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Incident reporting systems have been widely adopted to collect information about patient safety incidents. The value of incident reports often lies in the free-text section, but this is often inadequately processed. We developed a novel scoring system that uses machine learning to assess the severity of an incident using the semantic characteristics of text in incident reports. We retrospectively analyzed free-text data extracted from incident reports from between January 2012 and September 2021 at Nagoya University Hospital, Aichi, Japan. The sample was randomly allocated into a training dataset and validation dataset. Morphological analysis was used to segment terms using the training dataset. We calculated a severity term score, a severity report score and severity group score, by the report volume size. We then compared these with conventional severity classifications by patient safety experts and reporters. We allocated 96,082 incident reports into two groups. From the 48,041 reports in the training dataset, 1,802 severity term scores were calculated. There was a significant difference in severity report score between reports categorized as severe and not severe by experts (95% confidence interval [CI] − 0.83 to − 0.80, p

We practice patient safety as a model that links patient safety and quality improvement in healthcare. The most important activity is the incident report. The loop on the left is during usual situation activity related to quality improvement in healthcare. The loop on the right is during critical situations activity related to patient safety. What is important in these activities is the initial response to the critical situation, which is the first corner of the right loop. We practice emphasizing the initial response to the critical situation, creating the pattern, and taking measures without omissions. Although many patient safety measures have been taken, it has become clear that there is a shortage of doctors who can practice them. We have practiced that pattern and supported advanced healthcare. We want you to explain the pattern and use it in practice.