In today’s world, information systems are increasingly necessary for organizations to continue to provide their services with quality. These systems have become increasingly heterogeneous and complex. Executing them in an integrated manner with other systems has become a prerequisite. Due to the existence of legacy systems with stored data that needs to be maintained, the integration between systems is impaired. This situation is aggravated when it comes to health information systems because there are specific laws that require that data need to kept for decades. One well-known health information system is the Electronic Health Record (EHR). The EHR system is the electronic record of the patient’s health consisting of information coming from di erent systems. These systems are often developed by di erent companies and use di erent technologies. With this in mind, the use of a Service-Oriented Architecture (SOA) becomes very useful, since it is a solution capable of integrating heterogeneous structures using specific standards such as web services. However, designing SOA-based systems is not a trivial task. A robust and well-defined architecture is crucial to the success of applications based on SOA paradigm. Therefore, this study aimed to present a software architecture for the development of an EHR system based on SOA considering interoperability between legacy systems. Thus, a set of research methods were applied. Initially, a literature review was conducted in order to find relevant papers that could help in the development of applications in healthcare. This review was bounded on the studies related to EHR systems. The review of these studies aimed to first build a base of knowledge about problems, di culties and challenges regarding the implementation of EHR systems. The analysis of the literature showed that there was a deficiency in precisely defining a specific architecture for the development of EHR systems.The architecture is used a case study in order to test the applicability of the same. The object of this study was the University Hospital of the Federal University of Sergipe where it was developed an EHR system prototype. The architecture proposed in this work was of fundamental importance to the development of the EHR system prototype. The proposed architecture has enabled communication between the EHR system prototype and applications that mimicked the Legacies systems. Among the limitations of the case study, that were not possible to be used to the real legacy systems to the achievement of architecture tests. Applications were created that simulated real systems. However, these simulations did not a ect the result of the study which showed how to satisfactorily creating a software architecture based on SOA for building an EHR system considering interoperability between legacy system. / No mundo atual, sistemas de informação são cada vez mais necessários para que organizações continuem prestando seus serviços com qualidade. Estes sistemas têm se tornado cada vez mais heterogêneos e complexos. Funcionar de maneira integrada com outros sistemas passou a ser um pré-requisito. Devido à existência de sistemas legados com dados armazenados que precisam ser mantidos, a integração entre sistemas fica prejudicada. Essa situação é agravada quando se trata de sistemas de informação em saúde pois existem legislações específicas que exigem que os dados sejam mantidos por décadas. Um sistema de informação em saúde bem conhecido é o Electronic Health Record (EHR). O sistema EHR é o registro eletrônico de saúde do paciente composto por informações vindas de diversos sistemas. Estes sistemas muitas vezes são desenvolvidos por empresas diferentes e utilizam tecnologias diferentes. Com isso em mente, o uso de uma Service-Oriented Architecture (SOA) se torna bastante útil, visto que é uma solução capaz de integrar estruturas heterogêneas utilizando padrões específicos como por exemplo web services. No entanto, projetar sistemas baseados em SOA não é uma tarefa trivial. Uma arquitetura robusta e bem definida é crucial para o sucesso de aplicações baseadas no paradigma SOA. Por essa razão, este trabalho teve como objetivo apresentar uma arquitetura de software para desenvolvimento de um sistema EHR baseado em SOA considerando a interoperabilidade entre sistemas legados. Para tanto, um conjunto de métodos de pesquisa foram aplicados. Inicialmente foi realizada uma revisão da literatura com o intuito de encontrar trabalhos relevantes que pudessem auxiliar no desenvolvimento de aplicações na área de saúde. Esta revisão foi delimitada a estudos relacionados aos sistemas EHR. A revisão destes estudos visou primeiramente construir uma base de conhecimento a respeito de problemas, dificuldades e desafios em relação a implementação de sistemas EHR. A análise da literatura mostrou que existia uma deficiência justamente na definição de uma arquitetura específica para o desenvolvimento de sistemas EHR. Assim, foi definida uma arquitetura de implementação e esta foi utilizada em um estudo de caso com o objetivo de testar a aplicabilidade da mesma. O objeto deste estudo foi o Hospital Universitário da Universidade Federal de Sergipe onde foi desenvolvido um protótipo de sistema EHR. A arquitetura proposta neste trabalho foi de fundamental importância para o desenvolvimento do protótipo de sistema EHR. A arquitetura proposta permitiu a comunicação entre o protótipo de sistema EHR e as aplicações que simularam os sistemas legados. Entre as limitações do estudo de caso, destaca-se a não utilização de sistemas legados reais para a realização dos testes da arquitetura. Foram criadas aplicações que simularam os sistemas reais. No entanto, estas simulações não interferiram no resultado do estudo que mostrou de maneira satisfatória a criação de uma arquitetura de software baseada em SOA para construção de um sistema EHR considerando a interoperabilidade entre sistema legados.

Digital transformation is evolving, and it is driving at the helm of the digital evolution. The amount of information accessible to us has revolutionized the way we gather information. Mobile technology and the immediate and ubiquitous access to information has changed how we engage with services including healthcare. Digital technology and digital transformation have afforded people the ability to self-manage in different ways than face-to-face and paper-based methods through different technologies. This study focuses on exploring the use of the most commonly used digital health technologies in the healthcare sector and how it affects physicians’ daily routine practice. The study presents findings from a qualitative methodology involving semi-structured, personal interviews with physicians from Sweden and a physician from Spain. The interviews capture what physicians feel towards digital transformation, digital health technologies and how it affects their work. In a field where a lack of information regarding how physicians work is affected by digital health technologies, this study reveals a general aspect of how reality looks for physicians. A new way of conducting medicine and the changed role of the physician is presented along with the societal implications for physicians and the healthcare sector. The findings demonstrate that physicians’ role, work and the digital transformation in healthcare on a societal level are important in shaping the future for the healthcare industry and the role of the physician in this future. / Den digitala transformationen växer och den drivs vid rodret för den digitala utvecklingen. Mängden information som är tillgänglig för oss har revolutionerat hur vi samlar in information. Mobila tekniker och den omedelbara och allmänt förekommande tillgången till information har förändrat hur vi tillhandahåller oss tjänster inklusive inom vården. Digital teknik och digital transformation har gett människor möjlighet att kontrollera sig själv och sin egen hälsa på olika sätt än ansikte mot ansikte och pappersbaserade metoder genom olika tekniker. Denna studie fokuserar på att utforska användningen av de vanligaste digitala hälsoteknologierna inom hälso- och sjukvårdssektorn och hur det påverkar läkarnas dagliga rutin. Studien presenterar resultat från en kvalitativ metod som involverar semistrukturerade, personliga intervjuer med läkare från Sverige och en läkare från Spanien. Intervjuerna fångar vad läkare tycker om digital transformation, digital hälsoteknik och hur det påverkar deras arbete. I ett fält där brist på information om hur läkare arbetar påverkas av digital hälsoteknik avslöjar denna studie en allmän aspekt av hur verkligheten ser ut för läkare. Ett nytt sätt att bedriva medicin och läkarens förändrade roll presenteras tillsammans med de samhälleliga konsekvenserna för läkare och vårdsektorn. Resultaten visar att läkarnas roll, arbete och den digitala transformationen inom hälso- och sjukvården på samhällsnivå är viktiga för att utforma framtiden för vårdindustrin och läkarens roll i framtiden.

Denna studie undersöker användarupplevelsen av utbildning i journalsystemet Cosmic i Region Jönköpings län. Utbildning är av största vikt för personalens välmående samt för optimal användning av systemet. Syftet med undersökningen är att utforma en enkät för kontinuerligt förbättringsarbete inom området. Enkäten undersöker både kvantitativa och kvalitativa element hos ett urval som stratifierats utifrån användarnas yrkesroller. Enkäten skickades till deltagarnas respektive arbetsmejl, och svaren samlades in och bearbetades med hjälp av enkätprogrammet EsMaker. Ordinalskalor användes som mätverktyg i många av enkätens kvantitativa frågor, medan de kvalitativa frågorna analyserades med hjälp av The constant comparative method.  Studiens resultat visar en godtycklighet gentemot det material som finns samt med kollegor som instruktörer. Dock framkommer starka önskemål om organiserade utbildningar. Ett tydligt mönster är att användarna föredrar utbildningsmetoder som bygger på synkron kommunikation, samt att metoder som bygger på demonstration av programvaran är mer uppskattade än andra. Resultaten visar dock att dessa metoder bör kombineras med övningar för bästa effekt.  Slutsatser som undersökningen genererat är att kommande utbildningsinsatser bör innebära organiserade utbildningar på arbetsplatsen. Vidare forskning kopplat till Ställföreträdande lärande och Aktivitetsbaserat lärande skulle kunna användas för att optimera utbildningens resultat samt användarnas nöjdhet. En djupare analys av enkätresultatet med avseende på yrkesrollernas respektive behov skulle ytterligare kunna höja kvalitén och effektivisera utbildningarna. Studiens absolut viktigaste fynd är vikten av att chefer avsätter tid för sina medarbetare att ta del av de utbildningsmöjligheter som finns. Detta är kärnan i allt, för utan tid till utbildning spelar utbildningsmaterialets kvalitet ingen som helst roll. / This study investigates the end-user experience of education in Cosmic, a system for electronic health records, in Region Jönköping County. Training is of paramount importance for the well-being of the staff and for optimal use of the system. The purpose of the survey is to design a questionnaire that can be used for continuous improvement of the end-user training in the county.  The survey examines both quantitative and qualitative elements of a sample that is stratified based on the end‑users' professions. The survey was sent to the participants' work emails, and the responses were collected and processed using the EsMaker survey program. Ordinal scales were used as a measurement tool in many of the survey's quantitative questions, while the qualitative questions were analyzed using The constant comparative method.  The results of the study show an arbitrary attitude towards the available training material as well as towards colleagues as instructors. However, there are strong desires for organized training. A clear pattern is that users prefer training methods based on synchronous communication, as well as methods based on demonstration of the software. However, the results show that these methods should be combined with individual tasks for the best effect.  Conclusions generated by the survey are that future training efforts should involve organized training at the workplace. Further research linked to vicarious modeling and enactive learning could be used to optimize the results of the education as well as end-user satisfaction. A deeper analysis of the survey results regarding the respective needs of the professional roles could further increase the quality and streamline the education. The study's most important finding is the importance of managers to dedicate time for their employees to use the training opportunities available. This is the essence of everything, because without time for training, the quality of the educational material does not matter whatsoever. / <p>Examensarbete i vårdadministration, YH-utbildning: 20 Yh-poäng.</p>

Disease subtyping has been a critical aim of precision and personalized medicine. With the potential to improve patient outcomes, unsupervised and semi-supervised methods for determining phenotypes of subtypes have emerged with a recent focus on matrix and tensor factorization. However, interpretability of proposed models is debatable. Principal component analysis (PCA), a traditional method of dimensionality reduction, does not impose non-negativity constraints. Thus coefficients of the principal components are, in cases, difficult to translate to real physical units. Non-negative matrix factorization (NMF) constrains the factorization to positive numbers such that representative types resulting from the factorization are additive. Archetypal analysis (AA) extends this idea and seeks to identify pure types, archetypes, at the extremes of the data from which all other data can be expressed as a convex combination, or by proportion, of the archetypes. Using AA, this study sought to evaluate the sufficiency of AKI staging criteria through unsupervised subtyping. Archetype analysis failed to find a direct 1:1 mapping of archetypes to physician staging and also did not provide additional insight into patient outcomes. Several factors of the analysis such as quality of the data source and the difficulty in selecting features contributed to the outcome. Additionally, after performing feature selection with lasso across data subsets, it was determined that current staging criteria is sufficient to determine patient phenotype with serum creatinine at time of diagnosis to be a necessary factor.

Indiana University-Purdue University Indianapolis (IUPUI) / With the increasing availability of Electronic Health Records (EHRs) and advances in deep learning techniques, developing deep predictive models that use EHR data to solve healthcare problems has gained momentum in recent years. The majority of clinical predictive models benefit from structured data in EHR (e.g., lab measurements and medications). Still, learning clinical outcomes from all possible information sources is one of the main challenges when building predictive models. This work focuses mainly on two sources of information that have been underused by researchers; unstructured data (e.g., clinical notes) and a patient network. We propose a novel hybrid deep learning model, DeepNote-GNN, that integrates clinical notes information and patient network topological structure to improve 30-day hospital readmission prediction. DeepNote-GNN is a robust deep learning framework consisting of two modules: DeepNote and patient network. DeepNote extracts deep representations of clinical notes using a feature aggregation unit on top of a state-of-the-art Natural Language Processing (NLP) technique - BERT. By exploiting these deep representations, a patient network is built, and Graph Neural Network (GNN) is used to train the network for hospital readmission predictions. Performance evaluation on the MIMIC-III dataset demonstrates that DeepNote-GNN achieves superior results compared to the state-of-the-art baselines on the 30-day hospital readmission task. We extensively analyze the DeepNote-GNN model to illustrate the effectiveness and contribution of each component of it. The model analysis shows that patient network has a significant contribution to the overall performance, and DeepNote-GNN is robust and can consistently perform well on the 30-day readmission prediction task. To evaluate the generalization of DeepNote and patient network modules on new prediction tasks, we create a multimodal model and train it on structured and unstructured data of MIMIC-III dataset to predict patient mortality and Length of Stay (LOS). Our proposed multimodal model consists of four components: DeepNote, patient network, DeepTemporal, and score aggregation. While DeepNote keeps its functionality and extracts representations of clinical notes, we build a DeepTemporal module using a fully connected layer stacked on top of a one-layer Gated Recurrent Unit (GRU) to extract the deep representations of temporal signals. Independent to DeepTemporal, we extract feature vectors of temporal signals and use them to build a patient network. Finally, the DeepNote, DeepTemporal, and patient network scores are linearly aggregated to fit the multimodal model on downstream prediction tasks. Our results are very competitive to the baseline model. The multimodal model analysis reveals that unstructured text data better help to estimate predictions than temporal signals. Moreover, there is no limitation in applying a patient network on structured data. In comparison to other modules, the patient network makes a more significant contribution to prediction tasks. We believe that our efforts in this work have opened up a new study area that can be used to enhance the performance of clinical predictive models.

Electronic health records (EHRs) are used in Sweden's healthcare systems to store patients' medical information. Patients in Sweden have the right to access and read their health records. Unfortunately, the language used in EHRs is very complex and presents a challenge for readers who lack medical knowledge. Simplifying the language used in EHRs could facilitate the transfer of information between medical staff and patients. This project investigates the possibility of generating Swedish medical synonyms automatically. These synonyms are intended to be used in future systems for lexical simplification that can enhance the readability of Swedish EHRs and simplify medical terminology. Current publicly available Swedish corpora that provide synonyms for medical terminology are insufficient in size to be utilized in a system for lexical simplification. To overcome the obstacle of insufficient corpora, machine learning models are trained to generate synonyms and terms that convey medical concepts in a more understandable way. With the purpose of establishing a foundation for analyzing complex medical terms, a simple mechanism for Complex Word Identification (CWI) is implemented. The mechanism relies on matching strings and substrings from a pre-existing corpus containing hand-curated medical terms in Swedish. To find a suitable strategy for generating medical synonyms automatically, seven different machine learning models are queried for synonym suggestions for 50 complex sample terms. To explore the effect of different input data, we trained our models on different datasets with varying sizes. Three of the seven models are based on BERT and four of them are based on Word2Vec. For each model, results for the 50 complex sample terms are generated and raters with medical knowledge are asked to assess whether the automatically generated suggestions could be considered synonyms. The results vary between the different models and seem to be connected to the amount and quality of the data they have been trained on. Furthermore, the raters involved in judging the synonyms exhibit great disagreement, revealing the complexity and subjectivity of the task to find suitable and widely accepted medical synonyms. The method and models applied in this project do not succeed in creating a stable source of suitable synonyms. The chosen BERT approach based on Masked Language Modelling cannot reliably generate suitable synonyms due to the limitation of generating one term per synonym suggestion only. The Word2Vec models demonstrate some weaknesses due to the lack of context consideration. Despite the fact that the current performance of our models in generating automatic synonym suggestions is not entirely satisfactory, we have observed a promising number of accurate suggestions. This gives us reason to believe that with enhanced training and a larger amount of input data consisting of Swedish medical text, the models could be improved and eventually effectively applied.

[ES] La interoperabilidad semántica de la información sanitaria es un requisito imprescindible para la sostenibilidad de la atención sanitaria, y es fundamental para afrontar los nuevos retos sanitarios de un mundo globalizado. Esta tesis aporta nuevas metodologías para abordar algunos de los aspectos fundamentales de la interoperabilidad semántica, específicamente aquellos relacionados con la definición y gobernanza de modelos de información clínica expresados en forma de arquetipo. Las aportaciones de la tesis son: - Estudio de las metodologías de modelado existentes de componentes de interoperabilidad semántica que influirán en la definición de una metodología de modelado de arquetipos. - Análisis comparativo de los sistemas e iniciativas existentes para la gobernanza de modelos de información clínica. - Una propuesta de Metodología de Modelado de Arquetipos unificada que formalice las fases de desarrollo del arquetipo, los participantes requeridos y las buenas prácticas a seguir. - Identificación y definición de principios y características de gobernanza de arquetipos. - Diseño y desarrollo de herramientas que brinden soporte al modelado y la gobernanza de arquetipos. Las aportaciones de esta tesis se han puesto en práctica en múltiples proyectos y experiencias de desarrollo. Estas experiencias varían desde un proyecto local dentro de una sola organización que requirió la reutilización de datos clínicos basados en principios de interoperabilidad semántica, hasta el desarrollo de proyectos de historia clínica electrónica de alcance nacional. / [CA] La interoperabilitat semàntica de la informació sanitària és un requisit imprescindible per a la sostenibilitat de l'atenció sanitària, i és fonamental per a afrontar els nous reptes sanitaris d'un món globalitzat. Aquesta tesi aporta noves metodologies per a abordar alguns dels aspectes fonamentals de la interoperabilitat semàntica, específicament aquells relacionats amb la definició i govern de models d'informació clínica expressats en forma d'arquetip. Les aportacions de la tesi són: - Estudi de les metodologies de modelatge existents de components d'interoperabilitat semàntica que influiran en la definició d'una metodologia de modelatge d'arquetips. - Anàlisi comparativa dels sistemes i iniciatives existents per al govern de models d'informació clínica. - Una proposta de Metodologia de Modelatge d'Arquetips unificada que formalitza les fases de desenvolupament de l'arquetip, els participants requerits i les bones pràctiques a seguir. - Identificació i definició de principis i característiques de govern d'arquetips. - Disseny i desenvolupament d'eines que brinden suport al modelatge i al govern d'arquetips. Les aportacions d'aquesta tesi s'han posat en pràctica en múltiples projectes i experiències de desenvolupament. Aquestes experiències varien des d'un projecte local dins d'una sola organització que va requerir la reutilització de dades clíniques basades en principis d'interoperabilitat semàntica, fins al desenvolupament de projectes d'història clínica electrònica d'abast nacional. / [EN] Semantic interoperability of health information is an essential requirement for the sustainability of healthcare, and it is essential to face the new health challenges of a globalized world. This thesis provides new methodologies to tackle some of the fundamental aspects of semantic interoperability, specifically those aspects related to the definition and governance of clinical information models expressed in the form of archetypes. The contributions of the thesis are: - Study of existing modeling methodologies of semantic interoperability components that will influence in the definition of an archetype modeling methodology. - Comparative analysis of existing clinical information model governance systems and initiatives. - A proposal of a unified Archetype Modeling Methodology that formalizes the phases of archetype development, the required participants, and the good practices to be followed. - Identification and definition of archetype governance principles and characteristics. - Design and development of tools that provide support to archetype modeling and governance. The contributions of this thesis have been put into practice in multiple projects and development experiences. These experiences vary from a local project inside a single organization that required a reuse on clinical data based on semantic interoperability principles, to the development of national electronic health record projects. / This thesis was partially funded by the Ministerio de Economía y Competitividad, ayudas para contratos para la formación de doctores en empresas “Doctorados Industriales”, grant DI-14-06564 and by the Agencia Valenciana de la Innovación, ayudas del Programa de Promoción del Talento – Doctorados empresariales (INNODOCTO), grant INNTA3/2020/12. / Moner Cano, D. (2021). Archetype development and governance methodologies for the electronic health record [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/164916 / TESIS

Indiana University-Purdue University Indianapolis (IUPUI) / Graph Neural Networks (GNNs) have become excessively popular and prominent deep learning techniques to analyze structural graph data for their ability to solve complex real-world problems. Because graphs provide an efficient approach to contriving abstract hypothetical concepts, modern research overcomes the limitations of classical graph theory, requiring prior knowledge of the graph structure before employing traditional algorithms. GNNs, an impressive framework for representation learning of graphs, have already produced many state-of-the-art techniques to solve node classification, link prediction, and graph classification tasks. GNNs can learn meaningful representations of graphs incorporating topological structure, node attributes, and neighborhood aggregation to solve supervised, semi-supervised, and unsupervised graph-based problems. In this study, the usefulness of GNNs has been analyzed primarily from two aspects - clustering and classification. We focus on these two techniques, as they are the most popular strategies in data mining to discern collected data and employ predictive analysis.

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The Emergency department at Södersjukhuset currently suffers from very long waiting times. This is partly due to problems within visualisation and mapping of patient data and other information that is fundamental in the handling of patients at the Emergency department. This led to a need in the creation of improvement suggestions to the visualisation of the patient flow between the Emergency department and the Gastroenterology department at Södersjukhuset. During the project, a simulated graphical user interface was created with the purpose of mimicking Södersjukhusets current patient flow. This simulated user interface would visualise the patient flow between the Emergency department and the Gastroenterology department. Additionally, a patient symptoms estimation algorithm was implemented to guess the likelihood of a patient being admitted to a department. The result shows that there are many possible improvements to Södersjukhusets current hospital information system, TakeCare, that would facilitate the care coordinators work and in turn lower the waiting times at the Emergency department. / Akutmottagningen på Södersjukhuset har i dagsläget väldigt långa väntetider. Detta beror till viss del utav problem inom visualiseringen och kartläggning av patient data och annan fundamental information för att hantera patienter på akutmottagningen. Detta ledde till att det finns ett behov att skapa förbättringsförslag på visualiseringen av patientflödet mellan akutmottagningen och gastroenterologiavdelningen på Södersjukhuset. Under projektets gång skapades ett simulerat användargränssnitt med syfte att efterlikna Södersjukhusets nuvarande patientflöde. Denna lösning visualiserar patientflödet mellan akutmottagningen och gastroenterologiavdelningen. Dessutom implementerades en enkel sorteringsalgoritm som kan bedöma sannolikheten om en patient skall bli inlagd på en avdelning. Resultatet visar att det finns flera möjliga förbättringar i Södersjukhusets nuvarande elektroniska journalsystemet, TakeCare, som skulle underlätta vårdkoordinatorernas arbete och därmed sänka väntetiderna på akutmottagningen.