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Background: Crowding in emergency departments (ED) has a negative impact on quality of care and can be averted by allocating additional resources based on predictive crowding models. However, there is a lack in effective external overall predictors, particularly those representing public activity.
Objectives: This study, therefore, examines public activity measured by regional road traffic flow as an external predictor of ED crowding in an urban hospital.
Methods: Seasonal autoregressive cross-validated models (SARIMA) were compared with respect to their forecasting error on ED crowding data.
Results: It could be shown that inclusion of inflowing road traffic into a SARIMA model effectively improved prediction errors.
Conclusion: The results provide evidence that circadian patterns of medical emergencies are connected to human activity levels in the region and could be captured by public monitoring of traffic flow. In order to corroborate this model, data from further years and additional regions need to be considered. It would also be interesting to study public activity by additional variables.
Use of Emergency Departments by Frail Elderly Patients : Temporal Patterns and Case Complexity
(2019)
Emergency department (ED) care for frail elderly patients is associated with an increased use of resources due to their complex medical needs and frequently difficult psycho-social situation. To better target their needs with specially trained staff, it is vital to determine the times during which these particular patients present to the ED. Recent research was inconclusive regarding this question and the applied methods were limited to coarse time windows. Moreover, there is little research on time variation of frail ED patients’ case complexity. This study examines differences in arrival rates for frail vs. non-frail patients in detail and compares case complexity in frail patients within vs. outside of regular GP working hours. Arrival times and case variables (admission rate, ED length of stay [LOS], triage level and comorbidities) were extracted from the EHR of an ED in an urban German teaching hospital. We employed Poisson time series regression to determine patterns in hourly arrival rates over the week. Frail elderly patients presented more likely to the ED during already high frequented hours, especially at midday and in the afternoon. Case complexity for frail patients was significantly higher compared to non-frail patients, but varied marginally in time only with respect to triage level and ED LOS. The results suggest that frailty-attuned emergency care should be available in EDs during the busiest hours. Based on EHR data, hospitals thus can tailor their staff needs.
Although user participation may facilitate the realisation of IT innovations, various literature analyses show only minimal to moderate evidence for such effects possibly due to disregard of mediating factors. Against this background, this study examines the extent to which joint intrapreneurship of clinical leaders and IT leaders as well as a distinct innovation culture mediate the effect of user participation on hospitals’ IT innovativeness. IT innovativeness was measured by the availability and usability of IT functions and by the perceived ‘innovative power’ of a hospital. An empirical model was developed and tested with data from 168 clinical leaders and IT leaders who participated pairwise in a survey representing 84 German hospitals. Three parallel mediation analyses indicated that the participation of users could only lead to IT innovativeness if they were accompanied by intrapreneurial leadership on the part of clinical directors and IT leaders and if a pronounced innovation culture prevailed.
Das Ausmaß der Digitalisierung im Gesundheitswesen bemisst sich daran, wie gut die vorhandene IT Informationslogistik bedienen kann. Der IT-Report Gesundheitswesen ist eine Umfragereihe, die seit 16 Jahren den Digitalisierungsgrad in Krankenhäusern untersucht und eine Familie von Composite Scores bereitstellt, insbesondere den Workflow Composite Score (WCS) zur Messung der klinischen Informationslogistik. Dieser lag mit durchschnittlich 56 von 100 Punkten im Jahr 2017 nur knapp über der Marke von 50 Punkten. Weitere Sub-Scores wie z. B. der für den Aufnahmeprozess lagen mit 44 Punkten sogar darunter. Dieses Ergebnis zeigt, dass es ein großes Potenzial zur Verbesserung gibt, das ausgeschöpft werden muss, soll Digitalisierung ihren Effekt der Vernetzung, Transparenz, Datenanalytik und Wissensgenerierung entfalten.
Background:
While aiming for the same goal of building a national eHealth Infrastructure, Germany and the United States pursued different strategic approaches – particularly regarding the role of promoting the adoption and usage of hospital Electronic Health Records (EHR).
Objective:
To measure and model the diffusion dynamics of EHRs in German hospital care and to contrast the results with the developments in the US.
Materials and methods:
All acute care hospitals that were members of the German statutory health system were surveyed during the period 2007–2017 for EHR adoption. Bass models were computed based on the German data and the corresponding data of the American Hospital Association (AHA) from non-federal hospitals in order to model and explain the diffusion of innovation.
Results:
While the diffusion dynamics observed in the US resembled the typical s-shaped curve with high imitation effects (q = 0.583) but with a relatively low innovation effect (p = 0.025), EHR diffusion in Germany stagnated with adoption rates of approx. 50% (imitation effect q = -0.544) despite a higher innovation effect (p = 0.303).
Discussion:
These findings correlate with different governmental strategies in the US and Germany of financially supporting EHR adoption. Imitation only seems to work if there are financial incentives, e.g. those of the HITECH Act in the US. They are lacking in Germany, where the government left health IT adoption strategies solely to the free market and the consensus among all of the stakeholders.
Conclusion:
Bass diffusion models proved to be useful for distinguishing the diffusion dynamics in German and US non-federal hospitals. When applying the Bass model, the imitation parameter needs a broader interpretation beyond the network effects, including driving forces such as incentives and regulations, as was demonstrated by this study.