Ischemic stroke: Process perspective, clinical and profile characteristics, and external factors.

OBJECTIVE: To describe a method of analysis for understanding the health care process, enriched with information on the clinical and profile characteristics of the patients. To apply the proposed technique to analyze an ischemic stroke dataset. MATERIALS AND METHODS: We analyzed 4,830 electronic health records (EHRs) from patients with ischemic stroke (2010-2017), containing information about events realized during treatment and clinical and profile information of the patients. The proposed method combined process mining techniques with data analysis, grouping the data by primary care units (PCU - units responsible for the primary care of patients residing in a geographical area). RESULTS: A novel method, named process, data, and management (PDM) analysis method was used for ischemic stroke data and it provided the following outcomes: health care process for patients with ischemic stroke with time statistics; analysis of potential factors for slow hospital admission indicating an increase in the time to hospital admission of 3.4 h (mean value) for patients with an origin at the urgent care center (UCC) - 30% of patients; analysis of PCUs with distinct secondary stroke rates indicating that the social class of patients is the main difference between them; and the visualization of risk factors (before the stroke) by the PCU to inform the health manager about the potential of prevention. DISCUSSION: PDM analysis describes a step-by-step method for combining process analysis with data analysis considering a management focus. The results obtained on the stroke context can support the definition of more refined action plans by the health manager, improving the stroke health care process and preventing new events. CONCLUSION: When a patient is diagnosed with ischemic stroke, immediate treatment is needed. Moreover, it is possible to prevent new events to some degree by monitoring and treating risk factors. PDM analysis provides an overview of the health care process with time, combining elements that affect the treatment flow and factors, which can indicate a potential for preventing new events. We also can apply PDM analysis in different scenarios, when there is information about activities from treatment flow and other characteristics related to the treatment or the prevention of the analyzed disease. The management focus of the results aids in the formulation of service policies, action plans, and resource allocation.

'Quer N0 AVC' for Monitoring Stroke Patients' Healthcare Using a Mobile App.

Stroke is one of the leading causes of death and impairments worldwide. After hospital discharge, it is necessary to monitor these patients during their recovery. This research addresses the implementation of a mobile app, entitled 'Quer N0 AVC', to improve the quality of stroke patient care in Joinville, Brazil. The study method was divided into two parts. The adaptation phase included all the necessary information in the app for monitoring stroke patients. The implementation phase aimed to prepare a routine for the Quer mobile app installation. One of the questionnaires collected data from 42 patients and identified that before hospital admission 29% of them did not have medical appointments, 36% had one or two appointments, 11% had three appointments, and 24% had four or more appointments. This research portrayed adaptation feasibility and the implementation of a cell phone app for following up on stroke patients.

Prediction of the occurrence of leprosy reactions based on Bayesian networks.

Introduction: Leprosy reactions (LR) are severe episodes of intense activation of the host inflammatory response of uncertain etiology, today the leading cause of permanent nerve damage in leprosy patients. Several genetic and non-genetic risk factors for LR have been described; however, there are limited attempts to combine this information to estimate the risk of a leprosy patient developing LR. Here we present an artificial intelligence (AI)-based system that can assess LR risk using clinical, demographic, and genetic data. Methods: The study includes four datasets from different regions of Brazil, totalizing 1,450 leprosy patients followed prospectively for at least 2 years to assess the occurrence of LR. Data mining using WEKA software was performed following a two-step protocol to select the variables included in the AI system, based on Bayesian Networks, and developed using the NETICA software. Results: Analysis of the complete database resulted in a system able to estimate LR risk with 82.7% accuracy, 79.3% sensitivity, and 86.2% specificity. When using only databases for which host genetic information associated with LR was included, the performance increased to 87.7% accuracy, 85.7% sensitivity, and 89.4% specificity. Conclusion: We produced an easy-to-use, online, free-access system that identifies leprosy patients at risk of developing LR. Risk assessment of LR for individual patients may detect candidates for close monitoring, with a potentially positive impact on the prevention of permanent disabilities, the quality of life of the patients, and upon leprosy control programs.

[Essential data set's archetypes for nursing care of endometriosis patients]. / Arquétipos do conjunto de dados essenciais de enfermagem para atendimento de portadoras de endometriose.

This study aimed to develop an Essential Data Set for Nursing Care of Patients with Endometriosis (CDEEPE), represented by archetypes. An exploratory applied research with specialists' participation that was carried out at Heath Informatics Laboratory of PUCPR, between February and November of 2010. It was divided in two stages: CDEEPE construction and evaluation including Nursing Process phases and Basic Human Needs, and archetypes development based on this data set. CDEEPE was evaluated by doctors and nurses with 95.9% of consensus and containing 51 data items. The archetype "Perception of Organs and Senses" was created to represents this data set. This study allowed identifying important information for nursing practices contributing to computerization and application of nursing process during care. The CDEEPE was the basis for archetype creation, that will make possible structured, organized, efficient, interoperable, and semantics records.

Analysis of Stroke Assistance in Covid-19 Pandemic by Process Mining Techniques.

Medical assistance to stroke patients must start as early as possible; however, several changes have impacted healthcare services during the Covid-19 pandemic. This research aimed to identify the stroke onset-to-door time during the Covid-19 pandemic considering the different paths a patient can take until receiving specialized care. It is a retrospective study based on process mining (PM) techniques applied to 221 electronic healthcare records of stroke patients during the pandemic. The results are two process models representing the patient's path and performance, from the onset of the first symptoms to admission to specialized care. PM techniques have discovered the patient journey in providing fast stroke assistance.

SemClinBr - a multi-institutional and multi-specialty semantically annotated corpus for Portuguese clinical NLP tasks.

BACKGROUND: The high volume of research focusing on extracting patient information from electronic health records (EHRs) has led to an increase in the demand for annotated corpora, which are a precious resource for both the development and evaluation of natural language processing (NLP) algorithms. The absence of a multipurpose clinical corpus outside the scope of the English language, especially in Brazilian Portuguese, is glaring and severely impacts scientific progress in the biomedical NLP field. METHODS: In this study, a semantically annotated corpus was developed using clinical text from multiple medical specialties, document types, and institutions. In addition, we present, (1) a survey listing common aspects, differences, and lessons learned from previous research, (2) a fine-grained annotation schema that can be replicated to guide other annotation initiatives, (3) a web-based annotation tool focusing on an annotation suggestion feature, and (4) both intrinsic and extrinsic evaluation of the annotations. RESULTS: This study resulted in SemClinBr, a corpus that has 1000 clinical notes, labeled with 65,117 entities and 11,263 relations. In addition, both negation cues and medical abbreviation dictionaries were generated from the annotations. The average annotator agreement score varied from 0.71 (applying strict match) to 0.92 (considering a relaxed match) while accepting partial overlaps and hierarchically related semantic types. The extrinsic evaluation, when applying the corpus to two downstream NLP tasks, demonstrated the reliability and usefulness of annotations, with the systems achieving results that were consistent with the agreement scores. CONCLUSION: The SemClinBr corpus and other resources produced in this work can support clinical NLP studies, providing a common development and evaluation resource for the research community, boosting the utilization of EHRs in both clinical practice and biomedical research. To the best of our knowledge, SemClinBr is the first available Portuguese clinical corpus.

Decision-Making Support in Stroke Diagnosis Process: An Approach Based on the PROMETHEE Method and Decision Model Notation.

Decision-making in the field of healthcare is a very complex activity. Several tools have been developed to support the decision-making process. DMN, a modeling technique focused on decisions, is among these and has been gaining prominence in both, literature and business, as has the multi-criteria method PROMETHEE II that helps decision-makers with multi-criteria in analyses. Thus, this research targets combining these two techniques and analyzing the decision support that these two tools afford together. The diagnostic stage of stroke patients was used to perform this work. The research demonstrated that this proposal can drive major gains in efficiency and assertiveness in decision-making in time-sensitive hospital processes. After all, there is a noticeable dearth of hospitals with specialized teams as well as a shortfall of adequate infrastructure for this treatment.

Representation of Diagnosis and Nursing Interventions in OpenEHR Archetypes.

OBJECTIVE: The study aimed to represent the content of nursing diagnosis and interventions in the openEHR standard. METHODS: This is a developmental study with the models developed according to ISO 18104: 2014. The Ocean Archetype Editor tool from the openEHR Foundation was used. RESULTS: Two archetypes were created; one to represent the nursing diagnosis concept and the other the nursing intervention concept. Existing archetypes available in the Clinical Knowledge Manager were reused in modeling. CONCLUSION: The representation of nursing diagnosis and interventions based on the openEHR standard contributes to representing nursing care phenomena and needs in health information systems.

ISO/TR 12300:2016 for clinical cross-terminology mapping: contribution to nursing.

This theoretical and reflective study aimed to assess the contribution of the ISO/TR 12300:2016 document for the mapping of nursing terminology. The referred document and related articles were used as an empirical framework. The study analyzed the content of the document, highlighting cardinality and equivalence principles. The standard presents conceptual and operational basis for mapping, with cardinality and equivalence as the support for the categorization of cross-terminology mapping in the area of nursing. Cardinality verifies candidate target terms to represent the source term, while the equivalence degree scale checks semantic correspondence. Among the principles included in the ISO/TR 12300:2016, cardinality and equivalence contribute to the accurate representation of the results of the cross-terminology mapping process and its use should decrease inconsistencies.

Comparison of the Results of Manual and Automated Processes of Cross-Mapping Between Nursing Terms: Quantitative Study.

BACKGROUND: Cross-mapping establishes equivalence between terms from different terminology systems, which is useful for interoperability, updated terminological versions, and reuse of terms. Due to the number of terms to be mapped, this work can be extensive, tedious, and thorough, and it is susceptible to errors; this can be minimized by automated processes, which use computational tools. OBJECTIVE: The aim of this study was to compare the results of manual and automated term mapping processes. METHODS: In this descriptive, quantitative study, we used the results of two mapping processes as an empirical basis: manual, which used 2638 terms of nurses' records from a university hospital in southern Brazil and the International Classification for Nursing Practice (ICNP); and automated, which used the same university hospital terms and the primitive terms of the ICNP through MappICNP, an algorithm based on rules of natural language processing. The two processes were compared via equality and exclusivity assessments of new terms of the automated process and of candidate terms. RESULTS: The automated process mapped 569/2638 (21.56%) of the source bank's terms as identical, and the manual process mapped 650/2638 (24.63%) as identical. Regarding new terms, the automated process mapped 1031/2638 (39.08%) of the source bank's terms as new, while the manual process mapped 1251 (47.42%). In particular, manual mapping identified 101/2638 (3.82%) terms as identical and 429 (16.26%) as new, whereas the automated process identified 20 (0.75%) terms as identical and 209 (7.92%) as new. Of the 209 terms mapped as new by the automated process, it was possible to establish an equivalence with ICNP terms in 48 (23.0%) cases. An analysis of the candidate terms offered by the automated process to the 429 new terms mapped exclusively by the manual process resulted in 100 (23.3%) candidates that had a semantic relationship with the source term. CONCLUSIONS: The automated and manual processes map identical and new terms in similar ways and can be considered complementary. Direct identification of identical terms and the offering of candidate terms through the automated process facilitate and enhance the results of the mapping; confirmation of the precision of the automated mapping requires further analysis by researchers.

Automatic Mapping Between Brazilian Portuguese Clinical Terms and International Classification for Nursing Practice.

This study describes MappICNP, an automatic method for mapping between Brazilian Portuguese clinical narratives in free text and International Classification for Nursing Practice (ICNP) concepts. It's composed of six natural language processing rules, related to terms comparison. A set of 2,638 terms extracted from hospitals nursing notes was mapped. MappICNP helps to map 1,607 terms, 113 less than a manual approach. The results demostrate its advantages in minimizing the time spent and reducing the scope of analysis through candidate terms of ICNP.

Learning Portuguese Clinical Word Embeddings: A Multi-Specialty and Multi-Institutional Corpus of Clinical Narratives Supporting a Downstream Biomedical Task.

In this paper, we trained a set of Portuguese clinical word embedding models of different granularities from multi-specialty and multi-institutional clinical narrative datasets. Then, we assessed their impact on a downstream biomedical NLP task of Urinary Tract Infection disease identification. Additionally, we intrinsically evaluated our main model using an adapted version of Bio-SimLex for the Portuguese language. Our empirical results showed that the larger, coarse-grained model achieved a slightly better outcome when compared with the small, fine-grained model in the proposed task. Moreover, we obtained satisfactory results with Bio-SimLex intrinsic evaluation.

Exploiting Temporal Constraints of Clinical Guidelines by Applying OpenEHR Archetypes.

Studies describing Computer-Interpretable Clinical Guidelines (CIG) with temporal constrains (TC) generally have not addressed issues related to their integration into Electronic Health Record (EHR) systems. This study aimed to represent TCs contained in clinical guidelines by applying archetypes and Guideline Definition Language (GDL) to incorporate decision support into EHRs. An example of each TC class in the clinical guideline for management of Atrial Fibrillation was represented using archetypes and GDL.

Early Identification of Patients at Risk of Sepsis in a Hospital Environment

Abstract Sepsis is a systematic response to an infectious disease, being a concerning factor because of the increase in the mortality ratio for every delayed hour in the identification and start of patient's treatment. Studies that aim to identify sepsis early are valuable for the healthcare domain. Further, studies that propose machine learning-based models to identify sepsis risk are scarce for the Brazilian scenario. Hence, we propose the early identification of sepsis considering data from a Brazilian hospital. We developed a temporal series based on LSTM to predict sepsis in patients considering a three-day timestep. The patients were selected using both criteria, ICD-10, and qSOFA, where we supplemented qSOFA with the additional identification of words referring to infections in the clinical texts. Additionally, we tested a Random Forest classifier to classify patients with sepsis with a single timestep before the sepsis event, evaluating the most relevant features. We achieved an accuracy of 0.907, a sensitivity of 0.912, and a specificity of 0.971 when considering a three-day timestep with LSTM. The Random Forest classifier achieved an accuracy of 0.971, a sensitivity of 0.611, and a specificity of 0.998. The features age, blood glucose, systolic blood pressure, diastolic blood pressure, heart rate, respiratory rate, and admission days had the most influence over the algorithm classification, with age being the most relevant feature. We achieved satisfactory results compared with the literature considering a scenario of spaced measures and a high amount of missing data.

Hospitalization Discharge Summary: Standardization of Information Model.

Brazil has a long tradition in the use of health information systems, however--until now--there is no consensus on the minimum data set from which to compose discharge summaries. This article describes the methodology used by a group of experts--members of WG1 of ISOTC 215 Health Informatics Brazilian mirror committee--to define the information model of the discharge summary. This paper describes the current status of the standardization process and the first pilot tests with this infomation content.

Bridging the Gap between Clinical Practice Guidelines and Archetype-Based Electronic Health Records: A Novel Model Proposal.

UNLABELLED: The lack of a unique, standardized format for representing data and knowledge is one of the existing difficulties to integrating decision support into Electronic Health Records (EHRs). OBJECTIVE: Propose an archetype-based model to allow the integration of Clinical Practice Guidelines (CPG) and EHRs; design and implement this proposed model. RESULTS: A generic model was designed for the integration of CPG into EHRs, and an archetype-based EHR for Chronic Kidney Disease Prevention based on rules from CPGs, was made as a proof of concept of this novel integration.

ISO/TR 12300:2016 for clinical cross-terminology mapping: contribution to nursing / ISO/TR 12300:2016 para mapeo entre terminologías clínicas: aporte para la enfermeira / ISO/TR 12300:2016 para mapeamento entre terminologias clínicas: contribuição para a enfermagem

Abstract This theoretical and reflective study aimed to assess the contribution of the ISO/TR 12300:2016 document for the mapping of nursing terminology. The referred document and related articles were used as an empirical framework. The study analyzed the content of the document, highlighting cardinality and equivalence principles. The standard presents conceptual and operational basis for mapping, with cardinality and equivalence as the support for the categorization of cross-terminology mapping in the area of nursing. Cardinality verifies candidate target terms to represent the source term, while the equivalence degree scale checks semantic correspondence. Among the principles included in the ISO/TR 12300:2016, cardinality and equivalence contribute to the accurate representation of the results of the cross-terminology mapping process and its use should decrease inconsistencies.

Resumen Este estudio teórico reflexivo tiene como fin reflexionar acerca del aporte de la norma ISO/TR 12300:2016 para el mapeo de terminologías en el área de enfermería. Fueron utilizados como base empírica la mencionada norma y artículos relacionados, analizando el contenido de la norma y destacando los principios de cardinalidad y equivalencia. La norma presenta bases conceptuales y operativas para el mapeo, con la cardinalidad y la equivalencia, anclando la categorización de los resultados de los mapeos entre terminologías en el área de enfermería. La cardinalidad verifica los términos meta candidatos para representar el término fuente, mientras que la escala de grado de equivalencia verifica la correspondencia semántica. Entre los principios incluidos en la ISO/TR 12300:2016, la cardinalidad y la equivalencia contribuyen a la representación precisa de los resultados del proceso de mapeo cruzado y su empleo debe de reducir inconsistencias.

Resumo Este estudo teórico-reflexivo teve como objetivo refletir sobre a contribuição da norma ISO/TR 12300:2016 para mapeamento de terminologias na área de enfermagem. Foram utilizados como base empírica a referida norma e artigos relacionados, analisando o conteúdo da norma e destacando os princípios de cardinalidade e equivalência. A norma apresenta bases conceituais e operacionais para o mapeamento, com a cardinalidade e a equivalência, ancorando a categorização dos resultados dos mapeamentos entre terminologias na área de enfermagem. A cardinalidade verifica os termos-alvo candidatos para representar o termo-fonte, enquanto a escala de grau de equivalência verifica a correspondência semântica. Entre os princípios inclusos na ISO/TR 12300:2016, a cardinalidade e a equivalência contribuem para a representação precisa dos resultados do processo de mapeamento cruzado e seu uso deve diminuir inconsistências.

Integrating Methods to Evaluate Health Information Systems.

There are different methods to evaluate Health Information Systems (HIS), such as Quality Evaluation of software products, human factors, and socio-technical approaches. This work aims to identify the main aspects used to evaluate HIS, and whether there are relationships between issues considered in assessment of software quality and the ones applied specific to the health domain. This was an exploratory study that included a literature search related to HIS evaluation and software quality analyses applying the norms of the International Organization for Standardization (ISO/IEC), to identify aspects and features applied during the assessment process. The result is a proposal of an evaluation method based on the integration of these two evaluative approaches, combining or complementing the considered aspects. The method was applied to an evaluation of a natural language processing system to identify continuity of care in discharge summaries.

Protocol for physical assessment in patients with fibromyalgia syndrome.

INTRODUCTION: Fibromyalgia syndrome (FMS) is a chronic disease that causes pain and fatigue, presenting a negative impact on quality of life. Exercise helps maintaining physical fitness and influences directly on the improvement of quality of life. OBJECTIVE: Develop a protocol for health-related physical fitness assessment of patients with FMS with tests that are feasible and appropriate for this population. METHOD: An exploratory and analytical literature review was performed, seeking to determine the tests used by the scientific community. With this in mind, we performed a literature revision through the use of virtual libraries databases: PubMed, Bireme, Banco de Teses e Dissertações da Capes and Biblioteca Digital Brasileira de Teses e Dissertações, published in between 1992-2012. RESULTS: A variety of tests was found; the following, by number of citations, stood out: Body Mass Index (BMI) and bioimpedance; 6-minute walk; handgrip strength (dynamometer, 1RM [Repetition Maximum]); Sit and reach and Shoulder flexibility; Foot Up and Go, and Flamingo balance. CONCLUSION: These are the tests that should make up the protocol for the physical evaluation of FMS patients, emphasizing their ease of use.