Language：English   Publisher：Journal of Medical Systems  

Incident reporting systems have been widely adopted to collect information about patient safety incidents. Much of the value of incident reports lies in the free-text section. Computer processing of semantic information may be helpful to analyze this. We developed a novel scoring system for decision making to assess the severity of incidents using the semantic characteristics of the text in incident reports, and compared its results with experts’ opinions. We retrospectively analyzed free-text data from incident reports from January 2012 to September 2021 at Nagoya University Hospital, Aichi, Japan. The sample was allocated to training and validation datasets using the hold-out method. Morphological analysis was used to segment terms in the training dataset. We calculated a severity term score, a severity report score and severity group score, by report volume size, and compared these with conventional severity classifications by patient safety experts and reporters. We allocated 96,082 incident reports into two groups. We calculated 1,802 severity term scores from the 48,041 reports in the training dataset. 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 < 0.001, d = 0.81). Severity group scores were positively associated with severity ratings from experts and reporters (correlation coefficients 0.73 [95% CI 0.63–0.80, p < 0.001] and 0.79 [95% CI 0.71–0.85, p < 0.001]) for all departments. Our severity scoring system could therefore contribute to better organizational patient safety.

DOI： 10.1007/s10916-022-01893-1

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Language：English   Publisher：Nagoya Journal of Medical Science  

Communication errors are the most important cause of adverse events in healthcare. The current study aimed to improve hospital-wide employee teamwork and reduce adverse medical events for patients arising from miscommunication. In our hospital, when patient safety incidents and accidents occur, staff from various occupations submit incident reports to the Department of Patient Safety via an electronic reporting system; over 11,000 cases are reported each year. We surveyed the incident reports submitted in our institution from 2016 to 2018. All incidents related to miscommunication were identified, and relevant information was collected from the original electronic incident reports. Incident severity classification is commonly divided into near-miss or adverse events. We extracted only the required incident information items for this study, and processed information concerning individuals (e.g., reporters and target patients) anonymously. This study was approved by the Institutional Review Board of the study hospital. The authors declare no conflicts of interest associated with this study. Team training for all employees reduced adverse events for patients. The coefficient of determination (R squared value) was −0.32. This suggests our approach may be slightly but significantly effective for developing the fundamental strengths of the medical team. Quality improvement is continuous, and seamless efforts to improve the effectiveness of medical teams at our hospital will continue.

DOI： 10.18999/nagjms.82.4.697

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Language：English   Publisher：Annals of Medicine and Surgery  

DOI： 10.1016/j.amsu.2020.06.032

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Language：Japanese   Publisher：Nagoya Journal of Medical Science  

This study aimed to evaluate the efficacy of interventions to reduce patient misidentification incidents classified as level 2 and over (adverse events occurred for patients) with the step-by-step problem-solving method. All incidents related to patient misidentification were selected, and relevant information was collected from the original electronic incident reports. We then conducted an eight-step problem-solving process with the aim of reducing patient misclassification and improving patient safety. Step 1: the number of misidentification-related incident reports and the percentage of these reports in the total incident reports increased each year. Step 2: the most frequent misidentification type was sample collection tubes, followed by drug administration and hospital meals. Step 3: we set a target of an 20% decrease in patient misidentification cases classified as level 2 or over compared with the previous year, and established this as a hospital priority. Step 4: we found that discrepancies in patient identification procedures were the most important causes of misidentification. Step 5: we standardized the patient identification process to achieve an 10% reduction in misidentification. Step 6: we disseminated instructional videos to all staff members. Step 7: we confirmed there was an 18% reduction in level 2 and over patient misidentification compared with the previous year. Step 8: we intend to make additional effort to decrease misidentification of patients by a further 10%. Level 2 and over patient misidentification can be reduced by a patient identification policy using a step-by-step problem-solving procedure. This study aimed to evaluate the efficacy of interventions to reduce patient misidentification incidents with step-by-step problem-solving method. Continued seamless efforts to eliminate patient misidentification are mandatory for this activity.

DOI： 10.18999/nagjms.82.2.315

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Language：Japanese   Publisher：Patient Safety in Surgery  

Background: Incident reporting is an effective strategy used to enhance patient safety and quality improvement in healthcare. An incident is an event that could eventually result in harm to a patient. The aim of this study is to re-evaluate the importance of reporting by medical doctors to improve quality in healthcare and patient safety. Methods: We conducted a retrospective analysis of the reported incidents registered in our institutional database from April 1st 2015 to March 31st 2019, classified according to eight variables proposed by the National University Hospital Council of Japan, to determine the type of incidents and their potential harm to patients. Results: Registered reports totalled 43,775, approximately 8% of which arise annually from medical doctors in clinical departments. Incidents with higher impact on patients have significantly increased the rate of reporting by medical doctors. The most frequent types of report overall concerned medication incidents, followed by infusion lines, drainage-tube devices, cure, examination, and treatment outside the operating room. The most frequent reports by medical doctors involved operation-related incidents, followed by cure, examination, treatment outside the operation room, and medications. Conclusion: Reporting by medical doctors reflects the organizational transparency and the driving forces behind patient safety and quality improvement in healthcare. Efforts toward seamless improvement in patient safety and quality at our hospital continue apace.

DOI： 10.1186/s13037-020-00240-y

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Event date： 2023.11

Language：Japanese   Presentation type：Poster presentation  

Venue：兵庫県神戸市   Country：Japan  

Event date： 2022.10

Venue：名古屋大学医学部附属病院患者安全推進部  

Event date： 2022.9

Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Venue：名古屋大学医学部附属病院患者安全推進部  

Work type：Software   Location：名古屋大学医学部附属病院  

Work type：Software   Location：名古屋大学医学部附属病院  

Work type：Software   Location：名古屋大学  

・人工知能を搭載（リスク量測定、患者誤認・過失判定）
・汎用性、メンテナンス性、レスポンスに優れ、小～大規模病院まで幅広く適用可能。