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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.
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.
In the context of the ongoing digitization of interdisciplinary subjects, the need for digital literacy is increasing in all areas of everyday life. Furthermore, communication between science and society is facing new challenges, not least since the COVID-19 pandemic. In order to deal with these challenges and to provide target-oriented online teaching, new educational concepts for the transfer of knowledge to society are necessary. In the transfer project “Zukunftslabor Gesundheit” (ZLG), a didactic concept for the creation of E-Learning classes was developed. A key factor for the didactic concept is addressing heterogeneous target groups to reach the broadest possible spectrum of participants. The concept has already been used for the creation of the first ZLG E-Learning courses. This article outlines the central elements of the developed didactic concept and addresses the creation of the ZLG courses. The courses created so far appeal to different target groups and convey diverse types of knowledge at different levels of difficulty.
Einleitung: Whiteboards können als ein Instrument des Lean Managements zur Steuerung der Verweildauer auf Stationen eingesetzt werden, um aktuelle Patienteninformationen zu bündeln und in regelmäßigen strukturierten sowie interdisziplinären Besprechungen die Patientenversorgung zu steuern, die interdisziplinäre Zusammenarbeit zu optimieren und das Entlassungsmanagement zu verbessern. Das Ziel dieser Studie bestand darin, zu untersuchen, inwiefern die Einführung von Whiteboards in zwei Kliniken mit einer Veränderung der Verweildauer einherging.
Methode: Um die Forschungsfrage zu beantworten, wurden retrospektive Zeitreihen aus den DRG-Routinedaten vor und nach Installation der Whiteboards aus den beiden Kliniken in einem Interrupted Time Series Design genutzt. In der einen Klinik (Chirurgie) lagen 3.734 Fälle für den Zeitraum von Januar 2018 bis Dezember 2019 und in der anderen Klinik (Innere Medizin) 54.049 Fälle für den Zeitraum Juli 2013 bis Dezember 2019 vor.
Ergebnisse: In dem gemittelten Vergleich der Verweildauer (relative Verweildauerabweichung pro DRG von dem jeweiligen Verweildauermittel) konnte in der ersten Klinik kein signifikanter Unterschied zwischen den Werten vor und nach Einführung des Boards festgestellt werden. Am zweiten Klinikum zeigte sich sogar im Vorher-Nachher-Vergleich eine signifikante Verschlechterung der Verweildauer. Eine deskriptive Zeitreihenanalyse vor und nach Einführung zeigte in beiden Kliniken, dass kurz nach der Einführung der Boards sich die Verweildauer verschlechterte, anschließend jedoch verbesserte, d.h. dass die Patienten durchschnittlich früher entlassen wurden. Dieser Unterschied ging jedoch im Zeitverlauf wieder zurück.
Diskussion: Zusammenfassend lässt sich festhalten, dass keine Verbesserung in der Verweildauer im Zuge der Nutzung der Whiteboards durch einen reinen Vorher-Nachher-Vergleich nachweisbar war. In der anschließenden Zeitreihenbetrachtung zeigten sich starke Schwankungen, die zunächst mit einer kurzzeitigen Verschlechterung der Verweildauer nach der Implementierung einhergingen und dann zu einer Verbesserung führten. Im Zeitverlauf verblasste der Unterschied jedoch, sodass die Patienten wieder später entlassen wurden. Methodisch zeigt sich, dass im Gegensatz zu der reinen Vorher-Nachher-Analyse erst eine Zeitreihenbetrachtung einen Einblick in das Geschehen und seine Variabilität lieferte. Für die Praxis ergeben sich folgende Implikationen: Whiteboards können als ein hilfreiches Instrument von Lean Management zur Verweildauersteuerung angesehen werden, wie die zwischenzeitlichen Verbesserungen nahelegen. Dies erfordert jedoch eine kontinuierliche, unter Einbezug der Mitarbeiter durchgeführte Pflege der Informationen und einen erkennbaren Mehrwert. Perspektivisch empfiehlt sich zudem eine Digitalisierung der Boards, um den Nachteilen wie der manuellen Pflege entgegenzuwirken.
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.
Background:
Chronic health conditions are on the rise and are putting high economic pressure on health systems, as they require well-coordinated prevention and treatment. Among chronic conditions, chronic wounds such as cardiovascular leg ulcers have a high prevalence. Their treatment is highly interdisciplinary and regularly spans multiple care settings and organizations; this places particularly high demands on interoperable information exchange that can be achieved using international semantic standards, such as Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT).
Objective:
This study aims to investigate the expressiveness of SNOMED CT in the domain of wound care, and thereby its clinical usefulness and the potential need for extensions.
Methods:
A clinically consented and profession-independent wound care item set, the German National Consensus for the Documentation of Leg Wounds (NKDUC), was mapped onto the precoordinated concepts of the international reference terminology SNOMED CT. Before the mapping took place, the NKDUC was transformed into an information model that served to systematically identify relevant items. The mapping process was carried out in accordance with the ISO/TR 12300 formalism. As a result, the reliability, equivalence, and coverage rate were determined for all NKDUC items and sections.
Results:
The developed information model revealed 268 items to be mapped. Conducted by 3 health care professionals, the mapping resulted in moderate reliability (κ=0.512). Regarding the two best equivalence categories (symmetrical equivalence of meaning), the coverage rate of SNOMED CT was 67.2% (180/268) overall and 64.3% (108/168) specifically for wounds. The sections general medical condition (55/66, 83%), wound assessment (18/24, 75%), and wound status (37/57, 65%), showed higher coverage rates compared with the sections therapy (45/73, 62%), wound diagnostics (8/14, 57%), and patient demographics (17/34, 50%).
Conclusions:
The results yielded acceptable reliability values for the mapping procedure. The overall coverage rate shows that two-thirds of the items could be mapped symmetrically, which is a substantial portion of the source item set. Some wound care sections, such as general medical conditions and wound assessment, were covered better than other sections (wound status, diagnostics, and therapy). These deficiencies can be mitigated either by postcoordination or by the inclusion of new concepts in SNOMED CT. This study contributes to pushing interoperability in the domain of wound care, thereby responding to the high demand for information exchange in this field. Overall, this study adds another puzzle piece to the general knowledge about SNOMED CT in terms of its clinical usefulness and its need for further extensions.
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.
This study describes the eHealth4all@eu course development pipeline that builds upon the TIGER educational recommendations and allows a systematic development grounded on scientific and field requirements of competencies, a case/problem-based pedagogical approach and finally results in the syllabus and the course content. The pipeline is exemplified by the course Learning Healthcare in Action: Clinical Data Analytics.
Hintergrund
Sprunggelenksverletzungen (SGV) sind die häufigsten Verletzungen des muskuloskeletalen Systems. Neben Schmerz, Schwellung und Funktionseinschränkung werden Zusammenhänge zwischen einem Sprunggelenkstrauma und Veränderungen am Becken bzw. Sakroiliakalgelenk (SIG) diskutiert. In der vorliegenden Studie wird geprüft, ob Wechselwirkungen von SGV und Veränderungen am Becken bzw. SIG bestehen.
Material und Methoden
In dieser Querschnittsstudie ohne Verblindung wurden 18 Probanden mit SGV und 22 gesunde Probanden am Becken und SIG untersucht. Der Zustand nach der SGV wurde anhand des FAAM-G-Fragebogens ermittelt. Die Evaluation der Beckenposition erfolgte mit Photometrie. Dabei wurden die Referenzpunkte SIAS und SIPS zueinander verglichen. Am SIG erfolgten Schmerzprovokationstests, um Veränderungen am SIG zu ermitteln. Die in beiden Gruppen erhobenen Daten wurden statistisch ausgewertet und verglichen.
Ergebnisse
Der funktionelle Zustand der Sprunggelenke unterschied sich zwischen der Kontrollgruppe und der Experimentalgruppe signifikant. Die Unterschiede bei den photometrischen Ergebnissen waren für die Beckensymmetrie nicht signifikant (SIAS p = 0,426; SIPS p = 0,779). Hinsichtlich der Schmerzhaftigkeit des SIG zeigte sich ebenfalls kein signifikanter Unterschied (p = 0,477).
Schlussfolgerung
Es konnten keine Positionsveränderungen des Beckens infolge eines Sprunggelenktraumas beobachtet werden. Auch zeigten sich keine Assoziationen zwischen SGV und Becken- bzw. SIG-Position.