Integrated Clinical Pathways: A Model-based Holistic Method

Against the background of increasing multidisciplinarity as well as the focus on quality, transparency and economic efficiency of medical services, clinical pathways (CPs) have been established as a promising tool at the organizational level in recent years. They are primarily intended to ensure an adequate description of the care processes and to manage the balance between best treatment practice and economic viability. CPs standardize the internal care services by explicating the institution-specific knowledge with regard to recommendations for action, service portfolio, organizational structures, infrastructure, etc. of a specific service provider. The development of hospital information systems (HIS) has so far been characterized by an evolutionary development of modules in the field of laboratory, radiology, nursing and picture archiving systems as well as in the area of administrative systems. As one result of this development, the HIS usually comprises a heterogeneous network of software systems of different types and manufacturers. However, the actual control of patients by means of evidence-based processes and integration of CPs into HIS was not addressed until the recent years, when HIS manufacturers started developing modules for CP modeling and workflow support. The objective of this thesis is to provide a holistic methodical support for the description of clinical pathways and their integration into a hospital information system to finally improve the compliance of daily care to standard process definitions. Therefore, conceptual models provide an adequate mean to describe and communicate complex matters in a comprehensible form as well as to configure IT systems due to their semi-formal nature. Hence, a first research thread investigates the question, how clinical pathways can be described adequately using conceptual models. This results in an iterative design of adequate modeling languages for clinical pathways. A second research thread further investigates the question, how conceptual models of clinical pathways can be used to configure process-oriented application systems in health care. This thread therefore describes the design of a model-based method, that enables a consecutive transformation of CPs into technical (workflow) specifications, based on the principles of the Model-Driven Architecture.:A. Synopsis of the Doctoral Dissertation B. Agility in Medical Treatment Processes C. Domain Specific Modeling Language - CPmod D. BPMN4CP - Version 1.0 E. BPMN4CP - Version 2.0 F. BPMN4CP - Version 2.1 G. MDA in Health Care IS Development H. Transforming Clinical Pathways into Care Workflows I. CDA Templates - Utilizing the MediCUB

Encoding models for scholarly literature

We examine the issue of digital formats for document encoding, archiving and publishing, through the specific example of "born-digital" scholarly journal articles. We will begin by looking at the traditional workflow of journal editing and publication, and how these practices have made the transition into the online domain. We will examine the range of different file formats in which electronic articles are currently stored and published. We will argue strongly that, despite the prevalence of binary and proprietary formats such as PDF and MS Word, XML is a far superior encoding choice for journal articles. Next, we look at the range of XML document structures (DTDs, Schemas) which are in common use for encoding journal articles, and consider some of their strengths and weaknesses. We will suggest that, despite the existence of specialized schemas intended specifically for journal articles (such as NLM), and more broadly-used publication-oriented schemas such as DocBook, there are strong arguments in favour of developing a subset or customization of the Text Encoding Initiative (TEI) schema for the purpose of journal-article encoding; TEI is already in use in a number of journal publication projects, and the scale and precision of the TEI tagset makes it particularly appropriate for encoding scholarly articles. We will outline the document structure of a TEI-encoded journal article, and look in detail at suggested markup patterns for specific features of journal articles

Informatics innovation in clinical care: A visionary scenario for dentistry

Health information technology (HIT) is one of the most significant developments in health care in recent years. However, there is still a large gap between how HIT could support clinical work versus how it does. In this project, we developed a visionary scenario to identify opportunities for improving patient care in dentistry. In the scenario, patients and care providers are supported by a ubiquitous, embedded computing infrastructure that captures and processes data streams from multiple sources. Practical decision support, as well as automated background data processing (e.g., to screen for common conditions), helps clinicians provide quality care. A holistic view of clinical information technology (IT) focuses on supporting clinicians and patients in a user-centered manner. While clinical IT is still in very much a work in progress, scenarios such as the one presented may be helpful to keep us focused on the possibilities of tomorrow, not on the limitations of today

De-identifying Hospital Discharge Summaries: An End-to-End Framework using Ensemble of Deep Learning Models

Electronic Medical Records (EMRs) contain clinical narrative text that is of great potential value to medical researchers. However, this information is mixed with Personally Identifiable Information (PII) that presents risks to patient and clinician confidentiality. This paper presents an end-to-end de-identification framework to automatically remove PII from hospital discharge summaries. Our corpus included 600 hospital discharge summaries which were extracted from the EMRs of two principal referral hospitals in Sydney, Australia. Our end-to-end de-identification framework consists of three components: 1) Annotation: labelling of PII in the 600 hospital discharge summaries using five pre-defined categories: person, address, date of birth, identification number, phone number; 2) Modelling: training six named entity recognition (NER) deep learning base-models on balanced and imbalanced datasets; and evaluating ensembles that combine all six base-models, the three base-models with the best F1 scores and the three base-models with the best recall scores respectively, using token-level majority voting and stacking methods; and 3) De-identification: removing PII from the hospital discharge summaries. Our results showed that the ensemble model combined using the stacking Support Vector Machine (SVM) method on the three base-models with the best F1 scores achieved excellent results with a F1 score of 99.16% on the test set of our corpus. We also evaluated the robustness of our modelling component on the 2014 i2b2 de-identification dataset. Our ensemble model, which uses the token-level majority voting method on all six base-models, achieved the highest F1 score of 96.24% at strict entity matching and the highest F1 score of 98.64% at binary token-level matching compared to two state-of-the-art methods. The framework provides a robust solution to de-identifying clinical narrative text safely

Recent advances of HCI in decision-making tasks for optimized clinical workflows and precision medicine.

The ever-increasing amount of biomedical data is enabling new large-scale studies, even though ad hoc computational solutions are required. The most recent Machine Learning (ML) and Artificial Intelligence (AI) techniques have been achieving outstanding performance and an important impact in clinical research, aiming at precision medicine, as well as improving healthcare workflows. However, the inherent heterogeneity and uncertainty in the healthcare information sources pose new compelling challenges for clinicians in their decision-making tasks. Only the proper combination of AI and human intelligence capabilities, by explicitly taking into account effective and safe interaction paradigms, can permit the delivery of care that outperforms what either can do separately. Therefore, Human-Computer Interaction (HCI) plays a crucial role in the design of software oriented to decision-making in medicine. In this work, we systematically review and discuss several research fields strictly linked to HCI and clinical decision-making, by subdividing the articles into six themes, namely: Interfaces, Visualization, Electronic Health Records, Devices, Usability, and Clinical Decision Support Systems. However, these articles typically present overlaps among the themes, revealing that HCI inter-connects multiple topics. With the goal of focusing on HCI and design aspects, the articles under consideration were grouped into four clusters. The advances in AI can effectively support the physicians' cognitive processes, which certainly play a central role in decision-making tasks because the human mental behavior cannot be completely emulated and captured; the human mind might solve a complex problem even without a statistically significant amount of data by relying upon domain knowledge. For this reason, technology must focus on interactive solutions for supporting the physicians effectively in their daily activities, by exploiting their unique knowledge and evidence-based reasoning, as well as improving the various aspects highlighted in this review

Game-based learning and Gamification to promote engagement and motivation in medical learning contexts

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Examining the Effects of a Virtual Character on Learning and Engagement in Serious Games

Virtual characters have been employed for many purposes including interacting with players of serious games, with a purpose to increase engagement. These characters are often embodied conversational agents playing diverse roles, such as demonstrators, guides, teachers or interviewers. Recently, much research has been conducted into properties that affect the realism and plausibility of virtual characters, but it is less clear whether the inclusion of interactive agents in serious applications can enhance a user’s engagement with the application, or indeed increase efficacy. In a first step towards answering these questions, we conducted a study where a Virtual Learning Environment was used to examine the effect of employing a virtual character to deliver a lesso

10. Interuniversitäres Doktorandenseminar Wirtschaftsinformatik Juli 2009

Begonnen im Jahr 2000, ist das Interuniversitäre Wirtschaftsinformatik-Doktorandenseminar mittlerweile zu einer schönen Tradition geworden. Zunächst unter Beteiligung der Universitäten Leipzig und Halle-Wittenberg gestartet. Seit 2003 wird das Seminar zusammen mit der Jenaer Universität durchgeführt, in diesem Jahr sind erstmals auch die Technische Universität Dresden und die TU Bergakademie Freiberg dabei. Ziel der Interuniversitären Doktorandenseminare ist der über die eigenen Institutsgrenzen hinausgehende Gedankenaustausch zu aktuellen, in Promotionsprojekten behandelten Forschungsthemen. Indem der Schwerpunkt der Vorträge auch auf das Forschungsdesign gelegt wird, bietet sich allen Doktoranden die Möglichkeit, bereits in einer frühen Phase ihrer Arbeit wichtige Hinweise und Anregungen aus einem breiten Hörerspektrum zu bekommen. In den vorliegenden Research Papers sind elf Beiträge zum diesjährigen Doktorandenseminar in Jena enthalten. Sie stecken ein weites Feld ab - vom Data Mining und Wissensmanagement über die Unterstützung von Prozessen in Unternehmen bis hin zur RFID-Technologie. Die Wirtschaftsinformatik als typische Bindestrich-Informatik hat den Ruf einer thematischen Breite. Die Dissertationsprojekte aus fünf Universitäten belegen dies eindrucksvoll.