Differences in Physicians' Ratings of Work Stressors and Resources Associated With Digital Transformation: Cross-Sectional Study.

BACKGROUND: The emergence of the COVID-19 pandemic rapidly accelerated the need and implementation of digital innovations, especially in medicine. OBJECTIVE: To gain a better understanding of the stress associated with digital transformation in physicians, this study aims to identify working conditions that are stress relevant for physicians and differ in dependence on digital transformation. In addition, we examined the potential role of individual characteristics (ie, age, gender, and actual implementation of a digital innovation within the last 3 years) in digitalization-associated differences in these working conditions. METHODS: Cross-sectional web-based questionnaire data of 268 physicians (mean age 40.9, SD 12.3 y; n=150, 56% women) in Germany were analyzed. Physicians rated their chronic stress level and 11 relevant working conditions (ie, work stressors such as time pressure and work resources such as influence on sequence) both before and after either a fictional or real implementation of a relevant digital transformation at their workplace. In addition, a subsample of individuals (60; n=33, 55% women) submitted self-collected hair samples for cortisol analysis. RESULTS: The stress relevance of the selected working conditions was confirmed by significant correlations with self-rated chronic stress and hair cortisol levels (hair F) within the sample, all of them in the expected direction (P values between .01 and <.001). Multilevel modeling revealed significant differences associated with digital transformation in the rating of 8 (73%) out of 11 working conditions. More precisely, digital transformation was associated with potentially stress-enhancing effects in 6 working conditions (ie, influence on procedures and complexity of tasks) and stress-reducing effects in 2 other working conditions (ie, perceived workload and time pressure). Younger individuals, women, and individuals whose workplaces have implemented digital innovations tended to perceive digitalization-related differences in working conditions as rather stress-reducing. CONCLUSIONS: Our study lays the foundation for future hypothesis-based longitudinal research by identifying those working conditions that are stress relevant for physicians and prone to differ as a function of digital transformation and individual characteristics.

Burden of hospital admissions and resulting patient interhospital transports during the 2020/2021 SARS-CoV-2 pandemic in Saxony, Germany.

Secondary transports of patients from one hospital to another are indicated for medical reasons or to address local constraints in capacity. In particular, interhospital transports of critically ill infectious patients present a logistical challenge and can be key in the effective management of pandemic situations. The state of Saxony in Germany has two characteristics that allow for an extensive evaluation of secondary transports in the pandemic year 2020/2021. First, all secondary transports are centrally coordinated by a single institution. Second, Saxony had the highest SARS-CoV-2 infection rates and the highest COVID-19 associated mortality in Germany. This study evaluates secondary interhospital transports from March 2019 to February 2021 in Saxony with a detailed analysis of transport behaviour during the pandemic phase March 2020 to February 2021. Our analysis includes secondary transports of SARS-CoV-2 patients and compares them to secondary transports of non-infectious patients. In addition, our data show differences in demographics, SARS-CoV-2- incidences, ICU occupancy of COVID-19 patients, and COVID-19 associated mortality in all three regional health clusters in Saxony. In total, 12,282 secondary transports were analysed between March 1st, 2020 and February 28th, 2021, of which 632 were associated with SARS-CoV-2 (5.1%) The total number of secondary transports changed slightly during the study period March 2020 to February 2021. Transport capacities for non-infectious patients were reduced due to in-hospital and out-of-hospital measures and could be used for transport of SARS-CoV-2 patients. Infectious transfers lasted longer despite shorter distance, occurred more frequently on weekends and transported patients were older. Primary transport vehicles were emergency ambulances, transport ambulances and intensive care transport vehicles. Data analysis based on hospital structures showed that secondary transports in correlation to weekly case numbers depend on the hospital type. Maximum care hospitals and specialized hospitals show a maximum of infectious transports approximately 4 weeks after the highest incidences. In contrast, standard care hospitals transfer their patients at the time of highest SARS-CoV-2 case numbers. Two incidence peaks were accompanied by two peaks of increased secondary transport. Our findings show that interhospital transfers of SARS-CoV-2 and non-SARS-CoV-2 patients differ and that different hospital care levels initiated secondary transports at different times during the pandemic.

Transparent Quality Optimization for Machine Learning-Based Regression in Neurology.

The clinical monitoring of walking generates enormous amounts of data that contain extremely valuable information. Therefore, machine learning (ML) has rapidly entered the research arena to analyze and make predictions from large heterogeneous datasets. Such data-driven ML-based applications for various domains become increasingly applicable, and thus their software qualities are taken into focus. This work provides a proof of concept for applying state-of-the-art ML technology to predict the distance travelled of the 2-min walk test, an important neurological measurement which is an indicator of walking endurance. A transparent lean approach was emphasized to optimize the results in an explainable way and simultaneously meet the specified software requirements for a generic approach. It is a general-purpose strategy as a fractional−factorial design benchmark combined with standardized quality metrics based on a minimal technology build and a resulting optimized software prototype. Based on 400 training and 100 validation data, the achieved prediction yielded a relative error of 6.1% distributed over multiple experiments with an optimized configuration. The Adadelta algorithm (LR=0.000814, fModelSpread=5, nModelDepth=6, nepoch=1000) performed as the best model, with 90% of the predictions with an absolute error of <15 m. Factors such as gender, age, disease duration, or use of walking aids showed no effect on the relative error. For multiple sclerosis patients with high walking impairment (EDSS Ambulation Score ≥6), the relative difference was significant (n=30; 24.0%; p<0.050). The results show that it is possible to create a transparently working ML prototype for a given medical use case while meeting certain software qualities.

Regional responsibility and coordination of appropriate inpatient care capacities for patients with COVID-19 - the German DISPENSE model.

As of late 2019, the COVID-19 pandemic has been a challenge to health care systems worldwide. Rapidly rising local COVID-19 incidence rates, result in demand for high hospital and intensive care bed capacities on short notice. A detailed up-to-date regional surveillance of the dynamics of the pandemic, precise prediction of required inpatient capacities of care as well as a centralized coordination of the distribution of regional patient fluxes is needed to ensure optimal patient care. In March 2020, the German federal state of Saxony established three COVID-19 coordination centers located at each of its maximum care hospitals, namely the University Hospitals Dresden and Leipzig and the hospital Chemnitz. Each center has coordinated inpatient care facilities for the three regions East, Northwest and Southwest Saxony with 36, 18 and 29 hospital sites, respectively. Fed by daily data flows from local public health authorities capturing the dynamics of the pandemic as well as daily reports on regional inpatient care capacities, we established the information and prognosis tool DISPENSE. It provides a regional overview of the current pandemic situation combined with daily prognoses for up to seven days as well as outlooks for up to 14 days of bed requirements. The prognosis precision varies from 21% and 38% to 12% and 15% relative errors in normal ward and ICU bed demand, respectively, depending on the considered time period. The deployment of DISPENSE has had a major positive impact to stay alert for the second wave of the COVID-19 pandemic and to allocate resources as needed. The application of a mathematical model to forecast required bed capacities enabled concerted actions for patient allocation and strategic planning. The ad-hoc implementation of these tools substantiates the need of a detailed data basis that enables appropriate responses, both on regional scales in terms of clinic resource planning and on larger scales concerning political reactions to pandemic situations.

[Digital Health Innovation in Gastroenterology]. / Digitalisierung in der Gastroenterologie.

While digital health is changing the way we perform medicine, implementation of novel diagnostic or therapeutic approaches into clinical practice remains challenging. In this paper we discuss strategies and problems that need to be addressed to enable the translation of new technologies. With its broad spectrum of diseases and procedures the field of gastroenterology offers a perfect test bed for digital innovation. Two clusters of excellence funded by the German Research Foundation focus on data driven personalized therapeutic strategies for oncological and autoimmune diseases. On experimental level digital innovations in endoscopy and for ultrasound-guided interventions are being developed. In endoscopy artificial intelligence-driven decision support systems for adenoma detection have been developed that show promising results in clinical trails. However, for future development it will be crucial to follow an interdisciplinary approach with medical professionals and patients guiding the innovation process. Therefore digital health literacy will need to be implemented in medical education. In the academic field a strong cooperation of clinicians, patients, computer scientists and engineers will be essential.

Constitutive Kit activity triggers B-cell acute lymphoblastic leukemia-like disease in mice.

Acute lymphoblastic leukemia (ALL) is the most common childhood malignancy and, in most cases, is of pro- or pre-B cell origin (B-ALL). The receptor tyrosine kinase KIT is expressed by hematopoietic stem and precursor cells. Gain-of-function mutations of KIT cause systemic mastocytosis, which is characterized by abnormal accumulations of mast cells. We previously reported a mouse model of mastocytosis based on conditional expression of a constitutively active Kit protein. Half of these animals developed leukemic disease of B-lineage origin. Herein, we report that this condition bears striking similarities to human B-ALL. The immuno-phenotype of the leukemic cells was compatible with a pro-B-cell origin, as was the finding of immunoglobulin heavy-chain gene rearrangements in all cases, whereas light-chain loci were mostly not rearranged. Leukemogenesis was independent of pre-B-cell receptor expression. Primary leukemic cells and permanent cell lines derived from these were serially transplantable and rapidly killed the recipients. In a few animals, the leukemia was of T-cell origin with abnormal CD4/8 double-positive T-cell precursors dominating in the circulation. In summary, we report a novel ALL mouse model that may prove useful for in vivo drug testing and identification of novel oncogenic mutations and principles.