Abstract
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.
Highlights
Artificial intelligence (AI) and machine learning (ML) for supporting healthcare have been a constant in medical informatics research over decades [1, 2]
Several barriers and concerns have been raised for the implementation of ML-based predictive models in clinical decision support systems [5,6,7,8]
“Due to the delirium prediction application, we were already able to prevent the sliding into a strong delirium with simple interventions.”
Summary
Artificial intelligence (AI) and machine learning (ML) for supporting healthcare have been a constant in medical informatics research over decades [1, 2]. Health-related prediction modelling has gained much attention since wellknown companies have been developing prediction models for different clinical outcomes [3]. This has given rise to various prediction models with high predictive performance in retrospective data sets. Few of these models have ever been adopted to support healthcare professionals in clinical routine [4, 5]. Several barriers and concerns have been raised for the implementation of ML-based predictive models in clinical decision support systems [5,6,7,8]. As the final decision is always the responsibility of the user, it is crucial to open the often criticized black box of ML decisions so that healthcare professionals can detect bias or error [9]
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