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The aim of this European interprofessional Health Informatics (HI) Summer School was (i) to make advanced healthcare students familiar with what HI can offer in terms of knowledge development for patient care and (ii) to give them an idea about the underlying technical and legal mechanisms. According to the students’ evaluation, interprofessional education was very well received, problem-based learning focussing on cases was rated positively and the learning goals were met. However, it was criticised that the online material provided was rather detailed and comprehensive and could have been a bit overcharging for beginners. These drawbacks were obviously compensated by the positive experience of working in international and interprofessional groups and a generally welcoming environment.
Access to digital technologies depends on the availability of technical infrastructure, but this access is unequally distributed among social groups and newly summarized under the term digital divide. The aim is to analyze the perception of a tracing app to contain Covid-19 in Germany. The results showed that participants with the highest level of formal education rate the app as beneficial and were the most likely to use the app.
The PosiThera project focuses on the management of chronic wounds, which is multi-professional and multi-disciplinary. For this context, a software prototype was developed in the project, which is intended to support medical and nursing staff with the assistance of artificial intelligence. In accordance with the user-centred design, national workshops were held at the beginning of the project with the involvement of domain experts in wound care in order to identify requirements and use cases of IT systems in wound care, with a focus on AI. In this study, the focus was on involving nursing and nursing science staff in testing the software prototype to gain insights into its functionality and usability. The overarching goal of the iterative testing and adaptation process is to further develop the prototype in a way that is close to care.
Building on Rogers’ Diffusion of Innovation Theory, Bass models describe the diffusion processes distinguishing between innovation (p) and imitation (q). This study aimed at modelling the uptake of RIS, PACS and EHR systems in Germany and Finland. The Bass models revealed a quick and almost identical uptake process across all three systems for Finland. In contrast, the Bass models mirrored a slower uptake in Germany. Consequently, the Finnish “imitation” coefficients were larger than the German ones. While in Germany almost free market forces were driving the adoption through imitation but without tail wind from policy, the adoption process in Finland was centrally governed. This suggests that the diffusion process in Finland reflected a well-managed roll-out of the systems rather than imitation behaviour. Thus, in order for Bass model coefficients to be understood properly, additional contextual information is required.
This paper provides a discourse based upon the key development of nursing in response to the emerging 4Ds of health technology re-design. Building informatics capability among health professionals is a workforce issue necessitated through the increasing prevalence of information technology and digitization of healthcare affecting the entire health workforce, specifically front-line nurses. The key concepts will be explored of Digitization, Distribution, Disruption and Diversity, a framework recognising the tsunami of technology such as Big Data analytics, comprehensive decision support systems for nursing, nanobots, robotics, and pharmacogenomics and the impact these have upon the nursing workforce.
The diabetic foot ulcer, which 2% – 6% of diabetes patients experience, is a severe health threat. It is closely linked to the risk of lower extremity amputation (LEA). When a DFU is present, the chief imperative is to initiate tertiary preventive actions to avoid amputation. In this light, clinical decision support systems (CDSS) can guide clinicians to identify DFU patients early. In this study, the PEDIS classification and a Bayesian logistic regression model are utilised to develop and evaluate a decision method for patient stratification. Therefore, we conducted a Bayesian cutpoint analysis. The CDSS revealed an optimal cutpoint for the amputation risk of 0.28. Sensitivity and specificity were 0.83 and 0.66. These results show that although the specificity is low, the decision method includes most actual patients at risk, which is a desirable feature in monitoring patients at risk for major amputation. This study shows that the PEDIS classification promises to provide a valid basis for a DFU risk stratification in CDSS.
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.
Communication deficits belong to the most frequent errors in patient handovers calling upon specialized training approaches to be implemented. This study aims to harness problem-based learning (PBL) methods in handover education and evaluated the learning process. A digitally enabled PBL course was developed and implemented at Klinikum Osnabrück from which eight nurses participated in the course. They agreed on the stimulating effect of the setting regarding self-directed learning and on the potential to translate the new knowledge and skills into the daily clinical practice. In conclusion, the findings are promising that a digitally enabled PBL course is a suitable learning format for handover education.
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.