Objective Machine learning models trained on electronic health records have achieved high prognostic accuracy in test datasets, but little is known about their embedding into clinical workflows. We implemented a random forest–based algorithm to identify hospitalized patients at high risk for delirium, and evaluated its performance in a clinical setting. Materials and Methods Delirium was predicted at admission and recalculated on the evening of admission. The defined prediction outcome was a delirium coded for the recent hospital stay. During 7 months of prospective evaluation, 5530 predictions were analyzed. In addition, 119 predictions for internal medicine patients were compared with ratings of clinical experts in a blinded and nonblinded setting. Results During clinical application, the algorithm achieved a sensitivity of 74.1% and a specificity of 82.2%. Discrimination on prospective data (area under the receiver-operating characteristic curve = 0.86) was as good as in the test dataset, but calibration was poor. The predictions correlated strongly with delirium risk perceived by experts in the blinded (r = 0.81) and nonblinded (r = 0.62) settings. A major advantage of our setting was the timely prediction without additional data entry. Discussion The implemented machine learning algorithm achieved a stable performance predicting delirium in high agreement with expert ratings, but improvement of calibration is needed. Future research should evaluate the acceptance of implemented machine learning algorithms by health professionals. Conclusions Our study provides new insights into the implementation process of a machine learning algorithm into a clinical workflow and demonstrates its predictive power for delirium.

Early identification of patients with life-threatening risks such as delirium is crucial in order to initiate preventive actions as quickly as possible. Despite intense research on machine learning for the prediction of clinical outcomes, the acceptance of the integration of such complex models in clinical routine remains unclear. The aim of this study was to evaluate user acceptance of an already implemented machine learning-based application predicting the risk of delirium for in-patients. We applied a mixed methods design to collect opinions and concerns from health care professionals including physicians and nurses who regularly used the application. The evaluation was framed by the Technology Acceptance Model assessing perceived ease of use, perceived usefulness, actual system use and output quality of the application. Questionnaire results from 47 nurses and physicians as well as qualitative results of four expert group meetings rated the overall usefulness of the delirium prediction positively. For healthcare professionals, the visualization and presented information was understandable, the application was easy to use and the additional information for delirium management was appreciated. The application did not increase their workload, but the actual system use was still low during the pilot study. Our study provides insights into the user acceptance of a machine learning-based application supporting delirium management in hospitals. In order to improve quality and safety in healthcare, computerized decision support should predict actionable events and be highly accepted by users.

BACKGROUND: Fever in critically ill patients is often treated with antipyretics or physical cooling methods. Although fever is a host defense response that may benefit some critically ill patients, others may not tolerate the cardiovascular demands associated with fever. OBJECTIVES: To compare antipyretics and physical cooling for their effects on core body temperature and cardiovascular responses in critically ill patients. METHODS: The antipyretic administered was 650 mg of acetaminophen. Physical cooling was accomplished by anterior placement of a cooling blanket at 18 degrees C. Core temperature and cardiovascular responses were measured in 14 febrile (body temperature, 38.8 degrees C) critically ill patients at baseline before treatment and up to 3 hours after treatment. Patients able to receive acetaminophen were randomly assigned to receive either acetaminophen only (n = 5) or acetaminophen in combination with a cooling blanket (n = 3). Patients not able to receive acetaminophen were treated with physical cooling only (n = 6). RESULTS: Mean body temperature decreased minimally from baseline to 3 hours after treatment in the physical-cooling-only group (from 39.1 degrees C to 39.0 degrees C) and in the physical cooling and acetaminophen group (from 39.1 degrees C to 38.6 degrees C), but the mean body temperature increased in the acetaminophen-only group (from 39.2 degrees C to 39.4 degrees C). Other notable findings included a slight increase in systemic vascular resistance index in the physical-cooling-only group and in the physical-cooling-plus-acetaminophen group. CONCLUSIONS: Although the study included only 14 subjects, the findings will provide information for future studies in febrile critically ill patients.

In cities and densely populated areas, several corvid species are considered nuisance animals. In Austria, particularly carrion (Corvus corone) and hooded crows (C. cornix) are regarded as pests by the general public that frequently cause damage to crops, feed on human waste, and thus spread trash. We conducted a detailed one-year field survey to estimate the abundance of carrion crows in relation to potential anthropogenic food sources and reference sites in the Austrian Rhine valley. Our results demonstrated that the number and proximity of waste management facilities, animal feeding areas, and agricultural areas, and the productive capacity of agricultural areas, predominantly influenced habitat choice and abundance of carrion crows. In the current study, the probability of observing more than two carrion crows at a survey site decreased with increasing human population density. Moreover, the abundance of crows increased despite a continuous increase in crow hunting kills registered during the past 25 years. Our study suggests a regionally comprehensive waste management plan could serve as a promising strategy to manage nuisance birds. A reduction in anthropogenic food supply through improved waste management practices is required for long-term, sustainable management to limit the abundance of crow populations in and close to urban environments.

Background: Various machine learning (ML) models have been developed for the prediction of clinical outcomes, but there is missing evidence on their performance in clinical routine and external validation. Objectives: Our aim was to deploy and prospectively evaluate an already developed delirium prediction software in clinical routine of an external hospital. Methods: We compared updated ML models of the software and models re-trained with the external hospital’s data. The best models were deployed in clinical routine for one month, and risk predictions for all admitted patients were compared to the risk ratings of a senior physician. After using the software, clinicians completed a questionnaire assessing technology acceptance. Results: Re-trained models achieved a high discriminative performance (AUROC > 0.92). Compared to clinical risk ratings, the software achieved a sensitivity of 100.0% and a specificity of 90.6%. Usefulness, ease of use and output quality were rated positively by the users. Conclusion: A ML based delirium prediction software achieved a high discriminative performance and high technology acceptance at an external hospital using re-trained ML models.