Social Relations at the Collective Level: The Meaning and Measurement of Collective Control in Research on the Psychosocial Work Environment

In this article, we suggest that organizational-level social relations should be defined and measured as workplace norms. We base this argument on new research on the components of the psychosocial work environment and on the availability of new techniques for measuring and analyzing workplace norms as organizational properties. Workplace norms emerge from interactions and negotiations among organizational actors, through which patterns of behavior, attitudes, and perspectives become defined as legitimate. This is an underestimated dimension of the psychosocial work environment that should be assessed with two types of data: self-reports by employees of their experiences in the workplace (task-level control) and self-reports by employees and employers of collective or group-level norms. Hierarchical linear modeling is an especially useful tool for analyzing the relationships between workplace norms and different organizational outcomes because it allows researchers to separate the effects of individual-level variables from group or organizational-level factors. Our approach is anchored in the Nordic perspective of the work environment developed over the past 50 years

Visual Exploration and Cohort Identification of Acute Patient Histories Aggregated from Heterogeneous Sources

How can we use information visualization to support retrospective, exploratory analysis of collections of histories for patients admitted to acute care? This paper describes a novel design for visual cohort identification and exploration. We have developed a tool that integrates multiple, heterogeneous clinical data sources and allows alignment, querying and abstraction in a common workbench. This paper presents results from two projects and a review of related work in the field of information visualization including both presentation and interactive navigation of the information. We have developed an interactive prototype and present the visualization aspect of this prototype and a brief demonstration of its use in a research project with a large cohort of patients. The prototype represents and reasons with patient events in different OWL-formalizations according to the perspective and use: One for integration and alignment of patient records and observations; Another for visual presentation of individual or cohort trajectories. Health researchers have successfully analyzed large cohorts (over 100,000 individuals) using the tool. We have also used the tool to produce interactive personal health time-lines (for more than 10,000 individuals) on the web. Utility, usability and effect have been tested extensively and the results so far are promising. We envision that clinicians who want to learn more about groups of patients and their treatment processes will find the tool valuable. In addition, we believe that the visualization can be useful to researchers looking at data to be statistically evaluated, in order to discover new hypotheses or get ideas for the best analysis strategies. Our main conclusion is that the tool is usable, but it can be challenging to use for large data sets.Postprint version. © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works

Method for Designing Semantic Annotation of Sepsis Signs in Clinical Text

Annotated clinical text corpora are essential for machine learning studies that model and predict care processes and disease progression. However, few studies describe the necessary experimental design of the annotation guideline and annotation phases. This makes replication, reuse, and adoption challenging. Using clinical questions about sepsis, we designed a semantic annotation guideline to capture sepsis signs from clinical text. The clinical questions aid guideline design, application, and evaluation. Our method incrementally evaluates each change in the guideline by testing the resulting annotated corpus using clinical questions. Additionally, our method uses inter-annotator agreement to judge the annotator compliance and quality of the guideline. We show that the method, combined with controlled design increments, is simple and allows the development and measurable improvement of a purpose-built semantic annotation guideline. We believe that our approach is useful for incremental design of semantic annotation guidelines in general

Terminology and ontology development for semantic annotation : A use case on sepsis and adverse events

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Predicting in-hospital death from derived EHR trajectory features

Medical histories of patients can provide insight into the immediate future of a patient. While most studies propose to predict survival from vital signs and hospital tests within one episode of care, we carry out selective feature engineering from longitudinal historical medical records in this study to develop a dataset with derived features. We then train multiple machine learning models for the binary prediction whether an episode of care will culminate in death among patients suspected of bloodstream infections. The machine learning classifier performance is evaluated and compared and the feature importance impacting the model output is explored. The findings indicated that the logistic regression model achieved the best performance for predicting death in the next hospital episode with an accuracy of 98% and an almost perfect area under the receiver operating characteristic curve. Exploring the feature importance reveals that time to and severity of the last episode and previous history of sepsis episodes were the most critical features

Success factors of an early EHR system for child and adolescent mental health: Lessons learned for future practice data-driven decision aids

This paper recounts the successful BUPdata, a discontinued electronic health record (EHR) system for Child and Adolescent Mental Health Services (CAMHS) in Norway. It was developed and owned by the national association for CAMHS and fulfilled needs for collaborative care, practice insight, and service management. It aimed to unify the requirements of government, administration, clinicians, patients, and researchers alike, with the goal of providing uniform quality of care nationally. When CAMHS became integrated with specialist healthcare, BUPdata was replaced with more a general EHR system offering far less functionality and insight into CAMHS practice. We have studied BUPdata, and interviewed stakeholders in order to develop decision aids based on practice data analysis and give clinicians and patients insight into successful local practice, collaboration patterns, and overview of local resources

Usability of the IDDEAS prototype in child and adolescent mental health services: A qualitative study for clinical decision support system development

Introduction: Child and adolescent mental health services (CAMHS) clinical decision support system (CDSS) provides clinicians with real-time support as they assess and treat patients. CDSS can integrate diverse clinical data for identifying child and adolescent mental health needs earlier and more comprehensively. Individualized Digital Decision Assist System (IDDEAS) has the potential to improve quality of care with enhanced efficiency and effectiveness. Methods: We examined IDDEAS usability and functionality in a prototype for attention deficit hyperactivity disorder (ADHD), using a user-centered design process and qualitative methods with child and adolescent psychiatrists and clinical psychologists. Participants were recruited from Norwegian CAMHS and were randomly assigned patient case vignettes for clinical evaluation, with and without IDDEAS. Semi-structured interviews were conducted as one part of testing the usability of the prototype following a five-question interview guide. All interviews were recorded, transcribed, and analyzed following qualitative content analysis. Results: Participants were the first 20 individuals from the larger IDDEAS prototype usability study. Seven participants explicitly stated a need for integration with the patient electronic health record system. Three participants commended the step-by-step guidance as potentially helpful for novice clinicians. One participant did not like the aesthetics of the IDDEAS at this stage. All participants were pleased about the display of the patient information along with guidelines and suggested that wider guideline coverage will make IDDEAS much more useful. Overall, participants emphasized the importance of maintaining the clinician as the decision-maker in the clinical process, and the overall potential utility of IDDEAS within Norwegian CAMHS. Conclusion: Child and adolescent mental health services psychiatrists and psychologists expressed strong support for the IDDEAS clinical decision support system if better integrated in daily workflow. Further usability assessments and identification of additional IDDEAS requirements are necessary. A fully functioning, integrated version of IDDEAS has the potential to be an important support for clinicians in the early identification of risks for youth mental disorders and contribute to improved assessment and treatment of children and adolescents

Improving the Quality and Utility of Electronic Health Record Data through Ontologies

The translational research community, in general, and the Clinical and Translational Science Awards (CTSA) community, in particular, share the vision of repurposing EHRs for research that will improve the quality of clinical practice. Many members of these communities are also aware that electronic health records (EHRs) suffer limitations of data becoming poorly structured, biased, and unusable out of original context. This creates obstacles to the continuity of care, utility, quality improvement, and translational research. Analogous limitations to sharing objective data in other areas of the natural sciences have been successfully overcome by developing and using common ontologies. This White Paper presents the authors’ rationale for the use of ontologies with computable semantics for the improvement of clinical data quality and EHR usability formulated for researchers with a stake in clinical and translational science and who are advocates for the use of information technology in medicine but at the same time are concerned by current major shortfalls. This White Paper outlines pitfalls, opportunities, and solutions and recommends increased investment in research and development of ontologies with computable semantics for a new generation of EHRs

EVICARE Sluttrapport: Fra forskning til praksis – fra praksis til kunnskap

EviCare var det første, offentlige innovasjonsprosjektet innen IKT i Norge som fikk støtte fra Norges Forskningsråd. Hovedmålet med prosjektet var å utvikle metoder og teknologi som leverer forskningsbasert kunnskap der behandling og omsorg gis, integrert i elektronisk pasientjournal (EPJ). Forventede effekter er at behandlingen får høyere kvalitet og at dokumentasjonen av arbeidsprosessene blir mer forståelig

Reviewing Clinical Guideline Development Tools: Features and Characteristics

Background To improve consistency and streamline development and publication of clinical guidelines (GL), there is a need for appropriate software support. We have found few specific tools for the actual authoring and maintaining of GLs, and correspondingly few analyses or reviews of GL development tool functionality. In order to assist GL developers in selecting and evaluating tools, this study tries to address the perceived gap by pursuing four goals: 1) identifying available tools, 2) reviewing a representative group of tools and their supported functionalities, 3) uncovering themes of features that the studied tools support, and 4) compare the selected tools with respect to the themes. Methods We conducted a literature search using PubMed and Google Scholar in order to find GL development tools (GDT). We also explored tools and Content Management Systems (CMS) used in representative organisations and international communities that develop and maintain GLs. By reading a selected representative group of five GL tool manuals, exploring tools hands-on, we uncovered 8 themes of features. All found tools were compared according to these themes in order to identify the level of functionality they offer to support the GL development and publishing process. In order to limit the scope, tools for designing computer-interpretable/executable GL are excluded. Results After finding 1552 published papers, contacting 7 organizations and international communities, we identified a total of 19 unique tools, of which 5 tools were selected as representative in this paper. We uncovered a total of 8 themes of features according to the identified functionalities that each tool provides. Four features were common among tools: Collaborative authoring process support, user access control, GL repository management, electronic publishing. We found that the GRADE methodology was supported by three of the reviewed tools, while only two tools support annotating GL with MeSH terms. We also identified that monitoring progress, reference management, Managing versions (version control), and Change control (tracking) were often the missing features. Conclusion The results can promote sector discussion and eventual agreement on important tool functionality. It may aid tool and GL developers towards more efficient, and effective, GL authoring