Understanding patient trajectories and identifying patterns in episodes of care is critical for effective healthcare decision-making. We present a patient timeline visualization using clustered episodes of care derived from over 35 years of Child and Adolescent Mental Health Services (CAMHS) data. Patients were categorized into 12 groups based on three features: age group (preschoolers, middle childhood, teenagers) at the start of the first episode, gender, and presence or absence of Attention-Deficit Hyperactivity Disorder (ADHD), in order to group similar patients. The patients, timeline with demographics, and episode of care information are displayed in the trajectory to facilitate understanding of the patient and associated events, allowing observation of temporal patterns and variations. These plots reveal similarities and differences in care needs and patterns across groups. Females without ADHD have a steady increase in the number of episodes of care with age. Females with ADHD and all males experienced a peak in the number of episodes during middle childhood, followed by a decline in the teenage years. To compare and understand the intensity and co-occurring disorders with ADHD across different groups, we plotted an ADHD co-occurrence graph, and Tourette’s syndrome was co-occurring predominantly in all age groups. We evaluated and refined our visualizations with the involvement of clinicians, who found them useful for understanding the context of CAMHS care. These visual tools make the population data in the Electronic Health Records (EHR) available for decision-making and enhancing the understanding of care and disorder patterns across groups.

OBJECTIVE: To determine the effects of using unstructured clinical text in machine learning (ML) for prediction, early detection, and identification of sepsis. MATERIALS AND METHODS: PubMed, Scopus, ACM DL, dblp, and IEEE Xplore databases were searched. Articles utilizing clinical text for ML or natural language processing (NLP) to detect, identify, recognize, diagnose, or predict the onset, development, progress, or prognosis of systemic inflammatory response syndrome, sepsis, severe sepsis, or septic shock were included. Sepsis definition, dataset, types of data, ML models, NLP techniques, and evaluation metrics were extracted. RESULTS: The clinical text used in models include narrative notes written by nurses, physicians, and specialists in varying situations. This is often combined with common structured data such as demographics, vital signs, laboratory data, and medications. Area under the receiver operating characteristic curve (AUC) comparison of ML methods showed that utilizing both text and structured data predicts sepsis earlier and more accurately than structured data alone. No meta-analysis was performed because of incomparable measurements among the 9 included studies. DISCUSSION: Studies focused on sepsis identification or early detection before onset; no studies used patient histories beyond the current episode of care to predict sepsis. Sepsis definition affects reporting methods, outcomes, and results. Many methods rely on continuous vital sign measurements in intensive care, making them not easily transferable to general ward units. CONCLUSIONS: Approaches were heterogeneous, but studies showed that utilizing both unstructured text and structured data in ML can improve identification and early detection of sepsis.

In the clinical domain, patient states such as sepsis due to bloodstream infection (BSI) result in observable symptoms and signs used to determine diagnosis and treatment, all of which often is documented in electronic health records. However, clinical text is brief and implicit, making it challenging to infer patient conditions by reasoning tasks and supervised machine learning. To study sepsis-related BSIs, we developed an ontology from an annotation guideline and annotated corpus that empirically captures BSIs from adverse event notes containing procedural deviations, guideline deviations, and unwanted incidents that can bring harm to patients. The resulting ontology represents (1) the physical patient state, clinical observations, and clinical documentation, and (2) background clinical knowledge for artificial intelligence, reasoning, and machine learning.

Adverse event (AE) reports contain notes detailing procedural and guideline deviations, and unwanted incidents that can bring harm to patients. Available datasets mainly focus on vigilance or post-market surveillance of adverse drug reactions or medical device failures. The lack of clinical-related AE datasets makes it challenging to study healthcare-related AEs. AEs affect 10% of hospitalized patients, and almost half are preventable. Having an AE dataset can assist in identifying possible patient safety interventions and performing quality surveillance to lower AE rates. The free-text notes can provide insight into the cause of incidents and lead to better patient care. The objective of this study is to introduce a Norwegian AE dataset and present preliminary processing and analysis for sepsis-related events, specifically peripheral intravenous catheter-related bloodstream infections. Therefore, the methods focus on performing a domain analysis to prepare and better understand the data through screening, generating synthetic free-text notes, and annotating notes.

BACKGROUND: The Living Evidence Map Project at the Norwegian Institute of Public Health (NIPH) gives an updated overview of research results and publications. As part of NIPH's mandate to inform evidence-based infection prevention, control and treatment, a large group of experts are continously monitoring, assessing, coding and summarising new COVID-19 publications. Screening tools, coding practice and workflow are incrementally improved, but remain largely manual. RESULTS: This paper describes how deep learning methods have been employed to learn classification and coding from the steadily growing NIPH COVID-19 dashboard data, so as to aid manual classification, screening and preprocessing of the rapidly growing influx of new papers on the subject. Our main objective is to make manual screening scalable through semi-automation, while ensuring high-quality Evidence Map content. CONCLUSIONS: We report early results on classifying publication topic and type from titles and abstracts, showing that even simple neural network architectures and text representations can yield acceptable performance.

Software engineering courses include practical and theoretical elements that give many options for pedagogical combinations among them. In this paper, we report on two different pedagogical approaches for an undergraduate, introductory project-based software engineering course with more than 500 students working in collaborative scrum teams. We call one approach `Every Student is an Innovator', and the other `No Student Left Behind'. This SE course has been long-running, with stable learning objectives and content. However, from one year to another, we radically changed the pedagogical approach of the course along several dimensions, among them the technical framework, software tools, project topic, mentor roles, assessment form and frequency, feedback and degree of student innovativeness. We report on the perceived challenges, detailed changes, the anticipated effects on the course learning outcomes. The results showed that innovativeness and fun need freedom and flexibility with processes and technology. However, strict design requirements and systematic guidance ensure fulfillment of learning objectives. Analyzing student and staff feedback, we find that both approaches lead to students using more time than intended and worrying about unknown assessment criteria.

Publishing clinical guidelines (GLs) on the web improves their accessibility. Although such publication is common, usability evaluation of GLs and their web presentation has been neglected. In this study we have carried out such an evaluation. Four of the most commonly used GL websites in Norway were selected for evaluation. In addition, we included UpToDate, which is widely used under a national licence in Norway. A total of 14 volunteer GL users participated in our case study. A pretest survey, scenario-based task completion, system usability scale (SUS) questionnaire, observation, and semi-structured interview were methods we employed. A step-by-step thematic synthesis method was used on the interview transcripts to identify themes. Analysis of the SUS results show that except for UpToDate, there was no correlation between user familiarity with the studied website and higher SUS score. Users were mostly concerned about the amount of text and scrolling, font size, no more than one navigation bar and no redirection to other websites. Keeping the same format and structure for presentation in all web pages and presenting numerical information in tabular format were the other suggestions for improvement. The results of this paper can be used by GL publishers to improve their websites usability.

Health information about a patient is usually scattered among several clinical systems, which limits the availability of the information. Integration of the most central systems is a possible solution to this problem. In this paper we present one such integration effort, with a focus on how access control is handled in the integrated system. Although this effort has not yet solved all the issues of access control integration, it demonstrates a practical approach for creating something that works today and serves as input to the discussion on future challenges for access control when integrating multiple systems

In contrast to existing patient-record systems, which merely offer static applications for storage and presentation, a helpful patient-record system is a problem-oriented, knowledge-based system, which provides clinicians with situation-dependent information. We propose a practical approach to extend the current data model with (1) means to recognize and interpret situations, (2) knowledge of how clinicians work and what information they need, and (3) means to rank information according to its relevance in a given care situation. Following the methodology of second-generation knowledge-based systems, that use ontologies to define fundamental concepts, their properties, and interrelationships within a particular domain, we present an ontology that supports three prerequisite features for a future helpful patient-record system: a family-care workflow process, a problem-oriented patient record, and means to identify relevant information to the care process and medical problems.

To reduce the effort spent on system comprehension during software maintenance, easy access to different types of information describing the system features is necessary. This is used by the maintainer to build a mental model of the software. Links relating different types of information are often implicit, and are called traceability links. Extracting all traceability links of possible interest from the system documentation would be an extremely complex operation. Storing and updating a database of such information manually would be an expensive task. We propose that the identification of information about a feature is done dynamically. The maintainer is provided with a powerful query mechanism for identifying a starting point for collecting the information. Further information is identified dynamically by automatically expanding several predefined types of traceability links. This avoids the problems of the traditional database approach. To make the query mechanism scalable, an architectural description of the system is used to limit the size of information which must be inspected by a query