Background Machine learning algorithms are currently used in a wide array of clinical domains to produce models that can predict clinical risk events. Most models are developed and evaluated with retrospective data, very few are evaluated in a clinical workflow, and even fewer report performances in different hospitals. In this study, we provide detailed evaluations of clinical risk prediction models in live clinical workflows for three different use cases in three different hospitals. Objective The main objective of this study was to evaluate clinical risk prediction models in live clinical workflows and compare their performance in these setting with their performance when using retrospective data. We also aimed at generalizing the results by applying our investigation to three different use cases in three different hospitals. Methods We trained clinical risk prediction models for three use cases (ie, delirium, sepsis, and acute kidney injury) in three different hospitals with retrospective data. We used machine learning and, specifically, deep learning to train models that were based on the Transformer model. The models were trained using a calibration tool that is common for all hospitals and use cases. The models had a common design but were calibrated using each hospital’s specific data. The models were deployed in these three hospitals and used in daily clinical practice. The predictions made by these models were logged and correlated with the diagnosis at discharge. We compared their performance with evaluations on retrospective data and conducted cross-hospital evaluations. Results The performance of the prediction models with data from live clinical workflows was similar to the performance with retrospective data. The average value of the area under the receiver operating characteristic curve (AUROC) decreased slightly by 0.6 percentage points (from 94.8% to 94.2% at discharge). The cross-hospital evaluations exhibited severely reduced performance: the average AUROC decreased by 8 percentage points (from 94.2% to 86.3% at discharge), which indicates the importance of model calibration with data from the deployment hospital. Conclusions Calibrating the prediction model with data from different deployment hospitals led to good performance in live settings. The performance degradation in the cross-hospital evaluation identified limitations in developing a generic model for different hospitals. Designing a generic process for model development to generate specialized prediction models for each hospital guarantees model performance in different hospitals.

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ZusammenfassungDie perioperative Medizin ist ein Hochrisikobereich, der besonders anfällig für Kommunikationsdefizite und -fehler ist. Das Schema „situation, background, assessment, recommendation“ (SBAR) bietet einen einfach anzuwendenden Kommunikationsleitfaden, der mit einer verbesserten Qualität der Übergabe assoziiert ist. Im März 2022 ist die Verwendung des SBAR-Schemas in der Perioperativmedizin durch die DGAI schon in zweiter Auflage empfohlen worden. Darüber hinaus hat die moderne Kommunikationsforschung ein ganzes Bündel von Maßnahmen identifiziert, die essenzielle Voraussetzungen für eine effektive Teamarbeit und die Gewährleistung der Patientensicherheit schaffen. Das SBAR-Schema ist eine Möglichkeit, strukturierte Kommunikation im klinischen Alltag umzusetzen. Entscheidend sind die konsequente Nutzung und eine klare Definition der Handlungsabläufe. Nur so können Kommunikationsdefizite in Hochrisikobereichen schneller identifiziert und durch Einführung eines strukturierten Übergabekonzeptes reduziert werden. Unabdingbar bleibt das gemeinsame Verständnis für die Notwendigkeit, diese Konzepte zu erlernen, umzusetzen und als Team zu trainieren. Das übergeordnete Ziel einer Kultur der Patientensicherheit ist nur durch die konsequente Zusammenarbeit des interprofessionellen Teams und durch das Vorleben der Führungskräfte erreichbar.