First recognizing between smoker (S) and non-smoker (NS); second recognizing between current smoker (CS) and past smoker (PS). For each feature representation and classification level, we used two classifiers: Support Vector Machines (SVM) and Bayesian Networks (BN). We split our dataset as follows: a training set containing 66% of the available documents that was used to build classifiers and a test set containing the remaining 34% of the documents that was used to test and evaluate the model. Our results show that SVM together with the bigram representation performed better in both classification levels. For S vs NS classification level performance measures were: ACC=85%, Precision=85%, and Recall=90%. For CS vs PS classification level performance measures were: ACC=87%, Precision=91%, and Recall=94%.

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O registro eletrônico tem a finalidade de proporcionar maior legibilidade e segurança na documentação das ações do enfermeiro. A utilização das classificações de enfermagem inseridas em um sistema informatizado organiza, uniformiza e resgata os dados essenciais para o planejamento do cuidado individualizado e a avaliação dos resultados advindos das intervenções. O objetivo foi descrever a elaboração das etapas da Sistematização da Assistência de Enfermagem (SAE) utilizando as Classificações de Enfermagem em um Sistema de Informação Hospitalar (SIH). Um mapeamento cruzado entre o seu conteúdo e o impresso do plano de cuidados da Unidade de Cuidado Semi-Intensiva foi realizado. Planilhas em Excel foram inseridas de acordo com as etapas da sistematização e telas eletrônicas permitiram o registro da avaliação clínica do enfermeiro. A documentação eletrônica proporcionou agilizar e resgatar dados, estimular a acurácia diagnóstica e avaliar as intervenções por meio dos indicadores de resultados.

Electronic health records aim at providing greater readability and safety in documenting nursing actions. Using the nursing classifications of an information system organizes, standardizes and retrieves the data considered essential to plan individualized care and the results evaluation from the interventions. The objective was to describe the development of the Nursing Care Systematization stages using Nursing Classifications in a Hospital Information System. Cross-mapping was performed of the care plan content and the printed version, at a Semi-Intensive Care Unit. Excel spreadsheets were included according to the systematization stage and electronic screens permitted to record the nurses' clinical evaluation. The electronic documentation permitted faster data input and retrieval, in addition to stimulating diagnosis accuracy and evaluating the interventions by means of result indicators.

El registro electrónico tiene la finalidad de proporcionar mayor legibilidad y seguridad a la documentación de las acciones del enfermero. La utilización de las clasificaciones de enfermería insertas en un sistema informático organiza, otorga uniformidad y resguarda los datos esenciales para la planificación del cuidado individualizado y la evaluación de los resultados derivados de las intervenciones. Se objetivó describir la elaboración de las etapas de la SAE utilizando las Clasificaciones de Enfermería en un Sistema Informático Hospitalario (SIH). Se realizó un mapeo cruzado entre su contenido y la versión impresa del plan de cuidados de la Unidad de Terapia Intermedia. Fueron incluidas planillas de Excel de acuerdo con las etapas de la sistematización, y las pantallas electrónicas permitieron registrar la evaluación clínica del enfermero. La documentación electrónica permitió agilizar y resguardar datos, estimular la precisión diagnóstica y evaluar las intervenciones mediante los indicadores de resultados.

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Surveillance Levels (SLs) are categories for medical patients (used in Brazil) that represent different types of medical recommendations. SLs are defined according to risk factors and the medical and developmental history of patients. Each SL is associated with specific educational and clinical measures. The objective of the present paper was to verify computer-aided, automatic assignment of SLs. The present paper proposes a computer-aided approach for automatic recommendation of SLs. The approach is based on the classification of information from patient electronic records. For this purpose, a software architecture composed of three layers was developed. The architecture is formed by a classification layer that includes a linguistic module and machine learning classification modules. The classification layer allows for the use of different classification methods, including the use of preprocessed, normalized language data drawn from the linguistic module. We report the verification and validation of the software architecture in a Brazilian pediatric healthcare institution. The results indicate that selection of attributes can have a great effect on the performance of the system. Nonetheless, our automatic recommendation of surveillance level can still benefit from improvements in processing procedures when the linguistic module is applied prior to classification. Results from our efforts can be applied to different types of medical systems. The results of systems supported by the framework presented in this paper may be used by healthcare and governmental institutions to improve healthcare services in terms of establishing preventive measures and alerting authorities about the possibility of an epidemic.

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