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RWD-Cockpit. Application for quality assessment of real-world data
Digital technologies are transforming the health care system. A large part of information is generated as real-world data (RWD). Data from electronic health records and digital biomarkers have the potential to reveal associations between the benefits and adverse events of medicines, establish new patient-stratification principles, expose unknown disease correlations, and inform on preventive measures. The impact for health care payers and providers, the biopharmaceutical industry, and governments is massive in terms of health outcomes, quality of care, and cost. However, a framework to assess the preliminary quality of RWD is missing, thus hindering the conduct of population-based observational studies to support regulatory decision-making and real-world evidence.
Kooperation von Logopädinnen und Logopäden in Forschung und klinischer Praxis im Aphasieprojekt «E-Inclusion»
Im Zuge der evidenzbasierten Praxis sind im Bereich der Logopädie/Sprachtherapie neben der Praxisexper - tise wissenschaftliche Erkenntnisse in die Therapie zu integrieren. Dafür wird mehr fachspezifische Forschung benötigt, die auf Unterstützung der Praxis angewiesen ist. Das Ziel des Artikels ist es, die für Logopädie-For - schung notwendige intradisziplinäre Kooperation von logopädischen Forscherinnen und Forschern sowie Prak- tikerinnen und Praktikern anhand des interdisziplinären Aphasieprojektes «E-Inclusion» der Fachhochschule Nordwestschweiz (FHNW) zu beleuchten. Dazu wird das Aphasieprojekt vorgestellt, welches vier Ziele verfolgte: (1) Auswirkungen von Dialekt und Hochdeutsch sowie (2) von Fotografien und Illustrationen auf die Bildbenenn- leistung bei u. a. Personen mit Aphasie zu untersuchen, (3) mithilfe akustisch-objektiver Sprachsignalanalysen Leistungsveränderungen der Aphasie zu ermitteln und (4) aus diesen Erkenntnissen eine Prototypen-Appli- kation zu entwickeln. Nach der Projektvorstellung gewähren logopädische Praktikerinnen und Praktiker aus Partnerinstitutionen sowie das logopädische Forschungsteam Einblick in die intradisziplinäre Kooperation. Ins- besondere die Rekrutierung der Studienteilnehmenden erforderte eine enge Zusammenarbeit. Als Resultat des Artikels zeigt sich, dass die Kooperation auch unter erschwerten Bedingungen wie zunehmender Zeitdruck im klinisch-therapeutischen Setting für beide Seiten erfolgreich, bedeutsam und bereichernd war. Daraus abgelei- tete Erkenntnisse für kooperative Projekte werden im Fazit dieses Artikels formuliert. Zukünftig können so Logo- pädinnen und Logopäden aus Forschung und Praxis, wie auch Betroffene, von der inter- und intradisziplinären Kooperation im Bereich der Logopädie bzw. in Folgeprojekten des «E-Inclusion»-Forschungsteams profitieren.
Naming images in aphasia: effects of illustrative and photographic images on naming performance in people with and without aphasia
Background: Picture naming is a common tool in aphasia diagnosis and therapy. However, opin-ions differ as to which type of image (e.g., photographs, drawings) is most suitable for naming tasks and whether there is a difference on naming correctness and latencies based on image type. Moreover, recent studies have mainly analysed colour photographs and black-and-white line drawings leaving out image types like graphic representations that apply image features that can facilitate naming such as colour, controlled size, or texture. Aims: To shed more light on ap-propriate image types for persons with aphasia, we created graphic representations depicting nouns and verbs and compared them to photographic stimuli in a naming task including persons with aphasia (PWA) and a control group (CG). Methods & Procedures: 33 PWA and 33 age matched persons (CG) participated in the study. Naming correctness and latencies were meas-ured in two different conditions: concepts depicted as coloured photographs vs. as graphic rep-resentations. 128 pictures of linguistically controlled German-language concepts (64 nouns, 64 verbs) had to be named. The designed graphic stimuli were developed by professional designers based on photographs. The photographs were selected from stock image databases according to a defined image concept. This image concept was based on empirical findings regarding im-age features that facilitate naming (e.g., colour, texture, shading) and was applied to the selec-tion of the photographs as well as to the creation of the graphic representations. The images were presented in pseudo-randomized sequences on a tablet and all reactions of the participants were videotaped. The data from the main study was analysed using generalized linear mixed models (GLMM) and linear mixed models (LMM).
Cloud-based three-dimensional pattern analysis and classification of proximal humeral fractures – A feasibility study
For the complex clinical issue of treatment decision for proximal humeral fractures, dedicated software based on three-dimensional (3D) computer tomography (CT) models would potentially allow for a more accurate fracture classification and help to plan the surgical strategy needed to reduce the fracture in the operating theatre. The aim of this study was to elaborate the feasibility of implementation of such software using state-of-the-art cloud technology to enable access to its functionalities in a distributed manner. Feasibility was studied by implementation of a prototype application, which was tested in a usability study with five biomedical engineers. Implementation of a cloud-based solution was feasible using state-of-the-art technology under application of a specific software architectural approach allowing to distribute computational load between client and server. Mean System Usability Scale (SUS) Score for the developed application was determined to be 63 (StDev 20.4). These results can be interpreted as a medium low usability with high standard deviation of the measured SUS score. We conclude that more test subjects should be included in future studies and the developed application should be evaluated with a representative user group such as orthopaedic shoulder surgeons in a clinical setting.
E-Inclusion – Ein interdisziplinäres, Schweizerisches Aphasie-Forschungsprojekt
«E-Inclusion» ist ein interdisziplinäres, schweizerisches Aphasie-Forschungsprojekt mit dem Ziel einen App-Prototypen für die Benenntherapie zu entwickeln. Dazu wurden wissenschaftliche Fragestellungen zu den drei Themengebieten «Bildart», «Sprachvarietät » und «neue Technologien» in zwei Hauptstudien und fünf ergänzenden Studien untersucht. In der Hauptstudie 1 wurde bei niederfrequenten Nomen und Verben in einer Benennstudie mit Menschen mit einer Aphasie und einer Kontrollgruppe getestet, ob die Bildart (Fotografie vs. Illustration) und die Sprachvarietät (Dialekt vs. Hochdeutsch) die Benennleistung beeinflussen. In der Hauptstudie 2 wurde die mündliche Benennreaktion auf Nomen aus dem AAT (Aachener Aphasie Test) von Menschen mit Aphasie auf Wortebene gemessen, um Veränderungen beispielsweise in der Benennlatenz objektiv aufgrund akustischer Parameter messbar zu machen. Die ergänzenden Studien dienten der Überprüfung des Materials in Bezug auf das schriftliche und mündliche «Name Agreement». Ausserdem wurden Daten zu sog. subjektiven Frequenzen erhoben, um beispielsweise logopädisches Therapie- und Diagnostikmaterial in der Schweiz besser psycholinguistisch kontrollieren zu können. Eine weitere Studie widmete sich der Einschätzung von Sprachkenntnissen im Dialekt und Hochdeutschen sowie dem Gebrauch der Varietäten in verschiedenen Kontexten und Modalitäten. Die Ergebnisse dieser Studien wurden in einer Prototypen-App zusammengeführt.
RWD-Cockpit: application for quality assessment of real-world data
Background: Digital technologies are transforming the health care system. A large part of information is generated as real-world data (RWD). Data from electronic health records and digital biomarkers have the potential to reveal associations between the benefits and adverse events of medicines, establish new patient-stratification principles, expose unknown disease correlations, and inform on preventive measures. The impact for health care payers and providers, the biopharmaceutical industry, and governments is massive in terms of health outcomes, quality of care, and cost. However, a framework to assess the preliminary quality of RWD is missing, thus hindering the conduct of population-based observational studies to support regulatory decision-making and real-world evidence. Objective: To address the need to qualify RWD, we aimed to build a web application as a tool to translate characterization of some quality parameters of RWD into a metric and propose a standard framework for evaluating the quality of the RWD. Methods: The RWD-Cockpit systematically scores data sets based on proposed quality metrics and customizable variables chosen by the user. Sleep RWD generated de novo and publicly available data sets were used to validate the usability and applicability of the web application. The RWD quality score is based on the evaluation of 7 variables: manageability specifies access and publication status; complexity defines univariate, multivariate, and longitudinal data; sample size indicates the size of the sample or samples; privacy and liability stipulates privacy rules; accessibility specifies how the data set can be accessed and to what granularity; periodicity specifies how often the data set is updated; and standardization specifies whether the data set adheres to any specific technical or metadata standard. These variables are associated with several descriptors that define specific characteristics of the data set. Results: To address the need to qualify RWD, we built the RWD-Cockpit web application, which proposes a framework and applies a common standard for a preliminary evaluation of RWD quality across data sets—molecular, phenotypical, and social—and proposes a standard that can be further personalized by the community retaining an internal standard. Applied to 2 different case studies—de novo–generated sleep data and publicly available data sets—the RWD-Cockpit could identify and provide researchers with variables that might increase quality Conclusions: The results from the application of the framework of RWD metrics implemented in the RWD-Cockpit application suggests that multiple data sets can be preliminarily evaluated in terms of quality using the proposed metrics. The output scores—quality identifiers—provide a first quality assessment for the use of RWD. Although extensive challenges remain to be addressed to set RWD quality standards, our proposal can serve as an initial blueprint for community efforts in the characterization of RWD quality for regulated settings.