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Venous leg ulcers and diabetic foot ulcers are the most common chronic wounds. Their prevalence has been increasing significantly over the last years, consuming scarce care resources. This study aimed to explore the performance of detection and classification algorithms for these types of wounds in images. To this end, algorithms of the YoloV5 family of pre-trained models were applied to 885 images containing at least one of the two wound types. The YoloV5m6 model provided the highest precision (0.942) and a high recall value (0.837). Its mAP_0.5:0.95 was 0.642. While the latter value is comparable to the ones reported in the literature, precision and recall were considerably higher. In conclusion, our results on good wound detection and classification may reveal a path towards (semi-) automated entry of wound information in patient records. To strengthen the trust of clinicians, we are currently incorporating a dashboard where clinicians can check the validity of the predictions against their expertise.
An Iterative Methodology for Developing National Recommendations for Nursing Informatics Curricula
(2016)
The increasing importance of IT in nursing requires educational measures to support its meaningful application. However, many countries do not yet have national recommendations for nursing informatics competencies. We thus developed an iterative triple methodology to yield validated and country specific recommendations for informatics core competencies in nursing. We identified relevant competencies from national sources (step 1), matched and enriched these with input from the international literature (step 2) and fed the resulting 24 core competencies into a survey (120 invited experts from which 87 responded) and two focus group sessions with a total of 48 experts (steps 3a/3b). The subsequent focus group sessions confirmed and expanded the findings. As a result, we were able to define role specific informatics core competencies for three countries.
Background:
Contact tracing apps are potentially useful tools for supporting national COVID-19 containment strategies. Various national apps with different technical design features have been commissioned and issued by governments worldwide.
Objective:
Our goal was to develop and propose an item set that was suitable for describing and monitoring nationally issued COVID-19 contact tracing apps. This item set could provide a framework for describing the key technical features of such apps and monitoring their use based on widely available information.
Methods:
We used an open-source intelligence approach (OSINT) to access a multitude of publicly available sources and collect data and information regarding the development and use of contact tracing apps in different countries over several months (from June 2020 to January 2021). The collected documents were then iteratively analyzed via content analysis methods. During this process, an initial set of subject areas were refined into categories for evaluation (ie, coherent topics), which were then examined for individual features. These features were paraphrased as items in the form of questions and applied to information materials from a sample of countries (ie, Brazil, China, Finland, France, Germany, Italy, Singapore, South Korea, Spain, and the United Kingdom [England and Wales]). This sample was purposefully selected; our intention was to include the apps of different countries from around the world and to propose a valid item set that can be relatively easily applied by using an OSINT approach.
Results:
Our OSINT approach and subsequent analysis of the collected documents resulted in the definition of the following five main categories and associated subcategories: (1) background information (open-source code, public information, and collaborators); (2) purpose and workflow (secondary data use and warning process design); (3) technical information (protocol, tracing technology, exposure notification system, and interoperability); (4) privacy protection (the entity of trust and anonymity); and (5) availability and use (release date and the number of downloads). Based on this structure, a set of items that constituted the evaluation framework were specified. The application of these items to the 10 selected countries revealed differences, especially with regard to the centralization of the entity of trust and the overall transparency of the apps’ technical makeup.
Conclusions:
We provide a set of criteria for monitoring and evaluating COVID-19 tracing apps that can be easily applied to publicly issued information. The application of these criteria might help governments to identify design features that promote the successful, widespread adoption of COVID-19 tracing apps among target populations and across national boundaries.
CIOs' innovation capability is regarded as a precondition of successful HIT adoption in hospitals. Based on the data of 142 CIOs, this study aimed at identifying antecedents of perceived innovation capability. Eight features describing the status quo of the hospital IT management (e.g. use of IT governance frameworks), four features of the hospital structure (e.g. functional diversification) and four CIO characteristics (e.g. duration of employment) were tested as potential antecedents in an exploratory stepwise regression approach. Perceived innovation capability in its entirety and its three sub-dimensions served as criterion. The results show that CIOs' perceived innovation capability could be explained significantly (R2=0.34) and exclusively by facts that described the degree of formalism and structure of IT management in a hospital, e.g. intensive and formalised strategic communication, the existence of an IT strategy and the use of IT governance frameworks. Breaking down innovation capability into its constituents revealed that “innovative organisational culture” contributed to a large extent (R2=0.26) to the overall result sharing several predictors. In contrast, “intrapreneurial personality” (R2=0.11) and “openness towards users” (R2=0.18) could be predicted less well. These results hint at the relationship between working in a well-structured, formalised and strategy oriented environment and the overall feeling of being capable to promote IT innovation.
Das Informationsmanagement steht im Zentrum erfolgreicher eHealth-Innovationsprozesse von Krankenhäusern. Im Kontext komplexer, zum Teil tradierter Krankenhausstrukturen kann die Gestaltungsfähigkeit des Informationsmanagements durch eine ausgeprägte Intrapreneurship-Kultur erhöht werden, wovon vermutlich auch der Digitalisierungsgrad der Einrichtungen profitiert. Vor diesem Hintergrund verfolgte die vorliegende Studie zwei Forschungsfragen: (1.) Welche Effekte hat Intrapreneurship auf den Digitalisierungsgrad der Krankenhäuser und (2.) inwiefern werden diese Effekte durch das Informationsmanagement beeinflusst? Zur Beantwortung der Forschungsfragen wurde ein konzeptionelles Untersuchungsmodell entwickelt, welches mit Daten von 224 IT-Leitern evaluiert wurde. Die Ergebnisse bestätigen, dass Intrapreneurship die Umsetzung von eHealth-Anwendungen positiv beeinflussen kann. Die identifizierten Effekte waren jedoch vorwiegend indirekter Art, vermittelt durch den Professionalisierungsgrad des Informationsmanagements. So kann Intrapreneurship auf IT-Leiter-Ebene und auf Ebene der Gesamtorganisation zu einer Professionalisierung des strategischen Informationsmanagements führen. Auf Ebene der IT-Abteilung profitiert vor allem das operative Informationsmanagement von einer ausgeprägten Intrapreneurship-Kultur.
Diabetic foot ulcer (DFU) is a chronic wound and a common diabetic complication as 2% – 6% of diabetic patients witness the onset thereof. The DFU can lead to severe health threats such as infection and lower leg amputations, Coordination of interdisciplinary wound care requires well-written but time-consuming wound documentation. Artificial intelligence (AI) systems lend themselves to be tested to extract information from wound images, e.g. maceration, to fill the wound documentation. A convolutional neural network was therefore trained on 326 augmented DFU images to distinguish macerated from unmacerated wounds. The system was validated on 108 unaugmented images. The classification system achieved a recall of 0.69 and a precision of 0.67. The overall accuracy was 0.69. The results show that AI systems can classify DFU images for macerations and that those systems could support clinicians with data entry. However, the validation statistics should be further improved for use in real clinical settings. In summary, this paper can contribute to the development of methods to automatic wound documentation.