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Background: Clinical handovers at changes of shifts are typical scenarios of time restricted and information intensive communication, which are highly cognitively demanding. The currently available applications supporting handovers typically present complex information in a textual checklist-like manner. This presentation style has been criticised for not meeting the specific user requirements.
Objectives: We, therefore, aimed at developing a concept for visualising the overview of a clinical case that serves as an alternative way to checklist-like presentations in clinical handovers. We also aimed at implementing this concept in a handoverEHR in order to support the pre-handover phase, the actual handover, and the post-handover phase as well as at evaluating its usability and attractiveness.
Results: We developed and implemented a concept that draws on Tolman's pioneering work on cognitive maps that we designed in accordance with Gestalt principles. These maps provide a pictorial overview of a clinical case. The application to build, manipulate, and store the cognitive maps was integrated into an openEHR based handover record that extends conventional records with handover specific information. Usability (n = 28) and attractiveness (n = 26) testing with experienced clinicians resulted in good ratings for suitability for the task as well as for attractiveness and pragmatism.
Conclusion: We propose cognitive maps to represent and visualise the clinical case in situations where there is limited time to present complex information.
Patient handovers are cognitively demanding, crucial for information continuity and patient safety, but error prone. This study investigated the effect of an electronic handover tool, i.e. the handoverEHR, on the memory and care planning performance of nurse students (n=32) in a randomised, controlled cross-over design with the factors handover task and handover role. On a descriptive level, handover recipients could improve their memory performance with electronic support, handover givers their performance of writing care plans. Statistically meaningful differences occurred, however, only when the participants were givers. Without handover experience and with low fluency to word problems, givers performed badly in the most demanding of the handover tasks. Final recommendations, however, can only be made after replicating this study in a clinical setting with mixed groups.
Health IT systems are employed to support continuity of care via information continuity, while management continuity is often neglected. This study aims at investigating issues of management continuity when developing a collaborative decision support system for chronic wounds. Thirty-three experts from a variety of professions and disciplines discussed problems and possible solutions in four workshops. The following topics emerged from the discussion: existing networks involving payers, responsibilities as well as good discharge management. These topics clearly address management continuity and are also relevant for the scenario of inter-professional wound care across different settings.
Lückenlose Versorgung
(2020)
IT-Standard für das pflegerische Entlassmanagement.
Der von der Hochschule Osnabrück entwickelte „ePflegebericht“ kann die bisherigen unterschiedlichen papierbasierten Überleitungen ablösen, indem die entsprechenden IT-Systeme interoperabel Dokumente austauschen. Die Pflege erhält mit diesem IT-Standard erstmals einen möglichen Zugang zur Telematikinfrastruktur, um zwischen Einrichtungen und über Sektorengrenzen hinweg pflegerisch relevante Informationen schnell und sicher zu übermitteln.
Innovations are typically characterised by their relative newness for the user. In order for new eHealth applications to be accepted as innovations more criteria were proposed including “use” and “usability”. The handoverEHR is a new approach that allows the user to translate the essentials of a clinical case into a graphical representation, the so-called cognitive map of the patient. This study aimed at testing the software usability. A convenience sample of 23 experienced nurses from different healthcare organisations across the country rated the usability of the handoverEHR after performing typical handover tasks. All usability scales of the IsoMetricsL questionnaire showed positive values (4 “I agree”) with the exception of “error tolerance” (3 “neutral statement”). A significant improvement was found in self-descriptiveness as compared to an initial usability testing prior to this study. Different subgroups of users tended to rate the usability of the system differently. This study demonstrated the benefits of formative evaluations in terms of improving the usability of an entirely new approach. It thus helps to transform a novel piece of software towards becoming a real innovation. Our findings also hint at the importance of user characteristics that could affect the usability ratings.
Restricted Versus Unrestricted Search Space : Experience from Mining a Large Japanese Database
(2015)
The aim of this study was to investigate whether standard Big Data mining methods lead to clinically useful results. An association analysis was performed using the apriori algorithm to discover associations among co-morbidities of diabetes patients. Selected data were further analyzed by using k-means clustering with age, long-term blood sugar and cholesterol values. The association analysis led to a multitude of trivial rules. Cluster analysis detected clusters of well and badly managed diabetes patients both belonging to different age groups. The study suggests the usage of cluster analysis on a restricted space to come to meaningful results.
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.